diff --git a/.github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh b/.github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh new file mode 100755 index 000000000..cf35f711b --- /dev/null +++ b/.github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh @@ -0,0 +1,86 @@ +#!/usr/bin/env bash + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +cd egs/aishell/ASR + +git lfs install + +fbank_url=https://huggingface.co/csukuangfj/aishell-test-dev-manifests +log "Downloading pre-commputed fbank from $fbank_url" + +git clone https://huggingface.co/csukuangfj/aishell-test-dev-manifests +ln -s $PWD/aishell-test-dev-manifests/data . + +log "Downloading pre-trained model from $repo_url" +repo_url=https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20 +git clone $repo_url +repo=$(basename $repo_url) + +log "Display test files" +tree $repo/ +soxi $repo/test_wavs/*.wav +ls -lh $repo/test_wavs/*.wav + +pushd $repo/exp +ln -s pretrained-epoch-29-avg-5-torch-1.10.pt pretrained.pt +popd + +for sym in 1 2 3; do + log "Greedy search with --max-sym-per-frame $sym" + + ./pruned_transducer_stateless3/pretrained.py \ + --method greedy_search \ + --max-sym-per-frame $sym \ + --checkpoint $repo/exp/pretrained.pt \ + --lang-dir $repo/data/lang_char \ + $repo/test_wavs/BAC009S0764W0121.wav \ + $repo/test_wavs/BAC009S0764W0122.wav \ + $rep/test_wavs/BAC009S0764W0123.wav +done + +for method in modified_beam_search beam_search fast_beam_search; do + log "$method" + + ./pruned_transducer_stateless3/pretrained.py \ + --method $method \ + --beam-size 4 \ + --checkpoint $repo/exp/pretrained.pt \ + --lang-dir $repo/data/lang_char \ + $repo/test_wavs/BAC009S0764W0121.wav \ + $repo/test_wavs/BAC009S0764W0122.wav \ + $rep/test_wavs/BAC009S0764W0123.wav +done + +echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}" +echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}" +if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode" ]]; then + mkdir -p pruned_transducer_stateless3/exp + ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless3/exp/epoch-999.pt + ln -s $PWD/$repo/data/lang_char data/ + + ls -lh data + ls -lh pruned_transducer_stateless3/exp + + log "Decoding test and dev" + + # use a small value for decoding with CPU + max_duration=100 + + for method in greedy_search fast_beam_search modified_beam_search; do + log "Decoding with $method" + + ./pruned_transducer_stateless3/decode.py \ + --decoding-method $method \ + --epoch 999 \ + --avg 1 \ + --max-duration $max_duration \ + --exp-dir pruned_transducer_stateless3/exp + done + + rm pruned_transducer_stateless3/exp/*.pt +fi diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh index 3d0c4e2ef..61210ac6e 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh @@ -32,6 +32,12 @@ for sym in 1 2 3; do --max-sym-per-frame $sym \ --checkpoint $repo/exp/pretrained.pt \ --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --num-encoder-layers 18 \ + --dim-feedforward 2048 \ + --nhead 8 \ + --encoder-dim 512 \ + --decoder-dim 512 \ + --joiner-dim 512 $repo/test_wavs/1089-134686-0001.wav \ $repo/test_wavs/1221-135766-0001.wav \ $repo/test_wavs/1221-135766-0002.wav diff --git a/.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh b/.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh new file mode 100755 index 000000000..85bbb919f --- /dev/null +++ b/.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh @@ -0,0 +1,86 @@ +#!/usr/bin/env bash + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +cd egs/librispeech/ASR + +repo_url=https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless2_20220625 + +log "Downloading pre-trained model from $repo_url" +git lfs install +git clone $repo_url +repo=$(basename $repo_url) + +log "Display test files" +tree $repo/ +soxi $repo/test_wavs/*.wav +ls -lh $repo/test_wavs/*.wav + +pushd $repo/exp +ln -s pretrained-epoch-24-avg-10.pt pretrained.pt +popd + +for sym in 1 2 3; do + log "Greedy search with --max-sym-per-frame $sym" + + ./pruned_transducer_stateless2/pretrained.py \ + --method greedy_search \ + --max-sym-per-frame $sym \ + --checkpoint $repo/exp/pretrained.pt \ + --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --simulate-streaming 1 \ + --causal-convolution 1 \ + $repo/test_wavs/1089-134686-0001.wav \ + $repo/test_wavs/1221-135766-0001.wav \ + $repo/test_wavs/1221-135766-0002.wav +done + +for method in modified_beam_search beam_search fast_beam_search; do + log "$method" + + ./pruned_transducer_stateless2/pretrained.py \ + --method $method \ + --beam-size 4 \ + --checkpoint $repo/exp/pretrained.pt \ + --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --simulate-streaming 1 \ + --causal-convolution 1 \ + $repo/test_wavs/1089-134686-0001.wav \ + $repo/test_wavs/1221-135766-0001.wav \ + $repo/test_wavs/1221-135766-0002.wav +done + +echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}" +echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}" +if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode" ]]; then + mkdir -p pruned_transducer_stateless2/exp + ln -s $PWD/$repo/exp/pretrained-epoch-24-avg-10.pt pruned_transducer_stateless2/exp/epoch-999.pt + ln -s $PWD/$repo/data/lang_bpe_500 data/ + + ls -lh data + ls -lh pruned_transducer_stateless2/exp + + log "Decoding test-clean and test-other" + + # use a small value for decoding with CPU + max_duration=100 + + for method in greedy_search fast_beam_search modified_beam_search; do + log "Decoding with $method" + + ./pruned_transducer_stateless2/decode.py \ + --decoding-method $method \ + --epoch 999 \ + --avg 1 \ + --max-duration $max_duration \ + --exp-dir pruned_transducer_stateless2/exp \ + --simulate-streaming 1 \ + --causal-convolution 1 + done + + rm pruned_transducer_stateless2/exp/*.pt +fi diff --git a/.github/workflows/run-aishell-2022-06-20.yml b/.github/workflows/run-aishell-2022-06-20.yml new file mode 100644 index 000000000..e684e598e --- /dev/null +++ b/.github/workflows/run-aishell-2022-06-20.yml @@ -0,0 +1,119 @@ +# Copyright 2022 Fangjun Kuang (csukuangfj@gmail.com) + +# See ../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: run-aishell-2022-06-20 +# pruned RNN-T + reworked model with random combiner +# https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20 + +on: + push: + branches: + - master + pull_request: + types: [labeled] + + schedule: + # minute (0-59) + # hour (0-23) + # day of the month (1-31) + # month (1-12) + # day of the week (0-6) + # nightly build at 15:50 UTC time every day + - cron: "50 15 * * *" + +jobs: + run_aishell_2022_06_20: + if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule' + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-18.04] + python-version: [3.7, 3.8, 3.9] + + fail-fast: false + + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 + + - name: Setup Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + cache-dependency-path: '**/requirements-ci.txt' + + - name: Install Python dependencies + run: | + grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf + + - name: Cache kaldifeat + id: my-cache + uses: actions/cache@v2 + with: + path: | + ~/tmp/kaldifeat + key: cache-tmp-${{ matrix.python-version }} + + - name: Install kaldifeat + if: steps.my-cache.outputs.cache-hit != 'true' + shell: bash + run: | + .github/scripts/install-kaldifeat.sh + + - name: Inference with pre-trained model + shell: bash + env: + GITHUB_EVENT_NAME: ${{ github.event_name }} + GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }} + run: | + sudo apt-get -qq install git-lfs tree sox + export PYTHONPATH=$PWD:$PYTHONPATH + export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH + export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH + + .github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh + + - name: Display decoding results for aishell pruned_transducer_stateless3 + if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' + shell: bash + run: | + cd egs/aishell/ASR/ + tree ./pruned_transducer_stateless3/exp + + cd pruned_transducer_stateless3 + echo "results for pruned_transducer_stateless3" + echo "===greedy search===" + find exp/greedy_search -name "log-*" -exec grep -n --color "best for test" {} + | sort -n -k2 + find exp/greedy_search -name "log-*" -exec grep -n --color "best for dev" {} + | sort -n -k2 + + echo "===fast_beam_search===" + find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test" {} + | sort -n -k2 + find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for dev" {} + | sort -n -k2 + + echo "===modified beam search===" + find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test" {} + | sort -n -k2 + find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for dev" {} + | sort -n -k2 + + - name: Upload decoding results for aishell pruned_transducer_stateless3 + uses: actions/upload-artifact@v2 + if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' + with: + name: aishell-torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless3-2022-06-20 + path: egs/aishell/ASR/pruned_transducer_stateless3/exp/ diff --git a/.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml b/.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml new file mode 100644 index 000000000..9ce8244da --- /dev/null +++ b/.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml @@ -0,0 +1,155 @@ +# Copyright 2021 Fangjun Kuang (csukuangfj@gmail.com) + +# See ../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: run-librispeech-streaming-2022-06-26 +# streaming conformer stateless transducer2 + +on: + push: + branches: + - master + pull_request: + types: [labeled] + + schedule: + # minute (0-59) + # hour (0-23) + # day of the month (1-31) + # month (1-12) + # day of the week (0-6) + # nightly build at 15:50 UTC time every day + - cron: "50 15 * * *" + +jobs: + run_librispeech_streaming_2022_06_26: + if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule' + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-18.04] + python-version: [3.7, 3.8, 3.9] + + fail-fast: false + + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 + + - name: Setup Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + cache-dependency-path: '**/requirements-ci.txt' + + - name: Install Python dependencies + run: | + grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf + + - name: Cache kaldifeat + id: my-cache + uses: actions/cache@v2 + with: + path: | + ~/tmp/kaldifeat + key: cache-tmp-${{ matrix.python-version }} + + - name: Install kaldifeat + if: steps.my-cache.outputs.cache-hit != 'true' + shell: bash + run: | + .github/scripts/install-kaldifeat.sh + + - name: Cache LibriSpeech test-clean and test-other datasets + id: libri-test-clean-and-test-other-data + uses: actions/cache@v2 + with: + path: | + ~/tmp/download + key: cache-libri-test-clean-and-test-other + + - name: Download LibriSpeech test-clean and test-other + if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true' + shell: bash + run: | + .github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh + + - name: Prepare manifests for LibriSpeech test-clean and test-other + shell: bash + run: | + .github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh + + - name: Cache LibriSpeech test-clean and test-other fbank features + id: libri-test-clean-and-test-other-fbank + uses: actions/cache@v2 + with: + path: | + ~/tmp/fbank-libri + key: cache-libri-fbank-test-clean-and-test-other-v2 + + - name: Compute fbank for LibriSpeech test-clean and test-other + if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' + shell: bash + run: | + .github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh + + - name: Inference with pre-trained model + shell: bash + env: + GITHUB_EVENT_NAME: ${{ github.event_name }} + GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }} + run: | + mkdir -p egs/librispeech/ASR/data + ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank + ls -lh egs/librispeech/ASR/data/* + + sudo apt-get -qq install git-lfs tree sox + export PYTHONPATH=$PWD:$PYTHONPATH + export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH + export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH + + .github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh + + - name: Display decoding results + if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' + shell: bash + run: | + cd egs/librispeech/ASR/ + tree ./pruned_transducer_stateless2/exp + + cd pruned_transducer_stateless2 + echo "results for pruned_transducer_stateless2" + echo "===greedy search===" + find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 + + echo "===fast_beam_search===" + find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 + + echo "===modified_beam_search===" + find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 + + - name: Upload decoding results for pruned_transducer_stateless2 + uses: actions/upload-artifact@v2 + if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' + with: + name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless2-2022-06-26 + path: egs/librispeech/ASR/pruned_transducer_stateless2/exp/ diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index f9dab7afe..1583926ec 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -33,13 +33,13 @@ jobs: # disable macOS test for now. os: [ubuntu-18.04] python-version: [3.7, 3.8] - torch: ["1.8.0", "1.10.0"] - torchaudio: ["0.8.0", "0.10.0"] - k2-version: ["1.9.dev20211101"] + torch: ["1.8.0", "1.11.0"] + torchaudio: ["0.8.0", "0.11.0"] + k2-version: ["1.15.1.dev20220427"] exclude: - torch: "1.8.0" - torchaudio: "0.10.0" - - torch: "1.10.0" + torchaudio: "0.11.0" + - torch: "1.11.0" torchaudio: "0.8.0" fail-fast: false @@ -67,7 +67,7 @@ jobs: # numpy 1.20.x does not support python 3.6 pip install numpy==1.19 pip install torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html - if [[ ${{ matrix.torchaudio }} == "0.10.0" ]]; then + if [[ ${{ matrix.torchaudio }} == "0.11.0" ]]; then pip install torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html else pip install torchaudio==${{ matrix.torchaudio }} diff --git a/README.md b/README.md index 2096681ea..be922c191 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,8 @@ We provide the following recipes: - [Aidatatang_200zh][aidatatang_200zh] - [WenetSpeech][wenetspeech] - [Alimeeting][alimeeting] + - [Aishell4][aishell4] + - [TAL_CSASR][tal_csasr] ### yesno @@ -270,6 +272,36 @@ We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tKr3f0mL17uO_ljdHGKtR7HOmthYHwJG?usp=sharing) +### Aishell4 + +We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aishell4_pruned_transducer_stateless5]. + +#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with all subsets) + +The best CER(%) results: +| | test | +|----------------------|--------| +| greedy search | 29.89 | +| fast beam search | 28.91 | +| modified beam search | 29.08 | + +We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing) + +### TAL_CSASR + +We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][TAL_CSASR_pruned_transducer_stateless5]. + +#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss + +The best results for Chinese CER(%) and English WER(%) respectivly (zh: Chinese, en: English): +|decoding-method | dev | dev_zh | dev_en | test | test_zh | test_en | +|--|--|--|--|--|--|--| +|greedy_search| 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13| +|modified_beam_search| 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 | +|fast_beam_search| 7.18 | 6.39| 18.90 | 7.27| 6.55 | 18.77| + +We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DmIx-NloI1CMU5GdZrlse7TRu4y3Dpf8?usp=sharing) + ## Deployment with C++ Once you have trained a model in icefall, you may want to deploy it with C++, @@ -298,6 +330,8 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad [Aidatatang_200zh_pruned_transducer_stateless2]: egs/aidatatang_200zh/ASR/pruned_transducer_stateless2 [WenetSpeech_pruned_transducer_stateless2]: egs/wenetspeech/ASR/pruned_transducer_stateless2 [Alimeeting_pruned_transducer_stateless2]: egs/alimeeting/ASR/pruned_transducer_stateless2 +[Aishell4_pruned_transducer_stateless5]: egs/aishell4/ASR/pruned_transducer_stateless5 +[TAL_CSASR_pruned_transducer_stateless5]: egs/tal_csasr/ASR/pruned_transducer_stateless5 [yesno]: egs/yesno/ASR [librispeech]: egs/librispeech/ASR [aishell]: egs/aishell/ASR @@ -307,5 +341,6 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad [aidatatang_200zh]: egs/aidatatang_200zh/ASR [wenetspeech]: egs/wenetspeech/ASR [alimeeting]: egs/alimeeting/ASR +[aishell4]: egs/aishell4/ASR +[tal_csasr]: egs/tal_csasr/ASR [k2]: https://github.com/k2-fsa/k2 -) diff --git a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/export.py b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/export.py index 43033e517..00b54c39f 100644 --- a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/export.py +++ b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/export.py @@ -114,8 +114,6 @@ def main(): args = get_parser().parse_args() args.exp_dir = Path(args.exp_dir) - assert args.jit is False, "Support torchscript will be added later" - params = get_params() params.update(vars(args)) @@ -155,6 +153,11 @@ def main(): model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/egs/aishell/ASR/README.md b/egs/aishell/ASR/README.md index d0a0c1829..75fc6326e 100644 --- a/egs/aishell/ASR/README.md +++ b/egs/aishell/ASR/README.md @@ -4,6 +4,8 @@ Please refer to for how to run models in this recipe. + + # Transducers There are various folders containing the name `transducer` in this folder. @@ -14,6 +16,7 @@ The following table lists the differences among them. | `transducer_stateless` | Conformer | Embedding + Conv1d | with `k2.rnnt_loss` | | `transducer_stateless_modified` | Conformer | Embedding + Conv1d | with modified transducer from `optimized_transducer` | | `transducer_stateless_modified-2` | Conformer | Embedding + Conv1d | with modified transducer from `optimized_transducer` + extra data | +| `pruned_transducer_stateless3` | Conformer (reworked) | Embedding + Conv1d | pruned RNN-T + reworked model with random combiner + using aidatatang_20zh as extra data| The decoder in `transducer_stateless` is modified from the paper [Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/). diff --git a/egs/aishell/ASR/RESULTS.md b/egs/aishell/ASR/RESULTS.md index ecc93c21b..b420a1982 100644 --- a/egs/aishell/ASR/RESULTS.md +++ b/egs/aishell/ASR/RESULTS.md @@ -1,10 +1,93 @@ ## Results -### Aishell training result(Transducer-stateless) + +### Aishell training result(Stateless Transducer) + +#### Pruned transducer stateless 3 + +See + + +[./pruned_transducer_stateless3](./pruned_transducer_stateless3) + +It uses pruned RNN-T. + +| | test | dev | comment | +|------------------------|------|------|---------------------------------------| +| greedy search | 5.39 | 5.09 | --epoch 29 --avg 5 --max-duration 600 | +| modified beam search | 5.05 | 4.79 | --epoch 29 --avg 5 --max-duration 600 | +| fast beam search | 5.13 | 4.91 | --epoch 29 --avg 5 --max-duration 600 | + +Training command is: + +```bash +./prepare.sh +./prepare_aidatatang_200zh.sh + +export CUDA_VISIBLE_DEVICES="4,5,6,7" + +./pruned_transducer_stateless3/train.py \ + --exp-dir ./pruned_transducer_stateless3/exp-context-size-1 \ + --world-size 4 \ + --max-duration 200 \ + --datatang-prob 0.5 \ + --start-epoch 1 \ + --num-epochs 30 \ + --use-fp16 1 \ + --num-encoder-layers 12 \ + --dim-feedforward 2048 \ + --nhead 8 \ + --encoder-dim 512 \ + --context-size 1 \ + --decoder-dim 512 \ + --joiner-dim 512 \ + --master-port 12356 +``` + +**Caution**: It uses `--context-size=1`. + +The tensorboard log is available at + + +The decoding command is: + +```bash +for epoch in 29; do + for avg in 5; do + for m in greedy_search modified_beam_search fast_beam_search; do + ./pruned_transducer_stateless3/decode.py \ + --exp-dir ./pruned_transducer_stateless3/exp-context-size-1 \ + --epoch $epoch \ + --avg $avg \ + --use-averaged-model 1 \ + --max-duration 600 \ + --decoding-method $m \ + --num-encoder-layers 12 \ + --dim-feedforward 2048 \ + --nhead 8 \ + --context-size 1 \ + --encoder-dim 512 \ + --decoder-dim 512 \ + --joiner-dim 512 + done + done +done +``` + +Pretrained models, training logs, decoding logs, and decoding results +are available at + + +We have a tutorial in [sherpa](https://github.com/k2-fsa/sherpa) about how +to use the pre-trained model for non-streaming ASR. See + #### 2022-03-01 [./transducer_stateless_modified-2](./transducer_stateless_modified-2) +It uses [optimized_transducer](https://github.com/csukuangfj/optimized_transducer) +for computing RNN-T loss. + Stateless transducer + modified transducer + using [aidatatang_200zh](http://www.openslr.org/62/) as extra training data. diff --git a/egs/aishell/ASR/conformer_ctc/conformer.py b/egs/aishell/ASR/conformer_ctc/conformer.py index 7bd0f95cf..1e3e7b492 100644 --- a/egs/aishell/ASR/conformer_ctc/conformer.py +++ b/egs/aishell/ASR/conformer_ctc/conformer.py @@ -364,7 +364,7 @@ class RelPositionalEncoding(torch.nn.Module): ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (ij) and negative relative positions otherwise (i Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + token_table: + It maps token ID to a string. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + else: + hyp_tokens = [] + batch_size = encoder_out.size(0) + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyp_tokens.append(hyp) + + hyps = [[token_table[t] for t in tokens] for tokens in hyp_tokens] + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + token_table: k2.SymbolTable, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + token_table: + It maps a token ID to a string. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + + hyps_dict = decode_one_batch( + params=params, + model=model, + token_table=token_table, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + # we compute CER for aishell dataset. + results_char = [] + for res in results: + results_char.append((list("".join(res[0])), list("".join(res[1])))) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results_char, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tCER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, CER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + args.lang_dir = Path(args.lang_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + params.blank_id = 0 + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict( + average_checkpoints(filenames, device=device), strict=False + ) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict( + average_checkpoints(filenames, device=device), strict=False + ) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ), + strict=False, + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ), + strict=False, + ) + + model.to(device) + model.eval() + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + asr_datamodule = AsrDataModule(args) + aishell = AIShell(manifest_dir=args.manifest_dir) + test_cuts = aishell.test_cuts() + dev_cuts = aishell.valid_cuts() + test_dl = asr_datamodule.test_dataloaders(test_cuts) + dev_dl = asr_datamodule.test_dataloaders(dev_cuts) + + test_sets = ["test", "dev"] + test_dls = [test_dl, dev_dl] + + for test_set, test_dl in zip(test_sets, test_dls): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + token_table=lexicon.token_table, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/decoder.py b/egs/aishell/ASR/pruned_transducer_stateless3/decoder.py new file mode 120000 index 000000000..722e1c894 --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/decoder.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/decoder.py \ No newline at end of file diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/encoder_interface.py b/egs/aishell/ASR/pruned_transducer_stateless3/encoder_interface.py new file mode 120000 index 000000000..f58253127 --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/encoder_interface.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/encoder_interface.py \ No newline at end of file diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/exp-context-size-1 b/egs/aishell/ASR/pruned_transducer_stateless3/exp-context-size-1 new file mode 120000 index 000000000..bcd4abc2f --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/exp-context-size-1 @@ -0,0 +1 @@ +/ceph-fj/fangjun/open-source/icefall-aishell/egs/aishell/ASR/pruned_transducer_stateless3/exp-context-size-1 \ No newline at end of file diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/export.py b/egs/aishell/ASR/pruned_transducer_stateless3/export.py new file mode 100755 index 000000000..307895a76 --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/export.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./pruned_transducer_stateless3/export.py \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --jit 0 \ + --epoch 29 \ + --avg 5 + +It will generate a file exp_dir/pretrained-epoch-29-avg-5.pt + +To use the generated file with `pruned_transducer_stateless3/decode.py`, +you can do:: + + cd /path/to/exp_dir + ln -s pretrained-epoch-29-avg-5.pt epoch-9999.pt + + cd /path/to/egs/aishell/ASR + ./pruned_transducer_stateless3/decode.py \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 100 \ + --lang-dir data/lang_char +""" + +import argparse +import logging +from pathlib import Path + +import torch +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=29, + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=Path, + default=Path("pruned_transducer_stateless3/exp"), + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--lang-dir", + type=Path, + default=Path("data/lang_char"), + help="The lang dir", + ) + + parser.add_argument( + "--context-size", + type=int, + default=1, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + add_model_arguments(parser) + + return parser + + +def main(): + args = get_parser().parse_args() + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + lexicon = Lexicon(params.lang_dir) + + params.blank_id = 0 + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to("cpu") + model.eval() + + if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = ( + params.exp_dir / f"cpu_jit-epoch-{params.epoch}-avg-{params.avg}.pt" + ) + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = ( + params.exp_dir + / f"pretrained-epoch-{params.epoch}-avg-{params.avg}.pt" + ) + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/joiner.py b/egs/aishell/ASR/pruned_transducer_stateless3/joiner.py new file mode 120000 index 000000000..9052f3cbb --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/joiner.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/joiner.py \ No newline at end of file diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/model.py b/egs/aishell/ASR/pruned_transducer_stateless3/model.py new file mode 100644 index 000000000..e150e8230 --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/model.py @@ -0,0 +1,236 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import Optional + +import k2 +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface +from scaling import ScaledLinear + +from icefall.utils import add_sos + + +class Transducer(nn.Module): + """It implements https://arxiv.org/pdf/1211.3711.pdf + "Sequence Transduction with Recurrent Neural Networks" + """ + + def __init__( + self, + encoder: EncoderInterface, + decoder: nn.Module, + joiner: nn.Module, + encoder_dim: int, + decoder_dim: int, + joiner_dim: int, + vocab_size: int, + decoder_datatang: Optional[nn.Module] = None, + joiner_datatang: Optional[nn.Module] = None, + ): + """ + Args: + encoder: + It is the transcription network in the paper. Its accepts + two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,). + It returns two tensors: `logits` of shape (N, T, encoder_dm) and + `logit_lens` of shape (N,). + decoder: + It is the prediction network in the paper. Its input shape + is (N, U) and its output shape is (N, U, decoder_dim). + It should contain one attribute: `blank_id`. + joiner: + It has two inputs with shapes: (N, T, encoder_dim) and + (N, U, decoder_dim). Its output shape is (N, T, U, vocab_size). + Note that its output contains + unnormalized probs, i.e., not processed by log-softmax. + encoder_dim: + Output dimension of the encoder network. + decoder_dim: + Output dimension of the decoder network. + joiner_dim: + Input dimension of the joiner network. + vocab_size: + Output dimension of the joiner network. + decoder_datatang: + Optional. The decoder network for the aidatatang_200zh dataset. + joiner_datatang: + Optional. The joiner network for the aidatatang_200zh dataset. + """ + super().__init__() + + assert isinstance(encoder, EncoderInterface), type(encoder) + assert hasattr(decoder, "blank_id") + + self.encoder = encoder + self.decoder = decoder + self.joiner = joiner + + self.decoder_datatang = decoder_datatang + self.joiner_datatang = joiner_datatang + + self.simple_am_proj = ScaledLinear( + encoder_dim, vocab_size, initial_speed=0.5 + ) + self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size) + + if decoder_datatang is not None: + self.simple_am_proj_datatang = ScaledLinear( + encoder_dim, vocab_size, initial_speed=0.5 + ) + self.simple_lm_proj_datatang = ScaledLinear(decoder_dim, vocab_size) + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: k2.RaggedTensor, + aishell: bool = True, + prune_range: int = 5, + am_scale: float = 0.0, + lm_scale: float = 0.0, + warmup: float = 1.0, + ) -> torch.Tensor: + """ + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 1-D tensor of shape (N,). It contains the number of frames in `x` + before padding. + y: + A ragged tensor with 2 axes [utt][label]. It contains labels of each + utterance. + aishell: + True to use the decoder and joiner for the aishell dataset. + False to use the decoder and joiner for the aidatatang_200zh + dataset. + prune_range: + The prune range for rnnt loss, it means how many symbols(context) + we are considering for each frame to compute the loss. + am_scale: + The scale to smooth the loss with am (output of encoder network) + part + lm_scale: + The scale to smooth the loss with lm (output of predictor network) + part + warmup: + A value warmup >= 0 that determines which modules are active, values + warmup > 1 "are fully warmed up" and all modules will be active. + Returns: + Return the transducer loss. + + Note: + Regarding am_scale & lm_scale, it will make the loss-function one of + the form: + lm_scale * lm_probs + am_scale * am_probs + + (1-lm_scale-am_scale) * combined_probs + """ + assert x.ndim == 3, x.shape + assert x_lens.ndim == 1, x_lens.shape + assert y.num_axes == 2, y.num_axes + + assert x.size(0) == x_lens.size(0) == y.dim0 + + encoder_out, encoder_out_lens = self.encoder(x, x_lens, warmup=warmup) + assert torch.all(encoder_out_lens > 0) + + if aishell: + decoder = self.decoder + simple_lm_proj = self.simple_lm_proj + simple_am_proj = self.simple_am_proj + joiner = self.joiner + else: + decoder = self.decoder_datatang + simple_lm_proj = self.simple_lm_proj_datatang + simple_am_proj = self.simple_am_proj_datatang + joiner = self.joiner_datatang + + # Now for the decoder, i.e., the prediction network + row_splits = y.shape.row_splits(1) + y_lens = row_splits[1:] - row_splits[:-1] + + blank_id = decoder.blank_id + sos_y = add_sos(y, sos_id=blank_id) + + # sos_y_padded: [B, S + 1], start with SOS. + sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id) + + # decoder_out: [B, S + 1, decoder_dim] + decoder_out = decoder(sos_y_padded) + + # Note: y does not start with SOS + # y_padded : [B, S] + y_padded = y.pad(mode="constant", padding_value=0) + + y_padded = y_padded.to(torch.int64) + boundary = torch.zeros( + (x.size(0), 4), dtype=torch.int64, device=x.device + ) + boundary[:, 2] = y_lens + boundary[:, 3] = encoder_out_lens + + lm = simple_lm_proj(decoder_out) + am = simple_am_proj(encoder_out) + + with torch.cuda.amp.autocast(enabled=False): + simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed( + lm=lm.float(), + am=am.float(), + symbols=y_padded, + termination_symbol=blank_id, + lm_only_scale=lm_scale, + am_only_scale=am_scale, + boundary=boundary, + reduction="sum", + return_grad=True, + ) + + # ranges : [B, T, prune_range] + ranges = k2.get_rnnt_prune_ranges( + px_grad=px_grad, + py_grad=py_grad, + boundary=boundary, + s_range=prune_range, + ) + + # am_pruned : [B, T, prune_range, encoder_dim] + # lm_pruned : [B, T, prune_range, decoder_dim] + am_pruned, lm_pruned = k2.do_rnnt_pruning( + am=joiner.encoder_proj(encoder_out), + lm=joiner.decoder_proj(decoder_out), + ranges=ranges, + ) + + # logits : [B, T, prune_range, vocab_size] + + # project_input=False since we applied the decoder's input projections + # prior to do_rnnt_pruning (this is an optimization for speed). + logits = joiner(am_pruned, lm_pruned, project_input=False) + + with torch.cuda.amp.autocast(enabled=False): + pruned_loss = k2.rnnt_loss_pruned( + logits=logits.float(), + symbols=y_padded, + ranges=ranges, + termination_symbol=blank_id, + boundary=boundary, + reduction="sum", + ) + + return (simple_loss, pruned_loss) diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/optim.py b/egs/aishell/ASR/pruned_transducer_stateless3/optim.py new file mode 120000 index 000000000..0a2f285aa --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/optim.py \ No newline at end of file diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/pretrained.py b/egs/aishell/ASR/pruned_transducer_stateless3/pretrained.py new file mode 100755 index 000000000..5cda411bc --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/pretrained.py @@ -0,0 +1,337 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: + +(1) greedy search +./pruned_transducer_stateless3/pretrained.py \ + --checkpoint /path/to/pretrained.pt \ + --lang-dir /path/to/lang_char \ + --method greedy_search \ + /path/to/foo.wav \ + /path/to/bar.wav + +(2) beam search +./pruned_transducer_stateless3/pretrained.py \ + --checkpoint /path/to/pretrained.pt \ + --lang-dir /path/to/lang_char \ + --method beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(3) modified beam search +./pruned_transducer_stateless3/pretrained.py \ + --checkpoint /path/to/pretrained.pt \ + --lang-dir /path/to/lang_char \ + --method modified_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(4) fast beam search +./pruned_transducer_stateless3/pretrained.py \ + --checkpoint /path/to/pretrained.pt \ + --lang-dir /path/to/lang_char \ + --method fast_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav +""" + +import argparse +import logging +import math +from pathlib import Path +from typing import List + +import k2 +import kaldifeat +import torch +import torchaudio +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.lexicon import Lexicon + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--lang-dir", + type=Path, + default=Path("data/lang_char"), + help="The lang dir", + ) + + parser.add_argument( + "--method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "sound_files", + type=str, + nargs="+", + help="The input sound file(s) to transcribe. " + "Supported formats are those supported by torchaudio.load(). " + "For example, wav and flac are supported. " + "The sample rate has to be 16kHz.", + ) + + parser.add_argument( + "--sample-rate", + type=int, + default=16000, + help="The sample rate of the input sound file", + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=1, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="Maximum number of symbols per frame. " + "Use only when --method is greedy_search", + ) + + add_model_arguments(parser) + + return parser + + +def read_sound_files( + filenames: List[str], expected_sample_rate: float +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + assert sample_rate == expected_sample_rate, ( + f"expected sample rate: {expected_sample_rate}. " + f"Given: {sample_rate}" + ) + # We use only the first channel + ans.append(wave[0]) + return ans + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + lexicon = Lexicon(params.lang_dir) + + params.blank_id = 0 + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"], strict=False) + model.to(device) + model.eval() + model.device = device + + logging.info("Constructing Fbank computer") + opts = kaldifeat.FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = params.sample_rate + opts.mel_opts.num_bins = params.feature_dim + + fbank = kaldifeat.Fbank(opts) + + logging.info(f"Reading sound files: {params.sound_files}") + waves = read_sound_files( + filenames=params.sound_files, expected_sample_rate=params.sample_rate + ) + waves = [w.to(device) for w in waves] + + logging.info("Decoding started") + features = fbank(waves) + feature_lens = [f.size(0) for f in features] + feature_lens = torch.tensor(feature_lens, device=device) + + features = pad_sequence( + features, batch_first=True, padding_value=math.log(1e-10) + ) + + encoder_out, encoder_out_lens = model.encoder( + x=features, x_lens=feature_lens + ) + + num_waves = encoder_out.size(0) + hyp_list = [] + logging.info(f"Using {params.method}") + + if params.method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + hyp_list = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + elif params.method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_list = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + elif params.method == "modified_beam_search": + hyp_list = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + else: + for i in range(num_waves): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.method}" + ) + hyp_list.append(hyp) + + hyps = [] + for hyp in hyp_list: + hyps.append([lexicon.token_table[i] for i in hyp]) + + s = "\n" + for filename, hyp in zip(params.sound_files, hyps): + words = " ".join(hyp) + s += f"{filename}:\n{words}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/scaling.py b/egs/aishell/ASR/pruned_transducer_stateless3/scaling.py new file mode 120000 index 000000000..c10cdfe12 --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/scaling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/scaling.py \ No newline at end of file diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/train.py b/egs/aishell/ASR/pruned_transducer_stateless3/train.py new file mode 100755 index 000000000..02efe94fe --- /dev/null +++ b/egs/aishell/ASR/pruned_transducer_stateless3/train.py @@ -0,0 +1,1229 @@ +#!/usr/bin/env python3 +# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo,) +# Zengwei Yao) +# Copyright 2021 (Pingfeng Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +./prepare.sh +./prepare_aidatatang_200zh.sh + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + + +./pruned_transducer_stateless3/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 0 \ + --exp-dir pruned_transducer_stateless3/exp \ + --max-duration 300 \ + --datatang-prob 0.2 + +# For mix precision training: + +./pruned_transducer_stateless3/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless3/exp \ + --max-duration 550 +""" + + +import argparse +import copy +import logging +import random +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from aidatatang_200zh import AIDatatang200zh +from aishell import AIShell +from asr_datamodule import AsrDataModule +from conformer import Conformer +from decoder import Decoder +from joiner import Joiner +from lhotse import CutSet, load_manifest +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import Transducer +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[ + torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler +] + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=int, + default=12, + help="Number of conformer encoder layers..", + ) + + parser.add_argument( + "--dim-feedforward", + type=int, + default=2048, + help="Feedforward dimension of the conformer encoder layer.", + ) + + parser.add_argument( + "--nhead", + type=int, + default=8, + help="Number of attention heads in the conformer encoder layer.", + ) + + parser.add_argument( + "--encoder-dim", + type=int, + default=512, + help="Attention dimension in the conformer encoder layer.", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Embedding dimension in the decoder model.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="""Dimension used in the joiner model. + Outputs from the encoder and decoder model are projected + to this dimension before adding. + """, + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless3/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="The initial learning rate. This value should not need " + "to be changed.", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate + decreases. We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=6, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=1, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=4000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=30, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=100, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + parser.add_argument( + "--datatang-prob", + type=float, + default=0.2, + help="The probability to select a batch from the " + "aidatatang_200zh dataset", + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 1000, + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + # parameters for Noam + "model_warm_step": 3000, # arg given to model, not for lrate + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Conformer and Transformer + encoder = Conformer( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + decoder_datatang = get_decoder_model(params) + joiner_datatang = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + decoder_datatang=decoder_datatang, + joiner_datatang=joiner_datatang, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def is_aishell(c: Cut) -> bool: + """Return True if this cut is from the AIShell dataset. + + Note: + During data preparation, we set the custom field in + the supervision segment of aidatatang_200zh to + dict(origin='aidatatang_200zh') + See ../local/process_aidatatang_200zh.py. + """ + return c.supervisions[0].custom is None + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + batch: dict, + is_training: bool, + warmup: float = 1.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute RNN-T loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = ( + model.device + if isinstance(model, DDP) + else next(model.parameters()).device + ) + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + aishell = is_aishell(supervisions["cut"][0]) + + texts = batch["supervisions"]["text"] + y = graph_compiler.texts_to_ids(texts) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + aishell=aishell, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + warmup=warmup, + ) + # after the main warmup step, we keep pruned_loss_scale small + # for the same amount of time (model_warm_step), to avoid + # overwhelming the simple_loss and causing it to diverge, + # in case it had not fully learned the alignment yet. + pruned_loss_scale = ( + 0.0 + if warmup < 1.0 + else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) + ) + loss = ( + params.simple_loss_scale * simple_loss + + pruned_loss_scale * pruned_loss + ) + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + graph_compiler: CharCtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + datatang_train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + rng: random.Random, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + aishell_tot_loss = MetricsTracker() + datatang_tot_loss = MetricsTracker() + tot_loss = MetricsTracker() + + # index 0: for LibriSpeech + # index 1: for GigaSpeech + # This sets the probabilities for choosing which datasets + dl_weights = [1 - params.datatang_prob, params.datatang_prob] + + iter_aishell = iter(train_dl) + iter_datatang = iter(datatang_train_dl) + + batch_idx = 0 + + while True: + idx = rng.choices((0, 1), weights=dl_weights, k=1)[0] + dl = iter_aishell if idx == 0 else iter_datatang + + try: + batch = next(dl) + except StopIteration: + break + batch_idx += 1 + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + aishell = is_aishell(batch["supervisions"]["cut"][0]) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + if aishell: + aishell_tot_loss = ( + aishell_tot_loss * (1 - 1 / params.reset_interval) + ) + loss_info + prefix = "aishell" # for logging only + else: + datatang_tot_loss = ( + datatang_tot_loss * (1 - 1 / params.reset_interval) + ) + loss_info + prefix = "datatang" + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + except: # noqa + display_and_save_batch( + batch, params=params, graph_compiler=graph_compiler + ) + raise + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, {prefix}_loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"aishell_tot_loss[{aishell_tot_loss}], " + f"datatang_tot_loss[{datatang_tot_loss}], " + f"batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, + f"train/current_{prefix}_", + params.batch_idx_train, + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + aishell_tot_loss.write_summary( + tb_writer, "train/aishell_tot_", params.batch_idx_train + ) + datatang_tot_loss.write_summary( + tb_writer, "train/datatang_tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def filter_short_and_long_utterances(cuts: CutSet) -> CutSet: + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 12 seconds + # + # Caution: There is a reason to select 12.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 12.0 + + return cuts + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + rng = random.Random(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + graph_compiler = CharCtcTrainingGraphCompiler( + lexicon=lexicon, + device=device, + oov="", + ) + + params.blank_id = 0 + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank], find_unused_parameters=True) + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2 ** 22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + aishell = AIShell(manifest_dir=args.manifest_dir) + train_cuts = aishell.train_cuts() + train_cuts = filter_short_and_long_utterances(train_cuts) + + datatang = AIDatatang200zh(manifest_dir=args.manifest_dir) + train_datatang_cuts = datatang.train_cuts() + train_datatang_cuts = filter_short_and_long_utterances(train_datatang_cuts) + train_datatang_cuts = train_datatang_cuts.repeat(times=None) + + if args.enable_musan: + cuts_musan = load_manifest( + Path(args.manifest_dir) / "musan_cuts.jsonl.gz" + ) + else: + cuts_musan = None + + asr_datamodule = AsrDataModule(args) + + train_dl = asr_datamodule.train_dataloaders( + train_cuts, + on_the_fly_feats=False, + cuts_musan=cuts_musan, + ) + + datatang_train_dl = asr_datamodule.train_dataloaders( + train_datatang_cuts, + on_the_fly_feats=False, + cuts_musan=cuts_musan, + ) + + valid_cuts = aishell.valid_cuts() + valid_dl = asr_datamodule.valid_dataloaders(valid_cuts) + + for dl in [ + train_dl, + # datatang_train_dl + ]: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=dl, + optimizer=optimizer, + graph_compiler=graph_compiler, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + logging.info(f"start training from epoch {params.start_epoch}") + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + datatang_train_dl.sampler.set_epoch(epoch) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + graph_compiler=graph_compiler, + train_dl=train_dl, + datatang_train_dl=datatang_train_dl, + valid_dl=valid_dl, + rng=rng, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def display_and_save_batch( + batch: dict, + params: AttributeDict, + graph_compiler: CharCtcTrainingGraphCompiler, +) -> None: + """Display the batch statistics and save the batch into disk. + + Args: + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + params: + Parameters for training. See :func:`get_params`. + """ + from lhotse.utils import uuid4 + + filename = f"{params.exp_dir}/batch-{uuid4()}.pt" + logging.info(f"Saving batch to {filename}") + torch.save(batch, filename) + + supervisions = batch["supervisions"] + features = batch["inputs"] + + logging.info(f"features shape: {features.shape}") + + y = graph_compiler.texts_to_ids(supervisions["text"]) + num_tokens = sum(len(i) for i in y) + logging.info(f"num tokens: {num_tokens}") + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + graph_compiler: CharCtcTrainingGraphCompiler, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + # warmup = 0.0 is so that the derivs for the pruned loss stay zero + # (i.e. are not remembered by the decaying-average in adam), because + # we want to avoid these params being subject to shrinkage in adam. + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=0.0 if params.start_epoch == 1 else 1.0, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except Exception as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + display_and_save_batch( + batch, params=params, graph_compiler=graph_compiler + ) + raise + + +def main(): + parser = get_parser() + AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + args.lang_dir = Path(args.lang_dir) + + assert 0 <= args.datatang_prob < 1, args.datatang_prob + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/aishell/ASR/transducer_stateless/conformer.py b/egs/aishell/ASR/transducer_stateless/conformer.py index 7e8dc4ace..66eb3eb63 100644 --- a/egs/aishell/ASR/transducer_stateless/conformer.py +++ b/egs/aishell/ASR/transducer_stateless/conformer.py @@ -362,7 +362,7 @@ class RelPositionalEncoding(torch.nn.Module): ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i nn.Module: def main(): args = get_parser().parse_args() - assert args.jit is False, "torchscript support will be added later" - params = get_params() params.update(vars(args)) @@ -223,6 +221,11 @@ def main(): model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/train.py b/egs/aishell/ASR/transducer_stateless_modified-2/train.py index 962fffdf5..225d0d709 100755 --- a/egs/aishell/ASR/transducer_stateless_modified-2/train.py +++ b/egs/aishell/ASR/transducer_stateless_modified-2/train.py @@ -405,7 +405,7 @@ def compute_loss( is_training: bool, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: diff --git a/egs/aishell/ASR/transducer_stateless_modified/export.py b/egs/aishell/ASR/transducer_stateless_modified/export.py index 9a20fab6f..11335a834 100755 --- a/egs/aishell/ASR/transducer_stateless_modified/export.py +++ b/egs/aishell/ASR/transducer_stateless_modified/export.py @@ -182,8 +182,6 @@ def get_transducer_model(params: AttributeDict) -> nn.Module: def main(): args = get_parser().parse_args() - assert args.jit is False, "torchscript support will be added later" - params = get_params() params.update(vars(args)) @@ -223,6 +221,11 @@ def main(): model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/egs/aishell4/ASR/README.md b/egs/aishell4/ASR/README.md new file mode 100644 index 000000000..3744032f8 --- /dev/null +++ b/egs/aishell4/ASR/README.md @@ -0,0 +1,19 @@ + +# Introduction + +This recipe includes some different ASR models trained with Aishell4 (including S, M and L three subsets). + +[./RESULTS.md](./RESULTS.md) contains the latest results. + +# Transducers + +There are various folders containing the name `transducer` in this folder. +The following table lists the differences among them. + +| | Encoder | Decoder | Comment | +|---------------------------------------|---------------------|--------------------|-----------------------------| +| `pruned_transducer_stateless5` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss | | + +The decoder in `transducer_stateless` is modified from the paper +[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/). +We place an additional Conv1d layer right after the input embedding layer. diff --git a/egs/aishell4/ASR/RESULTS.md b/egs/aishell4/ASR/RESULTS.md new file mode 100644 index 000000000..9bd062f1d --- /dev/null +++ b/egs/aishell4/ASR/RESULTS.md @@ -0,0 +1,117 @@ +## Results + +### Aishell4 Char training results (Pruned Transducer Stateless5) + +#### 2022-06-13 + +Using the codes from this PR https://github.com/k2-fsa/icefall/pull/399. + +When use-averaged-model=False, the CERs are +| | test | comment | +|------------------------------------|------------|------------------------------------------| +| greedy search | 30.05 | --epoch 30, --avg 25, --max-duration 800 | +| modified beam search (beam size 4) | 29.16 | --epoch 30, --avg 25, --max-duration 800 | +| fast beam search (set as default) | 29.20 | --epoch 30, --avg 25, --max-duration 1500| + +When use-averaged-model=True, the CERs are +| | test | comment | +|------------------------------------|------------|----------------------------------------------------------------------| +| greedy search | 29.89 | --iter 36000, --avg 8, --max-duration 800 --use-averaged-model=True | +| modified beam search (beam size 4) | 28.91 | --iter 36000, --avg 8, --max-duration 800 --use-averaged-model=True | +| fast beam search (set as default) | 29.08 | --iter 36000, --avg 8, --max-duration 1500 --use-averaged-model=True | + +The training command for reproducing is given below: + +``` +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless5/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless5/exp \ + --lang-dir data/lang_char \ + --max-duration 220 \ + --save-every-n 4000 + +``` + +The tensorboard training log can be found at +https://tensorboard.dev/experiment/tjaVRKERS8C10SzhpBcxSQ/#scalars + +When use-averaged-model=False, the decoding command is: +``` +epoch=30 +avg=25 + +## greedy search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 800 + +## modified beam search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 800 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +## fast beam search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 1500 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +``` + +When use-averaged-model=True, the decoding command is: +``` +iter=36000 +avg=8 + +## greedy search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 800 \ + --use-averaged-model True + +## modified beam search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 800 \ + --decoding-method modified_beam_search \ + --beam-size 4 \ + --use-averaged-model True + +## fast beam search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 1500 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 \ + --use-averaged-model True +``` + +A pre-trained model and decoding logs can be found at diff --git a/egs/aishell4/ASR/local/__init__.py b/egs/aishell4/ASR/local/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/aishell4/ASR/local/compute_fbank_aishell4.py b/egs/aishell4/ASR/local/compute_fbank_aishell4.py new file mode 100755 index 000000000..09f885636 --- /dev/null +++ b/egs/aishell4/ASR/local/compute_fbank_aishell4.py @@ -0,0 +1,123 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the aidatatang_200zh dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import argparse +import logging +import os +from pathlib import Path + +import torch +from lhotse import ChunkedLilcomHdf5Writer, CutSet, Fbank, FbankConfig +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_aishell4(num_mel_bins: int = 80): + src_dir = Path("data/manifests/aishell4") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + + dataset_parts = ( + "train_S", + "train_M", + "train_L", + "test", + ) + prefix = "aishell4" + suffix = "jsonl.gz" + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, + ) + assert manifests is not None + + extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins)) + + with get_executor() as ex: # Initialize the executor only once. + for partition, m in manifests.items(): + cuts_filename = f"{prefix}_cuts_{partition}.{suffix}" + if (output_dir / cuts_filename).is_file(): + logging.info(f"{partition} already exists - skipping.") + continue + logging.info(f"Processing {partition}") + cut_set = CutSet.from_manifests( + recordings=m["recordings"], + supervisions=m["supervisions"], + ) + if "train" in partition: + cut_set = ( + cut_set + + cut_set.perturb_speed(0.9) + + cut_set.perturb_speed(1.1) + ) + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/{prefix}_feats_{partition}", + # when an executor is specified, make more partitions + num_jobs=num_jobs if ex is None else 80, + executor=ex, + storage_type=ChunkedLilcomHdf5Writer, + ) + + logging.info("About splitting cuts into smaller chunks") + cut_set = cut_set.trim_to_supervisions( + keep_overlapping=False, + min_duration=None, + ) + + cut_set.to_file(output_dir / cuts_filename) + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--num-mel-bins", + type=int, + default=80, + help="""The number of mel bins for Fbank""", + ) + + return parser.parse_args() + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + args = get_args() + compute_fbank_aishell4(num_mel_bins=args.num_mel_bins) diff --git a/egs/aishell4/ASR/local/compute_fbank_musan.py b/egs/aishell4/ASR/local/compute_fbank_musan.py new file mode 120000 index 000000000..5833f2484 --- /dev/null +++ b/egs/aishell4/ASR/local/compute_fbank_musan.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/compute_fbank_musan.py \ No newline at end of file diff --git a/egs/aishell4/ASR/local/display_manifest_statistics.py b/egs/aishell4/ASR/local/display_manifest_statistics.py new file mode 100644 index 000000000..b79e55eef --- /dev/null +++ b/egs/aishell4/ASR/local/display_manifest_statistics.py @@ -0,0 +1,113 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This file displays duration statistics of utterances in a manifest. +You can use the displayed value to choose minimum/maximum duration +to remove short and long utterances during the training. +See the function `remove_short_and_long_utt()` +in ../../../librispeech/ASR/transducer/train.py +for usage. +""" + + +from lhotse import load_manifest + + +def main(): + paths = [ + "./data/fbank/cuts_train_S.json.gz", + "./data/fbank/cuts_train_M.json.gz", + "./data/fbank/cuts_train_L.json.gz", + "./data/fbank/cuts_test.json.gz", + ] + + for path in paths: + print(f"Starting display the statistics for {path}") + cuts = load_manifest(path) + cuts.describe() + + +if __name__ == "__main__": + main() + +""" +Starting display the statistics for ./data/fbank/cuts_train_S.json.gz +Cuts count: 91995 +Total duration (hours): 95.8 +Speech duration (hours): 95.8 (100.0%) +*** +Duration statistics (seconds): +mean 3.7 +std 7.1 +min 0.1 +25% 0.9 +50% 2.5 +75% 5.4 +99% 15.3 +99.5% 17.5 +99.9% 23.3 +max 1021.7 +Starting display the statistics for ./data/fbank/cuts_train_M.json.gz +Cuts count: 177195 +Total duration (hours): 179.5 +Speech duration (hours): 179.5 (100.0%) +*** +Duration statistics (seconds): +mean 3.6 +std 6.4 +min 0.0 +25% 0.9 +50% 2.4 +75% 5.2 +99% 14.9 +99.5% 17.0 +99.9% 23.5 +max 990.4 +Starting display the statistics for ./data/fbank/cuts_train_L.json.gz +Cuts count: 37572 +Total duration (hours): 49.1 +Speech duration (hours): 49.1 (100.0%) +*** +Duration statistics (seconds): +mean 4.7 +std 4.0 +min 0.2 +25% 1.6 +50% 3.7 +75% 6.7 +99% 17.5 +99.5% 19.8 +99.9% 26.2 +max 87.4 +Starting display the statistics for ./data/fbank/cuts_test.json.gz +Cuts count: 10574 +Total duration (hours): 12.1 +Speech duration (hours): 12.1 (100.0%) +*** +Duration statistics (seconds): +mean 4.1 +std 3.4 +min 0.2 +25% 1.4 +50% 3.2 +75% 5.8 +99% 14.4 +99.5% 14.9 +99.9% 16.5 +max 17.9 +""" diff --git a/egs/aishell4/ASR/local/prepare_char.py b/egs/aishell4/ASR/local/prepare_char.py new file mode 100755 index 000000000..d9e47d17a --- /dev/null +++ b/egs/aishell4/ASR/local/prepare_char.py @@ -0,0 +1,248 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" + +This script takes as input `lang_dir`, which should contain:: + + - lang_dir/text, + - lang_dir/words.txt + +and generates the following files in the directory `lang_dir`: + + - lexicon.txt + - lexicon_disambig.txt + - L.pt + - L_disambig.pt + - tokens.txt +""" + +import re +from pathlib import Path +from typing import Dict, List + +import k2 +import torch +from prepare_lang import ( + Lexicon, + add_disambig_symbols, + add_self_loops, + write_lexicon, + write_mapping, +) + + +def lexicon_to_fst_no_sil( + lexicon: Lexicon, + token2id: Dict[str, int], + word2id: Dict[str, int], + need_self_loops: bool = False, +) -> k2.Fsa: + """Convert a lexicon to an FST (in k2 format). + + Args: + lexicon: + The input lexicon. See also :func:`read_lexicon` + token2id: + A dict mapping tokens to IDs. + word2id: + A dict mapping words to IDs. + need_self_loops: + If True, add self-loop to states with non-epsilon output symbols + on at least one arc out of the state. The input label for this + self loop is `token2id["#0"]` and the output label is `word2id["#0"]`. + Returns: + Return an instance of `k2.Fsa` representing the given lexicon. + """ + loop_state = 0 # words enter and leave from here + next_state = 1 # the next un-allocated state, will be incremented as we go + + arcs = [] + + # The blank symbol is defined in local/train_bpe_model.py + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + + for word, pieces in lexicon: + assert len(pieces) > 0, f"{word} has no pronunciations" + cur_state = loop_state + + word = word2id[word] + pieces = [ + token2id[i] if i in token2id else token2id[""] for i in pieces + ] + + for i in range(len(pieces) - 1): + w = word if i == 0 else eps + arcs.append([cur_state, next_state, pieces[i], w, 0]) + + cur_state = next_state + next_state += 1 + + # now for the last piece of this word + i = len(pieces) - 1 + w = word if i == 0 else eps + arcs.append([cur_state, loop_state, pieces[i], w, 0]) + + if need_self_loops: + disambig_token = token2id["#0"] + disambig_word = word2id["#0"] + arcs = add_self_loops( + arcs, + disambig_token=disambig_token, + disambig_word=disambig_word, + ) + + final_state = next_state + arcs.append([loop_state, final_state, -1, -1, 0]) + arcs.append([final_state]) + + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + + fsa = k2.Fsa.from_str(arcs, acceptor=False) + return fsa + + +def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool: + """Check if all the given tokens are in token symbol table. + + Args: + token_sym_table: + Token symbol table that contains all the valid tokens. + tokens: + A list of tokens. + Returns: + Return True if there is any token not in the token_sym_table, + otherwise False. + """ + for tok in tokens: + if tok not in token_sym_table: + return True + return False + + +def generate_lexicon( + token_sym_table: Dict[str, int], words: List[str] +) -> Lexicon: + """Generate a lexicon from a word list and token_sym_table. + + Args: + token_sym_table: + Token symbol table that mapping token to token ids. + words: + A list of strings representing words. + Returns: + Return a dict whose keys are words and values are the corresponding + tokens. + """ + lexicon = [] + for word in words: + chars = list(word.strip(" \t")) + if contain_oov(token_sym_table, chars): + continue + lexicon.append((word, chars)) + + # The OOV word is + lexicon.append(("", [""])) + return lexicon + + +def generate_tokens(text_file: str) -> Dict[str, int]: + """Generate tokens from the given text file. + + Args: + text_file: + A file that contains text lines to generate tokens. + Returns: + Return a dict whose keys are tokens and values are token ids ranged + from 0 to len(keys) - 1. + """ + tokens: Dict[str, int] = dict() + tokens[""] = 0 + tokens[""] = 1 + tokens[""] = 2 + whitespace = re.compile(r"([ \t\r\n]+)") + with open(text_file, "r", encoding="utf-8") as f: + for line in f: + line = re.sub(whitespace, "", line) + chars = list(line) + for char in chars: + if char not in tokens: + tokens[char] = len(tokens) + return tokens + + +def main(): + lang_dir = Path("data/lang_char") + text_file = lang_dir / "text" + + word_sym_table = k2.SymbolTable.from_file(lang_dir / "words.txt") + + words = word_sym_table.symbols + + excluded = ["", "!SIL", "", "", "#0", "", ""] + for w in excluded: + if w in words: + words.remove(w) + + token_sym_table = generate_tokens(text_file) + + lexicon = generate_lexicon(token_sym_table, words) + + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + next_token_id = max(token_sym_table.values()) + 1 + for i in range(max_disambig + 1): + disambig = f"#{i}" + assert disambig not in token_sym_table + token_sym_table[disambig] = next_token_id + next_token_id += 1 + + word_sym_table.add("#0") + word_sym_table.add("") + word_sym_table.add("") + + write_mapping(lang_dir / "tokens.txt", token_sym_table) + + write_lexicon(lang_dir / "lexicon.txt", lexicon) + write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig) + + L = lexicon_to_fst_no_sil( + lexicon, + token2id=token_sym_table, + word2id=word_sym_table, + ) + + L_disambig = lexicon_to_fst_no_sil( + lexicon_disambig, + token2id=token_sym_table, + word2id=word_sym_table, + need_self_loops=True, + ) + torch.save(L.as_dict(), lang_dir / "L.pt") + torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt") + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/local/prepare_lang.py b/egs/aishell4/ASR/local/prepare_lang.py new file mode 100755 index 000000000..e5ae89ec4 --- /dev/null +++ b/egs/aishell4/ASR/local/prepare_lang.py @@ -0,0 +1,390 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input a lexicon file "data/lang_phone/lexicon.txt" +consisting of words and tokens (i.e., phones) and does the following: + +1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt + +2. Generate tokens.txt, the token table mapping a token to a unique integer. + +3. Generate words.txt, the word table mapping a word to a unique integer. + +4. Generate L.pt, in k2 format. It can be loaded by + + d = torch.load("L.pt") + lexicon = k2.Fsa.from_dict(d) + +5. Generate L_disambig.pt, in k2 format. +""" +import argparse +import math +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Tuple + +import k2 +import torch + +from icefall.lexicon import read_lexicon, write_lexicon + +Lexicon = List[Tuple[str, List[str]]] + + +def write_mapping(filename: str, sym2id: Dict[str, int]) -> None: + """Write a symbol to ID mapping to a file. + + Note: + No need to implement `read_mapping` as it can be done + through :func:`k2.SymbolTable.from_file`. + + Args: + filename: + Filename to save the mapping. + sym2id: + A dict mapping symbols to IDs. + Returns: + Return None. + """ + with open(filename, "w", encoding="utf-8") as f: + for sym, i in sym2id.items(): + f.write(f"{sym} {i}\n") + + +def get_tokens(lexicon: Lexicon) -> List[str]: + """Get tokens from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique tokens. + """ + ans = set() + for _, tokens in lexicon: + ans.update(tokens) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def get_words(lexicon: Lexicon) -> List[str]: + """Get words from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique words. + """ + ans = set() + for word, _ in lexicon: + ans.add(word) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]: + """It adds pseudo-token disambiguation symbols #1, #2 and so on + at the ends of tokens to ensure that all pronunciations are different, + and that none is a prefix of another. + + See also add_lex_disambig.pl from kaldi. + + Args: + lexicon: + It is returned by :func:`read_lexicon`. + Returns: + Return a tuple with two elements: + + - The output lexicon with disambiguation symbols + - The ID of the max disambiguation symbol that appears + in the lexicon + """ + + # (1) Work out the count of each token-sequence in the + # lexicon. + count = defaultdict(int) + for _, tokens in lexicon: + count[" ".join(tokens)] += 1 + + # (2) For each left sub-sequence of each token-sequence, note down + # that it exists (for identifying prefixes of longer strings). + issubseq = defaultdict(int) + for _, tokens in lexicon: + tokens = tokens.copy() + tokens.pop() + while tokens: + issubseq[" ".join(tokens)] = 1 + tokens.pop() + + # (3) For each entry in the lexicon: + # if the token sequence is unique and is not a + # prefix of another word, no disambig symbol. + # Else output #1, or #2, #3, ... if the same token-seq + # has already been assigned a disambig symbol. + ans = [] + + # We start with #1 since #0 has its own purpose + first_allowed_disambig = 1 + max_disambig = first_allowed_disambig - 1 + last_used_disambig_symbol_of = defaultdict(int) + + for word, tokens in lexicon: + tokenseq = " ".join(tokens) + assert tokenseq != "" + if issubseq[tokenseq] == 0 and count[tokenseq] == 1: + ans.append((word, tokens)) + continue + + cur_disambig = last_used_disambig_symbol_of[tokenseq] + if cur_disambig == 0: + cur_disambig = first_allowed_disambig + else: + cur_disambig += 1 + + if cur_disambig > max_disambig: + max_disambig = cur_disambig + last_used_disambig_symbol_of[tokenseq] = cur_disambig + tokenseq += f" #{cur_disambig}" + ans.append((word, tokenseq.split())) + return ans, max_disambig + + +def generate_id_map(symbols: List[str]) -> Dict[str, int]: + """Generate ID maps, i.e., map a symbol to a unique ID. + + Args: + symbols: + A list of unique symbols. + Returns: + A dict containing the mapping between symbols and IDs. + """ + return {sym: i for i, sym in enumerate(symbols)} + + +def add_self_loops( + arcs: List[List[Any]], disambig_token: int, disambig_word: int +) -> List[List[Any]]: + """Adds self-loops to states of an FST to propagate disambiguation symbols + through it. They are added on each state with non-epsilon output symbols + on at least one arc out of the state. + + See also fstaddselfloops.pl from Kaldi. One difference is that + Kaldi uses OpenFst style FSTs and it has multiple final states. + This function uses k2 style FSTs and it does not need to add self-loops + to the final state. + + The input label of a self-loop is `disambig_token`, while the output + label is `disambig_word`. + + Args: + arcs: + A list-of-list. The sublist contains + `[src_state, dest_state, label, aux_label, score]` + disambig_token: + It is the token ID of the symbol `#0`. + disambig_word: + It is the word ID of the symbol `#0`. + + Return: + Return new `arcs` containing self-loops. + """ + states_needs_self_loops = set() + for arc in arcs: + src, dst, ilabel, olabel, score = arc + if olabel != 0: + states_needs_self_loops.add(src) + + ans = [] + for s in states_needs_self_loops: + ans.append([s, s, disambig_token, disambig_word, 0]) + + return arcs + ans + + +def lexicon_to_fst( + lexicon: Lexicon, + token2id: Dict[str, int], + word2id: Dict[str, int], + sil_token: str = "SIL", + sil_prob: float = 0.5, + need_self_loops: bool = False, +) -> k2.Fsa: + """Convert a lexicon to an FST (in k2 format) with optional silence at + the beginning and end of each word. + + Args: + lexicon: + The input lexicon. See also :func:`read_lexicon` + token2id: + A dict mapping tokens to IDs. + word2id: + A dict mapping words to IDs. + sil_token: + The silence token. + sil_prob: + The probability for adding a silence at the beginning and end + of the word. + need_self_loops: + If True, add self-loop to states with non-epsilon output symbols + on at least one arc out of the state. The input label for this + self loop is `token2id["#0"]` and the output label is `word2id["#0"]`. + Returns: + Return an instance of `k2.Fsa` representing the given lexicon. + """ + assert sil_prob > 0.0 and sil_prob < 1.0 + # CAUTION: we use score, i.e, negative cost. + sil_score = math.log(sil_prob) + no_sil_score = math.log(1.0 - sil_prob) + + start_state = 0 + loop_state = 1 # words enter and leave from here + sil_state = 2 # words terminate here when followed by silence; this state + # has a silence transition to loop_state. + next_state = 3 # the next un-allocated state, will be incremented as we go. + arcs = [] + + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + + sil_token = token2id[sil_token] + + arcs.append([start_state, loop_state, eps, eps, no_sil_score]) + arcs.append([start_state, sil_state, eps, eps, sil_score]) + arcs.append([sil_state, loop_state, sil_token, eps, 0]) + + for word, tokens in lexicon: + assert len(tokens) > 0, f"{word} has no pronunciations" + cur_state = loop_state + + word = word2id[word] + tokens = [token2id[i] for i in tokens] + + for i in range(len(tokens) - 1): + w = word if i == 0 else eps + arcs.append([cur_state, next_state, tokens[i], w, 0]) + + cur_state = next_state + next_state += 1 + + # now for the last token of this word + # It has two out-going arcs, one to the loop state, + # the other one to the sil_state. + i = len(tokens) - 1 + w = word if i == 0 else eps + arcs.append([cur_state, loop_state, tokens[i], w, no_sil_score]) + arcs.append([cur_state, sil_state, tokens[i], w, sil_score]) + + if need_self_loops: + disambig_token = token2id["#0"] + disambig_word = word2id["#0"] + arcs = add_self_loops( + arcs, + disambig_token=disambig_token, + disambig_word=disambig_word, + ) + + final_state = next_state + arcs.append([loop_state, final_state, -1, -1, 0]) + arcs.append([final_state]) + + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + + fsa = k2.Fsa.from_str(arcs, acceptor=False) + return fsa + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", type=str, help="The lang dir, data/lang_phone" + ) + return parser.parse_args() + + +def main(): + out_dir = Path(get_args().lang_dir) + lexicon_filename = out_dir / "lexicon.txt" + sil_token = "SIL" + sil_prob = 0.5 + + lexicon = read_lexicon(lexicon_filename) + tokens = get_tokens(lexicon) + words = get_words(lexicon) + + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + for i in range(max_disambig + 1): + disambig = f"#{i}" + assert disambig not in tokens + tokens.append(f"#{i}") + + assert "" not in tokens + tokens = [""] + tokens + + assert "" not in words + assert "#0" not in words + assert "" not in words + assert "" not in words + + words = [""] + words + ["#0", "", ""] + + token2id = generate_id_map(tokens) + word2id = generate_id_map(words) + + write_mapping(out_dir / "tokens.txt", token2id) + write_mapping(out_dir / "words.txt", word2id) + write_lexicon(out_dir / "lexicon_disambig.txt", lexicon_disambig) + + L = lexicon_to_fst( + lexicon, + token2id=token2id, + word2id=word2id, + sil_token=sil_token, + sil_prob=sil_prob, + ) + + L_disambig = lexicon_to_fst( + lexicon_disambig, + token2id=token2id, + word2id=word2id, + sil_token=sil_token, + sil_prob=sil_prob, + need_self_loops=True, + ) + torch.save(L.as_dict(), out_dir / "L.pt") + torch.save(L_disambig.as_dict(), out_dir / "L_disambig.pt") + + if False: + # Just for debugging, will remove it + L.labels_sym = k2.SymbolTable.from_file(out_dir / "tokens.txt") + L.aux_labels_sym = k2.SymbolTable.from_file(out_dir / "words.txt") + L_disambig.labels_sym = L.labels_sym + L_disambig.aux_labels_sym = L.aux_labels_sym + L.draw(out_dir / "L.png", title="L") + L_disambig.draw(out_dir / "L_disambig.png", title="L_disambig") + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/local/prepare_words.py b/egs/aishell4/ASR/local/prepare_words.py new file mode 100755 index 000000000..65aca2983 --- /dev/null +++ b/egs/aishell4/ASR/local/prepare_words.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input words.txt without ids: + - words_no_ids.txt +and generates the new words.txt with related ids. + - words.txt +""" + + +import argparse +import logging + +from tqdm import tqdm + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Prepare words.txt", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input-file", + default="data/lang_char/words_no_ids.txt", + type=str, + help="the words file without ids for WenetSpeech", + ) + parser.add_argument( + "--output-file", + default="data/lang_char/words.txt", + type=str, + help="the words file with ids for WenetSpeech", + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input_file + output_file = args.output_file + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + new_lines = [] + add_words = [" 0", "!SIL 1", " 2", " 3"] + new_lines.extend(add_words) + + logging.info("Starting reading the input file") + for i in tqdm(range(len(lines))): + x = lines[i] + idx = 4 + i + new_line = str(x.strip("\n")) + " " + str(idx) + new_lines.append(new_line) + + logging.info("Starting writing the words.txt") + f_out = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_out.write(line) + f_out.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/local/test_prepare_lang.py b/egs/aishell4/ASR/local/test_prepare_lang.py new file mode 100755 index 000000000..d4cf62bba --- /dev/null +++ b/egs/aishell4/ASR/local/test_prepare_lang.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) + +import os +import tempfile + +import k2 +from prepare_lang import ( + add_disambig_symbols, + generate_id_map, + get_phones, + get_words, + lexicon_to_fst, + read_lexicon, + write_lexicon, + write_mapping, +) + + +def generate_lexicon_file() -> str: + fd, filename = tempfile.mkstemp() + os.close(fd) + s = """ + !SIL SIL + SPN + SPN + f f + a a + foo f o o + bar b a r + bark b a r k + food f o o d + food2 f o o d + fo f o + """.strip() + with open(filename, "w") as f: + f.write(s) + return filename + + +def test_read_lexicon(filename: str): + lexicon = read_lexicon(filename) + phones = get_phones(lexicon) + words = get_words(lexicon) + print(lexicon) + print(phones) + print(words) + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + print(lexicon_disambig) + print("max disambig:", f"#{max_disambig}") + + phones = ["", "SIL", "SPN"] + phones + for i in range(max_disambig + 1): + phones.append(f"#{i}") + words = [""] + words + + phone2id = generate_id_map(phones) + word2id = generate_id_map(words) + + print(phone2id) + print(word2id) + + write_mapping("phones.txt", phone2id) + write_mapping("words.txt", word2id) + + write_lexicon("a.txt", lexicon) + write_lexicon("a_disambig.txt", lexicon_disambig) + + fsa = lexicon_to_fst(lexicon, phone2id=phone2id, word2id=word2id) + fsa.labels_sym = k2.SymbolTable.from_file("phones.txt") + fsa.aux_labels_sym = k2.SymbolTable.from_file("words.txt") + fsa.draw("L.pdf", title="L") + + fsa_disambig = lexicon_to_fst( + lexicon_disambig, phone2id=phone2id, word2id=word2id + ) + fsa_disambig.labels_sym = k2.SymbolTable.from_file("phones.txt") + fsa_disambig.aux_labels_sym = k2.SymbolTable.from_file("words.txt") + fsa_disambig.draw("L_disambig.pdf", title="L_disambig") + + +def main(): + filename = generate_lexicon_file() + test_read_lexicon(filename) + os.remove(filename) + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/local/text2segments.py b/egs/aishell4/ASR/local/text2segments.py new file mode 100644 index 000000000..3df727c67 --- /dev/null +++ b/egs/aishell4/ASR/local/text2segments.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input "text", which refers to the transcript file for +WenetSpeech: + - text +and generates the output file text_word_segmentation which is implemented +with word segmenting: + - text_words_segmentation +""" + + +import argparse + +import jieba +from tqdm import tqdm + +jieba.enable_paddle() + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Chinese Word Segmentation for text", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input-file", + default="data/lang_char/text", + type=str, + help="the input text file for WenetSpeech", + ) + parser.add_argument( + "--output-file", + default="data/lang_char/text_words_segmentation", + type=str, + help="the text implemented with words segmenting for WenetSpeech", + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input_file + output_file = args.output_file + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + new_lines = [] + for i in tqdm(range(len(lines))): + x = lines[i].rstrip() + seg_list = jieba.cut(x, use_paddle=True) + new_line = " ".join(seg_list) + new_lines.append(new_line) + + f_new = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_new.write(line) + f_new.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/local/text2token.py b/egs/aishell4/ASR/local/text2token.py new file mode 100755 index 000000000..71be2a613 --- /dev/null +++ b/egs/aishell4/ASR/local/text2token.py @@ -0,0 +1,195 @@ +#!/usr/bin/env python3 +# Copyright 2017 Johns Hopkins University (authors: Shinji Watanabe) +# 2022 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import codecs +import re +import sys +from typing import List + +from pypinyin import lazy_pinyin, pinyin + +is_python2 = sys.version_info[0] == 2 + + +def exist_or_not(i, match_pos): + start_pos = None + end_pos = None + for pos in match_pos: + if pos[0] <= i < pos[1]: + start_pos = pos[0] + end_pos = pos[1] + break + + return start_pos, end_pos + + +def get_parser(): + parser = argparse.ArgumentParser( + description="convert raw text to tokenized text", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--nchar", + "-n", + default=1, + type=int, + help="number of characters to split, i.e., \ + aabb -> a a b b with -n 1 and aa bb with -n 2", + ) + parser.add_argument( + "--skip-ncols", "-s", default=0, type=int, help="skip first n columns" + ) + parser.add_argument( + "--space", default="", type=str, help="space symbol" + ) + parser.add_argument( + "--non-lang-syms", + "-l", + default=None, + type=str, + help="list of non-linguistic symobles, e.g., etc.", + ) + parser.add_argument( + "text", type=str, default=False, nargs="?", help="input text" + ) + parser.add_argument( + "--trans_type", + "-t", + type=str, + default="char", + choices=["char", "pinyin", "lazy_pinyin"], + help="""Transcript type. char/pinyin/lazy_pinyin""", + ) + return parser + + +def token2id( + texts, token_table, token_type: str = "lazy_pinyin", oov: str = "" +) -> List[List[int]]: + """Convert token to id. + Args: + texts: + The input texts, it refers to the chinese text here. + token_table: + The token table is built based on "data/lang_xxx/token.txt" + token_type: + The type of token, such as "pinyin" and "lazy_pinyin". + oov: + Out of vocabulary token. When a word(token) in the transcript + does not exist in the token list, it is replaced with `oov`. + + Returns: + The list of ids for the input texts. + """ + if texts is None: + raise ValueError("texts can't be None!") + else: + oov_id = token_table[oov] + ids: List[List[int]] = [] + for text in texts: + chars_list = list(str(text)) + if token_type == "lazy_pinyin": + text = lazy_pinyin(chars_list) + sub_ids = [ + token_table[txt] if txt in token_table else oov_id + for txt in text + ] + ids.append(sub_ids) + else: # token_type = "pinyin" + text = pinyin(chars_list) + sub_ids = [ + token_table[txt[0]] if txt[0] in token_table else oov_id + for txt in text + ] + ids.append(sub_ids) + return ids + + +def main(): + parser = get_parser() + args = parser.parse_args() + + rs = [] + if args.non_lang_syms is not None: + with codecs.open(args.non_lang_syms, "r", encoding="utf-8") as f: + nls = [x.rstrip() for x in f.readlines()] + rs = [re.compile(re.escape(x)) for x in nls] + + if args.text: + f = codecs.open(args.text, encoding="utf-8") + else: + f = codecs.getreader("utf-8")( + sys.stdin if is_python2 else sys.stdin.buffer + ) + + sys.stdout = codecs.getwriter("utf-8")( + sys.stdout if is_python2 else sys.stdout.buffer + ) + line = f.readline() + n = args.nchar + while line: + x = line.split() + print(" ".join(x[: args.skip_ncols]), end=" ") + a = " ".join(x[args.skip_ncols :]) # noqa E203 + + # get all matched positions + match_pos = [] + for r in rs: + i = 0 + while i >= 0: + m = r.search(a, i) + if m: + match_pos.append([m.start(), m.end()]) + i = m.end() + else: + break + if len(match_pos) > 0: + chars = [] + i = 0 + while i < len(a): + start_pos, end_pos = exist_or_not(i, match_pos) + if start_pos is not None: + chars.append(a[start_pos:end_pos]) + i = end_pos + else: + chars.append(a[i]) + i += 1 + a = chars + + if args.trans_type == "pinyin": + a = pinyin(list(str(a))) + a = [one[0] for one in a] + + if args.trans_type == "lazy_pinyin": + a = lazy_pinyin(list(str(a))) + + a = [a[j : j + n] for j in range(0, len(a), n)] # noqa E203 + + a_flat = [] + for z in a: + a_flat.append("".join(z)) + + a_chars = "".join(a_flat) + print(a_chars) + line = f.readline() + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/local/text_normalize.py b/egs/aishell4/ASR/local/text_normalize.py new file mode 100755 index 000000000..5650be502 --- /dev/null +++ b/egs/aishell4/ASR/local/text_normalize.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright 2022 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input "text_full", which includes three transcript files +(train_S, train_M and train_L) for AISHELL4: + - text_full +and generates the output file text_normalize which is implemented +to normalize text: + - text +""" + + +import argparse + +from tqdm import tqdm + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Normalizing for text", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input", + default="data/lang_char/text_full", + type=str, + help="the input text files for AISHELL4", + ) + parser.add_argument( + "--output", + default="data/lang_char/text", + type=str, + help="the text implemented with normalizer for AISHELL4", + ) + + return parser + + +def text_normalize(str_line: str): + line = str_line.strip().rstrip("\n") + line = line.replace(" ", "") + line = line.replace("", "") + line = line.replace("<%>", "") + line = line.replace("<->", "") + line = line.replace("<$>", "") + line = line.replace("<#>", "") + line = line.replace("<_>", "") + line = line.replace("", "") + line = line.replace("`", "") + line = line.replace("&", "") + line = line.replace(",", "") + line = line.replace("Ａ", "") + line = line.replace("ａ", "A") + line = line.replace("ｂ", "B") + line = line.replace("ｃ", "C") + line = line.replace("ｋ", "K") + line = line.replace("ｔ", "T") + line = line.replace("，", "") + line = line.replace("丶", "") + line = line.replace("。", "") + line = line.replace("、", "") + line = line.replace("？", "") + line = line.replace("·", "") + line = line.replace("*", "") + line = line.replace("！", "") + line = line.replace("$", "") + line = line.replace("+", "") + line = line.replace("-", "") + line = line.replace("\\", "") + line = line.replace("?", "") + line = line.replace("￥", "") + line = line.replace("%", "") + line = line.replace(".", "") + line = line.replace("<", "") + line = line.replace("＆", "") + line = line.upper() + + return line + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input + output_file = args.output + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + new_lines = [] + for i in tqdm(range(len(lines))): + new_line = text_normalize(lines[i]) + new_lines.append(new_line) + + f_new = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_new.write(line) + f_new.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/prepare.sh b/egs/aishell4/ASR/prepare.sh new file mode 100755 index 000000000..c351e3964 --- /dev/null +++ b/egs/aishell4/ASR/prepare.sh @@ -0,0 +1,160 @@ +#!/usr/bin/env bash + +set -eou pipefail + +stage=-1 +stop_stage=100 + +# We assume dl_dir (download dir) contains the following +# directories and files. If not, they will be downloaded +# by this script automatically. +# +# - $dl_dir/aishell4 +# You can find four directories:train_S, train_M, train_L and test. +# You can download it from https://openslr.org/111/ +# +# - $dl_dir/musan +# This directory contains the following directories downloaded from +# http://www.openslr.org/17/ +# +# - music +# - noise +# - speech + +dl_dir=$PWD/download + +. shared/parse_options.sh || exit 1 + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +log "dl_dir: $dl_dir" + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "Stage 0: Download data" + + # If you have pre-downloaded it to /path/to/aishell4, + # you can create a symlink + # + # ln -sfv /path/to/aishell4 $dl_dir/aishell4 + # + if [ ! -f $dl_dir/aishell4/train_L ]; then + lhotse download aishell4 $dl_dir/aishell4 + fi + + # If you have pre-downloaded it to /path/to/musan, + # you can create a symlink + # + # ln -sfv /path/to/musan $dl_dir/musan + # + if [ ! -d $dl_dir/musan ]; then + lhotse download musan $dl_dir + fi +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Prepare aishell4 manifest" + # We assume that you have downloaded the aishell4 corpus + # to $dl_dir/aishell4 + if [ ! -f data/manifests/aishell4/.manifests.done ]; then + mkdir -p data/manifests/aishell4 + lhotse prepare aishell4 $dl_dir/aishell4 data/manifests/aishell4 + touch data/manifests/aishell4/.manifests.done + fi +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Process aishell4" + if [ ! -f data/fbank/aishell4/.fbank.done ]; then + mkdir -p data/fbank/aishell4 + lhotse prepare aishell4 $dl_dir/aishell4 data/manifests/aishell4 + touch data/fbank/aishell4/.fbank.done + fi +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Prepare musan manifest" + # We assume that you have downloaded the musan corpus + # to data/musan + if [ ! -f data/manifests/.musan_manifests.done ]; then + log "It may take 6 minutes" + mkdir -p data/manifests + lhotse prepare musan $dl_dir/musan data/manifests + touch data/manifests/.musan_manifests.done + fi +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Compute fbank for musan" + if [ ! -f data/fbank/.msuan.done ]; then + mkdir -p data/fbank + ./local/compute_fbank_musan.py + touch data/fbank/.msuan.done + fi +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "Stage 5: Compute fbank for aishell4" + if [ ! -f data/fbank/.aishell4.done ]; then + mkdir -p data/fbank + ./local/compute_fbank_aishell4.py + touch data/fbank/.aishell4.done + fi +fi + +if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then + log "Stage 6: Prepare char based lang" + lang_char_dir=data/lang_char + mkdir -p $lang_char_dir + + # Prepare text. + # Note: in Linux, you can install jq with the following command: + # wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64 + gunzip -c data/manifests/aishell4/aishell4_supervisions_train_S.jsonl.gz \ + | jq ".text" | sed 's/"//g' \ + | ./local/text2token.py -t "char" > $lang_char_dir/text_S + + gunzip -c data/manifests/aishell4/aishell4_supervisions_train_M.jsonl.gz \ + | jq ".text" | sed 's/"//g' \ + | ./local/text2token.py -t "char" > $lang_char_dir/text_M + + gunzip -c data/manifests/aishell4/aishell4_supervisions_train_L.jsonl.gz \ + | jq ".text" | sed 's/"//g' \ + | ./local/text2token.py -t "char" > $lang_char_dir/text_L + + for r in text_S text_M text_L ; do + cat $lang_char_dir/$r >> $lang_char_dir/text_full + done + + # Prepare text normalize + python ./local/text_normalize.py \ + --input $lang_char_dir/text_full \ + --output $lang_char_dir/text + + # Prepare words segments + python ./local/text2segments.py \ + --input $lang_char_dir/text \ + --output $lang_char_dir/text_words_segmentation + + cat $lang_char_dir/text_words_segmentation | sed "s/ /\n/g" \ + | sort -u | sed "/^$/d" \ + | uniq > $lang_char_dir/words_no_ids.txt + + # Prepare words.txt + if [ ! -f $lang_char_dir/words.txt ]; then + ./local/prepare_words.py \ + --input-file $lang_char_dir/words_no_ids.txt \ + --output-file $lang_char_dir/words.txt + fi + + if [ ! -f $lang_char_dir/L_disambig.pt ]; then + ./local/prepare_char.py + fi +fi diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/__init__.py b/egs/aishell4/ASR/pruned_transducer_stateless5/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/asr_datamodule.py b/egs/aishell4/ASR/pruned_transducer_stateless5/asr_datamodule.py new file mode 100644 index 000000000..7aa53ddda --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/asr_datamodule.py @@ -0,0 +1,448 @@ +# Copyright 2021 Piotr Żelasko +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import inspect +import logging +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, List, Optional + +import torch +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy +from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures + CutConcatenate, + CutMix, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import ( # noqa F401 for AudioSamples + AudioSamples, + OnTheFlyFeatures, +) +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader + +from icefall.utils import str2bool + + +class _SeedWorkers: + def __init__(self, seed: int): + self.seed = seed + + def __call__(self, worker_id: int): + fix_random_seed(self.seed + worker_id) + + +class Aishell4AsrDataModule: + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + This class should be derived for specific corpora used in ASR tasks. + """ + + def __init__(self, args: argparse.Namespace): + self.args = args + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + + group.add_argument( + "--manifest-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/valid/test cuts.", + ) + + group.add_argument( + "--max-duration", + type=int, + default=200.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + + group.add_argument( + "--num-buckets", + type=int, + default=300, + help="The number of buckets for the DynamicBucketingSampler" + "(you might want to increase it for larger datasets).", + ) + + group.add_argument( + "--concatenate-cuts", + type=str2bool, + default=False, + help="When enabled, utterances (cuts) will be concatenated " + "to minimize the amount of padding.", + ) + + group.add_argument( + "--duration-factor", + type=float, + default=1.0, + help="Determines the maximum duration of a concatenated cut " + "relative to the duration of the longest cut in a batch.", + ) + + group.add_argument( + "--gap", + type=float, + default=1.0, + help="The amount of padding (in seconds) inserted between " + "concatenated cuts. This padding is filled with noise when " + "noise augmentation is used.", + ) + + group.add_argument( + "--on-the-fly-feats", + type=str2bool, + default=False, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + + group.add_argument( + "--drop-last", + type=str2bool, + default=True, + help="Whether to drop last batch. Used by sampler.", + ) + + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + group.add_argument( + "--enable-spec-aug", + type=str2bool, + default=True, + help="When enabled, use SpecAugment for training dataset.", + ) + + group.add_argument( + "--spec-aug-time-warp-factor", + type=int, + default=80, + help="Used only when --enable-spec-aug is True. " + "It specifies the factor for time warping in SpecAugment. " + "Larger values mean more warping. " + "A value less than 1 means to disable time warp.", + ) + + group.add_argument( + "--enable-musan", + type=str2bool, + default=True, + help="When enabled, select noise from MUSAN and mix it" + "with training dataset. ", + ) + + group.add_argument( + "--input-strategy", + type=str, + default="PrecomputedFeatures", + help="AudioSamples or PrecomputedFeatures", + ) + + def train_dataloaders( + self, + cuts_train: CutSet, + sampler_state_dict: Optional[Dict[str, Any]] = None, + ) -> DataLoader: + """ + Args: + cuts_train: + CutSet for training. + sampler_state_dict: + The state dict for the training sampler. + """ + logging.info("About to get Musan cuts") + cuts_musan = load_manifest( + self.args.manifest_dir / "musan_cuts.jsonl.gz" + ) + + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + transforms.append( + CutMix( + cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True + ) + ) + else: + logging.info("Disable MUSAN") + + if self.args.concatenate_cuts: + logging.info( + f"Using cut concatenation with duration factor " + f"{self.args.duration_factor} and gap {self.args.gap}." + ) + # Cut concatenation should be the first transform in the list, + # so that if we e.g. mix noise in, it will fill the gaps between + # different utterances. + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + input_transforms = [] + if self.args.enable_spec_aug: + logging.info("Enable SpecAugment") + logging.info( + f"Time warp factor: {self.args.spec_aug_time_warp_factor}" + ) + # Set the value of num_frame_masks according to Lhotse's version. + # In different Lhotse's versions, the default of num_frame_masks is + # different. + num_frame_masks = 10 + num_frame_masks_parameter = inspect.signature( + SpecAugment.__init__ + ).parameters["num_frame_masks"] + if num_frame_masks_parameter.default == 1: + num_frame_masks = 2 + logging.info(f"Num frame mask: {num_frame_masks}") + input_transforms.append( + SpecAugment( + time_warp_factor=self.args.spec_aug_time_warp_factor, + num_frame_masks=num_frame_masks, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ) + else: + logging.info("Disable SpecAugment") + + logging.info("About to create train dataset") + train = K2SpeechRecognitionDataset( + input_strategy=eval(self.args.input_strategy)(), + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + buffer_size=30000, + drop_last=self.args.drop_last, + ) + else: + logging.info("Using SingleCutSampler.") + train_sampler = SingleCutSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + ) + logging.info("About to create train dataloader") + + # 'seed' is derived from the current random state, which will have + # previously been set in the main process. + seed = torch.randint(0, 100000, ()).item() + worker_init_fn = _SeedWorkers(seed) + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + worker_init_fn=worker_init_fn, + ) + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_dl.sampler.load_state_dict(sampler_state_dict) + + return train_dl + + def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader: + transforms = [] + if self.args.concatenate_cuts: + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + logging.info("About to create dev dataset") + if self.args.on_the_fly_feats: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + return_cuts=self.args.return_cuts, + ) + else: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + return_cuts=self.args.return_cuts, + ) + valid_sampler = DynamicBucketingSampler( + cuts_valid, + max_duration=self.args.max_duration, + rank=0, + world_size=1, + shuffle=False, + ) + logging.info("About to create dev dataloader") + valid_dl = DataLoader( + validate, + sampler=valid_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + ) + + return valid_dl + + def test_dataloaders(self, cuts: CutSet) -> DataLoader: + logging.debug("About to create test dataset") + test = K2SpeechRecognitionDataset( + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) + if self.args.on_the_fly_feats + else eval(self.args.input_strategy)(), + return_cuts=self.args.return_cuts, + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + rank=0, + world_size=1, + shuffle=False, + ) + logging.info("About to create test dataloader") + test_dl = DataLoader( + test, + batch_size=None, + sampler=sampler, + num_workers=self.args.num_workers, + ) + return test_dl + + @lru_cache() + def train_S_cuts(self) -> CutSet: + logging.info("About to get S train cuts") + return load_manifest_lazy( + self.args.manifest_dir / "aishell4_cuts_train_S.jsonl.gz" + ) + + @lru_cache() + def train_M_cuts(self) -> CutSet: + logging.info("About to get M train cuts") + return load_manifest_lazy( + self.args.manifest_dir / "aishell4_cuts_train_M.jsonl.gz" + ) + + @lru_cache() + def train_L_cuts(self) -> CutSet: + logging.info("About to get L train cuts") + return load_manifest_lazy( + self.args.manifest_dir / "aishell4_cuts_train_L.jsonl.gz" + ) + + @lru_cache() + def valid_cuts(self) -> CutSet: + logging.info("About to get dev cuts") + # Aishell4 doesn't have dev data, here use test to replace dev. + return load_manifest_lazy( + self.args.manifest_dir / "aishell4_cuts_test.jsonl.gz" + ) + + @lru_cache() + def test_cuts(self) -> List[CutSet]: + logging.info("About to get test cuts") + return load_manifest_lazy( + self.args.manifest_dir / "aishell4_cuts_test.jsonl.gz" + ) diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/beam_search.py b/egs/aishell4/ASR/pruned_transducer_stateless5/beam_search.py new file mode 120000 index 000000000..ed78bd4bb --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/beam_search.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/conformer.py b/egs/aishell4/ASR/pruned_transducer_stateless5/conformer.py new file mode 100644 index 000000000..d7b29f37c --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/conformer.py @@ -0,0 +1,1332 @@ +#!/usr/bin/env python3 +# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import math +import warnings +from typing import List, Optional, Tuple + +import torch +from encoder_interface import EncoderInterface +from scaling import ( + ActivationBalancer, + BasicNorm, + DoubleSwish, + ScaledConv1d, + ScaledConv2d, + ScaledLinear, +) +from torch import Tensor, nn + +from icefall.utils import make_pad_mask + + +class Conformer(EncoderInterface): + """ + Args: + num_features (int): Number of input features + subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers) + d_model (int): attention dimension, also the output dimension + nhead (int): number of head + dim_feedforward (int): feedforward dimention + num_encoder_layers (int): number of encoder layers + dropout (float): dropout rate + layer_dropout (float): layer-dropout rate. + cnn_module_kernel (int): Kernel size of convolution module + vgg_frontend (bool): whether to use vgg frontend. + """ + + def __init__( + self, + num_features: int, + subsampling_factor: int = 4, + d_model: int = 256, + nhead: int = 4, + dim_feedforward: int = 2048, + num_encoder_layers: int = 12, + dropout: float = 0.1, + layer_dropout: float = 0.075, + cnn_module_kernel: int = 31, + aux_layer_period: int = 3, + ) -> None: + super(Conformer, self).__init__() + + self.num_features = num_features + self.subsampling_factor = subsampling_factor + if subsampling_factor != 4: + raise NotImplementedError("Support only 'subsampling_factor=4'.") + + # self.encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, T//subsampling_factor, d_model). + # That is, it does two things simultaneously: + # (1) subsampling: T -> T//subsampling_factor + # (2) embedding: num_features -> d_model + self.encoder_embed = Conv2dSubsampling(num_features, d_model) + + self.encoder_pos = RelPositionalEncoding(d_model, dropout) + + encoder_layer = ConformerEncoderLayer( + d_model, + nhead, + dim_feedforward, + dropout, + layer_dropout, + cnn_module_kernel, + ) + self.encoder = ConformerEncoder( + encoder_layer, + num_encoder_layers, + aux_layers=list(range(0, num_encoder_layers - 1, aux_layer_period)), + ) + + def forward( + self, x: torch.Tensor, x_lens: torch.Tensor, warmup: float = 1.0 + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + x: + The input tensor. Its shape is (batch_size, seq_len, feature_dim). + x_lens: + A tensor of shape (batch_size,) containing the number of frames in + `x` before padding. + warmup: + A floating point value that gradually increases from 0 throughout + training; when it is >= 1.0 we are "fully warmed up". It is used + to turn modules on sequentially. + Returns: + Return a tuple containing 2 tensors: + - embeddings: its shape is (batch_size, output_seq_len, d_model) + - lengths, a tensor of shape (batch_size,) containing the number + of frames in `embeddings` before padding. + """ + x = self.encoder_embed(x) + x, pos_emb = self.encoder_pos(x) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + # Caution: We assume the subsampling factor is 4! + lengths = ((x_lens - 1) // 2 - 1) // 2 + assert x.size(0) == lengths.max().item() + mask = make_pad_mask(lengths) + + x = self.encoder( + x, pos_emb, src_key_padding_mask=mask, warmup=warmup + ) # (T, N, C) + + x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + + return x, lengths + + +class ConformerEncoderLayer(nn.Module): + """ + ConformerEncoderLayer is made up of self-attn, feedforward and convolution networks. + See: "Conformer: Convolution-augmented Transformer for Speech Recognition" + + Args: + d_model: the number of expected features in the input (required). + nhead: the number of heads in the multiheadattention models (required). + dim_feedforward: the dimension of the feedforward network model (default=2048). + dropout: the dropout value (default=0.1). + cnn_module_kernel (int): Kernel size of convolution module. + + Examples:: + >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> pos_emb = torch.rand(32, 19, 512) + >>> out = encoder_layer(src, pos_emb) + """ + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + layer_dropout: float = 0.075, + cnn_module_kernel: int = 31, + ) -> None: + super(ConformerEncoderLayer, self).__init__() + + self.layer_dropout = layer_dropout + + self.d_model = d_model + + self.self_attn = RelPositionMultiheadAttention( + d_model, nhead, dropout=0.0 + ) + + self.feed_forward = nn.Sequential( + ScaledLinear(d_model, dim_feedforward), + ActivationBalancer(channel_dim=-1), + DoubleSwish(), + nn.Dropout(dropout), + ScaledLinear(dim_feedforward, d_model, initial_scale=0.25), + ) + + self.feed_forward_macaron = nn.Sequential( + ScaledLinear(d_model, dim_feedforward), + ActivationBalancer(channel_dim=-1), + DoubleSwish(), + nn.Dropout(dropout), + ScaledLinear(dim_feedforward, d_model, initial_scale=0.25), + ) + + self.conv_module = ConvolutionModule(d_model, cnn_module_kernel) + + self.norm_final = BasicNorm(d_model) + + # try to ensure the output is close to zero-mean (or at least, zero-median). + self.balancer = ActivationBalancer( + channel_dim=-1, min_positive=0.45, max_positive=0.55, max_abs=6.0 + ) + + self.dropout = nn.Dropout(dropout) + + def forward( + self, + src: Tensor, + pos_emb: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + warmup: float = 1.0, + ) -> Tensor: + """ + Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + pos_emb: Positional embedding tensor (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + warmup: controls selective bypass of of layers; if < 1.0, we will + bypass layers more frequently. + + Shape: + src: (S, N, E). + pos_emb: (N, 2*S-1, E) + src_mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, N is the batch size, E is the feature number + """ + src_orig = src + + warmup_scale = min(0.1 + warmup, 1.0) + # alpha = 1.0 means fully use this encoder layer, 0.0 would mean + # completely bypass it. + if self.training: + alpha = ( + warmup_scale + if torch.rand(()).item() <= (1.0 - self.layer_dropout) + else 0.1 + ) + else: + alpha = 1.0 + + # macaron style feed forward module + src = src + self.dropout(self.feed_forward_macaron(src)) + + # multi-headed self-attention module + src_att = self.self_attn( + src, + src, + src, + pos_emb=pos_emb, + attn_mask=src_mask, + key_padding_mask=src_key_padding_mask, + )[0] + src = src + self.dropout(src_att) + + # convolution module + src = src + self.dropout(self.conv_module(src)) + + # feed forward module + src = src + self.dropout(self.feed_forward(src)) + + src = self.norm_final(self.balancer(src)) + + if alpha != 1.0: + src = alpha * src + (1 - alpha) * src_orig + + return src + + +class ConformerEncoder(nn.Module): + r"""ConformerEncoder is a stack of N encoder layers + + Args: + encoder_layer: an instance of the ConformerEncoderLayer() class (required). + num_layers: the number of sub-encoder-layers in the encoder (required). + + Examples:: + >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) + >>> conformer_encoder = ConformerEncoder(encoder_layer, num_layers=6) + >>> src = torch.rand(10, 32, 512) + >>> pos_emb = torch.rand(32, 19, 512) + >>> out = conformer_encoder(src, pos_emb) + """ + + def __init__( + self, + encoder_layer: nn.Module, + num_layers: int, + aux_layers: List[int], + ) -> None: + super().__init__() + self.layers = nn.ModuleList( + [copy.deepcopy(encoder_layer) for i in range(num_layers)] + ) + self.num_layers = num_layers + + assert num_layers - 1 not in aux_layers + self.aux_layers = set(aux_layers + [num_layers - 1]) + + num_channels = encoder_layer.norm_final.num_channels + self.combiner = RandomCombine( + num_inputs=len(self.aux_layers), + num_channels=num_channels, + final_weight=0.5, + pure_prob=0.333, + stddev=2.0, + ) + + def forward( + self, + src: Tensor, + pos_emb: Tensor, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + warmup: float = 1.0, + ) -> Tensor: + r"""Pass the input through the encoder layers in turn. + + Args: + src: the sequence to the encoder (required). + pos_emb: Positional embedding tensor (required). + mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + src: (S, N, E). + pos_emb: (N, 2*S-1, E) + mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number + + """ + output = src + + outputs = [] + + for i, mod in enumerate(self.layers): + output = mod( + output, + pos_emb, + src_mask=mask, + src_key_padding_mask=src_key_padding_mask, + warmup=warmup, + ) + if i in self.aux_layers: + outputs.append(output) + + output = self.combiner(outputs) + + return output + + +class RelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module. + + See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" + Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py + + Args: + d_model: Embedding dimension. + dropout_rate: Dropout rate. + max_len: Maximum input length. + + """ + + def __init__( + self, d_model: int, dropout_rate: float, max_len: int = 5000 + ) -> None: + """Construct an PositionalEncoding object.""" + super(RelPositionalEncoding, self).__init__() + self.d_model = d_model + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: Tensor) -> None: + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + # Note: TorchScript doesn't implement operator== for torch.Device + if self.pe.dtype != x.dtype or str(self.pe.device) != str( + x.device + ): + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vector and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[Tensor, Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Encoded tensor (batch, 2*time-1, `*`). + + """ + self.extend_pe(x) + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 + - x.size(1) + + 1 : self.pe.size(1) // 2 # noqa E203 + + x.size(1), + ] + return self.dropout(x), self.dropout(pos_emb) + + +class RelPositionMultiheadAttention(nn.Module): + r"""Multi-Head Attention layer with relative position encoding + + See reference: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" + + Args: + embed_dim: total dimension of the model. + num_heads: parallel attention heads. + dropout: a Dropout layer on attn_output_weights. Default: 0.0. + + Examples:: + + >>> rel_pos_multihead_attn = RelPositionMultiheadAttention(embed_dim, num_heads) + >>> attn_output, attn_output_weights = multihead_attn(query, key, value, pos_emb) + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + ) -> None: + super(RelPositionMultiheadAttention, self).__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + + self.in_proj = ScaledLinear(embed_dim, 3 * embed_dim, bias=True) + self.out_proj = ScaledLinear( + embed_dim, embed_dim, bias=True, initial_scale=0.25 + ) + + # linear transformation for positional encoding. + self.linear_pos = ScaledLinear(embed_dim, embed_dim, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(num_heads, self.head_dim)) + self.pos_bias_v = nn.Parameter(torch.Tensor(num_heads, self.head_dim)) + self.pos_bias_u_scale = nn.Parameter(torch.zeros(()).detach()) + self.pos_bias_v_scale = nn.Parameter(torch.zeros(()).detach()) + self._reset_parameters() + + def _pos_bias_u(self): + return self.pos_bias_u * self.pos_bias_u_scale.exp() + + def _pos_bias_v(self): + return self.pos_bias_v * self.pos_bias_v_scale.exp() + + def _reset_parameters(self) -> None: + nn.init.normal_(self.pos_bias_u, std=0.01) + nn.init.normal_(self.pos_bias_v, std=0.01) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + pos_emb: Positional embedding tensor + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. When given a binary mask and a value is True, + the corresponding value on the attention layer will be ignored. When given + a byte mask and a value is non-zero, the corresponding value on the attention + layer will be ignored + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + + Shape: + - Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - pos_emb: :math:`(N, 2*L-1, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the position + with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + + - Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + return self.multi_head_attention_forward( + query, + key, + value, + pos_emb, + self.embed_dim, + self.num_heads, + self.in_proj.get_weight(), + self.in_proj.get_bias(), + self.dropout, + self.out_proj.get_weight(), + self.out_proj.get_bias(), + training=self.training, + key_padding_mask=key_padding_mask, + need_weights=need_weights, + attn_mask=attn_mask, + ) + + def rel_shift(self, x: Tensor) -> Tensor: + """Compute relative positional encoding. + + Args: + x: Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + Tensor: tensor of shape (batch, head, time1, time2) + (note: time2 has the same value as time1, but it is for + the key, while time1 is for the query). + """ + (batch_size, num_heads, time1, n) = x.shape + assert n == 2 * time1 - 1 + # Note: TorchScript requires explicit arg for stride() + batch_stride = x.stride(0) + head_stride = x.stride(1) + time1_stride = x.stride(2) + n_stride = x.stride(3) + return x.as_strided( + (batch_size, num_heads, time1, time1), + (batch_stride, head_stride, time1_stride - n_stride, n_stride), + storage_offset=n_stride * (time1 - 1), + ) + + def multi_head_attention_forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + embed_dim_to_check: int, + num_heads: int, + in_proj_weight: Tensor, + in_proj_bias: Tensor, + dropout_p: float, + out_proj_weight: Tensor, + out_proj_bias: Tensor, + training: bool = True, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + pos_emb: Positional embedding tensor + embed_dim_to_check: total dimension of the model. + num_heads: parallel attention heads. + in_proj_weight, in_proj_bias: input projection weight and bias. + dropout_p: probability of an element to be zeroed. + out_proj_weight, out_proj_bias: the output projection weight and bias. + training: apply dropout if is ``True``. + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. This is an binary mask. When the value is True, + the corresponding value on the attention layer will be filled with -inf. + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + + Shape: + Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - pos_emb: :math:`(N, 2*L-1, E)` or :math:`(1, 2*L-1, E)` where L is the target sequence + length, N is the batch size, E is the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions + will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + + Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == embed_dim_to_check + assert key.size(0) == value.size(0) and key.size(1) == value.size(1) + + head_dim = embed_dim // num_heads + assert ( + head_dim * num_heads == embed_dim + ), "embed_dim must be divisible by num_heads" + + scaling = float(head_dim) ** -0.5 + + if torch.equal(query, key) and torch.equal(key, value): + # self-attention + q, k, v = nn.functional.linear( + query, in_proj_weight, in_proj_bias + ).chunk(3, dim=-1) + + elif torch.equal(key, value): + # encoder-decoder attention + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = nn.functional.linear(query, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + k, v = nn.functional.linear(key, _w, _b).chunk(2, dim=-1) + + else: + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = nn.functional.linear(query, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = embed_dim * 2 + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + k = nn.functional.linear(key, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim * 2 + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + v = nn.functional.linear(value, _w, _b) + + if attn_mask is not None: + assert ( + attn_mask.dtype == torch.float32 + or attn_mask.dtype == torch.float64 + or attn_mask.dtype == torch.float16 + or attn_mask.dtype == torch.uint8 + or attn_mask.dtype == torch.bool + ), "Only float, byte, and bool types are supported for attn_mask, not {}".format( + attn_mask.dtype + ) + if attn_mask.dtype == torch.uint8: + warnings.warn( + "Byte tensor for attn_mask is deprecated. Use bool tensor instead." + ) + attn_mask = attn_mask.to(torch.bool) + + if attn_mask.dim() == 2: + attn_mask = attn_mask.unsqueeze(0) + if list(attn_mask.size()) != [1, query.size(0), key.size(0)]: + raise RuntimeError( + "The size of the 2D attn_mask is not correct." + ) + elif attn_mask.dim() == 3: + if list(attn_mask.size()) != [ + bsz * num_heads, + query.size(0), + key.size(0), + ]: + raise RuntimeError( + "The size of the 3D attn_mask is not correct." + ) + else: + raise RuntimeError( + "attn_mask's dimension {} is not supported".format( + attn_mask.dim() + ) + ) + # attn_mask's dim is 3 now. + + # convert ByteTensor key_padding_mask to bool + if ( + key_padding_mask is not None + and key_padding_mask.dtype == torch.uint8 + ): + warnings.warn( + "Byte tensor for key_padding_mask is deprecated. Use bool tensor instead." + ) + key_padding_mask = key_padding_mask.to(torch.bool) + + q = (q * scaling).contiguous().view(tgt_len, bsz, num_heads, head_dim) + k = k.contiguous().view(-1, bsz, num_heads, head_dim) + v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1) + + src_len = k.size(0) + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz, "{} == {}".format( + key_padding_mask.size(0), bsz + ) + assert key_padding_mask.size(1) == src_len, "{} == {}".format( + key_padding_mask.size(1), src_len + ) + + q = q.transpose(0, 1) # (batch, time1, head, d_k) + + pos_emb_bsz = pos_emb.size(0) + assert pos_emb_bsz in (1, bsz) # actually it is 1 + p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim) + p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + + q_with_bias_u = (q + self._pos_bias_u()).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + q_with_bias_v = (q + self._pos_bias_v()).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + # compute attention score + # first compute matrix a and matrix c + # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 + k = k.permute(1, 2, 3, 0) # (batch, head, d_k, time2) + matrix_ac = torch.matmul( + q_with_bias_u, k + ) # (batch, head, time1, time2) + + # compute matrix b and matrix d + matrix_bd = torch.matmul( + q_with_bias_v, p.transpose(-2, -1) + ) # (batch, head, time1, 2*time1-1) + matrix_bd = self.rel_shift(matrix_bd) + + attn_output_weights = ( + matrix_ac + matrix_bd + ) # (batch, head, time1, time2) + + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, -1 + ) + + assert list(attn_output_weights.size()) == [ + bsz * num_heads, + tgt_len, + src_len, + ] + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + attn_output_weights.masked_fill_(attn_mask, float("-inf")) + else: + attn_output_weights += attn_mask + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + attn_output_weights = attn_output_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + float("-inf"), + ) + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, src_len + ) + + attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1) + attn_output_weights = nn.functional.dropout( + attn_output_weights, p=dropout_p, training=training + ) + + attn_output = torch.bmm(attn_output_weights, v) + assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim] + attn_output = ( + attn_output.transpose(0, 1) + .contiguous() + .view(tgt_len, bsz, embed_dim) + ) + attn_output = nn.functional.linear( + attn_output, out_proj_weight, out_proj_bias + ) + + if need_weights: + # average attention weights over heads + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + return attn_output, attn_output_weights.sum(dim=1) / num_heads + else: + return attn_output, None + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model. + Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py + + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernerl size of conv layers. + bias (bool): Whether to use bias in conv layers (default=True). + + """ + + def __init__( + self, channels: int, kernel_size: int, bias: bool = True + ) -> None: + """Construct an ConvolutionModule object.""" + super(ConvolutionModule, self).__init__() + # kernerl_size should be a odd number for 'SAME' padding + assert (kernel_size - 1) % 2 == 0 + + self.pointwise_conv1 = ScaledConv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + + # after pointwise_conv1 we put x through a gated linear unit (nn.functional.glu). + # For most layers the normal rms value of channels of x seems to be in the range 1 to 4, + # but sometimes, for some reason, for layer 0 the rms ends up being very large, + # between 50 and 100 for different channels. This will cause very peaky and + # sparse derivatives for the sigmoid gating function, which will tend to make + # the loss function not learn effectively. (for most layers the average absolute values + # are in the range 0.5..9.0, and the average p(x>0), i.e. positive proportion, + # at the output of pointwise_conv1.output is around 0.35 to 0.45 for different + # layers, which likely breaks down as 0.5 for the "linear" half and + # 0.2 to 0.3 for the part that goes into the sigmoid. The idea is that if we + # constrain the rms values to a reasonable range via a constraint of max_abs=10.0, + # it will be in a better position to start learning something, i.e. to latch onto + # the correct range. + self.deriv_balancer1 = ActivationBalancer( + channel_dim=1, max_abs=10.0, min_positive=0.05, max_positive=1.0 + ) + + self.depthwise_conv = ScaledConv1d( + channels, + channels, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + groups=channels, + bias=bias, + ) + + self.deriv_balancer2 = ActivationBalancer( + channel_dim=1, min_positive=0.05, max_positive=1.0 + ) + + self.activation = DoubleSwish() + + self.pointwise_conv2 = ScaledConv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + initial_scale=0.25, + ) + + def forward(self, x: Tensor) -> Tensor: + """Compute convolution module. + + Args: + x: Input tensor (#time, batch, channels). + + Returns: + Tensor: Output tensor (#time, batch, channels). + + """ + # exchange the temporal dimension and the feature dimension + x = x.permute(1, 2, 0) # (#batch, channels, time). + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channels, time) + + x = self.deriv_balancer1(x) + x = nn.functional.glu(x, dim=1) # (batch, channels, time) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + + x = self.deriv_balancer2(x) + x = self.activation(x) + + x = self.pointwise_conv2(x) # (batch, channel, time) + + return x.permute(2, 0, 1) + + +class Conv2dSubsampling(nn.Module): + """Convolutional 2D subsampling (to 1/4 length). + + Convert an input of shape (N, T, idim) to an output + with shape (N, T', odim), where + T' = ((T-1)//2 - 1)//2, which approximates T' == T//4 + + It is based on + https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + layer1_channels: int = 8, + layer2_channels: int = 32, + layer3_channels: int = 128, + ) -> None: + """ + Args: + in_channels: + Number of channels in. The input shape is (N, T, in_channels). + Caution: It requires: T >=7, in_channels >=7 + out_channels + Output dim. The output shape is (N, ((T-1)//2 - 1)//2, out_channels) + layer1_channels: + Number of channels in layer1 + layer1_channels: + Number of channels in layer2 + """ + assert in_channels >= 7 + super().__init__() + + self.conv = nn.Sequential( + ScaledConv2d( + in_channels=1, + out_channels=layer1_channels, + kernel_size=3, + padding=1, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ScaledConv2d( + in_channels=layer1_channels, + out_channels=layer2_channels, + kernel_size=3, + stride=2, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ScaledConv2d( + in_channels=layer2_channels, + out_channels=layer3_channels, + kernel_size=3, + stride=2, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ) + self.out = ScaledLinear( + layer3_channels * (((in_channels - 1) // 2 - 1) // 2), out_channels + ) + # set learn_eps=False because out_norm is preceded by `out`, and `out` + # itself has learned scale, so the extra degree of freedom is not + # needed. + self.out_norm = BasicNorm(out_channels, learn_eps=False) + # constrain median of output to be close to zero. + self.out_balancer = ActivationBalancer( + channel_dim=-1, min_positive=0.45, max_positive=0.55 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Subsample x. + + Args: + x: + Its shape is (N, T, idim). + + Returns: + Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim) + """ + # On entry, x is (N, T, idim) + x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W) + x = self.conv(x) + # Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + # Now x is of shape (N, ((T-1)//2 - 1))//2, odim) + x = self.out_norm(x) + x = self.out_balancer(x) + return x + + +class RandomCombine(nn.Module): + """ + This module combines a list of Tensors, all with the same shape, to + produce a single output of that same shape which, in training time, + is a random combination of all the inputs; but which in test time + will be just the last input. + + All but the last input will have a linear transform before we + randomly combine them; these linear transforms will be initialized + to the identity transform. + + The idea is that the list of Tensors will be a list of outputs of multiple + conformer layers. This has a similar effect as iterated loss. (See: + DEJA-VU: DOUBLE FEATURE PRESENTATION AND ITERATED LOSS IN DEEP TRANSFORMER + NETWORKS). + """ + + def __init__( + self, + num_inputs: int, + num_channels: int, + final_weight: float = 0.5, + pure_prob: float = 0.5, + stddev: float = 2.0, + ) -> None: + """ + Args: + num_inputs: + The number of tensor inputs, which equals the number of layers' + outputs that are fed into this module. E.g. in an 18-layer neural + net if we output layers 16, 12, 18, num_inputs would be 3. + num_channels: + The number of channels on the input, e.g. 512. + final_weight: + The amount of weight or probability we assign to the + final layer when randomly choosing layers or when choosing + continuous layer weights. + pure_prob: + The probability, on each frame, with which we choose + only a single layer to output (rather than an interpolation) + stddev: + A standard deviation that we add to log-probs for computing + randomized weights. + + The method of choosing which layers, or combinations of layers, to use, + is conceptually as follows:: + + With probability `pure_prob`:: + With probability `final_weight`: choose final layer, + Else: choose random non-final layer. + Else:: + Choose initial log-weights that correspond to assigning + weight `final_weight` to the final layer and equal + weights to other layers; then add Gaussian noise + with variance `stddev` to these log-weights, and normalize + to weights (note: the average weight assigned to the + final layer here will not be `final_weight` if stddev>0). + """ + super().__init__() + assert 0 <= pure_prob <= 1, pure_prob + assert 0 < final_weight < 1, final_weight + assert num_inputs >= 1 + + self.linear = nn.ModuleList( + [ + nn.Linear(num_channels, num_channels, bias=True) + for _ in range(num_inputs - 1) + ] + ) + + self.num_inputs = num_inputs + self.final_weight = final_weight + self.pure_prob = pure_prob + self.stddev = stddev + + self.final_log_weight = ( + torch.tensor( + (final_weight / (1 - final_weight)) * (self.num_inputs - 1) + ) + .log() + .item() + ) + self._reset_parameters() + + def _reset_parameters(self): + for i in range(len(self.linear)): + nn.init.eye_(self.linear[i].weight) + nn.init.constant_(self.linear[i].bias, 0.0) + + def forward(self, inputs: List[Tensor]) -> Tensor: + """Forward function. + Args: + inputs: + A list of Tensor, e.g. from various layers of a transformer. + All must be the same shape, of (*, num_channels) + Returns: + A Tensor of shape (*, num_channels). In test mode + this is just the final input. + """ + num_inputs = self.num_inputs + assert len(inputs) == num_inputs + if not self.training: + return inputs[-1] + + # Shape of weights: (*, num_inputs) + num_channels = inputs[0].shape[-1] + num_frames = inputs[0].numel() // num_channels + + mod_inputs = [] + for i in range(num_inputs - 1): + mod_inputs.append(self.linear[i](inputs[i])) + mod_inputs.append(inputs[num_inputs - 1]) + + ndim = inputs[0].ndim + # stacked_inputs: (num_frames, num_channels, num_inputs) + stacked_inputs = torch.stack(mod_inputs, dim=ndim).reshape( + (num_frames, num_channels, num_inputs) + ) + + # weights: (num_frames, num_inputs) + weights = self._get_random_weights( + inputs[0].dtype, inputs[0].device, num_frames + ) + + weights = weights.reshape(num_frames, num_inputs, 1) + # ans: (num_frames, num_channels, 1) + ans = torch.matmul(stacked_inputs, weights) + # ans: (*, num_channels) + ans = ans.reshape(*tuple(inputs[0].shape[:-1]), num_channels) + + if __name__ == "__main__": + # for testing only... + print("Weights = ", weights.reshape(num_frames, num_inputs)) + return ans + + def _get_random_weights( + self, dtype: torch.dtype, device: torch.device, num_frames: int + ) -> Tensor: + """Return a tensor of random weights, of shape + `(num_frames, self.num_inputs)`, + Args: + dtype: + The data-type desired for the answer, e.g. float, double. + device: + The device needed for the answer. + num_frames: + The number of sets of weights desired + Returns: + A tensor of shape (num_frames, self.num_inputs), such that + `ans.sum(dim=1)` is all ones. + """ + pure_prob = self.pure_prob + if pure_prob == 0.0: + return self._get_random_mixed_weights(dtype, device, num_frames) + elif pure_prob == 1.0: + return self._get_random_pure_weights(dtype, device, num_frames) + else: + p = self._get_random_pure_weights(dtype, device, num_frames) + m = self._get_random_mixed_weights(dtype, device, num_frames) + return torch.where( + torch.rand(num_frames, 1, device=device) < self.pure_prob, p, m + ) + + def _get_random_pure_weights( + self, dtype: torch.dtype, device: torch.device, num_frames: int + ): + """Return a tensor of random one-hot weights, of shape + `(num_frames, self.num_inputs)`, + Args: + dtype: + The data-type desired for the answer, e.g. float, double. + device: + The device needed for the answer. + num_frames: + The number of sets of weights desired. + Returns: + A one-hot tensor of shape `(num_frames, self.num_inputs)`, with + exactly one weight equal to 1.0 on each frame. + """ + final_prob = self.final_weight + + # final contains self.num_inputs - 1 in all elements + final = torch.full((num_frames,), self.num_inputs - 1, device=device) + # nonfinal contains random integers in [0..num_inputs - 2], these are for non-final weights. + nonfinal = torch.randint( + self.num_inputs - 1, (num_frames,), device=device + ) + + indexes = torch.where( + torch.rand(num_frames, device=device) < final_prob, final, nonfinal + ) + ans = torch.nn.functional.one_hot( + indexes, num_classes=self.num_inputs + ).to(dtype=dtype) + return ans + + def _get_random_mixed_weights( + self, dtype: torch.dtype, device: torch.device, num_frames: int + ): + """Return a tensor of random one-hot weights, of shape + `(num_frames, self.num_inputs)`, + Args: + dtype: + The data-type desired for the answer, e.g. float, double. + device: + The device needed for the answer. + num_frames: + The number of sets of weights desired. + Returns: + A tensor of shape (num_frames, self.num_inputs), which elements + in [0..1] that sum to one over the second axis, i.e. + `ans.sum(dim=1)` is all ones. + """ + logprobs = ( + torch.randn(num_frames, self.num_inputs, dtype=dtype, device=device) + * self.stddev + ) + logprobs[:, -1] += self.final_log_weight + return logprobs.softmax(dim=1) + + +def _test_random_combine(final_weight: float, pure_prob: float, stddev: float): + print( + f"_test_random_combine: final_weight={final_weight}, pure_prob={pure_prob}, stddev={stddev}" + ) + num_inputs = 3 + num_channels = 50 + m = RandomCombine( + num_inputs=num_inputs, + num_channels=num_channels, + final_weight=final_weight, + pure_prob=pure_prob, + stddev=stddev, + ) + + x = [torch.ones(3, 4, num_channels) for _ in range(num_inputs)] + + y = m(x) + assert y.shape == x[0].shape + assert torch.allclose(y, x[0]) # .. since actually all ones. + + +def _test_random_combine_main(): + _test_random_combine(0.999, 0, 0.0) + _test_random_combine(0.5, 0, 0.0) + _test_random_combine(0.999, 0, 0.0) + _test_random_combine(0.5, 0, 0.3) + _test_random_combine(0.5, 1, 0.3) + _test_random_combine(0.5, 0.5, 0.3) + + feature_dim = 50 + c = Conformer( + num_features=feature_dim, output_dim=256, d_model=128, nhead=4 + ) + batch_size = 5 + seq_len = 20 + # Just make sure the forward pass runs. + f = c( + torch.randn(batch_size, seq_len, feature_dim), + torch.full((batch_size,), seq_len, dtype=torch.int64), + ) + f # to remove flake8 warnings + + +if __name__ == "__main__": + feature_dim = 50 + c = Conformer(num_features=feature_dim, d_model=128, nhead=4) + batch_size = 5 + seq_len = 20 + # Just make sure the forward pass runs. + f = c( + torch.randn(batch_size, seq_len, feature_dim), + torch.full((batch_size,), seq_len, dtype=torch.int64), + warmup=0.5, + ) + + _test_random_combine_main() diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/decode.py b/egs/aishell4/ASR/pruned_transducer_stateless5/decode.py new file mode 100755 index 000000000..705e34647 --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/decode.py @@ -0,0 +1,630 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao, +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +When use-averaged-model=True, usage: +(1) greedy search +./pruned_transducer_stateless5/decode.py \ + --iter 36000 \ + --avg 8 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 800 \ + --decoding-method greedy_search \ + --use-averaged-model True + +(2) modified beam search +./pruned_transducer_stateless5/decode.py \ + --iter 36000 \ + --avg 8 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 800 \ + --decoding-method modified_beam_search \ + --beam-size 4 \ + --use-averaged-model True + +(3) fast beam search +./pruned_transducer_stateless5/decode.py \ + --iter 36000 \ + --avg 8 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 800 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 \ + --use-averaged-model True +""" + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import torch +import torch.nn as nn +from asr_datamodule import Aishell4AsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from lhotse.cut import Cut +from local.text_normalize import text_normalize +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless5/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append([lexicon.token_table[idx] for idx in hyp]) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + texts = [list(str(text).replace(" ", "")) for text in texts] + + hyps_dict = decode_one_batch( + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + this_batch.append((ref_text, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + Aishell4AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + def text_normalize_for_cut(c: Cut): + # Text normalize for each sample + text = c.supervisions[0].text + text = text.strip("\n").strip("\t") + c.supervisions[0].text = text_normalize(text) + return c + + aishell4 = Aishell4AsrDataModule(args) + test_cuts = aishell4.test_cuts() + test_cuts = test_cuts.map(text_normalize_for_cut) + test_dl = aishell4.test_dataloaders(test_cuts) + + test_sets = ["test"] + test_dl = [test_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/decoder.py b/egs/aishell4/ASR/pruned_transducer_stateless5/decoder.py new file mode 120000 index 000000000..8a5e07bd5 --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/decoder.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py \ No newline at end of file diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/encoder_interface.py b/egs/aishell4/ASR/pruned_transducer_stateless5/encoder_interface.py new file mode 120000 index 000000000..2fc10439b --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/encoder_interface.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/encoder_interface.py \ No newline at end of file diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/export.py b/egs/aishell4/ASR/pruned_transducer_stateless5/export.py new file mode 100755 index 000000000..f42a85373 --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/export.py @@ -0,0 +1,278 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./pruned_transducer_stateless5/export.py \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --lang-dir data/lang_char \ + --epoch 20 \ + --avg 10 + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `pruned_transducer_stateless5/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/aishell4/ASR + ./pruned_transducer_stateless5/decode.py \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 600 \ + --decoding-method greedy_search \ + --lang-dir data/lang_char +""" + +import argparse +import logging +from pathlib import Path + +import torch +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless5/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + add_model_arguments(parser) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.eval() + + model.to("cpu") + model.eval() + + if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/joiner.py b/egs/aishell4/ASR/pruned_transducer_stateless5/joiner.py new file mode 120000 index 000000000..f31b5fd9b --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/joiner.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py \ No newline at end of file diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/model.py b/egs/aishell4/ASR/pruned_transducer_stateless5/model.py new file mode 120000 index 000000000..be059ba7c --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/model.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/model.py \ No newline at end of file diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/optim.py b/egs/aishell4/ASR/pruned_transducer_stateless5/optim.py new file mode 120000 index 000000000..661206562 --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/optim.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/optim.py \ No newline at end of file diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/pretrained.py b/egs/aishell4/ASR/pruned_transducer_stateless5/pretrained.py new file mode 100755 index 000000000..1fa893637 --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/pretrained.py @@ -0,0 +1,358 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +When use-averaged-model=True, usage: + +(1) greedy search +./pruned_transducer_stateless5/pretrained.py \ + --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \ + --lang-dir data/lang_char \ + --decoding-method greedy_search \ + --use-averaged-model True \ + /path/to/foo.wav \ + /path/to/bar.wav + +(2) beam search +./pruned_transducer_stateless5/pretrained.py \ + --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \ + --lang-dir data/lang_char \ + --use-averaged-model True \ + --decoding-method beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(3) modified beam search (not suggest) +./pruned_transducer_stateless5/pretrained.py \ + --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \ + --lang-dir data/lang_char \ + --use-averaged-model True \ + --decoding-method modified_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(4) fast beam search +./pruned_transducer_stateless5/pretrained.py \ + --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \ + --lang-dir data/lang_char \ + --use-averaged-model True \ + --decoding-method fast_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +You can also use `./pruned_transducer_stateless5/exp/epoch-xx.pt`. + +Note: ./pruned_transducer_stateless5/exp/pretrained.pt is generated by +./pruned_transducer_stateless5/export.py +""" + + +import argparse +import logging +import math +from typing import List + +import k2 +import kaldifeat +import torch +import torchaudio +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.lexicon import Lexicon + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--lang-dir", + type=str, + help="""Path to lang. + """, + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "sound_files", + type=str, + nargs="+", + help="The input sound file(s) to transcribe. " + "Supported formats are those supported by torchaudio.load(). " + "For example, wav and flac are supported. " + "The sample rate has to be 16kHz.", + ) + + parser.add_argument( + "--sample-rate", + type=int, + default=16000, + help="The sample rate of the input sound file", + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. Used only when + --decoding-method is greedy_search. + """, + ) + + add_model_arguments(parser) + + return parser + + +def read_sound_files( + filenames: List[str], expected_sample_rate: float +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + assert sample_rate == expected_sample_rate, ( + f"expected sample rate: {expected_sample_rate}. " + f"Given: {sample_rate}" + ) + # We use only the first channel + ans.append(wave[0]) + return ans + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + + params.update(vars(args)) + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(f"{params}") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + logging.info("Creating model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"], strict=False) + model.to(device) + model.eval() + model.device = device + + logging.info("Constructing Fbank computer") + opts = kaldifeat.FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = params.sample_rate + opts.mel_opts.num_bins = params.feature_dim + + fbank = kaldifeat.Fbank(opts) + + logging.info(f"Reading sound files: {params.sound_files}") + waves = read_sound_files( + filenames=params.sound_files, expected_sample_rate=params.sample_rate + ) + waves = [w.to(device) for w in waves] + + logging.info("Decoding started") + features = fbank(waves) + feature_lengths = [f.size(0) for f in features] + + features = pad_sequence( + features, batch_first=True, padding_value=math.log(1e-10) + ) + + feature_lengths = torch.tensor(feature_lengths, device=device) + + encoder_out, encoder_out_lens = model.encoder( + x=features, x_lens=feature_lengths + ) + + num_waves = encoder_out.size(0) + hyps = [] + msg = f"Using {params.decoding_method}" + if params.decoding_method == "beam_search": + msg += f" with beam size {params.beam_size}" + logging.info(msg) + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + else: + for i in range(num_waves): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding-method: {params.decoding_method}" + ) + hyps.append([lexicon.token_table[idx] for idx in hyp]) + + s = "\n" + for filename, hyp in zip(params.sound_files, hyps): + words = " ".join(hyp) + s += f"{filename}:\n{words}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/scaling.py b/egs/aishell4/ASR/pruned_transducer_stateless5/scaling.py new file mode 120000 index 000000000..be7b111c6 --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/scaling.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py \ No newline at end of file diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/test_model.py b/egs/aishell4/ASR/pruned_transducer_stateless5/test_model.py new file mode 100755 index 000000000..d42c3b4f4 --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/test_model.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +To run this file, do: + + cd icefall/egs/aishell4/ASR + python ./pruned_transducer_stateless5/test_model.py +""" + +from train import get_params, get_transducer_model + + +def test_model_1(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.num_encoder_layers = 24 + params.dim_feedforward = 1536 # 384 * 4 + params.encoder_dim = 384 + model = get_transducer_model(params) + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + + +# See Table 1 from https://arxiv.org/pdf/2005.08100.pdf +def test_model_M(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.num_encoder_layers = 18 + params.dim_feedforward = 1024 + params.encoder_dim = 256 + params.nhead = 4 + params.decoder_dim = 512 + params.joiner_dim = 512 + model = get_transducer_model(params) + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + + +def main(): + # test_model_1() + test_model_M() + + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/train.py b/egs/aishell4/ASR/pruned_transducer_stateless5/train.py new file mode 100755 index 000000000..0a48b9059 --- /dev/null +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/train.py @@ -0,0 +1,1108 @@ +#!/usr/bin/env python3 +# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo, +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless5/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless5/exp \ + --full-libri 1 \ + --max-duration 300 + +# For mix precision training: + +./pruned_transducer_stateless5/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless5/exp \ + --full-libri 1 \ + --max-duration 550 + +""" + + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import Aishell4AsrDataModule +from conformer import Conformer +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from local.text_normalize import text_normalize +from model import Transducer +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[ + torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler +] + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=int, + default=24, + help="Number of conformer encoder layers..", + ) + + parser.add_argument( + "--dim-feedforward", + type=int, + default=1536, + help="Feedforward dimension of the conformer encoder layer.", + ) + + parser.add_argument( + "--nhead", + type=int, + default=8, + help="Number of attention heads in the conformer encoder layer.", + ) + + parser.add_argument( + "--encoder-dim", + type=int, + default=384, + help="Attention dimension in the conformer encoder layer.", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Embedding dimension in the decoder model.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="""Dimension used in the joiner model. + Outputs from the encoder and decoder model are projected + to this dimension before adding. + """, + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless5/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="The initial learning rate. This value should not need " + "to be changed.", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate + decreases. We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=6, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=4000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=30, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=100, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 100, + "valid_interval": 200, + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + # parameters for Noam + "model_warm_step": 400, # arg given to model, not for lrate + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Conformer and Transformer + encoder = Conformer( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + batch: dict, + is_training: bool, + warmup: float = 1.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = ( + model.device + if isinstance(model, DDP) + else next(model.parameters()).device + ) + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = graph_compiler.texts_to_ids(texts) + if type(y) == list: + y = k2.RaggedTensor(y).to(device) + else: + y = y.to(device) + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + warmup=warmup, + ) + # after the main warmup step, we keep pruned_loss_scale small + # for the same amount of time (model_warm_step), to avoid + # overwhelming the simple_loss and causing it to diverge, + # in case it had not fully learned the alignment yet. + pruned_loss_scale = ( + 0.0 + if warmup < 1.0 + else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) + ) + loss = ( + params.simple_loss_scale * simple_loss + + pruned_loss_scale * pruned_loss + ) + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + graph_compiler: CharCtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + # print(batch["supervisions"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + graph_compiler = CharCtcTrainingGraphCompiler( + lexicon=lexicon, + device=device, + ) + + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2 ** 22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + aishell4 = Aishell4AsrDataModule(args) + # Combine all of the training data + train_cuts = aishell4.train_S_cuts() + train_cuts += aishell4.train_M_cuts() + train_cuts += aishell4.train_L_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + def text_normalize_for_cut(c: Cut): + # Text normalize for each sample + text = c.supervisions[0].text + text = text.strip("\n").strip("\t") + c.supervisions[0].text = text_normalize(text) + return c + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + train_cuts = train_cuts.map(text_normalize_for_cut) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = aishell4.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = aishell4.valid_cuts() + valid_cuts = valid_cuts.map(text_normalize_for_cut) + valid_dl = aishell4.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + graph_compiler=graph_compiler, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + graph_compiler=graph_compiler, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + graph_compiler: CharCtcTrainingGraphCompiler, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + # warmup = 0.0 is so that the derivs for the pruned loss stay zero + # (i.e. are not remembered by the decaying-average in adam), because + # we want to avoid these params being subject to shrinkage in adam. + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=0.0, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except Exception as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + Aishell4AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/aishell4/ASR/shared b/egs/aishell4/ASR/shared new file mode 120000 index 000000000..3a3b28f96 --- /dev/null +++ b/egs/aishell4/ASR/shared @@ -0,0 +1 @@ +../../../egs/aishell/ASR/shared \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/export.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/export.py index 0a69e0a57..8beec1b8a 100644 --- a/egs/alimeeting/ASR/pruned_transducer_stateless2/export.py +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/export.py @@ -114,8 +114,6 @@ def main(): args = get_parser().parse_args() args.exp_dir = Path(args.exp_dir) - assert args.jit is False, "Support torchscript will be added later" - params = get_params() params.update(vars(args)) @@ -155,6 +153,11 @@ def main(): model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/egs/gigaspeech/ASR/conformer_ctc/conformer.py b/egs/gigaspeech/ASR/conformer_ctc/conformer.py index 871712a46..36e0c7aea 100644 --- a/egs/gigaspeech/ASR/conformer_ctc/conformer.py +++ b/egs/gigaspeech/ASR/conformer_ctc/conformer.py @@ -369,7 +369,7 @@ class RelPositionalEncoding(torch.nn.Module): ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i for more details. + +##### Training on full librispeech +The WERs are (the number in the table formatted as test-clean & test-other): + +We only trained 25 epochs for saving time, if you want to get better results you can train more epochs. + +| decoding method | left context | chunk size = 2 | chunk size = 4 | chunk size = 8 | chunk size = 16| +|----------------------|--------------|----------------|----------------|----------------|----------------| +| greedy search | 32 | 4.74 & 11.38 | 4.57 & 10.86 | 4.18 & 10.37 | 3.87 & 9.85 | +| greedy search | 64 | 4.74 & 11.25 | 4.48 & 10.72 | 4.1 & 10.24 | 3.85 & 9.73 | +| fast beam search | 32 | 4.75 & 11.1 | 4.48 & 10.65 | 4.12 & 10.18 | 3.95 & 9.67 | +| fast beam search | 64 | 4.7 & 11 | 4.37 & 10.49 | 4.07 & 10.04 | 3.89 & 9.53 | +| modified beam search | 32 | 4.64 & 10.94 | 4.38 & 10.51 | 4.11 & 10.14 | 3.87 & 9.61 | +| modified beam search | 64 | 4.59 & 10.81 | 4.29 & 10.39 | 4.02 & 10.02 | 3.84 & 9.43 | + +**NOTE:** The WERs in table above were decoded with simulate streaming method (i.e. using masking strategy), see commands below. We also have [real streaming decoding](./pruned_transducer_stateless/streaming_decode.py) script which should produce almost the same results. We tried adding right context in the real streaming decoding, but it seemed not to benefit the performance for all the models, the reasons might be the training and decoding mismatching. + +The training command is: + +```bash +./pruned_transducer_stateless/train.py \ + --exp-dir pruned_transducer_stateless/exp \ + --full-libri 1 \ + --dynamic-chunk-training 1 \ + --causal-convolution 1 \ + --short-chunk-size 20 \ + --num-left-chunks 4 \ + --max-duration 300 \ + --world-size 4 \ + --start-epoch 0 \ + --num-epochs 25 +``` + +You can find the tensorboard log here + +The decoding command is: +```bash +decoding_method="greedy_search" # "fast_beam_search", "modified_beam_search" + +for chunk in 2 4 8 16; do + for left in 32 64; do + ./pruned_transducer_stateless/decode.py \ + --simulate-streaming 1 \ + --decode-chunk-size ${chunk} \ + --left-context ${left} \ + --causal-convolution 1 \ + --epoch 24 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --max-sym-per-frame 1 \ + --max-duration 1000 \ + --decoding-method ${decoding_method} + done +done +``` + +Pre-trained models, training and decoding logs, and decoding results are available at + +#### [pruned_transducer_stateless2](./pruned_transducer_stateless2) + +See for more details. + +##### Training on full librispeech +The WERs are (the number in the table formatted as test-clean & test-other): + +We only trained 25 epochs for saving time, if you want to get better results you can train more epochs. + +| decoding method | left context | chunk size = 2 | chunk size = 4 | chunk size = 8 | chunk size = 16| +|----------------------|--------------|----------------|----------------|----------------|----------------| +| greedy search | 32 | 4.2 & 10.64 | 3.97 & 10.03 | 3.83 & 9.58 | 3.7 & 9.11 | +| greedy search | 64 | 4.16 & 10.5 | 3.93 & 9.99 | 3.73 & 9.45 | 3.63 & 9.04 | +| fast beam search | 32 | 4.13 & 10.3 | 3.93 & 9.82 | 3.8 & 9.35 | 3.62 & 8.93 | +| fast beam search | 64 | 4.13 & 10.22 | 3.89 & 9.68 | 3.73 & 9.27 | 3.52 & 8.82 | +| modified beam search | 32 | 4.02 & 10.22 | 3.9 & 9.71 | 3.74 & 9.33 | 3.59 & 8.87 | +| modified beam search | 64 | 4.05 & 10.08 | 3.81 & 9.67 | 3.68 & 9.21 | 3.56 & 8.77 | + +**NOTE:** The WERs in table above were decoded with simulate streaming method (i.e. using masking strategy), see commands below. We also have [real streaming decoding](./pruned_transducer_stateless2/streaming_decode.py) script which should produce almost the same results. We tried adding right context in the real streaming decoding, but it seemed not to benefit the performance for all the models, the reasons might be the training and decoding mismatching. + +The training command is: + +```bash +./pruned_transducer_stateless2/train.py \ + --exp-dir pruned_transducer_stateless2/exp \ + --full-libri 1 \ + --dynamic-chunk-training 1 \ + --causal-convolution 1 \ + --short-chunk-size 20 \ + --num-left-chunks 4 \ + --max-duration 300 \ + --world-size 4 \ + --start-epoch 0 \ + --num-epochs 25 +``` + +You can find the tensorboard log here + +The decoding command is: +```bash +decoding_method="greedy_search" # "fast_beam_search", "modified_beam_search" + +for chunk in 2 4 8 16; do + for left in 32 64; do + ./pruned_transducer_stateless2/decode.py \ + --simulate-streaming 1 \ + --decode-chunk-size ${chunk} \ + --left-context ${left} \ + --causal-convolution 1 \ + --epoch 24 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-sym-per-frame 1 \ + --max-duration 1000 \ + --decoding-method ${decoding_method} + done +done +``` + +Pre-trained models, training and decoding logs, and decoding results are available at + +#### [pruned_transducer_stateless3](./pruned_transducer_stateless3) + +See for more details. + +##### Training on full librispeech (**Use giga_prob = 0.5**) + +The WERs are (the number in the table formatted as test-clean & test-other): + +| decoding method | left context | chunk size = 2 | chunk size = 4 | chunk size = 8 | chunk size = 16| +|----------------------|--------------|----------------|----------------|----------------|----------------| +| greedy search | 32 | 3.7 & 9.53 | 3.45 & 8.88 | 3.28 & 8.45 | 3.13 & 7.93 | +| greedy search | 64 | 3.69 & 9.36 | 3.39 & 8.68 | 3.28 & 8.19 | 3.08 & 7.83 | +| fast beam search | 32 | 3.71 & 9.18 | 3.36 & 8.65 | 3.23 & 8.23 | 3.17 & 7.78 | +| fast beam search | 64 | 3.61 & 9.03 | 3.46 & 8.43 | 3.2 & 8.0 | 3.11 & 7.63 | +| modified beam search | 32 | 3.56 & 9.08 | 3.34 & 8.58 | 3.21 & 8.14 | 3.06 & 7.73 | +| modified beam search | 64 | 3.55 & 8.86 | 3.29 & 8.34 | 3.16 & 8.01 | 3.05 & 7.57 | + +**NOTE:** The WERs in table above were decoded with simulate streaming method (i.e. using masking strategy), see commands below. We also have [real streaming decoding](./pruned_transducer_stateless3/streaming_decode.py) script which should produce almost the same results. We tried adding right context in the real streaming decoding, but it seemed not to benefit the performance for all the models, the reasons might be the training and decoding mismatching. + +The training command is (Note: this model was trained with mix-precision training): + +```bash +./pruned_transducer_stateless3/train.py \ + --exp-dir pruned_transducer_stateless3/exp \ + --full-libri 1 \ + --dynamic-chunk-training 1 \ + --causal-convolution 1 \ + --short-chunk-size 32 \ + --num-left-chunks 4 \ + --max-duration 300 \ + --world-size 4 \ + --use-fp16 1 \ + --start-epoch 0 \ + --num-epochs 37 \ + --num-workers 2 \ + --giga-prob 0.5 +``` + +You can find the tensorboard log here + +The decoding command is: +```bash +decoding_method="greedy_search" # "fast_beam_search", "modified_beam_search" + +for chunk in 2 4 8 16; do + for left in 32 64; do + ./pruned_transducer_stateless3/decode.py \ + --simulate-streaming 1 \ + --decode-chunk-size ${chunk} \ + --left-context ${left} \ + --causal-convolution 1 \ + --epoch 36 \ + --avg 8 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-sym-per-frame 1 \ + --max-duration 1000 \ + --decoding-method ${decoding_method} + done +done +``` + +Pre-trained models, training and decoding logs, and decoding results are available at + +##### Training on full librispeech (**Use giga_prob = 0.9**) + +The WERs are (the number in the table formatted as test-clean & test-other): + +| decoding method | left context | chunk size = 2 | chunk size = 4 | chunk size = 8 | chunk size = 16| +|----------------------|--------------|----------------|----------------|----------------|----------------| +| greedy search | 32 | 3.25 & 8.2 | 3.07 & 7.67 | 2.91 & 7.28 | 2.8 & 6.89 | +| greedy search | 64 | 3.22 & 8.12 | 3.05 & 7.59 | 2.91 & 7.07 | 2.78 & 6.81 | +| fast beam search | 32 | 3.26 & 8.2 | 3.06 & 7.56 | 2.98 & 7.08 | 2.77 & 6.75 | +| fast beam search | 64 | 3.24 & 8.09 | 3.06 & 7.43 | 2.88 & 7.03 | 2.73 & 6.68 | +| modified beam search | 32 | 3.13 & 7.91 | 2.99 & 7.45 | 2.83 & 6.98 | 2.68 & 6.75 | +| modified beam search | 64 | 3.08 & 7.8 | 2.97 & 7.37 | 2.81 & 6.82 | 2.66 & 6.67 | + +**NOTE:** The WERs in table above were decoded with simulate streaming method (i.e. using masking strategy), see commands below. We also have [real streaming decoding](./pruned_transducer_stateless3/streaming_decode.py) script which should produce almost the same results. We tried adding right context in the real streaming decoding, but it seemed not to benefit the performance for all the models, the reasons might be the training and decoding mismatching. + +The training command is: + +```bash +./pruned_transducer_stateless3/train.py \ + --exp-dir pruned_transducer_stateless3/exp \ + --full-libri 1 \ + --dynamic-chunk-training 1 \ + --causal-convolution 1 \ + --short-chunk-size 25 \ + --num-left-chunks 8 \ + --max-duration 300 \ + --world-size 8 \ + --start-epoch 0 \ + --num-epochs 26 \ + --num-workers 2 \ + --giga-prob 0.9 +``` + +You can find the tensorboard log here + +The decoding command is: +```bash +decoding_method="greedy_search" # "fast_beam_search", "modified_beam_search" + +for chunk in 2 4 8 16; do + for left in 32 64; do + ./pruned_transducer_stateless3/decode.py \ + --simulate-streaming 1 \ + --decode-chunk-size ${chunk} \ + --left-context ${left} \ + --causal-convolution 1 \ + --epoch 25 \ + --avg 12 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-sym-per-frame 1 \ + --max-duration 1000 \ + --decoding-method ${decoding_method} + done +done +``` + +Pre-trained models, training and decoding logs, and decoding results are available at + +#### [pruned_transducer_stateless4](./pruned_transducer_stateless4) + +See for more details. + +##### Training on full librispeech +The WERs are (the number in the table formatted as test-clean & test-other): + +We only trained 25 epochs for saving time, if you want to get better results you can train more epochs. + +| decoding method | left context | chunk size = 2 | chunk size = 4 | chunk size = 8 | chunk size = 16| +|----------------------|--------------|----------------|----------------|----------------|----------------| +| greedy search | 32 | 3.96 & 10.45 | 3.73 & 9.97 | 3.54 & 9.56 | 3.45 & 9.08 | +| greedy search | 64 | 3.9 & 10.34 | 3.7 & 9.9 | 3.53 & 9.41 | 3.39 & 9.03 | +| fast beam search | 32 | 3.9 & 10.09 | 3.69 & 9.65 | 3.58 & 9.28 | 3.46 & 8.91 | +| fast beam search | 64 | 3.82 & 10.03 | 3.67 & 9.56 | 3.51 & 9.18 | 3.43 & 8.78 | +| modified beam search | 32 | 3.78 & 10.0 | 3.63 & 9.54 | 3.43 & 9.29 | 3.39 & 8.84 | +| modified beam search | 64 | 3.76 & 9.95 | 3.54 & 9.48 | 3.4 & 9.13 | 3.33 & 8.74 | + +**NOTE:** The WERs in table above were decoded with simulate streaming method (i.e. using masking strategy), see commands below. We also have [real streaming decoding](./pruned_transducer_stateless4/streaming_decode.py) script which should produce almost the same results. We tried adding right context in the real streaming decoding, but it seemed not to benefit the performance for all the models, the reasons might be the training and decoding mismatching. + +The training command is: + +```bash +./pruned_transducer_stateless4/train.py \ + --exp-dir pruned_transducer_stateless4/exp \ + --full-libri 1 \ + --dynamic-chunk-training 1 \ + --causal-convolution 1 \ + --short-chunk-size 20 \ + --num-left-chunks 4 \ + --max-duration 300 \ + --world-size 4 \ + --start-epoch 1 \ + --num-epochs 25 +``` + +You can find the tensorboard log here + +The decoding command is: +```bash +decoding_method="greedy_search" # "fast_beam_search", "modified_beam_search" + +for chunk in 2 4 8 16; do + for left in 32 64; do + ./pruned_transducer_stateless4/decode.py \ + --simulate-streaming 1 \ + --decode-chunk-size ${chunk} \ + --left-context ${left} \ + --causal-convolution 1 \ + --epoch 25 \ + --avg 3 \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --max-sym-per-frame 1 \ + --max-duration 1000 \ + --decoding-method ${decoding_method} + done +done +``` + +Pre-trained models, training and decoding logs, and decoding results are available at + + ### LibriSpeech BPE training results (Pruned Stateless Conv-Emformer RNN-T) [conv_emformer_transducer_stateless](./conv_emformer_transducer_stateless) @@ -781,9 +1088,25 @@ The WERs are: The train and decode commands are: -`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp --world-size 8 --num-epochs 26 --full-libri 1 --max-duration 300` +```bash +python3 ./pruned_transducer_stateless2/train.py \ + --exp-dir=pruned_transducer_stateless2/exp \ + --world-size 8 \ + --num-epochs 26 \ + --full-libri 1 \ + --max-duration 300 +``` + and: -`python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp --epoch 25 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600` + +```bash +python3 ./pruned_transducer_stateless2/decode.py \ + --exp-dir pruned_transducer_stateless2/exp \ + --epoch 25 \ + --avg 8 \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --max-duration 600 +``` The Tensorboard log is at (apologies, log starts only from epoch 3). @@ -796,9 +1119,26 @@ can be found at #### Training on train-clean-100: Trained with 1 job: -`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_ws1 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 300` +``` +python3 ./pruned_transducer_stateless2/train.py \ + --exp-dir=pruned_transducer_stateless2/exp_100h_ws1 \ + --world-size 1 \ + --num-epochs 40 \ + --full-libri 0 \ + --max-duration 300 +``` + and decoded with: -`python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp_100h_ws1 --epoch 19 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600`. + +``` +python3 ./pruned_transducer_stateless2/decode.py \ + --exp-dir pruned_transducer_stateless2/exp_100h_ws1 \ + --epoch 19 \ + --avg 8 \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --max-duration 600 +``` + The Tensorboard log is at (learning rate schedule is not visible due to a since-fixed bug). @@ -812,9 +1152,26 @@ schedule is not visible due to a since-fixed bug). | fast beam search | 6.53 | 16.82 | --epoch 39 --avg 10 --decoding-method fast_beam_search | Trained with 2 jobs: -`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_ws2 --world-size 2 --num-epochs 40 --full-libri 0 --max-duration 300` + +```bash +python3 ./pruned_transducer_stateless2/train.py \ + --exp-dir=pruned_transducer_stateless2/exp_100h_ws2 \ + --world-size 2 \ + --num-epochs 40 \ + --full-libri 0 \ + --max-duration 300 +``` + and decoded with: -`python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp_100h_ws2 --epoch 19 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600`. + +``` +python3 ./pruned_transducer_stateless2/decode.py \ + --exp-dir pruned_transducer_stateless2/exp_100h_ws2 \ + --epoch 19 \ + --avg 8 \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --max-duration 600 +``` The Tensorboard log is at (learning rate schedule is not visible due to a since-fixed bug). @@ -827,9 +1184,26 @@ The Tensorboard log is at @@ -846,7 +1220,16 @@ The Tensorboard log is at . Train command was -`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_fp16 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 300 --use-fp16 True` + +``` +python3 ./pruned_transducer_stateless2/train.py \ + --exp-dir=pruned_transducer_stateless2/exp_100h_fp16 \ + --world-size 1 \ + --num-epochs 40 \ + --full-libri 0 \ + --max-duration 300 \ + --use-fp16 True +``` The Tensorboard log is at @@ -860,7 +1243,16 @@ The Tensorboard log is at . Train command was -`python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_fp16 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 500 --use-fp16 True` + +``` +python3 ./pruned_transducer_stateless2/train.py \ + --exp-dir=pruned_transducer_stateless2/exp_100h_fp16 \ + --world-size 1 \ + --num-epochs 40 \ + --full-libri 0 \ + --max-duration 500 \ + --use-fp16 True +``` The Tensorboard log is at @@ -872,7 +1264,6 @@ The Tensorboard log is at +and the RNN-LM pre-trained model: + + The tensorboard log for training is available at diff --git a/egs/librispeech/ASR/conformer_ctc/conformer.py b/egs/librispeech/ASR/conformer_ctc/conformer.py index 871712a46..36e0c7aea 100644 --- a/egs/librispeech/ASR/conformer_ctc/conformer.py +++ b/egs/librispeech/ASR/conformer_ctc/conformer.py @@ -369,7 +369,7 @@ class RelPositionalEncoding(torch.nn.Module): ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i AttributeDict: def decode_one_batch( params: AttributeDict, model: nn.Module, + rnn_lm_model: Optional[nn.Module], HLG: Optional[k2.Fsa], H: Optional[k2.Fsa], bpe_model: Optional[spm.SentencePieceProcessor], @@ -205,6 +268,8 @@ def decode_one_batch( model: The neural model. + rnn_lm_model: + The neural model for RNN LM. HLG: The decoding graph. Used only when params.method is NOT ctc-decoding. H: @@ -330,6 +395,7 @@ def decode_one_batch( "nbest-rescoring", "whole-lattice-rescoring", "attention-decoder", + "rnn-lm", ] lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7] @@ -357,8 +423,6 @@ def decode_one_batch( G_with_epsilon_loops=G, lm_scale_list=None, ) - # TODO: pass `lattice` instead of `rescored_lattice` to - # `rescore_with_attention_decoder` best_path_dict = rescore_with_attention_decoder( lattice=rescored_lattice, @@ -370,6 +434,26 @@ def decode_one_batch( eos_id=eos_id, nbest_scale=params.nbest_scale, ) + elif params.method == "rnn-lm": + # lattice uses a 3-gram Lm. We rescore it with a 4-gram LM. + rescored_lattice = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=None, + ) + + best_path_dict = rescore_with_rnn_lm( + lattice=rescored_lattice, + num_paths=params.num_paths, + rnn_lm_model=rnn_lm_model, + model=model, + memory=memory, + memory_key_padding_mask=memory_key_padding_mask, + sos_id=sos_id, + eos_id=eos_id, + blank_id=0, + nbest_scale=params.nbest_scale, + ) else: assert False, f"Unsupported decoding method: {params.method}" @@ -388,6 +472,7 @@ def decode_dataset( dl: torch.utils.data.DataLoader, params: AttributeDict, model: nn.Module, + rnn_lm_model: Optional[nn.Module], HLG: Optional[k2.Fsa], H: Optional[k2.Fsa], bpe_model: Optional[spm.SentencePieceProcessor], @@ -405,6 +490,8 @@ def decode_dataset( It is returned by :func:`get_params`. model: The neural model. + rnn_lm_model: + The neural model for RNN LM. HLG: The decoding graph. Used only when params.method is NOT ctc-decoding. H: @@ -442,6 +529,7 @@ def decode_dataset( hyps_dict = decode_one_batch( params=params, model=model, + rnn_lm_model=rnn_lm_model, HLG=HLG, H=H, bpe_model=bpe_model, @@ -490,7 +578,7 @@ def save_results( test_set_name: str, results_dict: Dict[str, List[Tuple[List[int], List[int]]]], ): - if params.method == "attention-decoder": + if params.method in ("attention-decoder", "rnn-lm"): # Set it to False since there are too many logs. enable_log = False else: @@ -566,6 +654,10 @@ def main(): sos_id = graph_compiler.sos_id eos_id = graph_compiler.eos_id + params.num_classes = num_classes + params.sos_id = sos_id + params.eos_id = eos_id + if params.method == "ctc-decoding": HLG = None H = k2.ctc_topo( @@ -590,6 +682,7 @@ def main(): "nbest-rescoring", "whole-lattice-rescoring", "attention-decoder", + "rnn-lm", ): if not (params.lm_dir / "G_4_gram.pt").is_file(): logging.info("Loading G_4_gram.fst.txt") @@ -621,7 +714,11 @@ def main(): d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device) G = k2.Fsa.from_dict(d) - if params.method in ["whole-lattice-rescoring", "attention-decoder"]: + if params.method in [ + "whole-lattice-rescoring", + "attention-decoder", + "rnn-lm", + ]: # Add epsilon self-loops to G as we will compose # it with the whole lattice later G = k2.add_epsilon_self_loops(G) @@ -648,20 +745,40 @@ def main(): if params.avg == 1: load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) else: - start = params.epoch - params.avg + 1 - filenames = [] - for i in range(start, params.epoch + 1): - if start >= 0: - filenames.append(f"{params.exp_dir}/epoch-{i}.pt") - logging.info(f"averaging {filenames}") - model.to(device) - model.load_state_dict(average_checkpoints(filenames, device=device)) + model = load_averaged_model( + params.exp_dir, model, params.epoch, params.avg, device + ) model.to(device) model.eval() num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") + rnn_lm_model = None + if params.method == "rnn-lm": + rnn_lm_model = RnnLmModel( + vocab_size=params.num_classes, + embedding_dim=params.rnn_lm_embedding_dim, + hidden_dim=params.rnn_lm_hidden_dim, + num_layers=params.rnn_lm_num_layers, + tie_weights=params.rnn_lm_tie_weights, + ) + if params.rnn_lm_avg == 1: + load_checkpoint( + f"{params.rnn_lm_exp_dir}/epoch-{params.rnn_lm_epoch}.pt", + rnn_lm_model, + ) + rnn_lm_model.to(device) + else: + rnn_lm_model = load_averaged_model( + params.rnn_lm_exp_dir, + rnn_lm_model, + params.rnn_lm_epoch, + params.rnn_lm_avg, + device, + ) + rnn_lm_model.eval() + librispeech = LibriSpeechAsrDataModule(args) test_clean_cuts = librispeech.test_clean_cuts() @@ -678,6 +795,7 @@ def main(): dl=test_dl, params=params, model=model, + rnn_lm_model=rnn_lm_model, HLG=HLG, H=H, bpe_model=bpe_model, diff --git a/egs/librispeech/ASR/conformer_mmi/conformer.py b/egs/librispeech/ASR/conformer_mmi/conformer.py index b19b94db1..b5f22825d 100644 --- a/egs/librispeech/ASR/conformer_mmi/conformer.py +++ b/egs/librispeech/ASR/conformer_mmi/conformer.py @@ -363,7 +363,7 @@ class RelPositionalEncoding(torch.nn.Module): ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i use full librispeech dataset for distillation +# "False" -> use train-clean-100 subset for distillation +full_libri=False + +# use_extracted_codebook can be "True" or "False" +# "True" -> stage 0 and stage 1 would be skipped, +# and directly download the extracted codebook indexes for distillation +# "False" -> start from scratch +use_extracted_codebook=False + +# teacher_model_id can be one of +# "hubert_xtralarge_ll60k_finetune_ls960" -> fine-tuned model, it is the one we currently use. +# "hubert_xtralarge_ll60k" -> pretrained model without fintuing +teacher_model_id=hubert_xtralarge_ll60k_finetune_ls960 + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ] && [ ! "$use_extracted_codebook" == "True" ]; then + log "Stage 0: Download HuBERT model" # Preparation stage. # Install fairseq according to: @@ -45,7 +72,7 @@ if [ $stage -eq 0 ]; then # commit 806855bf660ea748ed7ffb42fe8dcc881ca3aca0 is used. has_fairseq=$(python3 -c "import importlib; print(importlib.util.find_spec('fairseq') is not None)") if [ $has_fairseq == 'False' ]; then - echo "Please install fairseq before running following stages" + log "Please install fairseq before running following stages" exit 1 fi @@ -56,42 +83,41 @@ if [ $stage -eq 0 ]; then has_quantization=$(python3 -c "import importlib; print(importlib.util.find_spec('quantization') is not None)") if [ $has_quantization == 'False' ]; then - echo "Please install quantization before running following stages" + log "Please install quantization before running following stages" exit 1 fi - echo "Download hubert model." + log "Download HuBERT model." # Parameters about model. - exp_dir=./pruned_transducer_stateless6/exp/ - model_id=hubert_xtralarge_ll60k_finetune_ls960 hubert_model_dir=${exp_dir}/hubert_models - hubert_model=${hubert_model_dir}/${model_id}.pt + hubert_model=${hubert_model_dir}/${teacher_model_id}.pt mkdir -p ${hubert_model_dir} # For more models refer to: https://github.com/pytorch/fairseq/tree/main/examples/hubert if [ -f ${hubert_model} ]; then - echo "hubert model alread exists." + log "HuBERT model alread exists." else - wget -c https://dl.fbaipublicfiles.com/hubert/${model_id} -P ${hubert_model} + wget -c https://dl.fbaipublicfiles.com/hubert/${teacher_model_id}.pt -P ${hubert_model_dir} wget -c wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt -P ${hubert_model_dir} fi fi if [ ! -d ./data/fbank ]; then - echo "This script assumes ./data/fbank is already generated by prepare.sh" + log "This script assumes ./data/fbank is already generated by prepare.sh" exit 1 fi -if [ $stage -eq 1 ]; then +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ] && [ ! "$use_extracted_codebook" == "True" ]; then + log "Stage 1: Verify that the downloaded HuBERT model is correct." # This stage is not directly used by codebook indexes extraction. # It is a method to "prove" that the downloaed hubert model # is inferenced in an correct way if WERs look like normal. # Expect WERs: # [test-clean-ctc_greedy_search] %WER 2.04% [1075 / 52576, 92 ins, 104 del, 879 sub ] # [test-other-ctc_greedy_search] %WER 3.71% [1942 / 52343, 152 ins, 126 del, 1664 sub ] - ./pruned_transducer_stateless6/hubert_decode.py + ./pruned_transducer_stateless6/hubert_decode.py --exp-dir $exp_dir fi -if [ $stage -eq 2 ]; then +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then # Analysis of disk usage: # With num_codebooks==8, each teacher embedding is quantized into # a sequence of eight 8-bit integers, i.e. only eight bytes are needed. @@ -113,25 +139,61 @@ if [ $stage -eq 2 ]; then # During quantizer's training data(teacher embedding) and it's training, # only the first ONE GPU is used. # During codebook indexes extraction, ALL GPUs set by CUDA_VISIBLE_DEVICES are used. + + if [ "$use_extracted_codebook" == "True" ]; then + if [ ! "$teacher_model_id" == "hubert_xtralarge_ll60k_finetune_ls960" ]; then + log "Currently we only uploaded codebook indexes from teacher model hubert_xtralarge_ll60k_finetune_ls960" + exit 1 + fi + mkdir -p $exp_dir/vq + codebook_dir=$exp_dir/vq/$teacher_model_id + mkdir -p codebook_dir + codebook_download_dir=$exp_dir/download_codebook + if [ -d $codebook_download_dir ]; then + log "$codebook_download_dir exists, you should remove it first." + exit 1 + fi + log "Downloading extracted codebook indexes to $codebook_download_dir" + # Make sure you have git-lfs installed (https://git-lfs.github.com) + git lfs install + git clone https://huggingface.co/Zengwei/pruned_transducer_stateless6_hubert_xtralarge_ll60k_finetune_ls960 $codebook_download_dir + + mkdir -p data/vq_fbank + mv $codebook_download_dir/*.jsonl.gz data/vq_fbank/ + mkdir -p $codebook_dir/splits4 + mv $codebook_download_dir/*.h5 $codebook_dir/splits4/ + log "Remove $codebook_download_dir" + rm -rf $codebook_download_dir + fi + ./pruned_transducer_stateless6/extract_codebook_index.py \ - --full-libri False + --full-libri $full_libri \ + --exp-dir $exp_dir \ + --embedding-layer 36 \ + --num-utts 1000 \ + --num-codebooks 8 \ + --max-duration 100 \ + --teacher-model-id $teacher_model_id \ + --use-extracted-codebook $use_extracted_codebook fi -if [ $stage -eq 3 ]; then +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then # Example training script. # Note: it's better to set spec-aug-time-warpi-factor=-1 WORLD_SIZE=$(echo ${CUDA_VISIBLE_DEVICES} | awk '{n=split($1, _, ","); print n}') ./pruned_transducer_stateless6/train.py \ --manifest-dir ./data/vq_fbank \ --master-port 12359 \ - --full-libri False \ + --full-libri $full_libri \ --spec-aug-time-warp-factor -1 \ --max-duration 300 \ --world-size ${WORLD_SIZE} \ - --num-epochs 20 + --num-epochs 20 \ + --exp-dir $exp_dir \ + --enable-distillation True fi -if [ $stage -eq 4 ]; then +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then # Results should be similar to: # errs-test-clean-beam_size_4-epoch-20-avg-10-beam-4.txt:%WER = 5.67 # errs-test-other-beam_size_4-epoch-20-avg-10-beam-4.txt:%WER = 15.60 @@ -140,5 +202,6 @@ if [ $stage -eq 4 ]; then --epoch 20 \ --avg 10 \ --max-duration 200 \ - --exp-dir ./pruned_transducer_stateless6/exp + --exp-dir $exp_dir \ + --enable-distillation True fi diff --git a/egs/librispeech/ASR/local/download_lm.py b/egs/librispeech/ASR/local/download_lm.py index 94d23afed..030122aa7 100755 --- a/egs/librispeech/ASR/local/download_lm.py +++ b/egs/librispeech/ASR/local/download_lm.py @@ -23,6 +23,7 @@ This file downloads the following LibriSpeech LM files: - 4-gram.arpa.gz - librispeech-vocab.txt - librispeech-lexicon.txt + - librispeech-lm-norm.txt.gz from http://www.openslr.org/resources/11 and save them in the user provided directory. @@ -61,6 +62,7 @@ def main(out_dir: str): "4-gram.arpa.gz", "librispeech-vocab.txt", "librispeech-lexicon.txt", + "librispeech-lm-norm.txt.gz", ) for f in tqdm(files_to_download, desc="Downloading LibriSpeech LM files"): diff --git a/egs/librispeech/ASR/local/prepare_lm_training_data.py b/egs/librispeech/ASR/local/prepare_lm_training_data.py new file mode 100755 index 000000000..5070341f1 --- /dev/null +++ b/egs/librispeech/ASR/local/prepare_lm_training_data.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2021 Xiaomi Corporation (authors: Daniel Povey +# Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script takes a `bpe.model` and a text file such as +./download/lm/librispeech-lm-norm.txt +and outputs the LM training data to a supplied directory such +as data/lm_training_bpe_500. The format is as follows: + +It creates a PyTorch archive (.pt file), say data/lm_training.pt, which is a +representation of a dict with the following format: + + 'words' -> a k2.RaggedTensor of two axes [word][token] with dtype torch.int32 + containing the BPE representations of each word, indexed by + integer word ID. (These integer word IDS are present in + 'lm_data'). The sentencepiece object can be used to turn the + words and BPE units into string form. + 'sentences' -> a k2.RaggedTensor of two axes [sentence][word] with dtype + torch.int32 containing all the sentences, as word-ids (we don't + output the string form of this directly but it can be worked out + together with 'words' and the bpe.model). + 'sentence_lengths' -> a 1-D torch.Tensor of dtype torch.int32, containing + number of BPE tokens of each sentence. +""" + +import argparse +import logging +from pathlib import Path + +import k2 +import sentencepiece as spm +import torch + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--bpe-model", + type=str, + help="Input BPE model, e.g. data/bpe_500/bpe.model", + ) + parser.add_argument( + "--lm-data", + type=str, + help="""Input LM training data as text, e.g. + download/pb.train.txt""", + ) + parser.add_argument( + "--lm-archive", + type=str, + help="""Path to output archive, e.g. data/bpe_500/lm_data.pt; + look at the source of this script to see the format.""", + ) + + return parser.parse_args() + + +def main(): + args = get_args() + + if Path(args.lm_archive).exists(): + logging.warning(f"{args.lm_archive} exists - skipping") + return + + sp = spm.SentencePieceProcessor() + sp.load(args.bpe_model) + + # word2index is a dictionary from words to integer ids. No need to reserve + # space for epsilon, etc.; the words are just used as a convenient way to + # compress the sequences of BPE pieces. + word2index = dict() + + word2bpe = [] # Will be a list-of-list-of-int, representing BPE pieces. + sentences = [] # Will be a list-of-list-of-int, representing word-ids. + + if "librispeech-lm-norm" in args.lm_data: + num_lines_in_total = 40418261.0 + step = 5000000 + elif "valid" in args.lm_data: + num_lines_in_total = 5567.0 + step = 3000 + elif "test" in args.lm_data: + num_lines_in_total = 5559.0 + step = 3000 + else: + num_lines_in_total = None + step = None + + processed = 0 + + with open(args.lm_data) as f: + while True: + line = f.readline() + if line == "": + break + + if step and processed % step == 0: + logging.info( + f"Processed number of lines: {processed} " + f"({processed/num_lines_in_total*100: .3f}%)" + ) + processed += 1 + + line_words = line.split() + for w in line_words: + if w not in word2index: + w_bpe = sp.encode(w) + word2index[w] = len(word2bpe) + word2bpe.append(w_bpe) + sentences.append([word2index[w] for w in line_words]) + + logging.info("Constructing ragged tensors") + words = k2.ragged.RaggedTensor(word2bpe) + sentences = k2.ragged.RaggedTensor(sentences) + + output = dict(words=words, sentences=sentences) + + num_sentences = sentences.dim0 + logging.info(f"Computing sentence lengths, num_sentences: {num_sentences}") + sentence_lengths = [0] * num_sentences + for i in range(num_sentences): + if step and i % step == 0: + logging.info( + f"Processed number of lines: {i} " + f"({i/num_sentences*100: .3f}%)" + ) + + word_ids = sentences[i] + + # NOTE: If word_ids is a tensor with only 1 entry, + # token_ids is a torch.Tensor + token_ids = words[word_ids] + if isinstance(token_ids, k2.RaggedTensor): + token_ids = token_ids.values + + # token_ids is a 1-D tensor containing the BPE tokens + # of the current sentence + + sentence_lengths[i] = token_ids.numel() + + output["sentence_lengths"] = torch.tensor( + sentence_lengths, dtype=torch.int32 + ) + + torch.save(output, args.lm_archive) + logging.info(f"Saved to {args.lm_archive}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + main() diff --git a/egs/librispeech/ASR/local/sort_lm_training_data.py b/egs/librispeech/ASR/local/sort_lm_training_data.py new file mode 120000 index 000000000..bb26b5f5c --- /dev/null +++ b/egs/librispeech/ASR/local/sort_lm_training_data.py @@ -0,0 +1 @@ +../../../ptb/LM/local/sort_lm_training_data.py \ No newline at end of file diff --git a/egs/librispeech/ASR/local/train_bpe_model.py b/egs/librispeech/ASR/local/train_bpe_model.py index bc5812810..42aba9572 100755 --- a/egs/librispeech/ASR/local/train_bpe_model.py +++ b/egs/librispeech/ASR/local/train_bpe_model.py @@ -38,7 +38,6 @@ def get_args(): "--lang-dir", type=str, help="""Input and output directory. - It should contain the training corpus: transcript_words.txt. The generated bpe.model is saved to this directory. """, ) diff --git a/egs/librispeech/ASR/prepare.sh b/egs/librispeech/ASR/prepare.sh index 17a638502..94e003036 100755 --- a/egs/librispeech/ASR/prepare.sh +++ b/egs/librispeech/ASR/prepare.sh @@ -24,6 +24,7 @@ stop_stage=100 # - 4-gram.arpa # - librispeech-vocab.txt # - librispeech-lexicon.txt +# - librispeech-lm-norm.txt.gz # # - $dl_dir/musan # This directory contains the following directories downloaded from @@ -40,9 +41,9 @@ dl_dir=$PWD/download # It will generate data/lang_bpe_xxx, # data/lang_bpe_yyy if the array contains xxx, yyy vocab_sizes=( - # 5000 - # 2000 - # 1000 + 5000 + 2000 + 1000 500 ) @@ -278,3 +279,99 @@ if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then ./local/compile_lg.py --lang-dir $lang_dir done fi + +if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then + log "Stage 11: Generate LM training data" + + for vocab_size in ${vocab_sizes[@]}; do + log "Processing vocab_size == ${vocab_size}" + lang_dir=data/lang_bpe_${vocab_size} + out_dir=data/lm_training_bpe_${vocab_size} + mkdir -p $out_dir + + ./local/prepare_lm_training_data.py \ + --bpe-model $lang_dir/bpe.model \ + --lm-data $dl_dir/lm/librispeech-lm-norm.txt \ + --lm-archive $out_dir/lm_data.pt + done +fi + +if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then + log "Stage 12: Generate LM validation data" + + for vocab_size in ${vocab_sizes[@]}; do + log "Processing vocab_size == ${vocab_size}" + out_dir=data/lm_training_bpe_${vocab_size} + mkdir -p $out_dir + + if [ ! -f $out_dir/valid.txt ]; then + files=$( + find "$dl_dir/LibriSpeech/dev-clean" -name "*.trans.txt" + find "$dl_dir/LibriSpeech/dev-other" -name "*.trans.txt" + ) + for f in ${files[@]}; do + cat $f | cut -d " " -f 2- + done > $out_dir/valid.txt + fi + + lang_dir=data/lang_bpe_${vocab_size} + ./local/prepare_lm_training_data.py \ + --bpe-model $lang_dir/bpe.model \ + --lm-data $out_dir/valid.txt \ + --lm-archive $out_dir/lm_data-valid.pt + done +fi + +if [ $stage -le 13 ] && [ $stop_stage -ge 13 ]; then + log "Stage 13: Generate LM test data" + + for vocab_size in ${vocab_sizes[@]}; do + log "Processing vocab_size == ${vocab_size}" + out_dir=data/lm_training_bpe_${vocab_size} + mkdir -p $out_dir + + if [ ! -f $out_dir/test.txt ]; then + files=$( + find "$dl_dir/LibriSpeech/test-clean" -name "*.trans.txt" + find "$dl_dir/LibriSpeech/test-other" -name "*.trans.txt" + ) + for f in ${files[@]}; do + cat $f | cut -d " " -f 2- + done > $out_dir/test.txt + fi + + lang_dir=data/lang_bpe_${vocab_size} + ./local/prepare_lm_training_data.py \ + --bpe-model $lang_dir/bpe.model \ + --lm-data $out_dir/test.txt \ + --lm-archive $out_dir/lm_data-test.pt + done +fi + +if [ $stage -le 14 ] && [ $stop_stage -ge 14 ]; then + log "Stage 14: Sort LM training data" + # Sort LM training data by sentence length in descending order + # for ease of training. + # + # Sentence length equals to the number of BPE tokens + # in a sentence. + + for vocab_size in ${vocab_sizes[@]}; do + out_dir=data/lm_training_bpe_${vocab_size} + mkdir -p $out_dir + ./local/sort_lm_training_data.py \ + --in-lm-data $out_dir/lm_data.pt \ + --out-lm-data $out_dir/sorted_lm_data.pt \ + --out-statistics $out_dir/statistics.txt + + ./local/sort_lm_training_data.py \ + --in-lm-data $out_dir/lm_data-valid.pt \ + --out-lm-data $out_dir/sorted_lm_data-valid.pt \ + --out-statistics $out_dir/statistics-valid.txt + + ./local/sort_lm_training_data.py \ + --in-lm-data $out_dir/lm_data-test.pt \ + --out-lm-data $out_dir/sorted_lm_data-test.pt \ + --out-statistics $out_dir/statistics-test.txt + done +fi diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py index db23fd993..40c442e7a 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py @@ -75,6 +75,202 @@ def fast_beam_search_one_best( return hyps +def fast_beam_search_nbest_LG( + model: Transducer, + decoding_graph: k2.Fsa, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + beam: float, + max_states: int, + max_contexts: int, + num_paths: int, + nbest_scale: float = 0.5, + use_double_scores: bool = True, +) -> List[List[int]]: + """It limits the maximum number of symbols per frame to 1. + + The process to get the results is: + - (1) Use fast beam search to get a lattice + - (2) Select `num_paths` paths from the lattice using k2.random_paths() + - (3) Unique the selected paths + - (4) Intersect the selected paths with the lattice and compute the + shortest path from the intersection result + - (5) The path with the largest score is used as the decoding output. + + Args: + model: + An instance of `Transducer`. + decoding_graph: + Decoding graph used for decoding, may be a TrivialGraph or a HLG. + encoder_out: + A tensor of shape (N, T, C) from the encoder. + encoder_out_lens: + A tensor of shape (N,) containing the number of frames in `encoder_out` + before padding. + beam: + Beam value, similar to the beam used in Kaldi.. + max_states: + Max states per stream per frame. + max_contexts: + Max contexts pre stream per frame. + num_paths: + Number of paths to extract from the decoded lattice. + nbest_scale: + It's the scale applied to the lattice.scores. A smaller value + yields more unique paths. + use_double_scores: + True to use double precision for computation. False to use + single precision. + Returns: + Return the decoded result. + """ + lattice = fast_beam_search( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=beam, + max_states=max_states, + max_contexts=max_contexts, + ) + + nbest = Nbest.from_lattice( + lattice=lattice, + num_paths=num_paths, + use_double_scores=use_double_scores, + nbest_scale=nbest_scale, + ) + + # The following code is modified from nbest.intersect() + word_fsa = k2.invert(nbest.fsa) + if hasattr(lattice, "aux_labels"): + # delete token IDs as it is not needed + del word_fsa.aux_labels + word_fsa.scores.zero_() + word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa) + path_to_utt_map = nbest.shape.row_ids(1) + + if hasattr(lattice, "aux_labels"): + # lattice has token IDs as labels and word IDs as aux_labels. + # inv_lattice has word IDs as labels and token IDs as aux_labels + inv_lattice = k2.invert(lattice) + inv_lattice = k2.arc_sort(inv_lattice) + else: + inv_lattice = k2.arc_sort(lattice) + + if inv_lattice.shape[0] == 1: + path_lattice = k2.intersect_device( + inv_lattice, + word_fsa_with_epsilon_loops, + b_to_a_map=torch.zeros_like(path_to_utt_map), + sorted_match_a=True, + ) + else: + path_lattice = k2.intersect_device( + inv_lattice, + word_fsa_with_epsilon_loops, + b_to_a_map=path_to_utt_map, + sorted_match_a=True, + ) + + # path_lattice has word IDs as labels and token IDs as aux_labels + path_lattice = k2.top_sort(k2.connect(path_lattice)) + tot_scores = path_lattice.get_tot_scores( + use_double_scores=use_double_scores, + log_semiring=True, # Note: we always use True + ) + # See https://github.com/k2-fsa/icefall/pull/420 for why + # we always use log_semiring=True + + ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) + best_hyp_indexes = ragged_tot_scores.argmax() + best_path = k2.index_fsa(nbest.fsa, best_hyp_indexes) + + hyps = get_texts(best_path) + + return hyps + + +def fast_beam_search_nbest( + model: Transducer, + decoding_graph: k2.Fsa, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + beam: float, + max_states: int, + max_contexts: int, + num_paths: int, + nbest_scale: float = 0.5, + use_double_scores: bool = True, +) -> List[List[int]]: + """It limits the maximum number of symbols per frame to 1. + + The process to get the results is: + - (1) Use fast beam search to get a lattice + - (2) Select `num_paths` paths from the lattice using k2.random_paths() + - (3) Unique the selected paths + - (4) Intersect the selected paths with the lattice and compute the + shortest path from the intersection result + - (5) The path with the largest score is used as the decoding output. + + Args: + model: + An instance of `Transducer`. + decoding_graph: + Decoding graph used for decoding, may be a TrivialGraph or a HLG. + encoder_out: + A tensor of shape (N, T, C) from the encoder. + encoder_out_lens: + A tensor of shape (N,) containing the number of frames in `encoder_out` + before padding. + beam: + Beam value, similar to the beam used in Kaldi.. + max_states: + Max states per stream per frame. + max_contexts: + Max contexts pre stream per frame. + num_paths: + Number of paths to extract from the decoded lattice. + nbest_scale: + It's the scale applied to the lattice.scores. A smaller value + yields more unique paths. + use_double_scores: + True to use double precision for computation. False to use + single precision. + Returns: + Return the decoded result. + """ + lattice = fast_beam_search( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=beam, + max_states=max_states, + max_contexts=max_contexts, + ) + + nbest = Nbest.from_lattice( + lattice=lattice, + num_paths=num_paths, + use_double_scores=use_double_scores, + nbest_scale=nbest_scale, + ) + + # at this point, nbest.fsa.scores are all zeros. + + nbest = nbest.intersect(lattice) + # Now nbest.fsa.scores contains acoustic scores + + max_indexes = nbest.tot_scores().argmax() + + best_path = k2.index_fsa(nbest.fsa, max_indexes) + + hyps = get_texts(best_path) + + return hyps + + def fast_beam_search_nbest_oracle( model: Transducer, decoding_graph: k2.Fsa, diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless/decode.py index ea43836bd..b7558089c 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/decode.py @@ -50,22 +50,58 @@ Usage: --exp-dir ./pruned_transducer_stateless/exp \ --max-duration 600 \ --decoding-method fast_beam_search \ - --beam 4 \ - --max-contexts 4 \ - --max-states 8 + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 -(5) fast beam search using LG +(5) fast beam search (nbest) ./pruned_transducer_stateless/decode.py \ --epoch 28 \ --avg 15 \ --exp-dir ./pruned_transducer_stateless/exp \ - --use-LG True \ - --use-max False \ --max-duration 600 \ - --decoding-method fast_beam_search \ - --beam 8 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./pruned_transducer_stateless/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./pruned_transducer_stateless/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ --max-contexts 8 \ --max-states 64 + +(6) decode in streaming mode (take greedy search as an example) +./pruned_transducer_stateless/decode.py \ + --epoch 28 \ + --avg 15 \ + --simulate-streaming 1 \ + --causal-convolution 1 \ + --decode-chunk-size 16 \ + --left-context 64 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method greedy_search """ @@ -82,12 +118,15 @@ import torch.nn as nn from asr_datamodule import LibriSpeechAsrDataModule from beam_search import ( beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, ) -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model from icefall.checkpoint import ( average_checkpoints, @@ -153,7 +192,7 @@ def get_parser(): parser.add_argument( "--lang-dir", - type=str, + type=Path, default="data/lang_bpe_500", help="The lang dir containing word table and LG graph", ) @@ -167,6 +206,11 @@ def get_parser(): - beam_search - modified_beam_search - fast_beam_search + - fast_beam_search_nbest + - fast_beam_search_nbest_oracle + - fast_beam_search_nbest_LG + If you use fast_beam_search_nbest_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. """, ) @@ -182,30 +226,13 @@ def get_parser(): parser.add_argument( "--beam", type=float, - default=4, + default=20.0, help="""A floating point value to calculate the cutoff score during beam search (i.e., `cutoff = max-score - beam`), which is the same as the `beam` in Kaldi. - Used only when --decoding-method is fast_beam_search""", - ) - - parser.add_argument( - "--use-LG", - type=str2bool, - default=False, - help="""Whether to use an LG graph for FSA-based beam search. - Used only when --decoding_method is fast_beam_search. If setting true, - it assumes there is an LG.pt file in lang_dir.""", - ) - - parser.add_argument( - "--use-max", - type=str2bool, - default=False, - help="""If True, use max-op to select the hypothesis that have the - max log_prob in case of duplicate hypotheses. - If False, use log_add. - Used only for beam_search, modified_beam_search, and fast_beam_search + Used only when --decoding-method is fast_beam_search, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle """, ) @@ -214,7 +241,7 @@ def get_parser(): type=float, default=0.01, help=""" - Used only when --decoding_method is fast_beam_search. + Used only when --decoding_method is fast_beam_search_nbest_LG. It specifies the scale for n-gram LM scores. """, ) @@ -222,9 +249,10 @@ def get_parser(): parser.add_argument( "--max-contexts", type=int, - default=4, + default=8, help="""Used only when --decoding-method is - fast_beam_search""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( @@ -232,7 +260,8 @@ def get_parser(): type=int, default=8, help="""Used only when --decoding-method is - fast_beam_search""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( @@ -250,6 +279,47 @@ def get_parser(): Used only when --decoding_method is greedy_search""", ) + parser.add_argument( + "--simulate-streaming", + type=str2bool, + default=False, + help="""Whether to simulate streaming in decoding, this is a good way to + test a streaming model. + """, + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + add_model_arguments(parser) + return parser @@ -286,7 +356,8 @@ def decode_one_batch( The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -299,11 +370,21 @@ def decode_one_batch( # at entry, feature is (N, T, C) supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) - encoder_out, encoder_out_lens = model.encoder( - x=feature, x_lens=feature_lens - ) + if params.simulate_streaming: + encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward( + x=feature, + x_lens=feature_lens, + chunk_size=params.decode_chunk_size, + left_context=params.left_context, + simulate_streaming=True, + ) + else: + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) hyps = [] if params.decoding_method == "fast_beam_search": @@ -316,12 +397,51 @@ def decode_one_batch( max_contexts=params.max_contexts, max_states=params.max_states, ) - if params.use_LG: - for hyp in hyp_tokens: - hyps.append([word_table[i] for i in hyp]) - else: - for hyp in sp.decode(hyp_tokens): - hyps.append(hyp.split()) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + ref_texts=sp.encode(supervisions["text"]), + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) elif ( params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1 @@ -339,7 +459,6 @@ def decode_one_batch( encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, beam=params.beam_size, - use_max=params.use_max, ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) @@ -361,7 +480,6 @@ def decode_one_batch( model=model, encoder_out=encoder_out_i, beam=params.beam_size, - use_max=params.use_max, ) else: raise ValueError( @@ -371,14 +489,17 @@ def decode_one_batch( if params.decoding_method == "greedy_search": return {"greedy_search": hyps} - elif params.decoding_method == "fast_beam_search": - return { - ( - f"beam_{params.beam}_" - f"max_contexts_{params.max_contexts}_" - f"max_states_{params.max_states}" - ): hyps - } + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} else: return {f"beam_size_{params.beam_size}": hyps} @@ -406,7 +527,8 @@ def decode_dataset( The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -424,7 +546,7 @@ def decode_dataset( if params.decoding_method == "greedy_search": log_interval = 50 else: - log_interval = 10 + log_interval = 20 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -517,6 +639,9 @@ def main(): "greedy_search", "beam_search", "fast_beam_search", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", + "fast_beam_search_nbest_oracle", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method @@ -526,17 +651,23 @@ def main(): else: params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + if params.simulate_streaming: + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}" + params.suffix += f"-left-context-{params.left_context}" + if "fast_beam_search" in params.decoding_method: - params.suffix += f"-use-LG-{params.use_LG}" params.suffix += f"-beam-{params.beam}" params.suffix += f"-max-contexts-{params.max_contexts}" params.suffix += f"-max-states-{params.max_states}" - params.suffix += f"-use-max-{params.use_max}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" elif "beam_search" in params.decoding_method: params.suffix += ( f"-{params.decoding_method}-beam-size-{params.beam_size}" ) - params.suffix += f"-use-max-{params.use_max}" else: params.suffix += f"-context-{params.context_size}" params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" @@ -558,6 +689,11 @@ def main(): params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.simulate_streaming: + assert ( + params.causal_convolution + ), "Decoding in streaming requires causal convolution" + logging.info(params) logging.info("About to create model") @@ -596,12 +732,14 @@ def main(): model.eval() model.device = device - if params.decoding_method == "fast_beam_search": - if params.use_LG: + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": lexicon = Lexicon(params.lang_dir) word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") decoding_graph = k2.Fsa.from_dict( - torch.load(f"{params.lang_dir}/LG.pt", map_location=device) + torch.load(lg_filename, map_location=device) ) decoding_graph.scores *= params.ngram_lm_scale else: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/decode_stream.py b/egs/librispeech/ASR/pruned_transducer_stateless/decode_stream.py new file mode 100644 index 000000000..ba5e80555 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless/decode_stream.py @@ -0,0 +1,126 @@ +# Copyright 2022 Xiaomi Corp. (authors: Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import List, Optional, Tuple + +import k2 +import torch + +from icefall.utils import AttributeDict + + +class DecodeStream(object): + def __init__( + self, + params: AttributeDict, + initial_states: List[torch.Tensor], + decoding_graph: Optional[k2.Fsa] = None, + device: torch.device = torch.device("cpu"), + ) -> None: + """ + Args: + initial_states: + Initial decode states of the model, e.g. the return value of + `get_init_state` in conformer.py + decoding_graph: + Decoding graph used for decoding, may be a TrivialGraph or a HLG. + Used only when decoding_method is fast_beam_search. + device: + The device to run this stream. + """ + if decoding_graph is not None: + assert device == decoding_graph.device + + self.params = params + self.LOG_EPS = math.log(1e-10) + + self.states = initial_states + + # It contains a 2-D tensors representing the feature frames. + self.features: torch.Tensor = None + + self.num_frames: int = 0 + # how many frames have been processed. (before subsampling). + # we only modify this value in `func:get_feature_frames`. + self.num_processed_frames: int = 0 + + self._done: bool = False + + # The transcript of current utterance. + self.ground_truth: str = "" + + # The decoding result (partial or final) of current utterance. + self.hyp: List = [] + + # how many frames have been processed, after subsampling (i.e. a + # cumulative sum of the second return value of + # encoder.streaming_forward + self.done_frames: int = 0 + + self.pad_length = ( + params.right_context + 2 + ) * params.subsampling_factor + 3 + + if params.decoding_method == "greedy_search": + self.hyp = [params.blank_id] * params.context_size + elif params.decoding_method == "fast_beam_search": + # The rnnt_decoding_stream for fast_beam_search. + self.rnnt_decoding_stream: k2.RnntDecodingStream = ( + k2.RnntDecodingStream(decoding_graph) + ) + else: + assert ( + False + ), f"Decoding method :{params.decoding_method} do not support." + + @property + def done(self) -> bool: + """Return True if all the features are processed.""" + return self._done + + def set_features( + self, + features: torch.Tensor, + ) -> None: + """Set features tensor of current utterance.""" + assert features.dim() == 2, features.dim() + self.features = torch.nn.functional.pad( + features, + (0, 0, 0, self.pad_length), + mode="constant", + value=self.LOG_EPS, + ) + self.num_frames = self.features.size(0) + + def get_feature_frames(self, chunk_size: int) -> Tuple[torch.Tensor, int]: + """Consume chunk_size frames of features""" + chunk_length = chunk_size + self.pad_length + + ret_length = min( + self.num_frames - self.num_processed_frames, chunk_length + ) + + ret_features = self.features[ + self.num_processed_frames : self.num_processed_frames # noqa + + ret_length + ] + + self.num_processed_frames += chunk_size + if self.num_processed_frames >= self.num_frames: + self._done = True + + return ret_features, ret_length diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/export.py b/egs/librispeech/ASR/pruned_transducer_stateless/export.py index a4210831c..b5a151878 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/export.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/export.py @@ -49,7 +49,7 @@ from pathlib import Path import sentencepiece as spm import torch -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model from icefall.checkpoint import average_checkpoints, load_checkpoint from icefall.utils import str2bool @@ -109,6 +109,17 @@ def get_parser(): "2 means tri-gram", ) + parser.add_argument( + "--streaming-model", + type=str2bool, + default=False, + help="""Whether to export a streaming model, if the models in exp-dir + are streaming model, this should be True. + """, + ) + + add_model_arguments(parser) + return parser @@ -130,8 +141,12 @@ def main(): # is defined in local/train_bpe_model.py params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.streaming_model: + assert params.causal_convolution + logging.info(params) logging.info("About to create model") diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py index d21a737b8..eb95827af 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py @@ -77,7 +77,9 @@ from beam_search import ( modified_beam_search, ) from torch.nn.utils.rnn import pad_sequence -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.utils import str2bool def get_parser(): @@ -177,6 +179,29 @@ def get_parser(): --method is greedy_search. """, ) + parser.add_argument( + "--simulate-streaming", + type=str2bool, + default=False, + help="""Whether to simulate streaming in decoding, this is a good way to + test a streaming model. + """, + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + add_model_arguments(parser) return parser @@ -222,6 +247,11 @@ def main(): params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.simulate_streaming: + assert ( + params.causal_convolution + ), "Decoding in streaming requires causal convolution" + logging.info(f"{params}") device = torch.device("cpu") @@ -268,9 +298,18 @@ def main(): feature_lengths = torch.tensor(feature_lengths, device=device) - encoder_out, encoder_out_lens = model.encoder( - x=features, x_lens=feature_lengths - ) + if params.simulate_streaming: + encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward( + x=features, + x_lens=feature_lengths, + chunk_size=params.decode_chunk_size, + left_context=params.left_context, + simulate_streaming=True, + ) + else: + encoder_out, encoder_out_lens = model.encoder( + x=features, x_lens=feature_lengths + ) num_waves = encoder_out.size(0) hyps = [] diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py new file mode 100755 index 000000000..f05cf7a91 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py @@ -0,0 +1,678 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: +./pruned_transducer_stateless2/streaming_decode.py \ + --epoch 28 \ + --avg 15 \ + --decode-chunk-size 8 \ + --left-context 32 \ + --right-context 0 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --decoding_method greedy_search \ + --num-decode-streams 1000 +""" + +import argparse +import logging +import math +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import numpy as np +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from decode_stream import DecodeStream +from kaldifeat import Fbank, FbankOptions +from lhotse import CutSet +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.decode import one_best_decoding +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Support only greedy_search and fast_beam_search now. + """, + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=32, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--right-context", + type=int, + default=0, + help="right context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--num-decode-streams", + type=int, + default=2000, + help="The number of streams that can be decoded parallel.", + ) + + add_model_arguments(parser) + + return parser + + +def greedy_search( + model: nn.Module, + encoder_out: torch.Tensor, + streams: List[DecodeStream], +) -> List[List[int]]: + + assert len(streams) == encoder_out.size(0) + assert encoder_out.ndim == 3 + + blank_id = model.decoder.blank_id + context_size = model.decoder.context_size + device = model.device + T = encoder_out.size(1) + + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + # decoder_out is of shape (N, decoder_out_dim) + decoder_out = model.decoder(decoder_input, need_pad=False) + + for t in range(T): + # current_encoder_out's shape: (batch_size, 1, encoder_out_dim) + current_encoder_out = encoder_out[:, t : t + 1, :] # noqa + + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + ) + # logits'shape (batch_size, vocab_size) + logits = logits.squeeze(1).squeeze(1) + + assert logits.ndim == 2, logits.shape + y = logits.argmax(dim=1).tolist() + emitted = False + for i, v in enumerate(y): + if v != blank_id: + streams[i].hyp.append(v) + emitted = True + if emitted: + # update decoder output + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + decoder_out = model.decoder( + decoder_input, + need_pad=False, + ) + + hyp_tokens = [] + for stream in streams: + hyp_tokens.append(stream.hyp) + return hyp_tokens + + +def fast_beam_search( + model: nn.Module, + encoder_out: torch.Tensor, + processed_lens: torch.Tensor, + decoding_streams: k2.RnntDecodingStreams, +) -> List[List[int]]: + + B, T, C = encoder_out.shape + for t in range(T): + # shape is a RaggedShape of shape (B, context) + # contexts is a Tensor of shape (shape.NumElements(), context_size) + shape, contexts = decoding_streams.get_contexts() + # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64 + contexts = contexts.to(torch.int64) + # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim) + decoder_out = model.decoder(contexts, need_pad=False) + # current_encoder_out is of shape + # (shape.NumElements(), 1, joiner_dim) + # fmt: off + current_encoder_out = torch.index_select( + encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64) + ) + # fmt: on + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + ) + logits = logits.squeeze(1).squeeze(1) + log_probs = logits.log_softmax(dim=-1) + decoding_streams.advance(log_probs) + + decoding_streams.terminate_and_flush_to_streams() + + lattice = decoding_streams.format_output(processed_lens.tolist()) + best_path = one_best_decoding(lattice) + hyp_tokens = get_texts(best_path) + return hyp_tokens + + +def decode_one_chunk( + params: AttributeDict, + model: nn.Module, + decode_streams: List[DecodeStream], +) -> List[int]: + """Decode one chunk frames of features for each decode_streams and + return the indexes of finished streams in a List. + + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + decode_streams: + A List of DecodeStream, each belonging to a utterance. + Returns: + Return a List containing which DecodeStreams are finished. + """ + device = model.device + + features = [] + feature_lens = [] + states = [] + + rnnt_stream_list = [] + processed_lens = [] + + for stream in decode_streams: + feat, feat_len = stream.get_feature_frames( + params.decode_chunk_size * params.subsampling_factor + ) + features.append(feat) + feature_lens.append(feat_len) + states.append(stream.states) + processed_lens.append(stream.done_frames) + if params.decoding_method == "fast_beam_search": + rnnt_stream_list.append(stream.rnnt_decoding_stream) + + feature_lens = torch.tensor(feature_lens, device=device) + features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS) + + # if T is less than 7 there will be an error in time reduction layer, + # because we subsample features with ((x_len - 1) // 2 - 1) // 2 + # we plus 2 here because we will cut off one frame on each size of + # encoder_embed output as they see invalid paddings. so we need extra 2 + # frames. + tail_length = 7 + (2 + params.right_context) * params.subsampling_factor + if features.size(1) < tail_length: + feature_lens += tail_length - features.size(1) + features = torch.cat( + [ + features, + torch.tensor( + LOG_EPS, dtype=features.dtype, device=device + ).expand( + features.size(0), + tail_length - features.size(1), + features.size(2), + ), + ], + dim=1, + ) + + states = [ + torch.stack([x[0] for x in states], dim=2), + torch.stack([x[1] for x in states], dim=2), + ] + + processed_lens = torch.tensor(processed_lens, device=device) + + encoder_out, encoder_out_lens, states = model.encoder.streaming_forward( + x=features, + x_lens=feature_lens, + states=states, + left_context=params.left_context, + right_context=params.right_context, + processed_lens=processed_lens, + ) + + if params.decoding_method == "greedy_search": + hyp_tokens = greedy_search(model, encoder_out, decode_streams) + elif params.decoding_method == "fast_beam_search": + config = k2.RnntDecodingConfig( + vocab_size=params.vocab_size, + decoder_history_len=params.context_size, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config) + processed_lens = processed_lens + encoder_out_lens + hyp_tokens = fast_beam_search( + model, encoder_out, processed_lens, decoding_streams + ) + else: + assert False + + states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)] + + finished_streams = [] + for i in range(len(decode_streams)): + decode_streams[i].states = [states[0][i], states[1][i]] + decode_streams[i].done_frames += encoder_out_lens[i] + if params.decoding_method == "fast_beam_search": + decode_streams[i].hyp = hyp_tokens[i] + if decode_streams[i].done: + finished_streams.append(i) + + return finished_streams + + +def decode_dataset( + cuts: CutSet, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + cuts: + Lhotse Cutset containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + device = model.device + + opts = FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + + log_interval = 100 + + decode_results = [] + # Contain decode streams currently running. + decode_streams = [] + initial_states = model.encoder.get_init_state( + params.left_context, device=device + ) + for num, cut in enumerate(cuts): + # each utterance has a DecodeStream. + decode_stream = DecodeStream( + params=params, + initial_states=initial_states, + decoding_graph=decoding_graph, + device=device, + ) + + audio: np.ndarray = cut.load_audio() + # audio.shape: (1, num_samples) + assert len(audio.shape) == 2 + assert audio.shape[0] == 1, "Should be single channel" + assert audio.dtype == np.float32, audio.dtype + + # The trained model is using normalized samples + assert audio.max() <= 1, "Should be normalized to [-1, 1])" + + samples = torch.from_numpy(audio).squeeze(0) + + fbank = Fbank(opts) + decode_stream.set_features(fbank(samples.to(device))) + decode_stream.ground_truth = cut.supervisions[0].text + + decode_streams.append(decode_stream) + + while len(decode_streams) >= params.num_decode_streams: + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + hyp = decode_streams[i].hyp + if params.decoding_method == "greedy_search": + hyp = hyp[params.context_size :] # noqa + decode_results.append( + ( + decode_streams[i].ground_truth.split(), + sp.decode(hyp).split(), + ) + ) + del decode_streams[i] + + if num % log_interval == 0: + logging.info(f"Cuts processed until now is {num}.") + + # decode final chunks of last sequences + while len(decode_streams): + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + hyp = decode_streams[i].hyp + if params.decoding_method == "greedy_search": + hyp = hyp[params.context_size :] # noqa + decode_results.append( + ( + decode_streams[i].ground_truth.split(), + sp.decode(hyp).split(), + ) + ) + del decode_streams[i] + + key = "greedy_search" + if params.decoding_method == "fast_beam_search": + key = ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ) + return {key: decode_results} + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + # sort results so we can easily compare the difference between two + # recognition results + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + params.res_dir = params.exp_dir / "streaming" / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + # for streaming + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}" + params.suffix += f"-left-context-{params.left_context}" + params.suffix += f"-right-context-{params.right_context}" + + # for fast_beam_search + if params.decoding_method == "fast_beam_search": + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + params.causal_convolution = True + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.to(device) + model.eval() + model.device = device + + decoding_graph = None + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_sets = ["test-clean", "test-other"] + test_cuts = [test_clean_cuts, test_other_cuts] + + for test_set, test_cut in zip(test_sets, test_cuts): + results_dict = decode_dataset( + cuts=test_cut, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless/test_model.py index 5c49025bd..1858d6bf0 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/test_model.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/test_model.py @@ -34,6 +34,31 @@ def test_model(): params.context_size = 2 params.unk_id = 2 + params.dynamic_chunk_training = False + params.short_chunk_size = 25 + params.num_left_chunks = 4 + params.causal_convolution = False + + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + torch.jit.script(model) + + +def test_model_streaming(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.unk_id = 2 + + params.dynamic_chunk_training = True + params.short_chunk_size = 25 + params.num_left_chunks = 4 + params.causal_convolution = True + model = get_transducer_model(params) num_param = sum([p.numel() for p in model.parameters()]) @@ -44,6 +69,7 @@ def test_model(): def main(): test_model() + test_model_streaming() if __name__ == "__main__": diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/train.py b/egs/librispeech/ASR/pruned_transducer_stateless/train.py index 448419759..3708c17ef 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/train.py @@ -28,6 +28,19 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3" --exp-dir pruned_transducer_stateless/exp \ --full-libri 1 \ --max-duration 300 + +# train a streaming model +./pruned_transducer_stateless/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir pruned_transducer_stateless/exp \ + --full-libri 1 \ + --dynamic-chunk-training 1 \ + --causal-convolution 1 \ + --short-chunk-size 25 \ + --num-left-chunks 4 \ + --max-duration 300 """ @@ -73,6 +86,42 @@ from icefall.utils import ( ) +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--dynamic-chunk-training", + type=str2bool, + default=False, + help="""Whether to use dynamic_chunk_training, if you want a streaming + model, this requires to be True. + """, + ) + + parser.add_argument( + "--causal-convolution", + type=str2bool, + default=False, + help="""Whether to use causal convolution, this requires to be True when + using dynamic_chunk_training. + """, + ) + + parser.add_argument( + "--short-chunk-size", + type=int, + default=25, + help="""Chunk length of dynamic training, the chunk size would be either + max sequence length of current batch or uniformly sampled from (1, short_chunk_size). + """, + ) + + parser.add_argument( + "--num-left-chunks", + type=int, + default=4, + help="How many left context can be seen in chunks when calculating attention.", + ) + + def get_parser(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter @@ -222,6 +271,8 @@ def get_parser(): """, ) + add_model_arguments(parser) + return parser @@ -263,7 +314,7 @@ def get_params() -> AttributeDict: - subsampling_factor: The subsampling factor for the model. - - attention_dim: Hidden dim for multi-head attention model. + - encoder_dim: Hidden dim for multi-head attention model. - num_decoder_layers: Number of decoder layer of transformer decoder. @@ -283,7 +334,7 @@ def get_params() -> AttributeDict: # parameters for conformer "feature_dim": 80, "subsampling_factor": 4, - "attention_dim": 512, + "encoder_dim": 512, "nhead": 8, "dim_feedforward": 2048, "num_encoder_layers": 12, @@ -305,11 +356,15 @@ def get_encoder_model(params: AttributeDict) -> nn.Module: num_features=params.feature_dim, output_dim=params.vocab_size, subsampling_factor=params.subsampling_factor, - d_model=params.attention_dim, + d_model=params.encoder_dim, nhead=params.nhead, dim_feedforward=params.dim_feedforward, num_encoder_layers=params.num_encoder_layers, vgg_frontend=params.vgg_frontend, + dynamic_chunk_training=params.dynamic_chunk_training, + short_chunk_size=params.short_chunk_size, + num_left_chunks=params.num_left_chunks, + causal=params.causal_convolution, ) return encoder @@ -762,6 +817,11 @@ def run(rank, world_size, args): params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.dynamic_chunk_training: + assert ( + params.causal_convolution + ), "dynamic_chunk_training requires causal convolution" + logging.info(params) logging.info("About to create model") @@ -780,7 +840,7 @@ def run(rank, world_size, args): optimizer = Noam( model.parameters(), - model_size=params.attention_dim, + model_size=params.encoder_dim, factor=params.lr_factor, warm_step=params.warm_step, ) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py index 7c936b257..6b6190a09 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py @@ -37,7 +37,7 @@ def fast_beam_search_one_best( ) -> List[List[int]]: """It limits the maximum number of symbols per frame to 1. - A lattice is first obtained using modified beam search, and then + A lattice is first obtained using fast beam search, and then the shortest path within the lattice is used as the final output. Args: @@ -74,6 +74,202 @@ def fast_beam_search_one_best( return hyps +def fast_beam_search_nbest_LG( + model: Transducer, + decoding_graph: k2.Fsa, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + beam: float, + max_states: int, + max_contexts: int, + num_paths: int, + nbest_scale: float = 0.5, + use_double_scores: bool = True, +) -> List[List[int]]: + """It limits the maximum number of symbols per frame to 1. + + The process to get the results is: + - (1) Use fast beam search to get a lattice + - (2) Select `num_paths` paths from the lattice using k2.random_paths() + - (3) Unique the selected paths + - (4) Intersect the selected paths with the lattice and compute the + shortest path from the intersection result + - (5) The path with the largest score is used as the decoding output. + + Args: + model: + An instance of `Transducer`. + decoding_graph: + Decoding graph used for decoding, may be a TrivialGraph or a HLG. + encoder_out: + A tensor of shape (N, T, C) from the encoder. + encoder_out_lens: + A tensor of shape (N,) containing the number of frames in `encoder_out` + before padding. + beam: + Beam value, similar to the beam used in Kaldi.. + max_states: + Max states per stream per frame. + max_contexts: + Max contexts pre stream per frame. + num_paths: + Number of paths to extract from the decoded lattice. + nbest_scale: + It's the scale applied to the lattice.scores. A smaller value + yields more unique paths. + use_double_scores: + True to use double precision for computation. False to use + single precision. + Returns: + Return the decoded result. + """ + lattice = fast_beam_search( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=beam, + max_states=max_states, + max_contexts=max_contexts, + ) + + nbest = Nbest.from_lattice( + lattice=lattice, + num_paths=num_paths, + use_double_scores=use_double_scores, + nbest_scale=nbest_scale, + ) + + # The following code is modified from nbest.intersect() + word_fsa = k2.invert(nbest.fsa) + if hasattr(lattice, "aux_labels"): + # delete token IDs as it is not needed + del word_fsa.aux_labels + word_fsa.scores.zero_() + word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa) + path_to_utt_map = nbest.shape.row_ids(1) + + if hasattr(lattice, "aux_labels"): + # lattice has token IDs as labels and word IDs as aux_labels. + # inv_lattice has word IDs as labels and token IDs as aux_labels + inv_lattice = k2.invert(lattice) + inv_lattice = k2.arc_sort(inv_lattice) + else: + inv_lattice = k2.arc_sort(lattice) + + if inv_lattice.shape[0] == 1: + path_lattice = k2.intersect_device( + inv_lattice, + word_fsa_with_epsilon_loops, + b_to_a_map=torch.zeros_like(path_to_utt_map), + sorted_match_a=True, + ) + else: + path_lattice = k2.intersect_device( + inv_lattice, + word_fsa_with_epsilon_loops, + b_to_a_map=path_to_utt_map, + sorted_match_a=True, + ) + + # path_lattice has word IDs as labels and token IDs as aux_labels + path_lattice = k2.top_sort(k2.connect(path_lattice)) + tot_scores = path_lattice.get_tot_scores( + use_double_scores=use_double_scores, + log_semiring=True, # Note: we always use True + ) + # See https://github.com/k2-fsa/icefall/pull/420 for why + # we always use log_semiring=True + + ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) + best_hyp_indexes = ragged_tot_scores.argmax() + best_path = k2.index_fsa(nbest.fsa, best_hyp_indexes) + + hyps = get_texts(best_path) + + return hyps + + +def fast_beam_search_nbest( + model: Transducer, + decoding_graph: k2.Fsa, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + beam: float, + max_states: int, + max_contexts: int, + num_paths: int, + nbest_scale: float = 0.5, + use_double_scores: bool = True, +) -> List[List[int]]: + """It limits the maximum number of symbols per frame to 1. + + The process to get the results is: + - (1) Use fast beam search to get a lattice + - (2) Select `num_paths` paths from the lattice using k2.random_paths() + - (3) Unique the selected paths + - (4) Intersect the selected paths with the lattice and compute the + shortest path from the intersection result + - (5) The path with the largest score is used as the decoding output. + + Args: + model: + An instance of `Transducer`. + decoding_graph: + Decoding graph used for decoding, may be a TrivialGraph or a HLG. + encoder_out: + A tensor of shape (N, T, C) from the encoder. + encoder_out_lens: + A tensor of shape (N,) containing the number of frames in `encoder_out` + before padding. + beam: + Beam value, similar to the beam used in Kaldi.. + max_states: + Max states per stream per frame. + max_contexts: + Max contexts pre stream per frame. + num_paths: + Number of paths to extract from the decoded lattice. + nbest_scale: + It's the scale applied to the lattice.scores. A smaller value + yields more unique paths. + use_double_scores: + True to use double precision for computation. False to use + single precision. + Returns: + Return the decoded result. + """ + lattice = fast_beam_search( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=beam, + max_states=max_states, + max_contexts=max_contexts, + ) + + nbest = Nbest.from_lattice( + lattice=lattice, + num_paths=num_paths, + use_double_scores=use_double_scores, + nbest_scale=nbest_scale, + ) + + # at this point, nbest.fsa.scores are all zeros. + + nbest = nbest.intersect(lattice) + # Now nbest.fsa.scores contains acoustic scores + + max_indexes = nbest.tot_scores().argmax() + + best_path = k2.index_fsa(nbest.fsa, max_indexes) + + hyps = get_texts(best_path) + + return hyps + + def fast_beam_search_nbest_oracle( model: Transducer, decoding_graph: k2.Fsa, @@ -89,7 +285,7 @@ def fast_beam_search_nbest_oracle( ) -> List[List[int]]: """It limits the maximum number of symbols per frame to 1. - A lattice is first obtained using modified beam search, and then + A lattice is first obtained using fast beam search, and then we select `num_paths` linear paths from the lattice. The path that has the minimum edit distance with the given reference transcript is used as the output. diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py b/egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py index 840d847cb..fb8123838 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py @@ -18,7 +18,7 @@ import copy import math import warnings -from typing import Optional, Tuple +from typing import List, Optional, Tuple import torch from encoder_interface import EncoderInterface @@ -32,7 +32,7 @@ from scaling import ( ) from torch import Tensor, nn -from icefall.utils import make_pad_mask +from icefall.utils import make_pad_mask, subsequent_chunk_mask class Conformer(EncoderInterface): @@ -48,6 +48,26 @@ class Conformer(EncoderInterface): layer_dropout (float): layer-dropout rate. cnn_module_kernel (int): Kernel size of convolution module vgg_frontend (bool): whether to use vgg frontend. + dynamic_chunk_training (bool): whether to use dynamic chunk training, if + you want to train a streaming model, this is expected to be True. + When setting True, it will use a masking strategy to make the attention + see only limited left and right context. + short_chunk_threshold (float): a threshold to determinize the chunk size + to be used in masking training, if the randomly generated chunk size + is greater than ``max_len * short_chunk_threshold`` (max_len is the + max sequence length of current batch) then it will use + full context in training (i.e. with chunk size equals to max_len). + This will be used only when dynamic_chunk_training is True. + short_chunk_size (int): see docs above, if the randomly generated chunk + size equals to or less than ``max_len * short_chunk_threshold``, the + chunk size will be sampled uniformly from 1 to short_chunk_size. + This also will be used only when dynamic_chunk_training is True. + num_left_chunks (int): the left context (in chunks) attention can see, the + chunk size is decided by short_chunk_threshold and short_chunk_size. + A minus value means seeing full left context. + This also will be used only when dynamic_chunk_training is True. + causal (bool): Whether to use causal convolution in conformer encoder + layer. This MUST be True when using dynamic_chunk_training. """ def __init__( @@ -61,6 +81,11 @@ class Conformer(EncoderInterface): dropout: float = 0.1, layer_dropout: float = 0.075, cnn_module_kernel: int = 31, + dynamic_chunk_training: bool = False, + short_chunk_threshold: float = 0.75, + short_chunk_size: int = 25, + num_left_chunks: int = -1, + causal: bool = False, ) -> None: super(Conformer, self).__init__() @@ -76,6 +101,15 @@ class Conformer(EncoderInterface): # (2) embedding: num_features -> d_model self.encoder_embed = Conv2dSubsampling(num_features, d_model) + self.encoder_layers = num_encoder_layers + self.d_model = d_model + self.cnn_module_kernel = cnn_module_kernel + self.causal = causal + self.dynamic_chunk_training = dynamic_chunk_training + self.short_chunk_threshold = short_chunk_threshold + self.short_chunk_size = short_chunk_size + self.num_left_chunks = num_left_chunks + self.encoder_pos = RelPositionalEncoding(d_model, dropout) encoder_layer = ConformerEncoderLayer( @@ -85,8 +119,10 @@ class Conformer(EncoderInterface): dropout, layer_dropout, cnn_module_kernel, + causal, ) self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers) + self._init_state: List[torch.Tensor] = [torch.empty(0)] def forward( self, x: torch.Tensor, x_lens: torch.Tensor, warmup: float = 1.0 @@ -120,15 +156,249 @@ class Conformer(EncoderInterface): lengths = (((x_lens - 1) >> 1) - 1) >> 1 assert x.size(0) == lengths.max().item() - mask = make_pad_mask(lengths) - x = self.encoder( - x, pos_emb, src_key_padding_mask=mask, warmup=warmup - ) # (T, N, C) + src_key_padding_mask = make_pad_mask(lengths) + + if self.dynamic_chunk_training: + assert ( + self.causal + ), "Causal convolution is required for streaming conformer." + max_len = x.size(0) + chunk_size = torch.randint(1, max_len, (1,)).item() + if chunk_size > (max_len * self.short_chunk_threshold): + chunk_size = max_len + else: + chunk_size = chunk_size % self.short_chunk_size + 1 + + mask = ~subsequent_chunk_mask( + size=x.size(0), + chunk_size=chunk_size, + num_left_chunks=self.num_left_chunks, + device=x.device, + ) + x = self.encoder( + x, + pos_emb, + mask=mask, + src_key_padding_mask=src_key_padding_mask, + warmup=warmup, + ) # (T, N, C) + else: + x = self.encoder( + x, + pos_emb, + mask=None, + src_key_padding_mask=src_key_padding_mask, + warmup=warmup, + ) # (T, N, C) + + x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + return x, lengths + + @torch.jit.export + def get_init_state( + self, left_context: int, device: torch.device + ) -> List[torch.Tensor]: + """Return the initial cache state of the model. + + Args: + left_context: The left context size (in frames after subsampling). + + Returns: + Return the initial state of the model, it is a list containing two + tensors, the first one is the cache for attentions which has a shape + of (num_encoder_layers, left_context, encoder_dim), the second one + is the cache of conv_modules which has a shape of + (num_encoder_layers, cnn_module_kernel - 1, encoder_dim). + + NOTE: the returned tensors are on the given device. + """ + if ( + len(self._init_state) == 2 + and self._init_state[0].size(1) == left_context + ): + # Note: It is OK to share the init state as it is + # not going to be modified by the model + return self._init_state + + init_states: List[torch.Tensor] = [ + torch.zeros( + ( + self.encoder_layers, + left_context, + self.d_model, + ), + device=device, + ), + torch.zeros( + ( + self.encoder_layers, + self.cnn_module_kernel - 1, + self.d_model, + ), + device=device, + ), + ] + + self._init_state = init_states + + return init_states + + @torch.jit.export + def streaming_forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + states: Optional[List[Tensor]] = None, + processed_lens: Optional[Tensor] = None, + left_context: int = 64, + right_context: int = 4, + chunk_size: int = 16, + simulate_streaming: bool = False, + warmup: float = 1.0, + ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]: + """ + Args: + x: + The input tensor. Its shape is (batch_size, seq_len, feature_dim). + x_lens: + A tensor of shape (batch_size,) containing the number of frames in + `x` before padding. + states: + The decode states for previous frames which contains the cached data. + It has two elements, the first element is the attn_cache which has + a shape of (encoder_layers, left_context, batch, attention_dim), + the second element is the conv_cache which has a shape of + (encoder_layers, cnn_module_kernel-1, batch, conv_dim). + Note: states will be modified in this function. + processed_lens: + How many frames (after subsampling) have been processed for each sequence. + left_context: + How many previous frames the attention can see in current chunk. + Note: It's not that each individual frame has `left_context` frames + of left context, some have more. + right_context: + How many future frames the attention can see in current chunk. + Note: It's not that each individual frame has `right_context` frames + of right context, some have more. + chunk_size: + The chunk size for decoding, this will be used to simulate streaming + decoding using masking. + simulate_streaming: + If setting True, it will use a masking strategy to simulate streaming + fashion (i.e. every chunk data only see limited left context and + right context). The whole sequence is supposed to be send at a time + When using simulate_streaming. + warmup: + A floating point value that gradually increases from 0 throughout + training; when it is >= 1.0 we are "fully warmed up". It is used + to turn modules on sequentially. + Returns: + Return a tuple containing 2 tensors: + - logits, its shape is (batch_size, output_seq_len, output_dim) + - logit_lens, a tensor of shape (batch_size,) containing the number + of frames in `logits` before padding. + - decode_states, the updated states including the information + of current chunk. + """ + + # x: [N, T, C] + # Caution: We assume the subsampling factor is 4! + + # lengths = ((x_lens - 1) // 2 - 1) // 2 # issue an warning + # + # Note: rounding_mode in torch.div() is available only in torch >= 1.8.0 + lengths = (((x_lens - 1) >> 1) - 1) >> 1 + + if not simulate_streaming: + assert states is not None + assert processed_lens is not None + assert ( + len(states) == 2 + and states[0].shape + == (self.encoder_layers, left_context, x.size(0), self.d_model) + and states[1].shape + == ( + self.encoder_layers, + self.cnn_module_kernel - 1, + x.size(0), + self.d_model, + ) + ), f"""The length of states MUST be equal to 2, and the shape of + first element should be {(self.encoder_layers, left_context, x.size(0), self.d_model)}, + given {states[0].shape}. the shape of second element should be + {(self.encoder_layers, self.cnn_module_kernel - 1, x.size(0), self.d_model)}, + given {states[1].shape}.""" + + lengths -= 2 # we will cut off 1 frame on each side of encoder_embed output + + src_key_padding_mask = make_pad_mask(lengths) + + processed_mask = torch.arange(left_context, device=x.device).expand( + x.size(0), left_context + ) + processed_lens = processed_lens.view(x.size(0), 1) + processed_mask = (processed_lens <= processed_mask).flip(1) + + src_key_padding_mask = torch.cat( + [processed_mask, src_key_padding_mask], dim=1 + ) + + embed = self.encoder_embed(x) + + # cut off 1 frame on each size of embed as they see the padding + # value which causes a training and decoding mismatch. + embed = embed[:, 1:-1, :] + + embed, pos_enc = self.encoder_pos(embed, left_context) + embed = embed.permute(1, 0, 2) # (B, T, F) -> (T, B, F) + + x, states = self.encoder.chunk_forward( + embed, + pos_enc, + src_key_padding_mask=src_key_padding_mask, + warmup=warmup, + states=states, + left_context=left_context, + right_context=right_context, + ) # (T, B, F) + if right_context > 0: + x = x[0:-right_context, ...] + lengths -= right_context + else: + assert states is None + states = [] # just to make torch.script.jit happy + # this branch simulates streaming decoding using mask as we are + # using in training time. + src_key_padding_mask = make_pad_mask(lengths) + x = self.encoder_embed(x) + x, pos_emb = self.encoder_pos(x) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + + assert x.size(0) == lengths.max().item() + + num_left_chunks = -1 + if left_context >= 0: + assert left_context % chunk_size == 0 + num_left_chunks = left_context // chunk_size + + mask = ~subsequent_chunk_mask( + size=x.size(0), + chunk_size=chunk_size, + num_left_chunks=num_left_chunks, + device=x.device, + ) + x = self.encoder( + x, + pos_emb, + mask=mask, + src_key_padding_mask=src_key_padding_mask, + warmup=warmup, + ) # (T, N, C) x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C) - return x, lengths + return x, lengths, states class ConformerEncoderLayer(nn.Module): @@ -142,6 +412,8 @@ class ConformerEncoderLayer(nn.Module): dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). cnn_module_kernel (int): Kernel size of convolution module. + causal (bool): Whether to use causal convolution in conformer encoder + layer. This MUST be True when using dynamic_chunk_training and streaming decoding. Examples:: >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) @@ -158,6 +430,7 @@ class ConformerEncoderLayer(nn.Module): dropout: float = 0.1, layer_dropout: float = 0.075, cnn_module_kernel: int = 31, + causal: bool = False, ) -> None: super(ConformerEncoderLayer, self).__init__() @@ -185,7 +458,9 @@ class ConformerEncoderLayer(nn.Module): ScaledLinear(dim_feedforward, d_model, initial_scale=0.25), ) - self.conv_module = ConvolutionModule(d_model, cnn_module_kernel) + self.conv_module = ConvolutionModule( + d_model, cnn_module_kernel, causal=causal + ) self.norm_final = BasicNorm(d_model) @@ -214,7 +489,6 @@ class ConformerEncoderLayer(nn.Module): src_key_padding_mask: the mask for the src keys per batch (optional). warmup: controls selective bypass of of layers; if < 1.0, we will bypass layers more frequently. - Shape: src: (S, N, E). pos_emb: (N, 2*S-1, E) @@ -248,10 +522,12 @@ class ConformerEncoderLayer(nn.Module): attn_mask=src_mask, key_padding_mask=src_key_padding_mask, )[0] + src = src + self.dropout(src_att) # convolution module - src = src + self.dropout(self.conv_module(src)) + conv, _ = self.conv_module(src) + src = src + self.dropout(conv) # feed forward module src = src + self.dropout(self.feed_forward(src)) @@ -263,6 +539,100 @@ class ConformerEncoderLayer(nn.Module): return src + @torch.jit.export + def chunk_forward( + self, + src: Tensor, + pos_emb: Tensor, + states: List[Tensor], + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + warmup: float = 1.0, + left_context: int = 0, + right_context: int = 0, + ) -> Tuple[Tensor, List[Tensor]]: + """ + Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + pos_emb: Positional embedding tensor (required). + states: + The decode states for previous frames which contains the cached data. + It has two elements, the first element is the attn_cache which has + a shape of (left_context, batch, attention_dim), + the second element is the conv_cache which has a shape of + (cnn_module_kernel-1, batch, conv_dim). + Note: states will be modified in this function. + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + warmup: controls selective bypass of of layers; if < 1.0, we will + bypass layers more frequently. + left_context: + How many previous frames the attention can see in current chunk. + Note: It's not that each individual frame has `left_context` frames + of left context, some have more. + right_context: + How many future frames the attention can see in current chunk. + Note: It's not that each individual frame has `right_context` frames + of right context, some have more. + + Shape: + src: (S, N, E). + pos_emb: (N, 2*(S+left_context)-1, E). + src_mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, N is the batch size, E is the feature number + """ + + assert not self.training + assert len(states) == 2 + assert states[0].shape == (left_context, src.size(1), src.size(2)) + + # macaron style feed forward module + src = src + self.dropout(self.feed_forward_macaron(src)) + + # We put the attention cache this level (i.e. before linear transformation) + # to save memory consumption, when decoding in streaming fashion, the + # batch size would be thousands (for 32GB machine), if we cache key & val + # separately, it needs extra several GB memory. + # TODO(WeiKang): Move cache to self_attn level (i.e. cache key & val + # separately) if needed. + key = torch.cat([states[0], src], dim=0) + val = key + if right_context > 0: + states[0] = key[ + -(left_context + right_context) : -right_context, ... # noqa + ] + else: + states[0] = key[-left_context:, ...] + + # multi-headed self-attention module + src_att = self.self_attn( + src, + key, + val, + pos_emb=pos_emb, + attn_mask=src_mask, + key_padding_mask=src_key_padding_mask, + left_context=left_context, + )[0] + + src = src + self.dropout(src_att) + + # convolution module + conv, conv_cache = self.conv_module(src, states[1], right_context) + states[1] = conv_cache + + src = src + self.dropout(conv) + + # feed forward module + src = src + self.dropout(self.feed_forward(src)) + + src = self.norm_final(self.balancer(src)) + + return src, states + class ConformerEncoder(nn.Module): r"""ConformerEncoder is a stack of N encoder layers @@ -301,6 +671,8 @@ class ConformerEncoder(nn.Module): pos_emb: Positional embedding tensor (required). mask: the mask for the src sequence (optional). src_key_padding_mask: the mask for the src keys per batch (optional). + warmup: controls selective bypass of of layers; if < 1.0, we will + bypass layers more frequently. Shape: src: (S, N, E). @@ -312,7 +684,7 @@ class ConformerEncoder(nn.Module): """ output = src - for i, mod in enumerate(self.layers): + for layer_index, mod in enumerate(self.layers): output = mod( output, pos_emb, @@ -323,6 +695,79 @@ class ConformerEncoder(nn.Module): return output + @torch.jit.export + def chunk_forward( + self, + src: Tensor, + pos_emb: Tensor, + states: List[Tensor], + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + warmup: float = 1.0, + left_context: int = 0, + right_context: int = 0, + ) -> Tuple[Tensor, List[Tensor]]: + r"""Pass the input through the encoder layers in turn. + + Args: + src: the sequence to the encoder (required). + pos_emb: Positional embedding tensor (required). + states: + The decode states for previous frames which contains the cached data. + It has two elements, the first element is the attn_cache which has + a shape of (encoder_layers, left_context, batch, attention_dim), + the second element is the conv_cache which has a shape of + (encoder_layers, cnn_module_kernel-1, batch, conv_dim). + Note: states will be modified in this function. + mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + warmup: controls selective bypass of of layers; if < 1.0, we will + bypass layers more frequently. + left_context: + How many previous frames the attention can see in current chunk. + Note: It's not that each individual frame has `left_context` frames + of left context, some have more. + right_context: + How many future frames the attention can see in current chunk. + Note: It's not that each individual frame has `right_context` frames + of right context, some have more. + Shape: + src: (S, N, E). + pos_emb: (N, 2*(S+left_context)-1, E). + mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number + + """ + assert not self.training + assert len(states) == 2 + assert states[0].shape == ( + self.num_layers, + left_context, + src.size(1), + src.size(2), + ) + assert states[1].size(0) == self.num_layers + + output = src + + for layer_index, mod in enumerate(self.layers): + cache = [states[0][layer_index], states[1][layer_index]] + output, cache = mod.chunk_forward( + output, + pos_emb, + states=cache, + src_mask=mask, + src_key_padding_mask=src_key_padding_mask, + warmup=warmup, + left_context=left_context, + right_context=right_context, + ) + states[0][layer_index] = cache[0] + states[1][layer_index] = cache[1] + + return output, states + class RelPositionalEncoding(torch.nn.Module): """Relative positional encoding module. @@ -347,24 +792,25 @@ class RelPositionalEncoding(torch.nn.Module): self.pe = None self.extend_pe(torch.tensor(0.0).expand(1, max_len)) - def extend_pe(self, x: Tensor) -> None: + def extend_pe(self, x: Tensor, left_context: int = 0) -> None: """Reset the positional encodings.""" + x_size_1 = x.size(1) + left_context if self.pe is not None: # self.pe contains both positive and negative parts # the length of self.pe is 2 * input_len - 1 - if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.size(1) >= x_size_1 * 2 - 1: # Note: TorchScript doesn't implement operator== for torch.Device if self.pe.dtype != x.dtype or str(self.pe.device) != str( x.device ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i Tuple[Tensor, Tensor]: + def forward( + self, + x: torch.Tensor, + left_context: int = 0, + ) -> Tuple[Tensor, Tensor]: """Add positional encoding. Args: x (torch.Tensor): Input tensor (batch, time, `*`). + left_context (int): left context (in frames) used during streaming decoding. + this is used only in real streaming decoding, in other circumstances, + it MUST be 0. Returns: torch.Tensor: Encoded tensor (batch, time, `*`). torch.Tensor: Encoded tensor (batch, 2*time-1, `*`). """ - self.extend_pe(x) + self.extend_pe(x, left_context) + x_size_1 = x.size(1) + left_context pos_emb = self.pe[ :, self.pe.size(1) // 2 - - x.size(1) + - x_size_1 + 1 : self.pe.size(1) // 2 # noqa E203 + x.size(1), ] @@ -469,6 +923,7 @@ class RelPositionMultiheadAttention(nn.Module): key_padding_mask: Optional[Tensor] = None, need_weights: bool = True, attn_mask: Optional[Tensor] = None, + left_context: int = 0, ) -> Tuple[Tensor, Optional[Tensor]]: r""" Args: @@ -482,6 +937,9 @@ class RelPositionMultiheadAttention(nn.Module): need_weights: output attn_output_weights. attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch. + left_context (int): left context (in frames) used during streaming decoding. + this is used only in real streaming decoding, in other circumstances, + it MUST be 0. Shape: - Inputs: @@ -527,14 +985,18 @@ class RelPositionMultiheadAttention(nn.Module): key_padding_mask=key_padding_mask, need_weights=need_weights, attn_mask=attn_mask, + left_context=left_context, ) - def rel_shift(self, x: Tensor) -> Tensor: + def rel_shift(self, x: Tensor, left_context: int = 0) -> Tensor: """Compute relative positional encoding. Args: x: Input tensor (batch, head, time1, 2*time1-1). time1 means the length of query vector. + left_context (int): left context (in frames) used during streaming decoding. + this is used only in real streaming decoding, in other circumstances, + it MUST be 0. Returns: Tensor: tensor of shape (batch, head, time1, time2) @@ -542,14 +1004,19 @@ class RelPositionMultiheadAttention(nn.Module): the key, while time1 is for the query). """ (batch_size, num_heads, time1, n) = x.shape - assert n == 2 * time1 - 1 + + time2 = time1 + left_context + assert ( + n == left_context + 2 * time1 - 1 + ), f"{n} == {left_context} + 2 * {time1} - 1" + # Note: TorchScript requires explicit arg for stride() batch_stride = x.stride(0) head_stride = x.stride(1) time1_stride = x.stride(2) n_stride = x.stride(3) return x.as_strided( - (batch_size, num_heads, time1, time1), + (batch_size, num_heads, time1, time2), (batch_stride, head_stride, time1_stride - n_stride, n_stride), storage_offset=n_stride * (time1 - 1), ) @@ -571,6 +1038,7 @@ class RelPositionMultiheadAttention(nn.Module): key_padding_mask: Optional[Tensor] = None, need_weights: bool = True, attn_mask: Optional[Tensor] = None, + left_context: int = 0, ) -> Tuple[Tensor, Optional[Tensor]]: r""" Args: @@ -588,6 +1056,9 @@ class RelPositionMultiheadAttention(nn.Module): need_weights: output attn_output_weights. attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch. + left_context (int): left context (in frames) used during streaming decoding. + this is used only in real streaming decoding, in other circumstances, + it MUST be 0. Shape: Inputs: @@ -751,7 +1222,8 @@ class RelPositionMultiheadAttention(nn.Module): pos_emb_bsz = pos_emb.size(0) assert pos_emb_bsz in (1, bsz) # actually it is 1 p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim) - p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + # (batch, 2*time1, head, d_k) --> (batch, head, d_k, 2*time -1) + p = p.permute(0, 2, 3, 1) q_with_bias_u = (q + self._pos_bias_u()).transpose( 1, 2 @@ -771,9 +1243,9 @@ class RelPositionMultiheadAttention(nn.Module): # compute matrix b and matrix d matrix_bd = torch.matmul( - q_with_bias_v, p.transpose(-2, -1) + q_with_bias_v, p ) # (batch, head, time1, 2*time1-1) - matrix_bd = self.rel_shift(matrix_bd) + matrix_bd = self.rel_shift(matrix_bd, left_context) attn_output_weights = ( matrix_ac + matrix_bd @@ -808,6 +1280,39 @@ class RelPositionMultiheadAttention(nn.Module): ) attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1) + + # If we are using dynamic_chunk_training and setting a limited + # num_left_chunks, the attention may only see the padding values which + # will also be masked out by `key_padding_mask`, at this circumstances, + # the whole column of `attn_output_weights` will be `-inf` + # (i.e. be `nan` after softmax), so, we fill `0.0` at the masking + # positions to avoid invalid loss value below. + if ( + attn_mask is not None + and attn_mask.dtype == torch.bool + and key_padding_mask is not None + ): + if attn_mask.size(0) != 1: + attn_mask = attn_mask.view(bsz, num_heads, tgt_len, src_len) + combined_mask = attn_mask | key_padding_mask.unsqueeze( + 1 + ).unsqueeze(2) + else: + # attn_mask.shape == (1, tgt_len, src_len) + combined_mask = attn_mask.unsqueeze( + 0 + ) | key_padding_mask.unsqueeze(1).unsqueeze(2) + + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + attn_output_weights = attn_output_weights.masked_fill( + combined_mask, 0.0 + ) + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, src_len + ) + attn_output_weights = nn.functional.dropout( attn_output_weights, p=dropout_p, training=training ) @@ -841,16 +1346,21 @@ class ConvolutionModule(nn.Module): channels (int): The number of channels of conv layers. kernel_size (int): Kernerl size of conv layers. bias (bool): Whether to use bias in conv layers (default=True). - + causal (bool): Whether to use causal convolution. """ def __init__( - self, channels: int, kernel_size: int, bias: bool = True + self, + channels: int, + kernel_size: int, + bias: bool = True, + causal: bool = False, ) -> None: """Construct an ConvolutionModule object.""" super(ConvolutionModule, self).__init__() # kernerl_size should be a odd number for 'SAME' padding assert (kernel_size - 1) % 2 == 0 + self.causal = causal self.pointwise_conv1 = ScaledConv1d( channels, @@ -878,12 +1388,17 @@ class ConvolutionModule(nn.Module): channel_dim=1, max_abs=10.0, min_positive=0.05, max_positive=1.0 ) + self.lorder = kernel_size - 1 + padding = (kernel_size - 1) // 2 + if self.causal: + padding = 0 + self.depthwise_conv = ScaledConv1d( channels, channels, kernel_size, stride=1, - padding=(kernel_size - 1) // 2, + padding=padding, groups=channels, bias=bias, ) @@ -904,14 +1419,28 @@ class ConvolutionModule(nn.Module): initial_scale=0.25, ) - def forward(self, x: Tensor) -> Tensor: + def forward( + self, + x: Tensor, + cache: Optional[Tensor] = None, + right_context: int = 0, + ) -> Tuple[Tensor, Tensor]: """Compute convolution module. Args: x: Input tensor (#time, batch, channels). + cache: The cache of depthwise_conv, only used in real streaming + decoding. + right_context: + How many future frames the attention can see in current chunk. + Note: It's not that each individual frame has `right_context` frames + of right context, some have more. Returns: - Tensor: Output tensor (#time, batch, channels). + If cache is None return the output tensor (#time, batch, channels). + If cache is not None, return a tuple of Tensor, the first one is + the output tensor (#time, batch, channels), the second one is the + new cache for next chunk (#kernel_size - 1, batch, channels). """ # exchange the temporal dimension and the feature dimension @@ -924,6 +1453,26 @@ class ConvolutionModule(nn.Module): x = nn.functional.glu(x, dim=1) # (batch, channels, time) # 1D Depthwise Conv + if self.causal and self.lorder > 0: + if cache is None: + # Make depthwise_conv causal by + # manualy padding self.lorder zeros to the left + x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) + else: + assert ( + not self.training + ), "Cache should be None in training time" + assert cache.size(0) == self.lorder + x = torch.cat([cache.permute(1, 2, 0), x], dim=2) + if right_context > 0: + cache = x.permute(2, 0, 1)[ + -(self.lorder + right_context) : ( # noqa + -right_context + ), + ..., + ] + else: + cache = x.permute(2, 0, 1)[-self.lorder :, ...] # noqa x = self.depthwise_conv(x) x = self.deriv_balancer2(x) @@ -931,7 +1480,11 @@ class ConvolutionModule(nn.Module): x = self.pointwise_conv2(x) # (batch, channel, time) - return x.permute(2, 0, 1) + # torch.jit.script requires return types be the same as annotated above + if cache is None: + cache = torch.empty(0) + + return x.permute(2, 0, 1), cache class Conv2dSubsampling(nn.Module): diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py index d7d6b1202..60a948a99 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py @@ -43,21 +43,74 @@ Usage: --decoding-method modified_beam_search \ --beam-size 4 -(4) fast beam search +(4) fast beam search (one best) ./pruned_transducer_stateless2/decode.py \ --epoch 28 \ --avg 15 \ --exp-dir ./pruned_transducer_stateless2/exp \ --max-duration 600 \ --decoding-method fast_beam_search \ - --beam 4 \ - --max-contexts 4 \ - --max-states 8 + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest) +./pruned_transducer_stateless2/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./pruned_transducer_stateless2/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./pruned_transducer_stateless2/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(8) decode in streaming mode (take greedy search as an example) +./pruned_transducer_stateless2/decode.py \ + --epoch 28 \ + --avg 15 \ + --simulate-streaming 1 \ + --causal-convolution 1 \ + --decode-chunk-size 16 \ + --left-context 64 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 600 \ + --decoding-method greedy_search + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 """ import argparse import logging +import math from collections import defaultdict from pathlib import Path from typing import Dict, List, Optional, Tuple @@ -69,25 +122,32 @@ import torch.nn as nn from asr_datamodule import LibriSpeechAsrDataModule from beam_search import ( beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, ) -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model from icefall.checkpoint import ( average_checkpoints, find_checkpoints, load_checkpoint, ) +from icefall.lexicon import Lexicon from icefall.utils import ( AttributeDict, setup_logger, store_transcripts, + str2bool, write_error_stats, ) +LOG_EPS = math.log(1e-10) + def get_parser(): parser = argparse.ArgumentParser( @@ -136,6 +196,13 @@ def get_parser(): help="Path to the BPE model", ) + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_bpe_500", + help="The lang dir containing word table and LG graph", + ) + parser.add_argument( "--decoding-method", type=str, @@ -145,6 +212,11 @@ def get_parser(): - beam_search - modified_beam_search - fast_beam_search + - fast_beam_search_nbest + - fast_beam_search_nbest_oracle + - fast_beam_search_nbest_LG + If you use fast_beam_search_nbest_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. """, ) @@ -160,27 +232,42 @@ def get_parser(): parser.add_argument( "--beam", type=float, - default=4, + default=20.0, help="""A floating point value to calculate the cutoff score during beam search (i.e., `cutoff = max-score - beam`), which is the same as the `beam` in Kaldi. - Used only when --decoding-method is fast_beam_search""", + Used only when --decoding-method is fast_beam_search, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_nbest_LG. + It specifies the scale for n-gram LM scores. + """, ) parser.add_argument( "--max-contexts", type=int, - default=4, + default=8, help="""Used only when --decoding-method is - fast_beam_search""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( "--max-states", type=int, - default=8, + default=64, help="""Used only when --decoding-method is - fast_beam_search""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( @@ -190,6 +277,7 @@ def get_parser(): help="The context size in the decoder. 1 means bigram; " "2 means tri-gram", ) + parser.add_argument( "--max-sym-per-frame", type=int, @@ -198,6 +286,48 @@ def get_parser(): Used only when --decoding_method is greedy_search""", ) + parser.add_argument( + "--simulate-streaming", + type=str2bool, + default=False, + help="""Whether to simulate streaming in decoding, this is a good way to + test a streaming model. + """, + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + add_model_arguments(parser) return parser @@ -206,6 +336,7 @@ def decode_one_batch( model: nn.Module, sp: spm.SentencePieceProcessor, batch: dict, + word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the @@ -229,9 +360,12 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. + word_table: + The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -246,9 +380,26 @@ def decode_one_batch( supervisions = batch["supervisions"] feature_lens = supervisions["num_frames"].to(device) - encoder_out, encoder_out_lens = model.encoder( - x=feature, x_lens=feature_lens + feature_lens += params.left_context + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, params.left_context), + value=LOG_EPS, ) + + if params.simulate_streaming: + encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward( + x=feature, + x_lens=feature_lens, + chunk_size=params.decode_chunk_size, + left_context=params.left_context, + simulate_streaming=True, + ) + else: + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] if params.decoding_method == "fast_beam_search": @@ -263,6 +414,49 @@ def decode_one_batch( ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + ref_texts=sp.encode(supervisions["text"]), + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) elif ( params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1 @@ -318,6 +512,17 @@ def decode_one_batch( f"max_states_{params.max_states}" ): hyps } + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} else: return {f"beam_size_{params.beam_size}": hyps} @@ -327,6 +532,7 @@ def decode_dataset( params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, + word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -340,9 +546,12 @@ def decode_dataset( The neural model. sp: The BPE model. + word_table: + The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -360,7 +569,7 @@ def decode_dataset( if params.decoding_method == "greedy_search": log_interval = 50 else: - log_interval = 10 + log_interval = 20 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -370,6 +579,7 @@ def decode_dataset( params=params, model=model, sp=sp, + word_table=word_table, decoding_graph=decoding_graph, batch=batch, ) @@ -452,6 +662,9 @@ def main(): "greedy_search", "beam_search", "fast_beam_search", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", + "fast_beam_search_nbest_oracle", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method @@ -461,10 +674,19 @@ def main(): else: params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + if params.simulate_streaming: + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}" + params.suffix += f"-left-context-{params.left_context}" + if "fast_beam_search" in params.decoding_method: params.suffix += f"-beam-{params.beam}" params.suffix += f"-max-contexts-{params.max_contexts}" params.suffix += f"-max-states-{params.max_states}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" elif "beam_search" in params.decoding_method: params.suffix += ( f"-{params.decoding_method}-beam-size-{params.beam_size}" @@ -490,6 +712,11 @@ def main(): params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.simulate_streaming: + assert ( + params.causal_convolution + ), "Decoding in streaming requires causal convolution" + logging.info(params) logging.info("About to create model") @@ -528,10 +755,24 @@ def main(): model.eval() model.device = device - if params.decoding_method == "fast_beam_search": - decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": + lexicon = Lexicon(params.lang_dir) + word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + word_table = None + decoding_graph = k2.trivial_graph( + params.vocab_size - 1, device=device + ) else: decoding_graph = None + word_table = None num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -553,6 +794,7 @@ def main(): params=params, model=model, sp=sp, + word_table=word_table, decoding_graph=decoding_graph, ) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/decode_stream.py b/egs/librispeech/ASR/pruned_transducer_stateless2/decode_stream.py new file mode 120000 index 000000000..30f264813 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/decode_stream.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/decode_stream.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py b/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py index b6d94aaf1..1ddfce034 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py @@ -73,6 +73,9 @@ class Decoder(nn.Module): groups=decoder_dim, bias=False, ) + else: + # It is to support torch script + self.conv = nn.Identity() def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor: """ diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/export.py b/egs/librispeech/ASR/pruned_transducer_stateless2/export.py index cff9c7377..f1a8ea589 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/export.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/export.py @@ -49,7 +49,7 @@ from pathlib import Path import sentencepiece as spm import torch -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model from icefall.checkpoint import ( average_checkpoints, @@ -124,6 +124,16 @@ def get_parser(): "2 means tri-gram", ) + parser.add_argument( + "--streaming-model", + type=str2bool, + default=False, + help="""Whether to export a streaming model, if the models in exp-dir + are streaming model, this should be True. + """, + ) + + add_model_arguments(parser) return parser @@ -147,6 +157,9 @@ def main(): params.blank_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.streaming_model: + assert params.causal_convolution + logging.info(params) logging.info("About to create model") diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py b/egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py index 35f75ed2a..b916addf0 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py @@ -52,8 +52,10 @@ class Joiner(nn.Module): Returns: Return a tensor of shape (N, T, s_range, C). """ - assert encoder_out.ndim == decoder_out.ndim == 4 - assert encoder_out.shape[:-1] == decoder_out.shape[:-1] + + assert encoder_out.ndim == decoder_out.ndim + assert encoder_out.ndim in (2, 4) + assert encoder_out.shape == decoder_out.shape if project_input: logit = self.encoder_proj(encoder_out) + self.decoder_proj( diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py index 21bcf7cfd..f52cb22ab 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py @@ -77,7 +77,9 @@ from beam_search import ( modified_beam_search, ) from torch.nn.utils.rnn import pad_sequence -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.utils import str2bool def get_parser(): @@ -178,6 +180,30 @@ def get_parser(): """, ) + parser.add_argument( + "--simulate-streaming", + type=str2bool, + default=False, + help="""Whether to simulate streaming in decoding, this is a good way to + test a streaming model. + """, + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + add_model_arguments(parser) + return parser @@ -222,6 +248,11 @@ def main(): params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.simulate_streaming: + assert ( + params.causal_convolution + ), "Decoding in streaming requires causal convolution" + logging.info(f"{params}") device = torch.device("cpu") @@ -268,9 +299,18 @@ def main(): feature_lengths = torch.tensor(feature_lengths, device=device) - encoder_out, encoder_out_lens = model.encoder( - x=features, x_lens=feature_lengths - ) + if params.simulate_streaming: + encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward( + x=features, + x_lens=feature_lengths, + chunk_size=params.decode_chunk_size, + left_context=params.left_context, + simulate_streaming=True, + ) + else: + encoder_out, encoder_out_lens = model.encoder( + x=features, x_lens=feature_lengths + ) num_waves = encoder_out.size(0) hyps = [] diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py index 5ee4bab98..c190be626 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py @@ -52,7 +52,15 @@ class ActivationBalancerFunction(torch.autograd.Function): if x.requires_grad: if channel_dim < 0: channel_dim += x.ndim - sum_dims = [d for d in range(x.ndim) if d != channel_dim] + + # sum_dims = [d for d in range(x.ndim) if d != channel_dim] + # The above line is not torch scriptable for torch 1.6.0 + # torch.jit.frontend.NotSupportedError: comprehension ifs not supported yet: # noqa + sum_dims = [] + for d in range(x.ndim): + if d != channel_dim: + sum_dims.append(d) + xgt0 = x > 0 proportion_positive = torch.mean( xgt0.to(x.dtype), dim=sum_dims, keepdim=True @@ -214,8 +222,8 @@ class ScaledLinear(nn.Linear): def get_bias(self): if self.bias is None or self.bias_scale is None: return None - - return self.bias * self.bias_scale.exp() + else: + return self.bias * self.bias_scale.exp() def forward(self, input: Tensor) -> Tensor: return torch.nn.functional.linear( @@ -234,6 +242,9 @@ class ScaledConv1d(nn.Conv1d): ): super(ScaledConv1d, self).__init__(*args, **kwargs) initial_scale = torch.tensor(initial_scale).log() + + self.bias_scale: Optional[nn.Parameter] # for torchscript + self.weight_scale = nn.Parameter(initial_scale.clone().detach()) if self.bias is not None: self.bias_scale = nn.Parameter(initial_scale.clone().detach()) @@ -262,7 +273,8 @@ class ScaledConv1d(nn.Conv1d): bias_scale = self.bias_scale if bias is None or bias_scale is None: return None - return bias * bias_scale.exp() + else: + return bias * bias_scale.exp() def forward(self, input: Tensor) -> Tensor: F = torch.nn.functional @@ -331,7 +343,8 @@ class ScaledConv2d(nn.Conv2d): bias_scale = self.bias_scale if bias is None or bias_scale is None: return None - return bias * bias_scale.exp() + else: + return bias * bias_scale.exp() def _conv_forward(self, input, weight): F = torch.nn.functional @@ -412,16 +425,16 @@ class ActivationBalancer(torch.nn.Module): def forward(self, x: Tensor) -> Tensor: if torch.jit.is_scripting(): return x - - return ActivationBalancerFunction.apply( - x, - self.channel_dim, - self.min_positive, - self.max_positive, - self.max_factor, - self.min_abs, - self.max_abs, - ) + else: + return ActivationBalancerFunction.apply( + x, + self.channel_dim, + self.min_positive, + self.max_positive, + self.max_factor, + self.min_abs, + self.max_abs, + ) class DoubleSwishFunction(torch.autograd.Function): @@ -461,7 +474,8 @@ class DoubleSwish(torch.nn.Module): """ if torch.jit.is_scripting(): return x * torch.sigmoid(x - 1.0) - return DoubleSwishFunction.apply(x) + else: + return DoubleSwishFunction.apply(x) class ScaledEmbedding(nn.Module): diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py new file mode 100755 index 000000000..b3e1f04c3 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py @@ -0,0 +1,687 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: +./pruned_transducer_stateless2/streaming_decode.py \ + --epoch 28 \ + --avg 15 \ + --left-context 32 \ + --decode-chunk-size 8 \ + --right-context 0 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --decoding_method greedy_search \ + --num-decode-streams 1000 +""" + +import argparse +import logging +import math +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import numpy as np +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from decode_stream import DecodeStream +from kaldifeat import Fbank, FbankOptions +from lhotse import CutSet +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.decode import one_best_decoding +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Support only greedy_search and fast_beam_search now. + """, + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=32, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--right-context", + type=int, + default=0, + help="right context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--num-decode-streams", + type=int, + default=2000, + help="The number of streams that can be decoded parallel.", + ) + + add_model_arguments(parser) + + return parser + + +def greedy_search( + model: nn.Module, + encoder_out: torch.Tensor, + streams: List[DecodeStream], +) -> List[List[int]]: + + assert len(streams) == encoder_out.size(0) + assert encoder_out.ndim == 3 + + blank_id = model.decoder.blank_id + context_size = model.decoder.context_size + device = model.device + T = encoder_out.size(1) + + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + # decoder_out is of shape (N, decoder_out_dim) + decoder_out = model.decoder(decoder_input, need_pad=False) + decoder_out = model.joiner.decoder_proj(decoder_out) + # logging.info(f"decoder_out shape : {decoder_out.shape}") + + for t in range(T): + # current_encoder_out's shape: (batch_size, 1, encoder_out_dim) + current_encoder_out = encoder_out[:, t : t + 1, :] # noqa + + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + project_input=False, + ) + # logits'shape (batch_size, vocab_size) + logits = logits.squeeze(1).squeeze(1) + + assert logits.ndim == 2, logits.shape + y = logits.argmax(dim=1).tolist() + emitted = False + for i, v in enumerate(y): + if v != blank_id: + streams[i].hyp.append(v) + emitted = True + if emitted: + # update decoder output + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + decoder_out = model.decoder( + decoder_input, + need_pad=False, + ) + decoder_out = model.joiner.decoder_proj(decoder_out) + + hyp_tokens = [] + for stream in streams: + hyp_tokens.append(stream.hyp) + return hyp_tokens + + +def fast_beam_search( + model: nn.Module, + encoder_out: torch.Tensor, + processed_lens: torch.Tensor, + decoding_streams: k2.RnntDecodingStreams, +) -> List[List[int]]: + + B, T, C = encoder_out.shape + for t in range(T): + # shape is a RaggedShape of shape (B, context) + # contexts is a Tensor of shape (shape.NumElements(), context_size) + shape, contexts = decoding_streams.get_contexts() + # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64 + contexts = contexts.to(torch.int64) + # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim) + decoder_out = model.decoder(contexts, need_pad=False) + decoder_out = model.joiner.decoder_proj(decoder_out) + # current_encoder_out is of shape + # (shape.NumElements(), 1, joiner_dim) + # fmt: off + current_encoder_out = torch.index_select( + encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64) + ) + # fmt: on + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + project_input=False, + ) + logits = logits.squeeze(1).squeeze(1) + log_probs = logits.log_softmax(dim=-1) + decoding_streams.advance(log_probs) + + decoding_streams.terminate_and_flush_to_streams() + + lattice = decoding_streams.format_output(processed_lens.tolist()) + best_path = one_best_decoding(lattice) + hyp_tokens = get_texts(best_path) + return hyp_tokens + + +def decode_one_chunk( + params: AttributeDict, + model: nn.Module, + decode_streams: List[DecodeStream], +) -> List[int]: + """Decode one chunk frames of features for each decode_streams and + return the indexes of finished streams in a List. + + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + decode_streams: + A List of DecodeStream, each belonging to a utterance. + Returns: + Return a List containing which DecodeStreams are finished. + """ + device = model.device + + features = [] + feature_lens = [] + states = [] + + rnnt_stream_list = [] + processed_lens = [] + + for stream in decode_streams: + feat, feat_len = stream.get_feature_frames( + params.decode_chunk_size * params.subsampling_factor + ) + features.append(feat) + feature_lens.append(feat_len) + states.append(stream.states) + processed_lens.append(stream.done_frames) + if params.decoding_method == "fast_beam_search": + rnnt_stream_list.append(stream.rnnt_decoding_stream) + + feature_lens = torch.tensor(feature_lens, device=device) + features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS) + + # if T is less than 7 there will be an error in time reduction layer, + # because we subsample features with ((x_len - 1) // 2 - 1) // 2 + # we plus 2 here because we will cut off one frame on each size of + # encoder_embed output as they see invalid paddings. so we need extra 2 + # frames. + tail_length = 7 + (2 + params.right_context) * params.subsampling_factor + if features.size(1) < tail_length: + feature_lens += tail_length - features.size(1) + features = torch.cat( + [ + features, + torch.tensor( + LOG_EPS, dtype=features.dtype, device=device + ).expand( + features.size(0), + tail_length - features.size(1), + features.size(2), + ), + ], + dim=1, + ) + + states = [ + torch.stack([x[0] for x in states], dim=2), + torch.stack([x[1] for x in states], dim=2), + ] + processed_lens = torch.tensor(processed_lens, device=device) + + encoder_out, encoder_out_lens, states = model.encoder.streaming_forward( + x=features, + x_lens=feature_lens, + states=states, + left_context=params.left_context, + right_context=params.right_context, + processed_lens=processed_lens, + ) + + encoder_out = model.joiner.encoder_proj(encoder_out) + + if params.decoding_method == "greedy_search": + hyp_tokens = greedy_search(model, encoder_out, decode_streams) + elif params.decoding_method == "fast_beam_search": + config = k2.RnntDecodingConfig( + vocab_size=params.vocab_size, + decoder_history_len=params.context_size, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config) + processed_lens = processed_lens + encoder_out_lens + hyp_tokens = fast_beam_search( + model, encoder_out, processed_lens, decoding_streams + ) + else: + assert False + + states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)] + + finished_streams = [] + for i in range(len(decode_streams)): + decode_streams[i].states = [states[0][i], states[1][i]] + decode_streams[i].done_frames += encoder_out_lens[i] + if params.decoding_method == "fast_beam_search": + decode_streams[i].hyp = hyp_tokens[i] + if decode_streams[i].done: + finished_streams.append(i) + + return finished_streams + + +def decode_dataset( + cuts: CutSet, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + cuts: + Lhotse Cutset containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + device = model.device + + opts = FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + + log_interval = 50 + + decode_results = [] + # Contain decode streams currently running. + decode_streams = [] + initial_states = model.encoder.get_init_state( + params.left_context, device=device + ) + for num, cut in enumerate(cuts): + # each utterance has a DecodeStream. + decode_stream = DecodeStream( + params=params, + initial_states=initial_states, + decoding_graph=decoding_graph, + device=device, + ) + + audio: np.ndarray = cut.load_audio() + # audio.shape: (1, num_samples) + assert len(audio.shape) == 2 + assert audio.shape[0] == 1, "Should be single channel" + assert audio.dtype == np.float32, audio.dtype + + # The trained model is using normalized samples + assert audio.max() <= 1, "Should be normalized to [-1, 1])" + + samples = torch.from_numpy(audio).squeeze(0) + + fbank = Fbank(opts) + feature = fbank(samples.to(device)) + decode_stream.set_features(feature) + decode_stream.ground_truth = cut.supervisions[0].text + + decode_streams.append(decode_stream) + + while len(decode_streams) >= params.num_decode_streams: + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + hyp = decode_streams[i].hyp + if params.decoding_method == "greedy_search": + hyp = hyp[params.context_size :] # noqa + decode_results.append( + ( + decode_streams[i].ground_truth.split(), + sp.decode(hyp).split(), + ) + ) + del decode_streams[i] + + if num % log_interval == 0: + logging.info(f"Cuts processed until now is {num}.") + + # decode final chunks of last sequences + while len(decode_streams): + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + hyp = decode_streams[i].hyp + if params.decoding_method == "greedy_search": + hyp = hyp[params.context_size :] # noqa + decode_results.append( + ( + decode_streams[i].ground_truth.split(), + sp.decode(hyp).split(), + ) + ) + del decode_streams[i] + + key = "greedy_search" + if params.decoding_method == "fast_beam_search": + key = ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ) + return {key: decode_results} + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + # sort results so we can easily compare the difference between two + # recognition results + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + params.res_dir = params.exp_dir / "streaming" / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + # for streaming + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}" + params.suffix += f"-left-context-{params.left_context}" + params.suffix += f"-right-context-{params.right_context}" + + # for fast_beam_search + if params.decoding_method == "fast_beam_search": + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + # Decoding in streaming requires causal convolution + params.causal_convolution = True + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.to(device) + model.eval() + model.device = device + + decoding_graph = None + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_sets = ["test-clean", "test-other"] + test_cuts = [test_clean_cuts, test_other_cuts] + + for test_set, test_cut in zip(test_sets, test_cuts): + results_dict = decode_dataset( + cuts=test_cut, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless2/test_model.py deleted file mode 100755 index 9d5c6376d..000000000 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/test_model.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python3 -# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -""" -To run this file, do: - - cd icefall/egs/librispeech/ASR - python ./pruned_transducer_stateless2/test_model.py -""" - -import torch -from train import get_params, get_transducer_model - - -def test_model(): - params = get_params() - params.vocab_size = 500 - params.blank_id = 0 - params.context_size = 2 - params.unk_id = 2 - - model = get_transducer_model(params) - - num_param = sum([p.numel() for p in model.parameters()]) - print(f"Number of model parameters: {num_param}") - model.__class__.forward = torch.jit.ignore(model.__class__.forward) - torch.jit.script(model) - - -def main(): - test_model() - - -if __name__ == "__main__": - main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless2/test_model.py new file mode 120000 index 000000000..4196e587c --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/test_model.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/test_model.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/train.py b/egs/librispeech/ASR/pruned_transducer_stateless2/train.py index 36ee7ca74..13175c4c2 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/train.py @@ -40,6 +40,18 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3" --full-libri 1 \ --max-duration 550 +# train a streaming model +./pruned_transducer_stateless2/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir pruned_transducer_stateless/exp \ + --full-libri 1 \ + --dynamic-chunk-training 1 \ + --causal-convolution 1 \ + --short-chunk-size 25 \ + --num-left-chunks 4 \ + --max-duration 300 """ @@ -83,6 +95,42 @@ LRSchedulerType = Union[ ] +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--dynamic-chunk-training", + type=str2bool, + default=False, + help="""Whether to use dynamic_chunk_training, if you want a streaming + model, this requires to be True. + """, + ) + + parser.add_argument( + "--causal-convolution", + type=str2bool, + default=False, + help="""Whether to use causal convolution, this requires to be True when + using dynamic_chunk_training. + """, + ) + + parser.add_argument( + "--short-chunk-size", + type=int, + default=25, + help="""Chunk length of dynamic training, the chunk size would be either + max sequence length of current batch or uniformly sampled from (1, short_chunk_size). + """, + ) + + parser.add_argument( + "--num-left-chunks", + type=int, + default=4, + help="How many left context can be seen in chunks when calculating attention.", + ) + + def get_parser(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter @@ -263,6 +311,8 @@ def get_parser(): help="Whether to use half precision training.", ) + add_model_arguments(parser) + return parser @@ -349,6 +399,10 @@ def get_encoder_model(params: AttributeDict) -> nn.Module: nhead=params.nhead, dim_feedforward=params.dim_feedforward, num_encoder_layers=params.num_encoder_layers, + dynamic_chunk_training=params.dynamic_chunk_training, + short_chunk_size=params.short_chunk_size, + num_left_chunks=params.num_left_chunks, + causal=params.causal_convolution, ) return encoder @@ -806,6 +860,11 @@ def run(rank, world_size, args): params.blank_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.dynamic_chunk_training: + assert ( + params.causal_convolution + ), "dynamic_chunk_training requires causal convolution" + logging.info(params) logging.info("About to create model") @@ -883,6 +942,7 @@ def run(rank, world_size, args): optimizer=optimizer, sp=sp, params=params, + warmup=0.0 if params.start_epoch == 0 else 1.0, ) scaler = GradScaler(enabled=params.use_fp16) @@ -973,6 +1033,7 @@ def scan_pessimistic_batches_for_oom( optimizer: torch.optim.Optimizer, sp: spm.SentencePieceProcessor, params: AttributeDict, + warmup: float, ): from lhotse.dataset import find_pessimistic_batches @@ -983,9 +1044,6 @@ def scan_pessimistic_batches_for_oom( for criterion, cuts in batches.items(): batch = train_dl.dataset[cuts] try: - # warmup = 0.0 is so that the derivs for the pruned loss stay zero - # (i.e. are not remembered by the decaying-average in adam), because - # we want to avoid these params being subject to shrinkage in adam. with torch.cuda.amp.autocast(enabled=params.use_fp16): loss, _ = compute_loss( params=params, @@ -993,7 +1051,7 @@ def scan_pessimistic_batches_for_oom( sp=sp, batch=batch, is_training=True, - warmup=0.0, + warmup=warmup, ) loss.backward() optimizer.step() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py b/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py index b54d1aa39..1df7f9ee5 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py @@ -291,7 +291,6 @@ class AsrDataModule: max_duration=self.args.max_duration, shuffle=False, num_buckets=self.args.num_buckets, - drop_last=True, ) logging.debug("About to create test dataloader") test_dl = DataLoader( diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py index 5b3dce853..44fc34640 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py @@ -19,45 +19,83 @@ Usage: (1) greedy search ./pruned_transducer_stateless3/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless3/exp \ - --max-duration 600 \ - --decoding-method greedy_search + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method greedy_search (2) beam search (not recommended) ./pruned_transducer_stateless3/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless3/exp \ - --max-duration 600 \ - --decoding-method beam_search \ - --beam-size 4 + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 (3) modified beam search ./pruned_transducer_stateless3/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless3/exp \ - --max-duration 600 \ - --decoding-method modified_beam_search \ - --beam-size 4 + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 -(4) fast beam search +(4) fast beam search (one best) ./pruned_transducer_stateless3/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless3/exp \ - --max-duration 600 \ - --decoding-method fast_beam_search \ - --beam 4 \ - --max-contexts 4 \ - --max-states 8 + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest) +./pruned_transducer_stateless3/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./pruned_transducer_stateless3/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./pruned_transducer_stateless3/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 """ import argparse import logging +import math from collections import defaultdict from pathlib import Path from typing import Dict, List, Optional, Tuple @@ -69,6 +107,8 @@ import torch.nn as nn from asr_datamodule import AsrDataModule from beam_search import ( beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, fast_beam_search_nbest_oracle, fast_beam_search_one_best, greedy_search, @@ -76,20 +116,24 @@ from beam_search import ( modified_beam_search, ) from librispeech import LibriSpeech -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model from icefall.checkpoint import ( average_checkpoints, find_checkpoints, load_checkpoint, ) +from icefall.lexicon import Lexicon from icefall.utils import ( AttributeDict, setup_logger, store_transcripts, + str2bool, write_error_stats, ) +LOG_EPS = math.log(1e-10) + def get_parser(): parser = argparse.ArgumentParser( @@ -138,6 +182,13 @@ def get_parser(): help="Path to the BPE model", ) + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_bpe_500", + help="The lang dir containing word table and LG graph", + ) + parser.add_argument( "--decoding-method", type=str, @@ -147,7 +198,11 @@ def get_parser(): - beam_search - modified_beam_search - fast_beam_search + - fast_beam_search_nbest - fast_beam_search_nbest_oracle + - fast_beam_search_nbest_LG + If you use fast_beam_search_nbest_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. """, ) @@ -163,28 +218,42 @@ def get_parser(): parser.add_argument( "--beam", type=float, - default=4, + default=20.0, help="""A floating point value to calculate the cutoff score during beam search (i.e., `cutoff = max-score - beam`), which is the same as the `beam` in Kaldi. - Used only when --decoding-method is - fast_beam_search or fast_beam_search_nbest_oracle""", + Used only when --decoding-method is fast_beam_search, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_nbest_LG. + It specifies the scale for n-gram LM scores. + """, ) parser.add_argument( "--max-contexts", type=int, - default=4, + default=8, help="""Used only when --decoding-method is - fast_beam_search or fast_beam_search_nbest_oracle""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( "--max-states", type=int, - default=8, + default=64, help="""Used only when --decoding-method is - fast_beam_search or fast_beam_search_nbest_oracle""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( @@ -205,10 +274,10 @@ def get_parser(): parser.add_argument( "--num-paths", type=int, - default=100, - help="""Number of paths for computed nbest oracle WER - when the decoding method is fast_beam_search_nbest_oracle. - """, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", ) parser.add_argument( @@ -216,9 +285,35 @@ def get_parser(): type=float, default=0.5, help="""Scale applied to lattice scores when computing nbest paths. - Used only when the decoding_method is fast_beam_search_nbest_oracle. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--simulate-streaming", + type=str2bool, + default=False, + help="""Whether to simulate streaming in decoding, this is a good way to + test a streaming model. """, ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + add_model_arguments(parser) + return parser @@ -227,6 +322,7 @@ def decode_one_batch( model: nn.Module, sp: spm.SentencePieceProcessor, batch: dict, + word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the @@ -250,10 +346,12 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. + word_table: + The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is - fast_beam_search or fast_beam_search_nbest_oracle. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -268,9 +366,26 @@ def decode_one_batch( supervisions = batch["supervisions"] feature_lens = supervisions["num_frames"].to(device) - encoder_out, encoder_out_lens = model.encoder( - x=feature, x_lens=feature_lens + feature_lens += params.left_context + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, params.left_context), + value=LOG_EPS, ) + + if params.simulate_streaming: + encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward( + x=feature, + x_lens=feature_lens, + chunk_size=params.decode_chunk_size, + left_context=params.left_context, + simulate_streaming=True, + ) + else: + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] if params.decoding_method == "fast_beam_search": @@ -285,6 +400,34 @@ def decode_one_batch( ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) elif params.decoding_method == "fast_beam_search_nbest_oracle": hyp_tokens = fast_beam_search_nbest_oracle( model=model, @@ -355,16 +498,25 @@ def decode_one_batch( f"max_states_{params.max_states}" ): hyps } - elif params.decoding_method == "fast_beam_search_nbest_oracle": + elif params.decoding_method == "fast_beam_search": return { ( f"beam_{params.beam}_" f"max_contexts_{params.max_contexts}_" - f"max_states_{params.max_states}_" - f"num_paths_{params.num_paths}_" - f"nbest_scale_{params.nbest_scale}" + f"max_states_{params.max_states}" ): hyps } + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} else: return {f"beam_size_{params.beam_size}": hyps} @@ -374,6 +526,7 @@ def decode_dataset( params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, + word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -387,9 +540,12 @@ def decode_dataset( The neural model. sp: The BPE model. + word_table: + The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -407,7 +563,7 @@ def decode_dataset( if params.decoding_method == "greedy_search": log_interval = 50 else: - log_interval = 10 + log_interval = 20 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -417,6 +573,7 @@ def decode_dataset( params=params, model=model, sp=sp, + word_table=word_table, decoding_graph=decoding_graph, batch=batch, ) @@ -499,6 +656,8 @@ def main(): "greedy_search", "beam_search", "fast_beam_search", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", "fast_beam_search_nbest_oracle", "modified_beam_search", ) @@ -509,16 +668,19 @@ def main(): else: params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - if params.decoding_method == "fast_beam_search": + if params.simulate_streaming: + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}" + params.suffix += f"-left-context-{params.left_context}" + + if "fast_beam_search" in params.decoding_method: params.suffix += f"-beam-{params.beam}" params.suffix += f"-max-contexts-{params.max_contexts}" params.suffix += f"-max-states-{params.max_states}" - elif params.decoding_method == "fast_beam_search_nbest_oracle": - params.suffix += f"-beam-{params.beam}" - params.suffix += f"-max-contexts-{params.max_contexts}" - params.suffix += f"-max-states-{params.max_states}" - params.suffix += f"-num-paths-{params.num_paths}" - params.suffix += f"-nbest-scale-{params.nbest_scale}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" elif "beam_search" in params.decoding_method: params.suffix += ( f"-{params.decoding_method}-beam-size-{params.beam_size}" @@ -539,11 +701,16 @@ def main(): sp = spm.SentencePieceProcessor() sp.load(params.bpe_model) - # and is defined in local/train_bpe_model.py + # and are defined in local/train_bpe_model.py params.blank_id = sp.piece_to_id("") - params.unk_id = sp.unk_id() + params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.simulate_streaming: + assert ( + params.causal_convolution + ), "Decoding in streaming requires causal convolution" + logging.info(params) logging.info("About to create model") @@ -583,13 +750,24 @@ def main(): model.device = device model.unk_id = params.unk_id - if params.decoding_method in ( - "fast_beam_search", - "fast_beam_search_nbest_oracle", - ): - decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": + lexicon = Lexicon(params.lang_dir) + word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + word_table = None + decoding_graph = k2.trivial_graph( + params.vocab_size - 1, device=device + ) else: decoding_graph = None + word_table = None num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -612,6 +790,7 @@ def main(): params=params, model=model, sp=sp, + word_table=word_table, decoding_graph=decoding_graph, ) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/decode_stream.py b/egs/librispeech/ASR/pruned_transducer_stateless3/decode_stream.py new file mode 120000 index 000000000..30f264813 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/decode_stream.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/decode_stream.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/export.py b/egs/librispeech/ASR/pruned_transducer_stateless3/export.py index e674fb360..53ea306ff 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/export.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/export.py @@ -50,7 +50,7 @@ from pathlib import Path import sentencepiece as spm import torch -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model from icefall.checkpoint import ( average_checkpoints, @@ -125,6 +125,17 @@ def get_parser(): "2 means tri-gram", ) + parser.add_argument( + "--streaming-model", + type=str2bool, + default=False, + help="""Whether to export a streaming model, if the models in exp-dir + are streaming model, this should be True. + """, + ) + + add_model_arguments(parser) + return parser @@ -148,6 +159,9 @@ def main(): params.blank_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.streaming_model: + assert params.causal_convolution + logging.info(params) logging.info("About to create model") diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py index 7efa592f9..8b0389bc9 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py @@ -77,7 +77,9 @@ from beam_search import ( modified_beam_search, ) from torch.nn.utils.rnn import pad_sequence -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.utils import str2bool def get_parser(): @@ -178,6 +180,30 @@ def get_parser(): """, ) + parser.add_argument( + "--simulate-streaming", + type=str2bool, + default=False, + help="""Whether to simulate streaming in decoding, this is a good way to + test a streaming model. + """, + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + add_model_arguments(parser) + return parser @@ -222,6 +248,11 @@ def main(): params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.simulate_streaming: + assert ( + params.causal_convolution + ), "Decoding in streaming requires causal convolution" + logging.info(f"{params}") device = torch.device("cpu") @@ -268,9 +299,18 @@ def main(): feature_lengths = torch.tensor(feature_lengths, device=device) - encoder_out, encoder_out_lens = model.encoder( - x=features, x_lens=feature_lengths - ) + if params.simulate_streaming: + encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward( + x=features, + x_lens=feature_lengths, + chunk_size=params.decode_chunk_size, + left_context=params.left_context, + simulate_streaming=True, + ) + else: + encoder_out, encoder_out_lens = model.encoder( + x=features, x_lens=feature_lengths + ) num_waves = encoder_out.size(0) hyps = [] diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py new file mode 100755 index 000000000..8af2788be --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py @@ -0,0 +1,686 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: +./pruned_transducer_stateless2/streaming_decode.py \ + --epoch 28 \ + --avg 15 \ + --left-context 32 \ + --decode-chunk-size 8 \ + --right-context 0 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --decoding_method greedy_search \ + --num-decode-streams 1000 +""" + +import argparse +import logging +import math +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import numpy as np +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import AsrDataModule +from decode_stream import DecodeStream +from kaldifeat import Fbank, FbankOptions +from lhotse import CutSet +from librispeech import LibriSpeech +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.decode import one_best_decoding +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Support only greedy_search and fast_beam_search now. + """, + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=32, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--right-context", + type=int, + default=0, + help="right context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--num-decode-streams", + type=int, + default=2000, + help="The number of streams that can be decoded parallel.", + ) + + add_model_arguments(parser) + + return parser + + +def greedy_search( + model: nn.Module, + encoder_out: torch.Tensor, + streams: List[DecodeStream], +) -> List[List[int]]: + + assert len(streams) == encoder_out.size(0) + assert encoder_out.ndim == 3 + + blank_id = model.decoder.blank_id + context_size = model.decoder.context_size + device = model.device + T = encoder_out.size(1) + + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + # decoder_out is of shape (N, decoder_out_dim) + decoder_out = model.decoder(decoder_input, need_pad=False) + decoder_out = model.joiner.decoder_proj(decoder_out) + # logging.info(f"decoder_out shape : {decoder_out.shape}") + + for t in range(T): + # current_encoder_out's shape: (batch_size, 1, encoder_out_dim) + current_encoder_out = encoder_out[:, t : t + 1, :] # noqa + + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + project_input=False, + ) + # logits'shape (batch_size, vocab_size) + logits = logits.squeeze(1).squeeze(1) + + assert logits.ndim == 2, logits.shape + y = logits.argmax(dim=1).tolist() + emitted = False + for i, v in enumerate(y): + if v != blank_id: + streams[i].hyp.append(v) + emitted = True + if emitted: + # update decoder output + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + decoder_out = model.decoder( + decoder_input, + need_pad=False, + ) + decoder_out = model.joiner.decoder_proj(decoder_out) + + hyp_tokens = [] + for stream in streams: + hyp_tokens.append(stream.hyp) + return hyp_tokens + + +def fast_beam_search( + model: nn.Module, + encoder_out: torch.Tensor, + processed_lens: torch.Tensor, + decoding_streams: k2.RnntDecodingStreams, +) -> List[List[int]]: + + B, T, C = encoder_out.shape + for t in range(T): + # shape is a RaggedShape of shape (B, context) + # contexts is a Tensor of shape (shape.NumElements(), context_size) + shape, contexts = decoding_streams.get_contexts() + # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64 + contexts = contexts.to(torch.int64) + # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim) + decoder_out = model.decoder(contexts, need_pad=False) + decoder_out = model.joiner.decoder_proj(decoder_out) + # current_encoder_out is of shape + # (shape.NumElements(), 1, joiner_dim) + # fmt: off + current_encoder_out = torch.index_select( + encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64) + ) + # fmt: on + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + project_input=False, + ) + logits = logits.squeeze(1).squeeze(1) + log_probs = logits.log_softmax(dim=-1) + decoding_streams.advance(log_probs) + + decoding_streams.terminate_and_flush_to_streams() + + lattice = decoding_streams.format_output(processed_lens.tolist()) + best_path = one_best_decoding(lattice) + hyp_tokens = get_texts(best_path) + return hyp_tokens + + +def decode_one_chunk( + params: AttributeDict, + model: nn.Module, + decode_streams: List[DecodeStream], +) -> List[int]: + """Decode one chunk frames of features for each decode_streams and + return the indexes of finished streams in a List. + + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + decode_streams: + A List of DecodeStream, each belonging to a utterance. + Returns: + Return a List containing which DecodeStreams are finished. + """ + device = model.device + + features = [] + feature_lens = [] + states = [] + + rnnt_stream_list = [] + processed_lens = [] + + for stream in decode_streams: + feat, feat_len = stream.get_feature_frames( + params.decode_chunk_size * params.subsampling_factor + ) + features.append(feat) + feature_lens.append(feat_len) + states.append(stream.states) + processed_lens.append(stream.done_frames) + if params.decoding_method == "fast_beam_search": + rnnt_stream_list.append(stream.rnnt_decoding_stream) + + feature_lens = torch.tensor(feature_lens, device=device) + features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS) + + # if T is less than 7 there will be an error in time reduction layer, + # because we subsample features with ((x_len - 1) // 2 - 1) // 2 + # we plus 2 here because we will cut off one frame on each size of + # encoder_embed output as they see invalid paddings. so we need extra 2 + # frames. + tail_length = 7 + (2 + params.right_context) * params.subsampling_factor + if features.size(1) < tail_length: + feature_lens += tail_length - features.size(1) + features = torch.cat( + [ + features, + torch.tensor( + LOG_EPS, dtype=features.dtype, device=device + ).expand( + features.size(0), + tail_length - features.size(1), + features.size(2), + ), + ], + dim=1, + ) + + states = [ + torch.stack([x[0] for x in states], dim=2), + torch.stack([x[1] for x in states], dim=2), + ] + processed_lens = torch.tensor(processed_lens, device=device) + + encoder_out, encoder_out_lens, states = model.encoder.streaming_forward( + x=features, + x_lens=feature_lens, + states=states, + left_context=params.left_context, + right_context=params.right_context, + processed_lens=processed_lens, + ) + + encoder_out = model.joiner.encoder_proj(encoder_out) + + if params.decoding_method == "greedy_search": + hyp_tokens = greedy_search(model, encoder_out, decode_streams) + elif params.decoding_method == "fast_beam_search": + config = k2.RnntDecodingConfig( + vocab_size=params.vocab_size, + decoder_history_len=params.context_size, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config) + processed_lens = processed_lens + encoder_out_lens + hyp_tokens = fast_beam_search( + model, encoder_out, processed_lens, decoding_streams + ) + else: + assert False + + states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)] + + finished_streams = [] + for i in range(len(decode_streams)): + decode_streams[i].states = [states[0][i], states[1][i]] + decode_streams[i].done_frames += encoder_out_lens[i] + if params.decoding_method == "fast_beam_search": + decode_streams[i].hyp = hyp_tokens[i] + if decode_streams[i].done: + finished_streams.append(i) + + return finished_streams + + +def decode_dataset( + cuts: CutSet, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + cuts: + Lhotse Cutset containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + device = model.device + + opts = FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + + log_interval = 50 + + decode_results = [] + # Contain decode streams currently running. + decode_streams = [] + initial_states = model.encoder.get_init_state( + params.left_context, device=device + ) + for num, cut in enumerate(cuts): + # each utterance has a DecodeStream. + decode_stream = DecodeStream( + params=params, + initial_states=initial_states, + decoding_graph=decoding_graph, + device=device, + ) + + audio: np.ndarray = cut.load_audio() + # audio.shape: (1, num_samples) + assert len(audio.shape) == 2 + assert audio.shape[0] == 1, "Should be single channel" + assert audio.dtype == np.float32, audio.dtype + + # The trained model is using normalized samples + assert audio.max() <= 1, "Should be normalized to [-1, 1])" + + samples = torch.from_numpy(audio).squeeze(0) + + fbank = Fbank(opts) + feature = fbank(samples.to(device)) + decode_stream.set_features(feature) + decode_stream.ground_truth = cut.supervisions[0].text + + decode_streams.append(decode_stream) + + while len(decode_streams) >= params.num_decode_streams: + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + hyp = decode_streams[i].hyp + if params.decoding_method == "greedy_search": + hyp = hyp[params.context_size :] # noqa + decode_results.append( + ( + decode_streams[i].ground_truth.split(), + sp.decode(hyp).split(), + ) + ) + del decode_streams[i] + + if num % log_interval == 0: + logging.info(f"Cuts processed until now is {num}.") + + # decode final chunks of last sequences + while len(decode_streams): + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + hyp = decode_streams[i].hyp + if params.decoding_method == "greedy_search": + hyp = hyp[params.context_size :] # noqa + decode_results.append( + ( + decode_streams[i].ground_truth.split(), + sp.decode(hyp).split(), + ) + ) + del decode_streams[i] + + key = "greedy_search" + if params.decoding_method == "fast_beam_search": + key = ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ) + return {key: decode_results} + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + params.res_dir = params.exp_dir / "streaming" / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + # for streaming + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}" + params.suffix += f"-left-context-{params.left_context}" + params.suffix += f"-right-context-{params.right_context}" + + # for fast_beam_search + if params.decoding_method == "fast_beam_search": + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + # Decoding in streaming requires causal convolution + params.causal_convolution = True + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.to(device) + model.eval() + model.device = device + + decoding_graph = None + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeech(params.manifest_dir) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_sets = ["test-clean", "test-other"] + test_cuts = [test_clean_cuts, test_other_cuts] + + for test_set, test_cut in zip(test_sets, test_cuts): + results_dict = decode_dataset( + cuts=test_cut, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless3/test_model.py deleted file mode 100755 index 9a060c5fb..000000000 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/test_model.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python3 -# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -""" -To run this file, do: - - cd icefall/egs/librispeech/ASR - python ./pruned_transducer_stateless3/test_model.py -""" - -import torch -from train import get_params, get_transducer_model - - -def test_model(): - params = get_params() - params.vocab_size = 500 - params.blank_id = 0 - params.context_size = 2 - params.unk_id = 2 - - model = get_transducer_model(params) - - num_param = sum([p.numel() for p in model.parameters()]) - print(f"Number of model parameters: {num_param}") - model.__class__.forward = torch.jit.ignore(model.__class__.forward) - torch.jit.script(model) - - -def main(): - test_model() - - -if __name__ == "__main__": - main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless3/test_model.py new file mode 120000 index 000000000..4196e587c --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/test_model.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/test_model.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/train.py b/egs/librispeech/ASR/pruned_transducer_stateless3/train.py index 92eae78d1..3b9fb710c 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/train.py @@ -91,6 +91,42 @@ LRSchedulerType = Union[ ] +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--dynamic-chunk-training", + type=str2bool, + default=False, + help="""Whether to use dynamic_chunk_training, if you want a streaming + model, this requires to be True. + """, + ) + + parser.add_argument( + "--causal-convolution", + type=str2bool, + default=False, + help="""Whether to use causal convolution, this requires to be True when + using dynamic_chunk_training. + """, + ) + + parser.add_argument( + "--short-chunk-size", + type=int, + default=25, + help="""Chunk length of dynamic training, the chunk size would be either + max sequence length of current batch or uniformly sampled from (1, short_chunk_size). + """, + ) + + parser.add_argument( + "--num-left-chunks", + type=int, + default=4, + help="How many left context can be seen in chunks when calculating attention.", + ) + + def get_parser(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter @@ -372,6 +408,10 @@ def get_encoder_model(params: AttributeDict) -> nn.Module: nhead=params.nhead, dim_feedforward=params.dim_feedforward, num_encoder_layers=params.num_encoder_layers, + dynamic_chunk_training=params.dynamic_chunk_training, + short_chunk_size=params.short_chunk_size, + num_left_chunks=params.num_left_chunks, + causal=params.causal_convolution, ) return encoder @@ -905,6 +945,11 @@ def run(rank, world_size, args): params.blank_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.dynamic_chunk_training: + assert ( + params.causal_convolution + ), "dynamic_chunk_training requires causal convolution" + logging.info(params) logging.info("About to create model") @@ -1001,6 +1046,7 @@ def run(rank, world_size, args): optimizer=optimizer, sp=sp, params=params, + warmup=0.0 if params.start_epoch == 0 else 1.0, ) scaler = GradScaler(enabled=params.use_fp16) @@ -1061,6 +1107,7 @@ def scan_pessimistic_batches_for_oom( optimizer: torch.optim.Optimizer, sp: spm.SentencePieceProcessor, params: AttributeDict, + warmup: float, ): from lhotse.dataset import find_pessimistic_batches @@ -1071,9 +1118,6 @@ def scan_pessimistic_batches_for_oom( for criterion, cuts in batches.items(): batch = train_dl.dataset[cuts] try: - # warmup = 0.0 is so that the derivs for the pruned loss stay zero - # (i.e. are not remembered by the decaying-average in adam), because - # we want to avoid these params being subject to shrinkage in adam. with torch.cuda.amp.autocast(enabled=params.use_fp16): loss, _ = compute_loss( params=params, @@ -1081,7 +1125,7 @@ def scan_pessimistic_batches_for_oom( sp=sp, batch=batch, is_training=True, - warmup=0.0, + warmup=warmup, ) loss.backward() optimizer.step() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py index 70afc3ea3..d8ae8e026 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py @@ -44,21 +44,74 @@ Usage: --decoding-method modified_beam_search \ --beam-size 4 -(4) fast beam search +(4) fast beam search (one best) ./pruned_transducer_stateless4/decode.py \ --epoch 30 \ --avg 15 \ --exp-dir ./pruned_transducer_stateless4/exp \ --max-duration 600 \ --decoding-method fast_beam_search \ - --beam 4 \ - --max-contexts 4 \ - --max-states 8 + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest) +./pruned_transducer_stateless4/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless3/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./pruned_transducer_stateless4/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./pruned_transducer_stateless4/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(8) decode in streaming mode (take greedy search as an example) +./pruned_transducer_stateless4/decode.py \ + --epoch 30 \ + --avg 15 \ + --simulate-streaming 1 \ + --causal-convolution 1 \ + --decode-chunk-size 16 \ + --left-context 64 \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --max-duration 600 \ + --decoding-method greedy_search + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 """ import argparse import logging +import math from collections import defaultdict from pathlib import Path from typing import Dict, List, Optional, Tuple @@ -70,12 +123,15 @@ import torch.nn as nn from asr_datamodule import LibriSpeechAsrDataModule from beam_search import ( beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, ) -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model from icefall.checkpoint import ( average_checkpoints, @@ -83,6 +139,7 @@ from icefall.checkpoint import ( find_checkpoints, load_checkpoint, ) +from icefall.lexicon import Lexicon from icefall.utils import ( AttributeDict, setup_logger, @@ -91,6 +148,8 @@ from icefall.utils import ( write_error_stats, ) +LOG_EPS = math.log(1e-10) + def get_parser(): parser = argparse.ArgumentParser( @@ -150,6 +209,13 @@ def get_parser(): help="Path to the BPE model", ) + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_bpe_500", + help="The lang dir containing word table and LG graph", + ) + parser.add_argument( "--decoding-method", type=str, @@ -159,6 +225,11 @@ def get_parser(): - beam_search - modified_beam_search - fast_beam_search + - fast_beam_search_nbest + - fast_beam_search_nbest_oracle + - fast_beam_search_nbest_LG + If you use fast_beam_search_nbest_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. """, ) @@ -174,27 +245,42 @@ def get_parser(): parser.add_argument( "--beam", type=float, - default=4, + default=20.0, help="""A floating point value to calculate the cutoff score during beam search (i.e., `cutoff = max-score - beam`), which is the same as the `beam` in Kaldi. - Used only when --decoding-method is fast_beam_search""", + Used only when --decoding-method is fast_beam_search, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_nbest_LG. + It specifies the scale for n-gram LM scores. + """, ) parser.add_argument( "--max-contexts", type=int, - default=4, + default=8, help="""Used only when --decoding-method is - fast_beam_search""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( "--max-states", type=int, - default=8, + default=64, help="""Used only when --decoding-method is - fast_beam_search""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( @@ -212,6 +298,48 @@ def get_parser(): Used only when --decoding_method is greedy_search""", ) + parser.add_argument( + "--simulate-streaming", + type=str2bool, + default=False, + help="""Whether to simulate streaming in decoding, this is a good way to + test a streaming model. + """, + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + add_model_arguments(parser) + return parser @@ -220,6 +348,7 @@ def decode_one_batch( model: nn.Module, sp: spm.SentencePieceProcessor, batch: dict, + word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the @@ -243,9 +372,12 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. + word_table: + The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -260,9 +392,26 @@ def decode_one_batch( supervisions = batch["supervisions"] feature_lens = supervisions["num_frames"].to(device) - encoder_out, encoder_out_lens = model.encoder( - x=feature, x_lens=feature_lens + feature_lens += params.left_context + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, params.left_context), + value=LOG_EPS, ) + + if params.simulate_streaming: + encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward( + x=feature, + x_lens=feature_lens, + chunk_size=params.decode_chunk_size, + left_context=params.left_context, + simulate_streaming=True, + ) + else: + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] if params.decoding_method == "fast_beam_search": @@ -277,6 +426,49 @@ def decode_one_batch( ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + ref_texts=sp.encode(supervisions["text"]), + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) elif ( params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1 @@ -324,14 +516,17 @@ def decode_one_batch( if params.decoding_method == "greedy_search": return {"greedy_search": hyps} - elif params.decoding_method == "fast_beam_search": - return { - ( - f"beam_{params.beam}_" - f"max_contexts_{params.max_contexts}_" - f"max_states_{params.max_states}" - ): hyps - } + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} else: return {f"beam_size_{params.beam_size}": hyps} @@ -341,6 +536,7 @@ def decode_dataset( params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, + word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -354,9 +550,12 @@ def decode_dataset( The neural model. sp: The BPE model. + word_table: + The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -374,7 +573,7 @@ def decode_dataset( if params.decoding_method == "greedy_search": log_interval = 50 else: - log_interval = 10 + log_interval = 20 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -385,6 +584,7 @@ def decode_dataset( model=model, sp=sp, decoding_graph=decoding_graph, + word_table=word_table, batch=batch, ) @@ -466,6 +666,9 @@ def main(): "greedy_search", "beam_search", "fast_beam_search", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", + "fast_beam_search_nbest_oracle", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method @@ -475,10 +678,19 @@ def main(): else: params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + if params.simulate_streaming: + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}" + params.suffix += f"-left-context-{params.left_context}" + if "fast_beam_search" in params.decoding_method: params.suffix += f"-beam-{params.beam}" params.suffix += f"-max-contexts-{params.max_contexts}" params.suffix += f"-max-states-{params.max_states}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" elif "beam_search" in params.decoding_method: params.suffix += ( f"-{params.decoding_method}-beam-size-{params.beam_size}" @@ -507,6 +719,11 @@ def main(): params.unk_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.simulate_streaming: + assert ( + params.causal_convolution + ), "Decoding in streaming requires causal convolution" + logging.info(params) logging.info("About to create model") @@ -592,10 +809,24 @@ def main(): model.to(device) model.eval() - if params.decoding_method == "fast_beam_search": - decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": + lexicon = Lexicon(params.lang_dir) + word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + word_table = None + decoding_graph = k2.trivial_graph( + params.vocab_size - 1, device=device + ) else: decoding_graph = None + word_table = None num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -617,6 +848,7 @@ def main(): params=params, model=model, sp=sp, + word_table=word_table, decoding_graph=decoding_graph, ) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/decode_stream.py b/egs/librispeech/ASR/pruned_transducer_stateless4/decode_stream.py new file mode 120000 index 000000000..30f264813 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/decode_stream.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/decode_stream.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/export.py b/egs/librispeech/ASR/pruned_transducer_stateless4/export.py index 8f64b5d64..ce7518ceb 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/export.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/export.py @@ -41,7 +41,7 @@ you can do: --avg 1 \ --max-duration 100 \ --bpe-model data/lang_bpe_500/bpe.model \ - --use-averaged-model False + --use-averaged-model True """ import argparse @@ -50,7 +50,7 @@ from pathlib import Path import sentencepiece as spm import torch -from train import get_params, get_transducer_model +from train import add_model_arguments, get_params, get_transducer_model from icefall.checkpoint import ( average_checkpoints, @@ -94,10 +94,21 @@ def get_parser(): "'--epoch' and '--iter'", ) + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + parser.add_argument( "--exp-dir", type=str, - default="pruned_transducer_stateless2/exp", + default="pruned_transducer_stateless4/exp", help="""It specifies the directory where all training related files, e.g., checkpoints, log, etc, are saved """, @@ -127,16 +138,16 @@ def get_parser(): ) parser.add_argument( - "--use-averaged-model", + "--streaming-model", type=str2bool, - default=True, - help="Whether to load averaged model. Currently it only supports " - "using --epoch. If True, it would decode with the averaged model " - "over the epoch range from `epoch-avg` (excluded) to `epoch`." - "Actually only the models with epoch number of `epoch-avg` and " - "`epoch` are loaded for averaging. ", + default=False, + help="""Whether to export a streaming model, if the models in exp-dir + are streaming model, this should be True. + """, ) + add_model_arguments(parser) + return parser @@ -148,6 +159,8 @@ def main(): params.update(vars(args)) device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) logging.info(f"device: {device}") @@ -158,6 +171,9 @@ def main(): params.blank_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.streaming_model: + assert params.causal_convolution + logging.info(params) logging.info("About to create model") @@ -242,6 +258,7 @@ def main(): ) ) + model.to("cpu") model.eval() if params.jit: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py new file mode 100755 index 000000000..57fd06980 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py @@ -0,0 +1,750 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: +./pruned_transducer_stateless2/streaming_decode.py \ + --epoch 28 \ + --avg 15 \ + --left-context 32 \ + --decode-chunk-size 8 \ + --right-context 0 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --decoding_method greedy_search \ + --num-decode-streams 200 +""" + +import argparse +import logging +import math +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import numpy as np +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from decode_stream import DecodeStream +from kaldifeat import Fbank, FbankOptions +from lhotse import CutSet +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.decode import one_best_decoding +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Support only greedy_search and fast_beam_search now. + """, + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=32, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--left-context", + type=int, + default=64, + help="left context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--right-context", + type=int, + default=0, + help="right context can be seen during decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--num-decode-streams", + type=int, + default=2000, + help="The number of streams that can be decoded parallel.", + ) + + add_model_arguments(parser) + + return parser + + +def greedy_search( + model: nn.Module, + encoder_out: torch.Tensor, + streams: List[DecodeStream], +) -> List[List[int]]: + + assert len(streams) == encoder_out.size(0) + assert encoder_out.ndim == 3 + + blank_id = model.decoder.blank_id + context_size = model.decoder.context_size + device = model.device + T = encoder_out.size(1) + + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + # decoder_out is of shape (N, decoder_out_dim) + decoder_out = model.decoder(decoder_input, need_pad=False) + decoder_out = model.joiner.decoder_proj(decoder_out) + # logging.info(f"decoder_out shape : {decoder_out.shape}") + + for t in range(T): + # current_encoder_out's shape: (batch_size, 1, encoder_out_dim) + current_encoder_out = encoder_out[:, t : t + 1, :] # noqa + + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + project_input=False, + ) + # logits'shape (batch_size, vocab_size) + logits = logits.squeeze(1).squeeze(1) + + assert logits.ndim == 2, logits.shape + y = logits.argmax(dim=1).tolist() + emitted = False + for i, v in enumerate(y): + if v != blank_id: + streams[i].hyp.append(v) + emitted = True + if emitted: + # update decoder output + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + decoder_out = model.decoder( + decoder_input, + need_pad=False, + ) + decoder_out = model.joiner.decoder_proj(decoder_out) + + hyp_tokens = [] + for stream in streams: + hyp_tokens.append(stream.hyp) + return hyp_tokens + + +def fast_beam_search( + model: nn.Module, + encoder_out: torch.Tensor, + processed_lens: torch.Tensor, + decoding_streams: k2.RnntDecodingStreams, +) -> List[List[int]]: + + B, T, C = encoder_out.shape + for t in range(T): + # shape is a RaggedShape of shape (B, context) + # contexts is a Tensor of shape (shape.NumElements(), context_size) + shape, contexts = decoding_streams.get_contexts() + # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64 + contexts = contexts.to(torch.int64) + # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim) + decoder_out = model.decoder(contexts, need_pad=False) + decoder_out = model.joiner.decoder_proj(decoder_out) + # current_encoder_out is of shape + # (shape.NumElements(), 1, joiner_dim) + # fmt: off + current_encoder_out = torch.index_select( + encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64) + ) + # fmt: on + logits = model.joiner( + current_encoder_out.unsqueeze(2), + decoder_out.unsqueeze(1), + project_input=False, + ) + logits = logits.squeeze(1).squeeze(1) + log_probs = logits.log_softmax(dim=-1) + decoding_streams.advance(log_probs) + + decoding_streams.terminate_and_flush_to_streams() + + lattice = decoding_streams.format_output(processed_lens.tolist()) + best_path = one_best_decoding(lattice) + hyp_tokens = get_texts(best_path) + return hyp_tokens + + +def decode_one_chunk( + params: AttributeDict, + model: nn.Module, + decode_streams: List[DecodeStream], +) -> List[int]: + """Decode one chunk frames of features for each decode_streams and + return the indexes of finished streams in a List. + + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + decode_streams: + A List of DecodeStream, each belonging to a utterance. + Returns: + Return a List containing which DecodeStreams are finished. + """ + device = model.device + + features = [] + feature_lens = [] + states = [] + + rnnt_stream_list = [] + processed_lens = [] + + for stream in decode_streams: + feat, feat_len = stream.get_feature_frames( + params.decode_chunk_size * params.subsampling_factor + ) + features.append(feat) + feature_lens.append(feat_len) + states.append(stream.states) + processed_lens.append(stream.done_frames) + if params.decoding_method == "fast_beam_search": + rnnt_stream_list.append(stream.rnnt_decoding_stream) + + feature_lens = torch.tensor(feature_lens, device=device) + features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS) + + # if T is less than 7 there will be an error in time reduction layer, + # because we subsample features with ((x_len - 1) // 2 - 1) // 2 + # we plus 2 here because we will cut off one frame on each size of + # encoder_embed output as they see invalid paddings. so we need extra 2 + # frames. + tail_length = 7 + (2 + params.right_context) * params.subsampling_factor + if features.size(1) < tail_length: + feature_lens += tail_length - features.size(1) + features = torch.cat( + [ + features, + torch.tensor( + LOG_EPS, dtype=features.dtype, device=device + ).expand( + features.size(0), + tail_length - features.size(1), + features.size(2), + ), + ], + dim=1, + ) + + states = [ + torch.stack([x[0] for x in states], dim=2), + torch.stack([x[1] for x in states], dim=2), + ] + processed_lens = torch.tensor(processed_lens, device=device) + + encoder_out, encoder_out_lens, states = model.encoder.streaming_forward( + x=features, + x_lens=feature_lens, + states=states, + left_context=params.left_context, + right_context=params.right_context, + processed_lens=processed_lens, + ) + + encoder_out = model.joiner.encoder_proj(encoder_out) + + if params.decoding_method == "greedy_search": + hyp_tokens = greedy_search(model, encoder_out, decode_streams) + elif params.decoding_method == "fast_beam_search": + config = k2.RnntDecodingConfig( + vocab_size=params.vocab_size, + decoder_history_len=params.context_size, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config) + processed_lens = processed_lens + encoder_out_lens + hyp_tokens = fast_beam_search( + model, encoder_out, processed_lens, decoding_streams + ) + else: + assert False + + states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)] + + finished_streams = [] + for i in range(len(decode_streams)): + decode_streams[i].states = [states[0][i], states[1][i]] + decode_streams[i].done_frames += encoder_out_lens[i] + if params.decoding_method == "fast_beam_search": + decode_streams[i].hyp = hyp_tokens[i] + if decode_streams[i].done: + finished_streams.append(i) + + return finished_streams + + +def decode_dataset( + cuts: CutSet, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + cuts: + Lhotse Cutset containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + device = model.device + + opts = FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + + log_interval = 50 + + decode_results = [] + # Contain decode streams currently running. + decode_streams = [] + initial_states = model.encoder.get_init_state( + params.left_context, device=device + ) + for num, cut in enumerate(cuts): + # each utterance has a DecodeStream. + decode_stream = DecodeStream( + params=params, + initial_states=initial_states, + decoding_graph=decoding_graph, + device=device, + ) + + audio: np.ndarray = cut.load_audio() + # audio.shape: (1, num_samples) + assert len(audio.shape) == 2 + assert audio.shape[0] == 1, "Should be single channel" + assert audio.dtype == np.float32, audio.dtype + + # The trained model is using normalized samples + assert audio.max() <= 1, "Should be normalized to [-1, 1])" + + samples = torch.from_numpy(audio).squeeze(0) + + fbank = Fbank(opts) + feature = fbank(samples.to(device)) + decode_stream.set_features(feature) + decode_stream.ground_truth = cut.supervisions[0].text + + decode_streams.append(decode_stream) + + while len(decode_streams) >= params.num_decode_streams: + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + hyp = decode_streams[i].hyp + if params.decoding_method == "greedy_search": + hyp = hyp[params.context_size :] # noqa + decode_results.append( + ( + decode_streams[i].ground_truth.split(), + sp.decode(hyp).split(), + ) + ) + del decode_streams[i] + + if num % log_interval == 0: + logging.info(f"Cuts processed until now is {num}.") + + # decode final chunks of last sequences + while len(decode_streams): + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + hyp = decode_streams[i].hyp + if params.decoding_method == "greedy_search": + hyp = hyp[params.context_size :] # noqa + decode_results.append( + ( + decode_streams[i].ground_truth.split(), + sp.decode(hyp).split(), + ) + ) + del decode_streams[i] + + key = "greedy_search" + if params.decoding_method == "fast_beam_search": + key = ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ) + return {key: decode_results} + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + params.res_dir = params.exp_dir / "streaming" / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + # for streaming + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}" + params.suffix += f"-left-context-{params.left_context}" + params.suffix += f"-right-context-{params.right_context}" + + # for fast_beam_search + if params.decoding_method == "fast_beam_search": + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + # Decoding in streaming requires causal convolution + params.causal_convolution = True + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + model.device = device + + decoding_graph = None + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_sets = ["test-clean", "test-other"] + test_cuts = [test_clean_cuts, test_other_cuts] + + for test_set, test_cut in zip(test_sets, test_cuts): + results_dict = decode_dataset( + cuts=test_cut, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless4/test_model.py deleted file mode 100755 index b1832d0ec..000000000 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/test_model.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python3 -# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -""" -To run this file, do: - - cd icefall/egs/librispeech/ASR - python ./pruned_transducer_stateless4/test_model.py -""" - -import torch -from train import get_params, get_transducer_model - - -def test_model(): - params = get_params() - params.vocab_size = 500 - params.blank_id = 0 - params.context_size = 2 - params.unk_id = 2 - - model = get_transducer_model(params) - - num_param = sum([p.numel() for p in model.parameters()]) - print(f"Number of model parameters: {num_param}") - model.__class__.forward = torch.jit.ignore(model.__class__.forward) - torch.jit.script(model) - - -def main(): - test_model() - - -if __name__ == "__main__": - main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless4/test_model.py new file mode 120000 index 000000000..4196e587c --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/test_model.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/test_model.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/train.py b/egs/librispeech/ASR/pruned_transducer_stateless4/train.py index 48c0e683d..47e2ae1c1 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/train.py @@ -41,8 +41,20 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3" --full-libri 1 \ --max-duration 550 -""" +# train a streaming model +./pruned_transducer_stateless4/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless4/exp \ + --full-libri 1 \ + --dynamic-chunk-training 1 \ + --causal-convolution 1 \ + --short-chunk-size 25 \ + --num-left-chunks 4 \ + --max-duration 300 +""" import argparse import copy @@ -88,6 +100,42 @@ LRSchedulerType = Union[ ] +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--dynamic-chunk-training", + type=str2bool, + default=False, + help="""Whether to use dynamic_chunk_training, if you want a streaming + model, this requires to be True. + """, + ) + + parser.add_argument( + "--causal-convolution", + type=str2bool, + default=False, + help="""Whether to use causal convolution, this requires to be True when + using dynamic_chunk_training. + """, + ) + + parser.add_argument( + "--short-chunk-size", + type=int, + default=25, + help="""Chunk length of dynamic training, the chunk size would be either + max sequence length of current batch or uniformly sampled from (1, short_chunk_size). + """, + ) + + parser.add_argument( + "--num-left-chunks", + type=int, + default=4, + help="How many left context can be seen in chunks when calculating attention.", + ) + + def get_parser(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter @@ -281,6 +329,8 @@ def get_parser(): help="Whether to use half precision training.", ) + add_model_arguments(parser) + return parser @@ -367,6 +417,10 @@ def get_encoder_model(params: AttributeDict) -> nn.Module: nhead=params.nhead, dim_feedforward=params.dim_feedforward, num_encoder_layers=params.num_encoder_layers, + dynamic_chunk_training=params.dynamic_chunk_training, + short_chunk_size=params.short_chunk_size, + num_left_chunks=params.num_left_chunks, + causal=params.causal_convolution, ) return encoder @@ -847,6 +901,11 @@ def run(rank, world_size, args): params.blank_id = sp.piece_to_id("") params.vocab_size = sp.get_piece_size() + if params.dynamic_chunk_training: + assert ( + params.causal_convolution + ), "dynamic_chunk_training requires causal convolution" + logging.info(params) logging.info("About to create model") @@ -932,6 +991,7 @@ def run(rank, world_size, args): optimizer=optimizer, sp=sp, params=params, + warmup=0.0 if params.start_epoch == 1 else 1.0, ) scaler = GradScaler(enabled=params.use_fp16) @@ -992,6 +1052,7 @@ def scan_pessimistic_batches_for_oom( optimizer: torch.optim.Optimizer, sp: spm.SentencePieceProcessor, params: AttributeDict, + warmup: float, ): from lhotse.dataset import find_pessimistic_batches @@ -1002,9 +1063,6 @@ def scan_pessimistic_batches_for_oom( for criterion, cuts in batches.items(): batch = train_dl.dataset[cuts] try: - # warmup = 0.0 is so that the derivs for the pruned loss stay zero - # (i.e. are not remembered by the decaying-average in adam), because - # we want to avoid these params being subject to shrinkage in adam. with torch.cuda.amp.autocast(enabled=params.use_fp16): loss, _ = compute_loss( params=params, @@ -1012,7 +1070,7 @@ def scan_pessimistic_batches_for_oom( sp=sp, batch=batch, is_training=True, - warmup=0.0, + warmup=warmup, ) loss.backward() optimizer.step() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py b/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py index 6f7231f4b..49bc6a489 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py @@ -87,10 +87,17 @@ class Conformer(EncoderInterface): layer_dropout, cnn_module_kernel, ) + # aux_layers from 1/3 self.encoder = ConformerEncoder( encoder_layer, num_encoder_layers, - aux_layers=list(range(0, num_encoder_layers - 1, aux_layer_period)), + aux_layers=list( + range( + num_encoder_layers // 3, + num_encoder_layers - 1, + aux_layer_period, + ) + ), ) def forward( @@ -117,10 +124,7 @@ class Conformer(EncoderInterface): x, pos_emb = self.encoder_pos(x) x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - # Caution: We assume the subsampling factor is 4! - lengths = ((x_lens - 1) // 2 - 1) // 2 + lengths = (((x_lens - 1) >> 1) - 1) >> 1 assert x.size(0) == lengths.max().item() mask = make_pad_mask(lengths) @@ -293,13 +297,13 @@ class ConformerEncoder(nn.Module): ) self.num_layers = num_layers - assert num_layers - 1 not in aux_layers - self.aux_layers = set(aux_layers + [num_layers - 1]) + assert len(set(aux_layers)) == len(aux_layers) + + assert num_layers - 1 not in aux_layers + self.aux_layers = aux_layers + [num_layers - 1] - num_channels = encoder_layer.norm_final.num_channels self.combiner = RandomCombine( num_inputs=len(self.aux_layers), - num_channels=num_channels, final_weight=0.5, pure_prob=0.333, stddev=2.0, @@ -384,7 +388,7 @@ class RelPositionalEncoding(torch.nn.Module): ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i= 1 - self.linear = nn.ModuleList( - [ - nn.Linear(num_channels, num_channels, bias=True) - for _ in range(num_inputs - 1) - ] - ) - self.num_inputs = num_inputs self.final_weight = final_weight self.pure_prob = pure_prob @@ -1135,12 +1129,6 @@ class RandomCombine(nn.Module): .log() .item() ) - self._reset_parameters() - - def _reset_parameters(self): - for i in range(len(self.linear)): - nn.init.eye_(self.linear[i].weight) - nn.init.constant_(self.linear[i].bias, 0.0) def forward(self, inputs: List[Tensor]) -> Tensor: """Forward function. @@ -1154,21 +1142,16 @@ class RandomCombine(nn.Module): """ num_inputs = self.num_inputs assert len(inputs) == num_inputs - if not self.training: + if not self.training or torch.jit.is_scripting(): return inputs[-1] # Shape of weights: (*, num_inputs) num_channels = inputs[0].shape[-1] num_frames = inputs[0].numel() // num_channels - mod_inputs = [] - for i in range(num_inputs - 1): - mod_inputs.append(self.linear[i](inputs[i])) - mod_inputs.append(inputs[num_inputs - 1]) - ndim = inputs[0].ndim # stacked_inputs: (num_frames, num_channels, num_inputs) - stacked_inputs = torch.stack(mod_inputs, dim=ndim).reshape( + stacked_inputs = torch.stack(inputs, dim=ndim).reshape( (num_frames, num_channels, num_inputs) ) @@ -1181,11 +1164,13 @@ class RandomCombine(nn.Module): # ans: (num_frames, num_channels, 1) ans = torch.matmul(stacked_inputs, weights) # ans: (*, num_channels) - ans = ans.reshape(*tuple(inputs[0].shape[:-1]), num_channels) - if __name__ == "__main__": - # for testing only... - print("Weights = ", weights.reshape(num_frames, num_inputs)) + ans = ans.reshape(inputs[0].shape[:-1] + (num_channels,)) + + # The following if causes errors for torch script in torch 1.6.0 + # if __name__ == "__main__": + # # for testing only... + # print("Weights = ", weights.reshape(num_frames, num_inputs)) return ans def _get_random_weights( diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py index c2ca07480..f87d23cc9 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py @@ -44,16 +44,53 @@ Usage: --decoding-method modified_beam_search \ --beam-size 4 -(4) fast beam search +(4) fast beam search (one best) ./pruned_transducer_stateless5/decode.py \ --epoch 28 \ --avg 15 \ --exp-dir ./pruned_transducer_stateless5/exp \ --max-duration 600 \ --decoding-method fast_beam_search \ - --beam 4 \ - --max-contexts 4 \ - --max-states 8 + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest) +./pruned_transducer_stateless5/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./pruned_transducer_stateless5/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./pruned_transducer_stateless5/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 """ @@ -70,6 +107,9 @@ import torch.nn as nn from asr_datamodule import LibriSpeechAsrDataModule from beam_search import ( beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, fast_beam_search_one_best, greedy_search, greedy_search_batch, @@ -83,6 +123,7 @@ from icefall.checkpoint import ( find_checkpoints, load_checkpoint, ) +from icefall.lexicon import Lexicon from icefall.utils import ( AttributeDict, setup_logger, @@ -128,7 +169,7 @@ def get_parser(): parser.add_argument( "--use-averaged-model", type=str2bool, - default=False, + default=True, help="Whether to load averaged model. Currently it only supports " "using --epoch. If True, it would decode with the averaged model " "over the epoch range from `epoch-avg` (excluded) to `epoch`." @@ -150,6 +191,13 @@ def get_parser(): help="Path to the BPE model", ) + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_bpe_500", + help="The lang dir containing word table and LG graph", + ) + parser.add_argument( "--decoding-method", type=str, @@ -159,6 +207,11 @@ def get_parser(): - beam_search - modified_beam_search - fast_beam_search + - fast_beam_search_nbest + - fast_beam_search_nbest_oracle + - fast_beam_search_nbest_LG + If you use fast_beam_search_nbest_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. """, ) @@ -174,27 +227,42 @@ def get_parser(): parser.add_argument( "--beam", type=float, - default=4, + default=20.0, help="""A floating point value to calculate the cutoff score during beam search (i.e., `cutoff = max-score - beam`), which is the same as the `beam` in Kaldi. - Used only when --decoding-method is fast_beam_search""", + Used only when --decoding-method is fast_beam_search, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_nbest_LG. + It specifies the scale for n-gram LM scores. + """, ) parser.add_argument( "--max-contexts", type=int, - default=4, + default=8, help="""Used only when --decoding-method is - fast_beam_search""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( "--max-states", type=int, - default=8, + default=64, help="""Used only when --decoding-method is - fast_beam_search""", + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", ) parser.add_argument( @@ -212,6 +280,24 @@ def get_parser(): Used only when --decoding_method is greedy_search""", ) + parser.add_argument( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + add_model_arguments(parser) return parser @@ -222,6 +308,7 @@ def decode_one_batch( model: nn.Module, sp: spm.SentencePieceProcessor, batch: dict, + word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the @@ -245,9 +332,12 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. + word_table: + The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -279,6 +369,49 @@ def decode_one_batch( ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + ref_texts=sp.encode(supervisions["text"]), + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) elif ( params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1 @@ -326,14 +459,17 @@ def decode_one_batch( if params.decoding_method == "greedy_search": return {"greedy_search": hyps} - elif params.decoding_method == "fast_beam_search": - return { - ( - f"beam_{params.beam}_" - f"max_contexts_{params.max_contexts}_" - f"max_states_{params.max_states}" - ): hyps - } + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} else: return {f"beam_size_{params.beam_size}": hyps} @@ -343,6 +479,7 @@ def decode_dataset( params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, + word_table: Optional[k2.SymbolTable] = None, decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -356,9 +493,12 @@ def decode_dataset( The neural model. sp: The BPE model. + word_table: + The word symbol table. decoding_graph: The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used - only when --decoding_method is fast_beam_search. + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -387,6 +527,7 @@ def decode_dataset( model=model, sp=sp, decoding_graph=decoding_graph, + word_table=word_table, batch=batch, ) @@ -468,6 +609,9 @@ def main(): "greedy_search", "beam_search", "fast_beam_search", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", + "fast_beam_search_nbest_oracle", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method @@ -481,6 +625,11 @@ def main(): params.suffix += f"-beam-{params.beam}" params.suffix += f"-max-contexts-{params.max_contexts}" params.suffix += f"-max-states-{params.max_states}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" elif "beam_search" in params.decoding_method: params.suffix += ( f"-{params.decoding_method}-beam-size-{params.beam_size}" @@ -594,10 +743,24 @@ def main(): model.to(device) model.eval() - if params.decoding_method == "fast_beam_search": - decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": + lexicon = Lexicon(params.lang_dir) + word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + word_table = None + decoding_graph = k2.trivial_graph( + params.vocab_size - 1, device=device + ) else: decoding_graph = None + word_table = None num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -619,6 +782,7 @@ def main(): params=params, model=model, sp=sp, + word_table=word_table, decoding_graph=decoding_graph, ) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/export.py b/egs/librispeech/ASR/pruned_transducer_stateless5/export.py index f1269a4bd..936508900 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless5/export.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless5/export.py @@ -146,8 +146,6 @@ def main(): args = get_parser().parse_args() args.exp_dir = Path(args.exp_dir) - assert args.jit is False, "Support torchscript will be added later" - params = get_params() params.update(vars(args)) @@ -246,12 +244,15 @@ def main(): ) ) - model.eval() - model.to("cpu") model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py index e77eb19ff..eaf893997 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py @@ -980,6 +980,7 @@ def run(rank, world_size, args): optimizer=optimizer, sp=sp, params=params, + warmup=0.0 if params.start_epoch == 1 else 1.0, ) scaler = GradScaler(enabled=params.use_fp16) @@ -1072,6 +1073,7 @@ def scan_pessimistic_batches_for_oom( optimizer: torch.optim.Optimizer, sp: spm.SentencePieceProcessor, params: AttributeDict, + warmup: float, ): from lhotse.dataset import find_pessimistic_batches @@ -1082,9 +1084,6 @@ def scan_pessimistic_batches_for_oom( for criterion, cuts in batches.items(): batch = train_dl.dataset[cuts] try: - # warmup = 0.0 is so that the derivs for the pruned loss stay zero - # (i.e. are not remembered by the decaying-average in adam), because - # we want to avoid these params being subject to shrinkage in adam. with torch.cuda.amp.autocast(enabled=params.use_fp16): loss, _ = compute_loss( params=params, @@ -1092,7 +1091,7 @@ def scan_pessimistic_batches_for_oom( sp=sp, batch=batch, is_training=True, - warmup=0.0, + warmup=warmup, ) loss.backward() optimizer.step() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py b/egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py index a0781da1f..90f2c8b1d 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py @@ -382,7 +382,7 @@ class RelPositionalEncoding(torch.nn.Module): ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i AttributeDict: "model_warm_step": 3000, # arg given to model, not for lrate "env_info": get_env_info(), # parameters for distillation with codebook indexes. - "enable_distiallation": True, "distillation_layer": 5, # 0-based index # Since output rate of hubert is 50, while that of encoder is 8, # two successive codebook_index are concatenated together. - # Detailed in function Transducer::concat_sucessive_codebook_indexes. + # Detailed in function Transducer::concat_sucessive_codebook_indexes "num_codebooks": 16, # used to construct distillation loss } ) @@ -394,7 +400,7 @@ def get_encoder_model(params: AttributeDict) -> nn.Module: dim_feedforward=params.dim_feedforward, num_encoder_layers=params.num_encoder_layers, middle_output_layer=params.distillation_layer - if params.enable_distiallation + if params.enable_distillation else None, ) return encoder @@ -433,9 +439,7 @@ def get_transducer_model(params: AttributeDict) -> nn.Module: decoder_dim=params.decoder_dim, joiner_dim=params.joiner_dim, vocab_size=params.vocab_size, - num_codebooks=params.num_codebooks - if params.enable_distiallation - else 0, + num_codebooks=params.num_codebooks if params.enable_distillation else 0, ) return model @@ -615,7 +619,7 @@ def compute_loss( y = k2.RaggedTensor(y).to(device) info = MetricsTracker() - if is_training and params.enable_distiallation: + if is_training and params.enable_distillation: codebook_indexes, _ = extract_codebook_indexes(batch) codebook_indexes = codebook_indexes.to(device) else: @@ -645,7 +649,7 @@ def compute_loss( params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss ) - if is_training and params.enable_distiallation: + if is_training and params.enable_distillation: assert codebook_loss is not None loss += params.codebook_loss_scale * codebook_loss @@ -661,7 +665,7 @@ def compute_loss( info["loss"] = loss.detach().cpu().item() info["simple_loss"] = simple_loss.detach().cpu().item() info["pruned_loss"] = pruned_loss.detach().cpu().item() - if is_training and params.enable_distiallation: + if is_training and params.enable_distillation: info["codebook_loss"] = codebook_loss.detach().cpu().item() return loss, info @@ -988,6 +992,7 @@ def run(rank, world_size, args): optimizer=optimizer, sp=sp, params=params, + warmup=0.0 if params.start_epoch == 1 else 1.0, ) scaler = GradScaler(enabled=params.use_fp16) @@ -1048,6 +1053,7 @@ def scan_pessimistic_batches_for_oom( optimizer: torch.optim.Optimizer, sp: spm.SentencePieceProcessor, params: AttributeDict, + warmup: float, ): from lhotse.dataset import find_pessimistic_batches @@ -1058,9 +1064,6 @@ def scan_pessimistic_batches_for_oom( for criterion, cuts in batches.items(): batch = train_dl.dataset[cuts] try: - # warmup = 0.0 is so that the derivs for the pruned loss stay zero - # (i.e. are not remembered by the decaying-average in adam), because - # we want to avoid these params being subject to shrinkage in adam. with torch.cuda.amp.autocast(enabled=params.use_fp16): loss, _ = compute_loss( params=params, @@ -1068,7 +1071,7 @@ def scan_pessimistic_batches_for_oom( sp=sp, batch=batch, is_training=True, - warmup=0.0, + warmup=warmup, ) loss.backward() optimizer.step() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py b/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py index c4935f921..e3dcd039b 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py @@ -37,6 +37,7 @@ from icefall.utils import ( setup_logger, ) from lhotse import CutSet, load_manifest +from lhotse.cut import MonoCut from lhotse.features.io import NumpyHdf5Writer @@ -62,16 +63,15 @@ class CodebookIndexExtractor: setup_logger(f"{self.vq_dir}/log-vq_extraction") def init_dirs(self): - # vq_dir is the root dir for quantizer: - # training data/ quantizer / extracted codebook indexes + # vq_dir is the root dir for quantization, containing: + # training data, trained quantizer, and extracted codebook indexes self.vq_dir = ( self.params.exp_dir / f"vq/{self.params.teacher_model_id}/" ) self.vq_dir.mkdir(parents=True, exist_ok=True) - # manifest_dir for : - # splited original manifests, - # extracted codebook indexes and their related manifests + # manifest_dir contains: + # splited original manifests, extracted codebook indexes with related manifests # noqa self.manifest_dir = self.vq_dir / f"splits{self.params.world_size}" self.manifest_dir.mkdir(parents=True, exist_ok=True) @@ -135,6 +135,7 @@ class CodebookIndexExtractor: logging.warn(warn_message) return + logging.info("Start to extract embeddings for training the quantizer.") total_cuts = 0 with NumpyHdf5Writer(self.embedding_file_path) as writer: for batch_idx, batch in enumerate(self.quantizer_train_dl): @@ -187,14 +188,15 @@ class CodebookIndexExtractor: return assert self.embedding_file_path.exists() + logging.info("Start to train quantizer.") trainer = quantization.QuantizerTrainer( dim=self.params.embedding_dim, bytes_per_frame=self.params.num_codebooks, device=self.params.device, ) train, valid = quantization.read_hdf5_data(self.embedding_file_path) - B = 512 # Minibatch size, this is very arbitrary, it's close to what we used - # when we tuned this method. + B = 512 # Minibatch size, this is very arbitrary, + # it's close to what we used when we tuned this method. def minibatch_generator(data: torch.Tensor, repeat: bool): assert 3 * B < data.shape[0] @@ -222,18 +224,50 @@ class CodebookIndexExtractor: """ for subset in self.params.subsets: logging.info(f"About to split {subset}.") - ori_manifest = f"./data/fbank/cuts_train-{subset}.json.gz" + ori_manifest = ( + f"./data/fbank/librispeech_cuts_train-{subset}.jsonl.gz" + ) split_cmd = f"lhotse split {self.params.world_size} {ori_manifest} {self.manifest_dir}" os.system(f"{split_cmd}") + def join_manifests(self): + """ + Join the vq manifest to the original manifest according to cut id. + """ + logging.info("Start to join manifest files.") + for subset in self.params.subsets: + vq_manifest_path = ( + self.dst_manifest_dir + / f"librispeech_cuts_train-{subset}-vq.jsonl.gz" + ) + ori_manifest_path = ( + self.ori_manifest_dir + / f"librispeech_cuts_train-{subset}.jsonl.gz" + ) + dst_vq_manifest_path = ( + self.dst_manifest_dir + / f"librispeech_cuts_train-{subset}.jsonl.gz" + ) + cuts_vq = load_manifest(vq_manifest_path) + cuts_ori = load_manifest(ori_manifest_path) + cuts_vq = cuts_vq.sort_like(cuts_ori) + for cut_idx, (cut_vq, cut_ori) in enumerate(zip(cuts_vq, cuts_ori)): + assert cut_vq.id == cut_ori.id + cut_ori.codebook_indexes = cut_vq.codebook_indexes + + CutSet.from_cuts(cuts_ori).to_jsonl(dst_vq_manifest_path) + logging.info(f"Processed {subset}.") + logging.info(f"Saved to {dst_vq_manifest_path}.") + def merge_vq_manifests(self): """ Merge generated vq included manfiests and storage to self.dst_manifest_dir. """ for subset in self.params.subsets: - vq_manifests = f"{self.manifest_dir}/with_codebook_indexes-cuts_train-{subset}*.json.gz" + vq_manifests = f"{self.manifest_dir}/with_codebook_indexes-librispeech-cuts_train-{subset}*.jsonl.gz" dst_vq_manifest = ( - self.dst_manifest_dir / f"cuts_train-{subset}.json.gz" + self.dst_manifest_dir + / f"librispeech_cuts_train-{subset}-vq.jsonl.gz" ) if 1 == self.params.world_size: merge_cmd = f"cp {vq_manifests} {dst_vq_manifest}" @@ -273,7 +307,6 @@ class CodebookIndexExtractor: os.symlink(ori_manifest_path, dst_manifest_path) def create_vq_fbank(self): - self.reuse_manifests() self.merge_vq_manifests() @cached_property @@ -294,11 +327,13 @@ class CodebookIndexExtractor: def load_ori_dl(self, subset): if self.params.world_size == 1: - ori_manifest_path = f"./data/fbank/cuts_train-{subset}.json.gz" + ori_manifest_path = ( + f"./data/fbank/librispeech_cuts_train-{subset}.jsonl.gz" + ) else: ori_manifest_path = ( self.manifest_dir - / f"cuts_train-{subset}.{self.params.manifest_index}.json.gz" + / f"librispeech_cuts_train-{subset}.{self.params.manifest_index}.jsonl.gz" # noqa ) cuts = load_manifest(ori_manifest_path) @@ -311,6 +346,7 @@ class CodebookIndexExtractor: torch.cuda.empty_cache() def extract_codebook_indexes(self): + logging.info("Start to extract codebook indexes.") if self.params.world_size == 1: self.extract_codebook_indexes_imp() else: @@ -333,7 +369,7 @@ class CodebookIndexExtractor: def extract_codebook_indexes_imp(self): for subset in self.params.subsets: num_cuts = 0 - cuts = [] + new_cuts = [] if self.params.world_size == 1: manifest_file_id = f"{subset}" else: @@ -356,15 +392,23 @@ class CodebookIndexExtractor: assert len(cut_list) == codebook_indexes.shape[0] assert all(c.start == 0 for c in supervisions["cut"]) + new_cut_list = [] for idx, cut in enumerate(cut_list): - cut.codebook_indexes = writer.store_array( + new_cut = MonoCut( + id=cut.id, + start=cut.start, + duration=cut.duration, + channel=cut.channel, + ) + new_cut.codebook_indexes = writer.store_array( key=cut.id, value=codebook_indexes[idx][: num_frames[idx]], frame_shift=0.02, temporal_dim=0, start=0, ) - cuts += cut_list + new_cut_list.append(new_cut) + new_cuts += new_cut_list num_cuts += len(cut_list) message = f"Processed {num_cuts} cuts from {subset}" if self.params.world_size > 1: @@ -373,9 +417,9 @@ class CodebookIndexExtractor: json_file_path = ( self.manifest_dir - / f"with_codebook_indexes-cuts_train-{manifest_file_id}.json.gz" + / f"with_codebook_indexes-librispeech-cuts_train-{manifest_file_id}.jsonl.gz" # noqa ) - CutSet.from_cuts(cuts).to_json(json_file_path) + CutSet.from_cuts(new_cuts).to_jsonl(json_file_path) @torch.no_grad() diff --git a/egs/librispeech/ASR/streaming_conformer_ctc/conformer.py b/egs/librispeech/ASR/streaming_conformer_ctc/conformer.py index 33f8e2e15..ff4c91446 100644 --- a/egs/librispeech/ASR/streaming_conformer_ctc/conformer.py +++ b/egs/librispeech/ASR/streaming_conformer_ctc/conformer.py @@ -760,7 +760,7 @@ class RelPositionalEncoding(torch.nn.Module): ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i None: super(Conformer, self).__init__( num_features=num_features, @@ -70,6 +95,16 @@ class Conformer(Transformer): vgg_frontend=vgg_frontend, ) + self.encoder_layers = num_encoder_layers + self.d_model = d_model + self.cnn_module_kernel = cnn_module_kernel + self.causal = causal + + self.dynamic_chunk_training = dynamic_chunk_training + self.short_chunk_threshold = short_chunk_threshold + self.short_chunk_size = short_chunk_size + self.num_left_chunks = num_left_chunks + self.encoder_pos = RelPositionalEncoding(d_model, dropout) encoder_layer = ConformerEncoderLayer( @@ -79,6 +114,7 @@ class Conformer(Transformer): dropout, cnn_module_kernel, normalize_before, + causal, ) self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers) self.normalize_before = normalize_before @@ -89,6 +125,8 @@ class Conformer(Transformer): # and throws an error without this change. self.after_norm = identity + self._init_state: List[torch.Tensor] = [torch.empty(0)] + def forward( self, x: torch.Tensor, x_lens: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: @@ -117,9 +155,33 @@ class Conformer(Transformer): lengths = (((x_lens - 1) >> 1) - 1) >> 1 assert x.size(0) == lengths.max().item() - mask = make_pad_mask(lengths) - x = self.encoder(x, pos_emb, src_key_padding_mask=mask) # (T, N, C) + src_key_padding_mask = make_pad_mask(lengths) + + if self.dynamic_chunk_training: + assert ( + self.causal + ), "Causal convolution is required for streaming conformer." + max_len = x.size(0) + chunk_size = torch.randint(1, max_len, (1,)).item() + if chunk_size > (max_len * self.short_chunk_threshold): + chunk_size = max_len + else: + chunk_size = chunk_size % self.short_chunk_size + 1 + + mask = ~subsequent_chunk_mask( + size=x.size(0), + chunk_size=chunk_size, + num_left_chunks=self.num_left_chunks, + device=x.device, + ) + x = self.encoder( + x, pos_emb, mask=mask, src_key_padding_mask=src_key_padding_mask + ) # (T, N, C) + else: + x = self.encoder( + x, pos_emb, mask=None, src_key_padding_mask=src_key_padding_mask + ) # (T, N, C) if self.normalize_before: x = self.after_norm(x) @@ -129,6 +191,202 @@ class Conformer(Transformer): return logits, lengths + @torch.jit.export + def get_init_state( + self, left_context: int, device: torch.device + ) -> List[torch.Tensor]: + """Return the initial cache state of the model. + + Args: + left_context: The left context size (in frames after subsampling). + + Returns: + Return the initial state of the model, it is a list containing two + tensors, the first one is the cache for attentions which has a shape + of (num_encoder_layers, left_context, encoder_dim), the second one + is the cache of conv_modules which has a shape of + (num_encoder_layers, cnn_module_kernel - 1, encoder_dim). + + NOTE: the returned tensors are on the given device. + """ + if ( + len(self._init_state) == 2 + and self._init_state[0].size(1) == left_context + ): + # Note: It is OK to share the init state as it is + # not going to be modified by the model + return self._init_state + + init_states: List[torch.Tensor] = [ + torch.zeros( + ( + self.encoder_layers, + left_context, + self.d_model, + ), + device=device, + ), + torch.zeros( + ( + self.encoder_layers, + self.cnn_module_kernel - 1, + self.d_model, + ), + device=device, + ), + ] + + self._init_state = init_states + + return init_states + + @torch.jit.export + def streaming_forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + states: Optional[List[torch.Tensor]] = None, + processed_lens: Optional[Tensor] = None, + left_context: int = 64, + right_context: int = 0, + chunk_size: int = 16, + simulate_streaming: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]: + """ + Args: + x: + The input tensor. Its shape is (batch_size, seq_len, feature_dim). + x_lens: + A tensor of shape (batch_size,) containing the number of frames in + `x` before padding. + states: + The decode states for previous frames which contains the cached data. + It has two elements, the first element is the attn_cache which has + a shape of (encoder_layers, left_context, batch, attention_dim), + the second element is the conv_cache which has a shape of + (encoder_layers, cnn_module_kernel-1, batch, conv_dim). + Note: states will be modified in this function. + processed_lens: + How many frames (after subsampling) have been processed for each sequence. + left_context: + How many previous frames the attention can see in current chunk. + Note: It's not that each individual frame has `left_context` frames + of left context, some have more. + right_context: + How many future frames the attention can see in current chunk. + Note: It's not that each individual frame has `right_context` frames + of right context, some have more. + chunk_size: + The chunk size for decoding, this will be used to simulate streaming + decoding using masking. + simulate_streaming: + If setting True, it will use a masking strategy to simulate streaming + fashion (i.e. every chunk data only see limited left context and + right context). The whole sequence is supposed to be send at a time + When using simulate_streaming. + Returns: + Return a tuple containing 2 tensors: + - logits, its shape is (batch_size, output_seq_len, output_dim) + - logit_lens, a tensor of shape (batch_size,) containing the number + of frames in `logits` before padding. + - states, the updated states(i.e. caches) including the information + of current chunk. + """ + + # x: [N, T, C] + # Caution: We assume the subsampling factor is 4! + + # lengths = ((x_lens - 1) // 2 - 1) // 2 # issue an warning + # + # Note: rounding_mode in torch.div() is available only in torch >= 1.8.0 + lengths = (((x_lens - 1) >> 1) - 1) >> 1 + + if not simulate_streaming: + assert states is not None + assert processed_lens is not None + assert ( + len(states) == 2 + and states[0].shape + == (self.encoder_layers, left_context, x.size(0), self.d_model) + and states[1].shape + == ( + self.encoder_layers, + self.cnn_module_kernel - 1, + x.size(0), + self.d_model, + ) + ), f"""The length of states MUST be equal to 2, and the shape of + first element should be {(self.encoder_layers, left_context, x.size(0), self.d_model)}, + given {states[0].shape}. the shape of second element should be + {(self.encoder_layers, self.cnn_module_kernel - 1, x.size(0), self.d_model)}, + given {states[1].shape}.""" + + lengths -= 2 # we will cut off 1 frame on each side of encoder_embed output + src_key_padding_mask = make_pad_mask(lengths) + + processed_mask = torch.arange(left_context, device=x.device).expand( + x.size(0), left_context + ) + processed_lens = processed_lens.view(x.size(0), 1) + processed_mask = (processed_lens <= processed_mask).flip(1) + + src_key_padding_mask = torch.cat( + [processed_mask, src_key_padding_mask], dim=1 + ) + + embed = self.encoder_embed(x) + + # cut off 1 frame on each size of embed as they see the padding + # value which causes a training and decoding mismatch. + embed = embed[:, 1:-1, :] + + embed, pos_enc = self.encoder_pos(embed, left_context) + embed = embed.permute(1, 0, 2) # (B, T, F) -> (T, B, F) + + x, states = self.encoder.chunk_forward( + embed, + pos_enc, + src_key_padding_mask=src_key_padding_mask, + states=states, + left_context=left_context, + right_context=right_context, + ) # (T, B, F) + else: + assert states is None + states = [] # just to make torch.script.jit happy + src_key_padding_mask = make_pad_mask(lengths) + x = self.encoder_embed(x) + x, pos_emb = self.encoder_pos(x) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + + assert x.size(0) == lengths.max().item() + + num_left_chunks = -1 + if left_context >= 0: + assert left_context % chunk_size == 0 + num_left_chunks = left_context // chunk_size + + mask = ~subsequent_chunk_mask( + size=x.size(0), + chunk_size=chunk_size, + num_left_chunks=num_left_chunks, + device=x.device, + ) + x = self.encoder( + x, + pos_emb, + mask=mask, + src_key_padding_mask=src_key_padding_mask, + ) # (T, N, C) + + if self.normalize_before: + x = self.after_norm(x) + + logits = self.encoder_output_layer(x) + logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + + return logits, lengths, states + class ConformerEncoderLayer(nn.Module): """ @@ -141,7 +399,9 @@ class ConformerEncoderLayer(nn.Module): dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). cnn_module_kernel (int): Kernel size of convolution module. - normalize_before: whether to use layer_norm before the first block. + normalize_before (bool): whether to use layer_norm before the first block. + causal (bool): Whether to use causal convolution in conformer encoder + layer. This MUST be True when using dynamic_chunk_training and streaming decoding. Examples:: >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) @@ -158,6 +418,7 @@ class ConformerEncoderLayer(nn.Module): dropout: float = 0.1, cnn_module_kernel: int = 31, normalize_before: bool = True, + causal: bool = False, ) -> None: super(ConformerEncoderLayer, self).__init__() self.self_attn = RelPositionMultiheadAttention( @@ -178,7 +439,9 @@ class ConformerEncoderLayer(nn.Module): nn.Linear(dim_feedforward, d_model), ) - self.conv_module = ConvolutionModule(d_model, cnn_module_kernel) + self.conv_module = ConvolutionModule( + d_model, cnn_module_kernel, causal=causal + ) self.norm_ff_macaron = nn.LayerNorm( d_model @@ -212,10 +475,101 @@ class ConformerEncoderLayer(nn.Module): pos_emb: Positional embedding tensor (required). src_mask: the mask for the src sequence (optional). src_key_padding_mask: the mask for the src keys per batch (optional). - Shape: src: (S, N, E). - pos_emb: (N, 2*S-1, E) + pos_emb: (N, 2*S-1, E). + src_mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, N is the batch size, E is the feature number + """ + # macaron style feed forward module + residual = src + if self.normalize_before: + src = self.norm_ff_macaron(src) + src = residual + self.ff_scale * self.dropout( + self.feed_forward_macaron(src) + ) + if not self.normalize_before: + src = self.norm_ff_macaron(src) + + # multi-headed self-attention module + residual = src + if self.normalize_before: + src = self.norm_mha(src) + + src_att = self.self_attn( + src, + src, + src, + pos_emb=pos_emb, + attn_mask=src_mask, + key_padding_mask=src_key_padding_mask, + )[0] + src = residual + self.dropout(src_att) + if not self.normalize_before: + src = self.norm_mha(src) + + # convolution module + residual = src + if self.normalize_before: + src = self.norm_conv(src) + + src, _ = self.conv_module(src) + src = residual + self.dropout(src) + + if not self.normalize_before: + src = self.norm_conv(src) + + # feed forward module + residual = src + if self.normalize_before: + src = self.norm_ff(src) + src = residual + self.ff_scale * self.dropout(self.feed_forward(src)) + if not self.normalize_before: + src = self.norm_ff(src) + + if self.normalize_before: + src = self.norm_final(src) + + return src + + @torch.jit.export + def chunk_forward( + self, + src: Tensor, + pos_emb: Tensor, + states: List[Tensor], + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + left_context: int = 0, + right_context: int = 0, + ) -> Tuple[Tensor, List[Tensor]]: + """ + Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + pos_emb: Positional embedding tensor (required). + states: + The decode states for previous frames which contains the cached data. + It has two elements, the first element is the attn_cache which has + a shape of (left_context, batch, attention_dim), + the second element is the conv_cache which has a shape of + (cnn_module_kernel-1, batch, conv_dim). + Note: states will be modified in this function. + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + left_context: + How many previous frames the attention can see in current chunk. + Note: It's not that each individual frame has `left_context` frames + of left context, some have more. + right_context: + How many future frames the attention can see in current chunk. + Note: It's not that each individual frame has `right_context` frames + of right context, some have more. + Shape: + src: (S, N, E). + pos_emb: (N, 2*(S+left_context)-1, E). src_mask: (S, S). src_key_padding_mask: (N, S). S is the source sequence length, N is the batch size, E is the feature number @@ -235,13 +589,30 @@ class ConformerEncoderLayer(nn.Module): residual = src if self.normalize_before: src = self.norm_mha(src) + + # We put the attention cache this level (i.e. before linear transformation) + # to save memory consumption, when decoding in streaming fashion, the + # batch size would be thousands (for 32GB machine), if we cache key & val + # separately, it needs extra several GB memory. + # TODO(WeiKang): Move cache to self_attn level (i.e. cache key & val + # separately) if needed. + key = torch.cat([states[0], src], dim=0) + val = key + if right_context > 0: + states[0] = key[ + -(left_context + right_context) : -right_context, ... # noqa + ] + else: + states[0] = key[-left_context:, ...] + src_att = self.self_attn( src, - src, - src, + key, + val, pos_emb=pos_emb, attn_mask=src_mask, key_padding_mask=src_key_padding_mask, + left_context=left_context, )[0] src = residual + self.dropout(src_att) if not self.normalize_before: @@ -251,7 +622,13 @@ class ConformerEncoderLayer(nn.Module): residual = src if self.normalize_before: src = self.norm_conv(src) - src = residual + self.dropout(self.conv_module(src)) + + src, conv_cache = self.conv_module( + src, states[1], right_context=right_context + ) + states[1] = conv_cache + src = residual + self.dropout(src) + if not self.normalize_before: src = self.norm_conv(src) @@ -266,7 +643,7 @@ class ConformerEncoderLayer(nn.Module): if self.normalize_before: src = self.norm_final(src) - return src + return src, states class ConformerEncoder(nn.Module): @@ -305,10 +682,11 @@ class ConformerEncoder(nn.Module): pos_emb: Positional embedding tensor (required). mask: the mask for the src sequence (optional). src_key_padding_mask: the mask for the src keys per batch (optional). + Shape: Shape: src: (S, N, E). - pos_emb: (N, 2*S-1, E) + pos_emb: (N, 2*S-1, E). mask: (S, S). src_key_padding_mask: (N, S). S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number @@ -316,16 +694,75 @@ class ConformerEncoder(nn.Module): """ output = src - for mod in self.layers: + for layer_index, mod in enumerate(self.layers): output = mod( output, pos_emb, src_mask=mask, src_key_padding_mask=src_key_padding_mask, ) - return output + @torch.jit.export + def chunk_forward( + self, + src: Tensor, + pos_emb: Tensor, + states: List[Tensor], + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + left_context: int = 0, + right_context: int = 0, + ) -> Tuple[Tensor, List[Tensor]]: + r"""Pass the input through the encoder layers in turn. + + Args: + src: the sequence to the encoder (required). + pos_emb: Positional embedding tensor (required). + states: + The decode states for previous frames which contains the cached data. + It has two elements, the first element is the attn_cache which has + a shape of (encoder_layers, left_context, batch, attention_dim), + the second element is the conv_cache which has a shape of + (encoder_layers, cnn_module_kernel-1, batch, conv_dim). + Note: states will be modified in this function. + mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + left_context: + How many previous frames the attention can see in current chunk. + Note: It's not that each individual frame has `left_context` frames + of left context, some have more. + right_context: + How many future frames the attention can see in current chunk. + Note: It's not that each individual frame has `right_context` frames + of right context, some have more. + Shape: + src: (S, N, E). + pos_emb: (N, 2*(S+left_context)-1, E). + mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number + + """ + assert not self.training + output = src + + for layer_index, mod in enumerate(self.layers): + cache = [states[0][layer_index], states[1][layer_index]] + output, cache = mod.chunk_forward( + output, + pos_emb, + states=cache, + src_mask=mask, + src_key_padding_mask=src_key_padding_mask, + left_context=left_context, + right_context=right_context, + ) + states[0][layer_index] = cache[0] + states[1][layer_index] = cache[1] + + return output, states + class RelPositionalEncoding(torch.nn.Module): """Relative positional encoding module. @@ -351,24 +788,25 @@ class RelPositionalEncoding(torch.nn.Module): self.pe = None self.extend_pe(torch.tensor(0.0).expand(1, max_len)) - def extend_pe(self, x: Tensor) -> None: + def extend_pe(self, x: Tensor, left_context: int = 0) -> None: """Reset the positional encodings.""" + x_size_1 = x.size(1) + left_context if self.pe is not None: # self.pe contains both positive and negative parts # the length of self.pe is 2 * input_len - 1 - if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.size(1) >= x_size_1 * 2 - 1: # Note: TorchScript doesn't implement operator== for torch.Device if self.pe.dtype != x.dtype or str(self.pe.device) != str( x.device ): self.pe = self.pe.to(dtype=x.dtype, device=x.device) return - # Suppose `i` means to the position of query vecotr and `j` means the + # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i Tuple[Tensor, Tensor]: + def forward( + self, x: torch.Tensor, left_context: int = 0 + ) -> Tuple[Tensor, Tensor]: """Add positional encoding. Args: x (torch.Tensor): Input tensor (batch, time, `*`). - + left_context (int): left context (in frames) used during streaming decoding. + this is used only in real streaming decoding, in other circumstances, + it MUST be 0. Returns: torch.Tensor: Encoded tensor (batch, time, `*`). torch.Tensor: Encoded tensor (batch, 2*time-1, `*`). """ - self.extend_pe(x) + self.extend_pe(x, left_context) x = x * self.xscale + x_size_1 = x.size(1) + left_context pos_emb = self.pe[ :, self.pe.size(1) // 2 - - x.size(1) + - x_size_1 + 1 : self.pe.size(1) // 2 # noqa E203 + x.size(1), ] @@ -469,6 +912,7 @@ class RelPositionMultiheadAttention(nn.Module): key_padding_mask: Optional[Tensor] = None, need_weights: bool = True, attn_mask: Optional[Tensor] = None, + left_context: int = 0, ) -> Tuple[Tensor, Optional[Tensor]]: r""" Args: @@ -482,6 +926,9 @@ class RelPositionMultiheadAttention(nn.Module): need_weights: output attn_output_weights. attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch. + left_context (int): left context (in frames) used during streaming decoding. + this is used only in real streaming decoding, in other circumstances, + it MUST be 0. Shape: - Inputs: @@ -527,14 +974,18 @@ class RelPositionMultiheadAttention(nn.Module): key_padding_mask=key_padding_mask, need_weights=need_weights, attn_mask=attn_mask, + left_context=left_context, ) - def rel_shift(self, x: Tensor) -> Tensor: + def rel_shift(self, x: Tensor, left_context: int = 0) -> Tensor: """Compute relative positional encoding. Args: x: Input tensor (batch, head, time1, 2*time1-1). time1 means the length of query vector. + left_context (int): left context (in frames) used during streaming decoding. + this is used only in real streaming decoding, in other circumstances, + it MUST be 0. Returns: Tensor: tensor of shape (batch, head, time1, time2) @@ -542,14 +993,19 @@ class RelPositionMultiheadAttention(nn.Module): the key, while time1 is for the query). """ (batch_size, num_heads, time1, n) = x.shape - assert n == 2 * time1 - 1 + time2 = time1 + left_context + + assert ( + n == left_context + 2 * time1 - 1 + ), f"{n} == {left_context} + 2 * {time1} - 1" + # Note: TorchScript requires explicit arg for stride() batch_stride = x.stride(0) head_stride = x.stride(1) time1_stride = x.stride(2) n_stride = x.stride(3) return x.as_strided( - (batch_size, num_heads, time1, time1), + (batch_size, num_heads, time1, time2), (batch_stride, head_stride, time1_stride - n_stride, n_stride), storage_offset=n_stride * (time1 - 1), ) @@ -571,6 +1027,7 @@ class RelPositionMultiheadAttention(nn.Module): key_padding_mask: Optional[Tensor] = None, need_weights: bool = True, attn_mask: Optional[Tensor] = None, + left_context: int = 0, ) -> Tuple[Tensor, Optional[Tensor]]: r""" Args: @@ -588,6 +1045,9 @@ class RelPositionMultiheadAttention(nn.Module): need_weights: output attn_output_weights. attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch. + left_context (int): left context (in frames) used during streaming decoding. + this is used only in real streaming decoding, in other circumstances, + it MUST be 0. Shape: Inputs: @@ -750,7 +1210,9 @@ class RelPositionMultiheadAttention(nn.Module): pos_emb_bsz = pos_emb.size(0) assert pos_emb_bsz in (1, bsz) # actually it is 1 p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim) - p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + + # (batch, 2*time1, head, d_k) --> (batch, head, d_k, 2*time -1) + p = p.permute(0, 2, 3, 1) q_with_bias_u = (q + self.pos_bias_u).transpose( 1, 2 @@ -770,9 +1232,10 @@ class RelPositionMultiheadAttention(nn.Module): # compute matrix b and matrix d matrix_bd = torch.matmul( - q_with_bias_v, p.transpose(-2, -1) + q_with_bias_v, p ) # (batch, head, time1, 2*time1-1) - matrix_bd = self.rel_shift(matrix_bd) + + matrix_bd = self.rel_shift(matrix_bd, left_context=left_context) attn_output_weights = ( matrix_ac + matrix_bd @@ -807,6 +1270,31 @@ class RelPositionMultiheadAttention(nn.Module): ) attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1) + + # If we are using dynamic_chunk_training and setting a limited + # num_left_chunks, the attention may only see the padding values which + # will also be masked out by `key_padding_mask`, at this circumstances, + # the whole column of `attn_output_weights` will be `-inf` + # (i.e. be `nan` after softmax), so, we fill `0.0` at the masking + # positions to avoid invalid loss value below. + if ( + attn_mask is not None + and attn_mask.dtype == torch.bool + and key_padding_mask is not None + ): + combined_mask = attn_mask.unsqueeze(0) | key_padding_mask.unsqueeze( + 1 + ).unsqueeze(2) + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + attn_output_weights = attn_output_weights.masked_fill( + combined_mask, 0.0 + ) + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, src_len + ) + attn_output_weights = nn.functional.dropout( attn_output_weights, p=dropout_p, training=training ) @@ -840,16 +1328,21 @@ class ConvolutionModule(nn.Module): channels (int): The number of channels of conv layers. kernel_size (int): Kernerl size of conv layers. bias (bool): Whether to use bias in conv layers (default=True). - + causal (bool): Whether to use causal convolution. """ def __init__( - self, channels: int, kernel_size: int, bias: bool = True + self, + channels: int, + kernel_size: int, + bias: bool = True, + causal: bool = False, ) -> None: """Construct an ConvolutionModule object.""" super(ConvolutionModule, self).__init__() # kernerl_size should be a odd number for 'SAME' padding assert (kernel_size - 1) % 2 == 0 + self.causal = causal self.pointwise_conv1 = nn.Conv1d( channels, @@ -859,12 +1352,18 @@ class ConvolutionModule(nn.Module): padding=0, bias=bias, ) + + self.lorder = kernel_size - 1 + padding = (kernel_size - 1) // 2 + if self.causal: + padding = 0 + self.depthwise_conv = nn.Conv1d( channels, channels, kernel_size, stride=1, - padding=(kernel_size - 1) // 2, + padding=padding, groups=channels, bias=bias, ) @@ -879,7 +1378,12 @@ class ConvolutionModule(nn.Module): ) self.activation = Swish() - def forward(self, x: Tensor) -> Tensor: + def forward( + self, + x: Tensor, + cache: Optional[Tensor] = None, + right_context: int = 0, + ) -> Tuple[Tensor, Tensor]: """Compute convolution module. Args: @@ -897,6 +1401,27 @@ class ConvolutionModule(nn.Module): x = nn.functional.glu(x, dim=1) # (batch, channels, time) # 1D Depthwise Conv + if self.causal and self.lorder > 0: + if cache is None: + # Make depthwise_conv causal by + # manualy padding self.lorder zeros to the left + x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) + else: + assert ( + not self.training + ), "Cache should be None in training time" + assert cache.size(0) == self.lorder + x = torch.cat([cache.permute(1, 2, 0), x], dim=2) + if right_context > 0: + cache = x.permute(2, 0, 1)[ + -(self.lorder + right_context) : ( # noqa + -right_context + ), + ..., + ] + else: + cache = x.permute(2, 0, 1)[-self.lorder :, ...] # noqa + x = self.depthwise_conv(x) # x is (batch, channels, time) x = x.permute(0, 2, 1) @@ -907,7 +1432,10 @@ class ConvolutionModule(nn.Module): x = self.pointwise_conv2(x) # (batch, channel, time) - return x.permute(2, 0, 1) + if cache is None: + cache = torch.empty(0) + + return x.permute(2, 0, 1), cache class Swish(torch.nn.Module): diff --git a/egs/ptb/LM/README.md b/egs/ptb/LM/README.md new file mode 100644 index 000000000..7629a950d --- /dev/null +++ b/egs/ptb/LM/README.md @@ -0,0 +1,18 @@ +## Description + +(Note: the experiments here are only about language modeling) + +ptb is short for Penn Treebank. + + +About the Penn Treebank corpus: + - This corpus is free for research purposes + - ptb.train.txt: train set + - ptb.valid.txt: development set (should be used just for tuning hyper-parameters, but not for training) + - ptb.test.txt: test set for reporting perplexity + +You can download the dataset from one of the following URLs: + +- https://github.com/townie/PTB-dataset-from-Tomas-Mikolov-s-webpage +- http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz +- https://deepai.org/dataset/penn-treebank diff --git a/egs/ptb/LM/local/prepare_lm_training_data.py b/egs/ptb/LM/local/prepare_lm_training_data.py new file mode 120000 index 000000000..abc00d421 --- /dev/null +++ b/egs/ptb/LM/local/prepare_lm_training_data.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/prepare_lm_training_data.py \ No newline at end of file diff --git a/egs/ptb/LM/local/sort_lm_training_data.py b/egs/ptb/LM/local/sort_lm_training_data.py new file mode 100755 index 000000000..af54dbd07 --- /dev/null +++ b/egs/ptb/LM/local/sort_lm_training_data.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This file takes as input the filename of LM training data +generated by ./local/prepare_lm_training_data.py and sorts +it by sentence length. + +Sentence length equals to the number of BPE tokens in a sentence. +""" + +import argparse +import logging +from pathlib import Path + +import k2 +import numpy as np +import torch + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--in-lm-data", + type=str, + help="Input LM training data, e.g., data/bpe_500/lm_data.pt", + ) + + parser.add_argument( + "--out-lm-data", + type=str, + help="Input LM training data, e.g., data/bpe_500/sorted_lm_data.pt", + ) + + parser.add_argument( + "--out-statistics", + type=str, + help="Statistics about LM training data., data/bpe_500/statistics.txt", + ) + + return parser.parse_args() + + +def main(): + args = get_args() + in_lm_data = Path(args.in_lm_data) + out_lm_data = Path(args.out_lm_data) + assert in_lm_data.is_file(), f"{in_lm_data}" + if out_lm_data.is_file(): + logging.warning(f"{out_lm_data} exists - skipping") + return + data = torch.load(in_lm_data) + words2bpe = data["words"] + sentences = data["sentences"] + sentence_lengths = data["sentence_lengths"] + + num_sentences = sentences.dim0 + assert num_sentences == sentence_lengths.numel(), ( + num_sentences, + sentence_lengths.numel(), + ) + + indices = torch.argsort(sentence_lengths, descending=True) + + sorted_sentences = sentences[indices.to(torch.int32)] + sorted_sentence_lengths = sentence_lengths[indices] + + # Check that sentences are ordered by length + assert num_sentences == sorted_sentences.dim0, ( + num_sentences, + sorted_sentences.dim0, + ) + + cur = None + for i in range(num_sentences): + word_ids = sorted_sentences[i] + token_ids = words2bpe[word_ids] + if isinstance(token_ids, k2.RaggedTensor): + token_ids = token_ids.values + if cur is not None: + assert cur >= token_ids.numel(), (cur, token_ids.numel()) + + cur = token_ids.numel() + assert cur == sorted_sentence_lengths[i] + + data["sentences"] = sorted_sentences + data["sentence_lengths"] = sorted_sentence_lengths + torch.save(data, args.out_lm_data) + logging.info(f"Saved to {args.out_lm_data}") + + statistics = Path(args.out_statistics) + + # Write statistics + num_words = sorted_sentences.numel() + num_tokens = sentence_lengths.sum().item() + max_sentence_length = sentence_lengths[indices[0]] + min_sentence_length = sentence_lengths[indices[-1]] + + step = 10 + hist, bins = np.histogram( + sentence_lengths.numpy(), + bins=np.arange(1, max_sentence_length + step, step), + ) + + histogram = np.stack((bins[:-1], hist)).transpose() + + with open(statistics, "w") as f: + f.write(f"num_sentences: {num_sentences}\n") + f.write(f"num_words: {num_words}\n") + f.write(f"num_tokens: {num_tokens}\n") + f.write(f"max_sentence_length: {max_sentence_length}\n") + f.write(f"min_sentence_length: {min_sentence_length}\n") + f.write("histogram:\n") + f.write(" bin count percent\n") + for row in histogram: + f.write( + f"{int(row[0]):>5} {int(row[1]):>5} " + f"{100.*row[1]/num_sentences:.3f}%\n" + ) + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/ptb/LM/local/test_prepare_lm_training_data.py b/egs/ptb/LM/local/test_prepare_lm_training_data.py new file mode 100755 index 000000000..877720e7b --- /dev/null +++ b/egs/ptb/LM/local/test_prepare_lm_training_data.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from pathlib import Path + +import sentencepiece as spm +import torch + + +def main(): + lm_training_data = Path("./data/bpe_500/lm_data.pt") + bpe_model = Path("./data/bpe_500/bpe.model") + if not lm_training_data.exists(): + logging.warning(f"{lm_training_data} does not exist - skipping") + return + + if not bpe_model.exists(): + logging.warning(f"{bpe_model} does not exist - skipping") + return + + sp = spm.SentencePieceProcessor() + sp.load(str(bpe_model)) + + data = torch.load(lm_training_data) + words2bpe = data["words"] + sentences = data["sentences"] + + ss = [] + unk = sp.decode(sp.unk_id()).strip() + for i in range(10): + s = sp.decode(words2bpe[sentences[i]].values.tolist()) + s = s.replace(unk, "") + ss.append(s) + + for s in ss: + print(s) + # You can compare the output with the first 10 lines of ptb.train.txt + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/ptb/LM/local/train_bpe_model.py b/egs/ptb/LM/local/train_bpe_model.py new file mode 120000 index 000000000..6fad36421 --- /dev/null +++ b/egs/ptb/LM/local/train_bpe_model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/train_bpe_model.py \ No newline at end of file diff --git a/egs/ptb/LM/prepare.sh b/egs/ptb/LM/prepare.sh new file mode 100755 index 000000000..70586785d --- /dev/null +++ b/egs/ptb/LM/prepare.sh @@ -0,0 +1,115 @@ +#!/usr/bin/env bash + +set -eou pipefail + +nj=15 +stage=-1 +stop_stage=100 + +dl_dir=$PWD/download +# The following files will be downloaded to $dl_dir +# - ptb.train.txt +# - ptb.valid.txt +# - ptb.test.txt + +. shared/parse_options.sh || exit 1 + +# vocab size for sentence piece models. +# It will generate data/bpe_xxx, data/bpe_yyy +# if the array contains xxx, yyy +vocab_sizes=( + 500 + 1000 + 2000 + 5000 +) + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data +mkdir -p $dl_dir + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +log "dl_dir: $dl_dir" + +if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then + log "Stage -1: Download data" + if [ ! -f $dl_dir/.complete ]; then + url=https://raw.githubusercontent.com/townie/PTB-dataset-from-Tomas-Mikolov-s-webpage/master/data/ + wget --no-verbose --directory-prefix $dl_dir $url/ptb.train.txt + wget --no-verbose --directory-prefix $dl_dir $url/ptb.valid.txt + wget --no-verbose --directory-prefix $dl_dir $url/ptb.test.txt + touch $dl_dir/.complete + fi +fi + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "Stage 0: Train BPE model" + + for vocab_size in ${vocab_sizes[@]}; do + out_dir=data/bpe_${vocab_size} + mkdir -p $out_dir + ./local/train_bpe_model.py \ + --out-dir $out_dir \ + --vocab-size $vocab_size \ + --transcript $dl_dir/ptb.train.txt + done +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Generate LM training data" + # Note: ptb.train.txt has already been normalized + + for vocab_size in ${vocab_sizes[@]}; do + out_dir=data/bpe_${vocab_size} + mkdir -p $out_dir + ./local/prepare_lm_training_data.py \ + --bpe-model $out_dir/bpe.model \ + --lm-data $dl_dir/ptb.train.txt \ + --lm-archive $out_dir/lm_data.pt + + ./local/prepare_lm_training_data.py \ + --bpe-model $out_dir/bpe.model \ + --lm-data $dl_dir/ptb.valid.txt \ + --lm-archive $out_dir/lm_data-valid.pt + + ./local/prepare_lm_training_data.py \ + --bpe-model $out_dir/bpe.model \ + --lm-data $dl_dir/ptb.test.txt \ + --lm-archive $out_dir/lm_data-test.pt + done +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Sort LM training data" + # Sort LM training data generated in stage 1 + # by sentence length in descending order + # for ease of training. + # + # Sentence length equals to the number of BPE tokens + # in a sentence. + + for vocab_size in ${vocab_sizes[@]}; do + out_dir=data/bpe_${vocab_size} + mkdir -p $out_dir + ./local/sort_lm_training_data.py \ + --in-lm-data $out_dir/lm_data.pt \ + --out-lm-data $out_dir/sorted_lm_data.pt \ + --out-statistics $out_dir/statistics.txt + + ./local/sort_lm_training_data.py \ + --in-lm-data $out_dir/lm_data-valid.pt \ + --out-lm-data $out_dir/sorted_lm_data-valid.pt \ + --out-statistics $out_dir/statistics-valid.txt + + ./local/sort_lm_training_data.py \ + --in-lm-data $out_dir/lm_data-test.pt \ + --out-lm-data $out_dir/sorted_lm_data-test.pt \ + --out-statistics $out_dir/statistics-test.txt + done +fi diff --git a/egs/ptb/LM/shared b/egs/ptb/LM/shared new file mode 120000 index 000000000..4c5e91438 --- /dev/null +++ b/egs/ptb/LM/shared @@ -0,0 +1 @@ +../../../icefall/shared/ \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/export.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/export.py index 6119ecf2c..77faa3c0e 100755 --- a/egs/spgispeech/ASR/pruned_transducer_stateless2/export.py +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/export.py @@ -130,8 +130,6 @@ def main(): args = get_parser().parse_args() args.exp_dir = Path(args.exp_dir) - assert args.jit is False, "Support torchscript will be added later" - params = get_params() params.update(vars(args)) @@ -178,6 +176,11 @@ def main(): model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/egs/tal_csasr/ASR/README.md b/egs/tal_csasr/ASR/README.md new file mode 100644 index 000000000..a705a2f44 --- /dev/null +++ b/egs/tal_csasr/ASR/README.md @@ -0,0 +1,19 @@ + +# Introduction + +This recipe includes some different ASR models trained with TAL_CSASR. + +[./RESULTS.md](./RESULTS.md) contains the latest results. + +# Transducers + +There are various folders containing the name `transducer` in this folder. +The following table lists the differences among them. + +| | Encoder | Decoder | Comment | +|---------------------------------------|---------------------|--------------------|-----------------------------| +| `pruned_transducer_stateless5` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + more layers + random combiner| + +The decoder in `transducer_stateless` is modified from the paper +[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/). +We place an additional Conv1d layer right after the input embedding layer. diff --git a/egs/tal_csasr/ASR/RESULTS.md b/egs/tal_csasr/ASR/RESULTS.md new file mode 100644 index 000000000..ddff0ab61 --- /dev/null +++ b/egs/tal_csasr/ASR/RESULTS.md @@ -0,0 +1,87 @@ +## Results + +### TAL_CSASR Mix Chars and BPEs training results (Pruned Transducer Stateless5) + +#### 2022-06-22 + +Using the codes from this PR https://github.com/k2-fsa/icefall/pull/428. + +The WERs are + +|decoding-method | epoch(iter) | avg | dev | test | +|--|--|--|--|--| +|greedy_search | 30 | 24 | 7.49 | 7.58| +|modified_beam_search | 30 | 24 | 7.33 | 7.38| +|fast_beam_search | 30 | 24 | 7.32 | 7.42| +|greedy_search(use-averaged-model=True) | 30 | 24 | 7.30 | 7.39| +|modified_beam_search(use-averaged-model=True) | 30 | 24 | 7.15 | 7.22| +|fast_beam_search(use-averaged-model=True) | 30 | 24 | 7.18 | 7.27| +|greedy_search | 348000 | 30 | 7.46 | 7.54| +|modified_beam_search | 348000 | 30 | 7.24 | 7.36| +|fast_beam_search | 348000 | 30 | 7.25 | 7.39 | + +The results (CER(%) and WER(%)) for Chinese CER and English WER respectivly (zh: Chinese, en: English): +|decoding-method | epoch(iter) | avg | dev | dev_zh | dev_en | test | test_zh | test_en | +|--|--|--|--|--|--|--|--|--| +|greedy_search(use-averaged-model=True) | 30 | 24 | 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13| +|modified_beam_search(use-averaged-model=True) | 30 | 24 | 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 | +|fast_beam_search(use-averaged-model=True) | 30 | 24 | 7.18 | 6.39| 18.90 | 7.27| 6.55 | 18.77| + +The training command for reproducing is given below: + +``` +export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5" + +./pruned_transducer_stateless5/train.py \ + --world-size 6 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless5/exp \ + --lang-dir data/lang_char \ + --max-duration 90 +``` + +The tensorboard training log can be found at +https://tensorboard.dev/experiment/KaACzXOVR0OM6cy0qbN5hw/#scalars + +The decoding command is: +``` +epoch=30 +avg=24 +use_average_model=True + +## greedy search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 800 \ + --use-averaged-model $use_average_model + +## modified beam search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 800 \ + --decoding-method modified_beam_search \ + --beam-size 4 \ + --use-averaged-model $use_average_model + +## fast beam search +./pruned_transducer_stateless5/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --max-duration 1500 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 \ + --use-averaged-model $use_average_model +``` + +A pre-trained model and decoding logs can be found at diff --git a/egs/tal_csasr/ASR/local/__init__.py b/egs/tal_csasr/ASR/local/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/tal_csasr/ASR/local/compute_fbank_musan.py b/egs/tal_csasr/ASR/local/compute_fbank_musan.py new file mode 120000 index 000000000..5833f2484 --- /dev/null +++ b/egs/tal_csasr/ASR/local/compute_fbank_musan.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/compute_fbank_musan.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/local/compute_fbank_tal_csasr.py b/egs/tal_csasr/ASR/local/compute_fbank_tal_csasr.py new file mode 100755 index 000000000..367e098f7 --- /dev/null +++ b/egs/tal_csasr/ASR/local/compute_fbank_tal_csasr.py @@ -0,0 +1,115 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the tal_csasr dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import argparse +import logging +import os +from pathlib import Path + +import torch +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_tal_csasr(num_mel_bins: int = 80): + src_dir = Path("data/manifests/tal_csasr") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + + dataset_parts = ( + "train_set", + "dev_set", + "test_set", + ) + prefix = "tal_csasr" + suffix = "jsonl.gz" + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, + ) + assert manifests is not None + + extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins)) + + with get_executor() as ex: # Initialize the executor only once. + for partition, m in manifests.items(): + cuts_filename = f"{prefix}_cuts_{partition}.{suffix}" + if (output_dir / cuts_filename).is_file(): + logging.info(f"{partition} already exists - skipping.") + continue + logging.info(f"Processing {partition}") + cut_set = CutSet.from_manifests( + recordings=m["recordings"], + supervisions=m["supervisions"], + ) + if "train" in partition: + cut_set = ( + cut_set + + cut_set.perturb_speed(0.9) + + cut_set.perturb_speed(1.1) + ) + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/{prefix}_feats_{partition}", + # when an executor is specified, make more partitions + num_jobs=num_jobs if ex is None else 80, + executor=ex, + storage_type=LilcomChunkyWriter, + ) + cut_set.to_file(output_dir / cuts_filename) + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--num-mel-bins", + type=int, + default=80, + help="""The number of mel bins for Fbank""", + ) + + return parser.parse_args() + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + args = get_args() + compute_fbank_tal_csasr(num_mel_bins=args.num_mel_bins) diff --git a/egs/tal_csasr/ASR/local/display_manifest_statistics.py b/egs/tal_csasr/ASR/local/display_manifest_statistics.py new file mode 100644 index 000000000..7521bb55b --- /dev/null +++ b/egs/tal_csasr/ASR/local/display_manifest_statistics.py @@ -0,0 +1,96 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This file displays duration statistics of utterances in a manifest. +You can use the displayed value to choose minimum/maximum duration +to remove short and long utterances during the training. +See the function `remove_short_and_long_utt()` +in ../../../librispeech/ASR/transducer/train.py +for usage. +""" + + +from lhotse import load_manifest + + +def main(): + paths = [ + "./data/fbank/tal_csasr_cuts_train_set.jsonl.gz", + "./data/fbank/tal_csasr_cuts_dev_set.jsonl.gz", + "./data/fbank/tal_csasr_cuts_test_set.jsonl.gz", + ] + + for path in paths: + print(f"Displaying the statistics for {path}") + cuts = load_manifest(path) + cuts.describe() + + +if __name__ == "__main__": + main() + +""" +Displaying the statistics for ./data/fbank/tal_csasr_cuts_train_set.jsonl.gz +Cuts count: 1050000 +Total duration (hours): 1679.0 +Speech duration (hours): 1679.0 (100.0%) +*** +Duration statistics (seconds): +mean 5.8 +std 4.1 +min 0.3 +25% 2.8 +50% 4.4 +75% 7.3 +99% 18.0 +99.5% 18.8 +99.9% 20.8 +max 36.5 +Displaying the statistics for ./data/fbank/tal_csasr_cuts_dev_set.jsonl.gz +Cuts count: 5000 +Total duration (hours): 8.0 +Speech duration (hours): 8.0 (100.0%) +*** +Duration statistics (seconds): +mean 5.8 +std 4.0 +min 0.5 +25% 2.8 +50% 4.5 +75% 7.4 +99% 17.0 +99.5% 17.7 +99.9% 19.5 +max 21.5 +Displaying the statistics for ./data/fbank/tal_csasr_cuts_test_set.jsonl.gz +Cuts count: 15000 +Total duration (hours): 23.6 +Speech duration (hours): 23.6 (100.0%) +*** +Duration statistics (seconds): +mean 5.7 +std 4.0 +min 0.5 +25% 2.8 +50% 4.4 +75% 7.2 +99% 17.2 +99.5% 17.9 +99.9% 19.6 +max 32.3 +""" diff --git a/egs/tal_csasr/ASR/local/prepare_char.py b/egs/tal_csasr/ASR/local/prepare_char.py new file mode 100755 index 000000000..2c5b8b8b3 --- /dev/null +++ b/egs/tal_csasr/ASR/local/prepare_char.py @@ -0,0 +1,265 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" + +This script takes as input `lang_dir`, which should contain:: + + - lang_dir/text_with_bpe, + - lang_dir/words.txt + +and generates the following files in the directory `lang_dir`: + + - lexicon.txt + - lexicon_disambig.txt + - L.pt + - L_disambig.pt + - tokens.txt +""" + +import re +from pathlib import Path +from typing import Dict, List + +import k2 +import sentencepiece as spm +import torch +from prepare_lang import ( + Lexicon, + add_disambig_symbols, + add_self_loops, + write_lexicon, + write_mapping, +) + + +def lexicon_to_fst_no_sil( + lexicon: Lexicon, + token2id: Dict[str, int], + word2id: Dict[str, int], + need_self_loops: bool = False, +) -> k2.Fsa: + """Convert a lexicon to an FST (in k2 format). + + Args: + lexicon: + The input lexicon. See also :func:`read_lexicon` + token2id: + A dict mapping tokens to IDs. + word2id: + A dict mapping words to IDs. + need_self_loops: + If True, add self-loop to states with non-epsilon output symbols + on at least one arc out of the state. The input label for this + self loop is `token2id["#0"]` and the output label is `word2id["#0"]`. + Returns: + Return an instance of `k2.Fsa` representing the given lexicon. + """ + loop_state = 0 # words enter and leave from here + next_state = 1 # the next un-allocated state, will be incremented as we go + + arcs = [] + + # The blank symbol is defined in local/train_bpe_model.py + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + + for word, pieces in lexicon: + assert len(pieces) > 0, f"{word} has no pronunciations" + cur_state = loop_state + + word = word2id[word] + pieces = [ + token2id[i] if i in token2id else token2id[""] for i in pieces + ] + + for i in range(len(pieces) - 1): + w = word if i == 0 else eps + arcs.append([cur_state, next_state, pieces[i], w, 0]) + + cur_state = next_state + next_state += 1 + + # now for the last piece of this word + i = len(pieces) - 1 + w = word if i == 0 else eps + arcs.append([cur_state, loop_state, pieces[i], w, 0]) + + if need_self_loops: + disambig_token = token2id["#0"] + disambig_word = word2id["#0"] + arcs = add_self_loops( + arcs, + disambig_token=disambig_token, + disambig_word=disambig_word, + ) + + final_state = next_state + arcs.append([loop_state, final_state, -1, -1, 0]) + arcs.append([final_state]) + + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + + fsa = k2.Fsa.from_str(arcs, acceptor=False) + return fsa + + +def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool: + """Check if all the given tokens are in token symbol table. + + Args: + token_sym_table: + Token symbol table that contains all the valid tokens. + tokens: + A list of tokens. + Returns: + Return True if there is any token not in the token_sym_table, + otherwise False. + """ + for tok in tokens: + if tok not in token_sym_table: + return True + return False + + +def generate_lexicon( + token_sym_table: Dict[str, int], + words: List[str], + bpe_model=None, +) -> Lexicon: + """Generate a lexicon from a word list and token_sym_table. + + Args: + token_sym_table: + Token symbol table that mapping token to token ids. + words: + A list of strings representing words. + Returns: + Return a dict whose keys are words and values are the corresponding + tokens. + """ + sp = "" + if bpe_model is not None: + sp = spm.SentencePieceProcessor() + sp.load(str(bpe_model)) + + lexicon = [] + zhPattern = re.compile(r"([\u4e00-\u9fa5])") + for word in words: + match = zhPattern.search(word) + tokens = [] + if match: + tokens = list(word.strip(" \t")) + else: + tokens = sp.encode_as_pieces(word.strip(" \t")) + + if contain_oov(token_sym_table, tokens): + continue + lexicon.append((word, tokens)) + + # The OOV word is + lexicon.append(("", [""])) + return lexicon + + +def generate_tokens(text_file: str) -> Dict[str, int]: + """Generate tokens from the given text file. + + Args: + text_file: + A file that contains text lines to generate tokens. + Returns: + Return a dict whose keys are tokens and values are token ids ranged + from 0 to len(keys) - 1. + """ + tokens: Dict[str, int] = dict() + tokens[""] = 0 + tokens[""] = 1 + tokens[""] = 2 + whitespace = re.compile(r"([\t\r\n]+)") + with open(text_file, "r", encoding="utf-8") as f: + for line in f: + line = re.sub(whitespace, "", line) + chars = line.split(" ") + for char in chars: + if char not in tokens: + tokens[char] = len(tokens) + + return tokens + + +def main(): + lang_dir = Path("data/lang_char") + text_file = lang_dir / "text_with_bpe" + bpe_model = lang_dir / "bpe.model" + + word_sym_table = k2.SymbolTable.from_file(lang_dir / "words.txt") + + words = word_sym_table.symbols + + excluded = ["", "!SIL", "", "", "#0", "", ""] + for w in excluded: + if w in words: + words.remove(w) + + token_sym_table = generate_tokens(text_file) + + lexicon = generate_lexicon(token_sym_table, words, bpe_model=bpe_model) + + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + next_token_id = max(token_sym_table.values()) + 1 + for i in range(max_disambig + 1): + disambig = f"#{i}" + assert disambig not in token_sym_table + token_sym_table[disambig] = next_token_id + next_token_id += 1 + + word_sym_table.add("#0") + word_sym_table.add("") + word_sym_table.add("") + + write_mapping(lang_dir / "tokens.txt", token_sym_table) + + write_lexicon(lang_dir / "lexicon.txt", lexicon) + write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig) + + L = lexicon_to_fst_no_sil( + lexicon, + token2id=token_sym_table, + word2id=word_sym_table, + ) + + L_disambig = lexicon_to_fst_no_sil( + lexicon_disambig, + token2id=token_sym_table, + word2id=word_sym_table, + need_self_loops=True, + ) + torch.save(L.as_dict(), lang_dir / "L.pt") + torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt") + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/local/prepare_lang.py b/egs/tal_csasr/ASR/local/prepare_lang.py new file mode 100755 index 000000000..e5ae89ec4 --- /dev/null +++ b/egs/tal_csasr/ASR/local/prepare_lang.py @@ -0,0 +1,390 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input a lexicon file "data/lang_phone/lexicon.txt" +consisting of words and tokens (i.e., phones) and does the following: + +1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt + +2. Generate tokens.txt, the token table mapping a token to a unique integer. + +3. Generate words.txt, the word table mapping a word to a unique integer. + +4. Generate L.pt, in k2 format. It can be loaded by + + d = torch.load("L.pt") + lexicon = k2.Fsa.from_dict(d) + +5. Generate L_disambig.pt, in k2 format. +""" +import argparse +import math +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Tuple + +import k2 +import torch + +from icefall.lexicon import read_lexicon, write_lexicon + +Lexicon = List[Tuple[str, List[str]]] + + +def write_mapping(filename: str, sym2id: Dict[str, int]) -> None: + """Write a symbol to ID mapping to a file. + + Note: + No need to implement `read_mapping` as it can be done + through :func:`k2.SymbolTable.from_file`. + + Args: + filename: + Filename to save the mapping. + sym2id: + A dict mapping symbols to IDs. + Returns: + Return None. + """ + with open(filename, "w", encoding="utf-8") as f: + for sym, i in sym2id.items(): + f.write(f"{sym} {i}\n") + + +def get_tokens(lexicon: Lexicon) -> List[str]: + """Get tokens from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique tokens. + """ + ans = set() + for _, tokens in lexicon: + ans.update(tokens) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def get_words(lexicon: Lexicon) -> List[str]: + """Get words from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique words. + """ + ans = set() + for word, _ in lexicon: + ans.add(word) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]: + """It adds pseudo-token disambiguation symbols #1, #2 and so on + at the ends of tokens to ensure that all pronunciations are different, + and that none is a prefix of another. + + See also add_lex_disambig.pl from kaldi. + + Args: + lexicon: + It is returned by :func:`read_lexicon`. + Returns: + Return a tuple with two elements: + + - The output lexicon with disambiguation symbols + - The ID of the max disambiguation symbol that appears + in the lexicon + """ + + # (1) Work out the count of each token-sequence in the + # lexicon. + count = defaultdict(int) + for _, tokens in lexicon: + count[" ".join(tokens)] += 1 + + # (2) For each left sub-sequence of each token-sequence, note down + # that it exists (for identifying prefixes of longer strings). + issubseq = defaultdict(int) + for _, tokens in lexicon: + tokens = tokens.copy() + tokens.pop() + while tokens: + issubseq[" ".join(tokens)] = 1 + tokens.pop() + + # (3) For each entry in the lexicon: + # if the token sequence is unique and is not a + # prefix of another word, no disambig symbol. + # Else output #1, or #2, #3, ... if the same token-seq + # has already been assigned a disambig symbol. + ans = [] + + # We start with #1 since #0 has its own purpose + first_allowed_disambig = 1 + max_disambig = first_allowed_disambig - 1 + last_used_disambig_symbol_of = defaultdict(int) + + for word, tokens in lexicon: + tokenseq = " ".join(tokens) + assert tokenseq != "" + if issubseq[tokenseq] == 0 and count[tokenseq] == 1: + ans.append((word, tokens)) + continue + + cur_disambig = last_used_disambig_symbol_of[tokenseq] + if cur_disambig == 0: + cur_disambig = first_allowed_disambig + else: + cur_disambig += 1 + + if cur_disambig > max_disambig: + max_disambig = cur_disambig + last_used_disambig_symbol_of[tokenseq] = cur_disambig + tokenseq += f" #{cur_disambig}" + ans.append((word, tokenseq.split())) + return ans, max_disambig + + +def generate_id_map(symbols: List[str]) -> Dict[str, int]: + """Generate ID maps, i.e., map a symbol to a unique ID. + + Args: + symbols: + A list of unique symbols. + Returns: + A dict containing the mapping between symbols and IDs. + """ + return {sym: i for i, sym in enumerate(symbols)} + + +def add_self_loops( + arcs: List[List[Any]], disambig_token: int, disambig_word: int +) -> List[List[Any]]: + """Adds self-loops to states of an FST to propagate disambiguation symbols + through it. They are added on each state with non-epsilon output symbols + on at least one arc out of the state. + + See also fstaddselfloops.pl from Kaldi. One difference is that + Kaldi uses OpenFst style FSTs and it has multiple final states. + This function uses k2 style FSTs and it does not need to add self-loops + to the final state. + + The input label of a self-loop is `disambig_token`, while the output + label is `disambig_word`. + + Args: + arcs: + A list-of-list. The sublist contains + `[src_state, dest_state, label, aux_label, score]` + disambig_token: + It is the token ID of the symbol `#0`. + disambig_word: + It is the word ID of the symbol `#0`. + + Return: + Return new `arcs` containing self-loops. + """ + states_needs_self_loops = set() + for arc in arcs: + src, dst, ilabel, olabel, score = arc + if olabel != 0: + states_needs_self_loops.add(src) + + ans = [] + for s in states_needs_self_loops: + ans.append([s, s, disambig_token, disambig_word, 0]) + + return arcs + ans + + +def lexicon_to_fst( + lexicon: Lexicon, + token2id: Dict[str, int], + word2id: Dict[str, int], + sil_token: str = "SIL", + sil_prob: float = 0.5, + need_self_loops: bool = False, +) -> k2.Fsa: + """Convert a lexicon to an FST (in k2 format) with optional silence at + the beginning and end of each word. + + Args: + lexicon: + The input lexicon. See also :func:`read_lexicon` + token2id: + A dict mapping tokens to IDs. + word2id: + A dict mapping words to IDs. + sil_token: + The silence token. + sil_prob: + The probability for adding a silence at the beginning and end + of the word. + need_self_loops: + If True, add self-loop to states with non-epsilon output symbols + on at least one arc out of the state. The input label for this + self loop is `token2id["#0"]` and the output label is `word2id["#0"]`. + Returns: + Return an instance of `k2.Fsa` representing the given lexicon. + """ + assert sil_prob > 0.0 and sil_prob < 1.0 + # CAUTION: we use score, i.e, negative cost. + sil_score = math.log(sil_prob) + no_sil_score = math.log(1.0 - sil_prob) + + start_state = 0 + loop_state = 1 # words enter and leave from here + sil_state = 2 # words terminate here when followed by silence; this state + # has a silence transition to loop_state. + next_state = 3 # the next un-allocated state, will be incremented as we go. + arcs = [] + + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + + sil_token = token2id[sil_token] + + arcs.append([start_state, loop_state, eps, eps, no_sil_score]) + arcs.append([start_state, sil_state, eps, eps, sil_score]) + arcs.append([sil_state, loop_state, sil_token, eps, 0]) + + for word, tokens in lexicon: + assert len(tokens) > 0, f"{word} has no pronunciations" + cur_state = loop_state + + word = word2id[word] + tokens = [token2id[i] for i in tokens] + + for i in range(len(tokens) - 1): + w = word if i == 0 else eps + arcs.append([cur_state, next_state, tokens[i], w, 0]) + + cur_state = next_state + next_state += 1 + + # now for the last token of this word + # It has two out-going arcs, one to the loop state, + # the other one to the sil_state. + i = len(tokens) - 1 + w = word if i == 0 else eps + arcs.append([cur_state, loop_state, tokens[i], w, no_sil_score]) + arcs.append([cur_state, sil_state, tokens[i], w, sil_score]) + + if need_self_loops: + disambig_token = token2id["#0"] + disambig_word = word2id["#0"] + arcs = add_self_loops( + arcs, + disambig_token=disambig_token, + disambig_word=disambig_word, + ) + + final_state = next_state + arcs.append([loop_state, final_state, -1, -1, 0]) + arcs.append([final_state]) + + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + + fsa = k2.Fsa.from_str(arcs, acceptor=False) + return fsa + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", type=str, help="The lang dir, data/lang_phone" + ) + return parser.parse_args() + + +def main(): + out_dir = Path(get_args().lang_dir) + lexicon_filename = out_dir / "lexicon.txt" + sil_token = "SIL" + sil_prob = 0.5 + + lexicon = read_lexicon(lexicon_filename) + tokens = get_tokens(lexicon) + words = get_words(lexicon) + + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + for i in range(max_disambig + 1): + disambig = f"#{i}" + assert disambig not in tokens + tokens.append(f"#{i}") + + assert "" not in tokens + tokens = [""] + tokens + + assert "" not in words + assert "#0" not in words + assert "" not in words + assert "" not in words + + words = [""] + words + ["#0", "", ""] + + token2id = generate_id_map(tokens) + word2id = generate_id_map(words) + + write_mapping(out_dir / "tokens.txt", token2id) + write_mapping(out_dir / "words.txt", word2id) + write_lexicon(out_dir / "lexicon_disambig.txt", lexicon_disambig) + + L = lexicon_to_fst( + lexicon, + token2id=token2id, + word2id=word2id, + sil_token=sil_token, + sil_prob=sil_prob, + ) + + L_disambig = lexicon_to_fst( + lexicon_disambig, + token2id=token2id, + word2id=word2id, + sil_token=sil_token, + sil_prob=sil_prob, + need_self_loops=True, + ) + torch.save(L.as_dict(), out_dir / "L.pt") + torch.save(L_disambig.as_dict(), out_dir / "L_disambig.pt") + + if False: + # Just for debugging, will remove it + L.labels_sym = k2.SymbolTable.from_file(out_dir / "tokens.txt") + L.aux_labels_sym = k2.SymbolTable.from_file(out_dir / "words.txt") + L_disambig.labels_sym = L.labels_sym + L_disambig.aux_labels_sym = L.aux_labels_sym + L.draw(out_dir / "L.png", title="L") + L_disambig.draw(out_dir / "L_disambig.png", title="L_disambig") + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/local/prepare_words.py b/egs/tal_csasr/ASR/local/prepare_words.py new file mode 100755 index 000000000..41ab3b2cb --- /dev/null +++ b/egs/tal_csasr/ASR/local/prepare_words.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input words.txt without ids: + - words_no_ids.txt +and generates the new words.txt with related ids. + - words.txt +""" + + +import argparse +import logging + +from tqdm import tqdm + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Prepare words.txt", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input", + default="data/lang_char/words_no_ids.txt", + type=str, + help="the words file without ids for WenetSpeech", + ) + parser.add_argument( + "--output", + default="data/lang_char/words.txt", + type=str, + help="the words file with ids for WenetSpeech", + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input + output_file = args.output + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + new_lines = [] + add_words = [" 0", "!SIL 1", " 2", " 3"] + new_lines.extend(add_words) + + logging.info("Starting reading the input file") + for i in tqdm(range(len(lines))): + x = lines[i] + idx = 4 + i + new_line = str(x.strip("\n")) + " " + str(idx) + new_lines.append(new_line) + + logging.info("Starting writing the words.txt") + f_out = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_out.write(line) + f_out.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/local/test_prepare_lang.py b/egs/tal_csasr/ASR/local/test_prepare_lang.py new file mode 100755 index 000000000..d4cf62bba --- /dev/null +++ b/egs/tal_csasr/ASR/local/test_prepare_lang.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) + +import os +import tempfile + +import k2 +from prepare_lang import ( + add_disambig_symbols, + generate_id_map, + get_phones, + get_words, + lexicon_to_fst, + read_lexicon, + write_lexicon, + write_mapping, +) + + +def generate_lexicon_file() -> str: + fd, filename = tempfile.mkstemp() + os.close(fd) + s = """ + !SIL SIL + SPN + SPN + f f + a a + foo f o o + bar b a r + bark b a r k + food f o o d + food2 f o o d + fo f o + """.strip() + with open(filename, "w") as f: + f.write(s) + return filename + + +def test_read_lexicon(filename: str): + lexicon = read_lexicon(filename) + phones = get_phones(lexicon) + words = get_words(lexicon) + print(lexicon) + print(phones) + print(words) + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + print(lexicon_disambig) + print("max disambig:", f"#{max_disambig}") + + phones = ["", "SIL", "SPN"] + phones + for i in range(max_disambig + 1): + phones.append(f"#{i}") + words = [""] + words + + phone2id = generate_id_map(phones) + word2id = generate_id_map(words) + + print(phone2id) + print(word2id) + + write_mapping("phones.txt", phone2id) + write_mapping("words.txt", word2id) + + write_lexicon("a.txt", lexicon) + write_lexicon("a_disambig.txt", lexicon_disambig) + + fsa = lexicon_to_fst(lexicon, phone2id=phone2id, word2id=word2id) + fsa.labels_sym = k2.SymbolTable.from_file("phones.txt") + fsa.aux_labels_sym = k2.SymbolTable.from_file("words.txt") + fsa.draw("L.pdf", title="L") + + fsa_disambig = lexicon_to_fst( + lexicon_disambig, phone2id=phone2id, word2id=word2id + ) + fsa_disambig.labels_sym = k2.SymbolTable.from_file("phones.txt") + fsa_disambig.aux_labels_sym = k2.SymbolTable.from_file("words.txt") + fsa_disambig.draw("L_disambig.pdf", title="L_disambig") + + +def main(): + filename = generate_lexicon_file() + test_read_lexicon(filename) + os.remove(filename) + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/local/text2segments.py b/egs/tal_csasr/ASR/local/text2segments.py new file mode 100644 index 000000000..3df727c67 --- /dev/null +++ b/egs/tal_csasr/ASR/local/text2segments.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input "text", which refers to the transcript file for +WenetSpeech: + - text +and generates the output file text_word_segmentation which is implemented +with word segmenting: + - text_words_segmentation +""" + + +import argparse + +import jieba +from tqdm import tqdm + +jieba.enable_paddle() + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Chinese Word Segmentation for text", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input-file", + default="data/lang_char/text", + type=str, + help="the input text file for WenetSpeech", + ) + parser.add_argument( + "--output-file", + default="data/lang_char/text_words_segmentation", + type=str, + help="the text implemented with words segmenting for WenetSpeech", + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input_file + output_file = args.output_file + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + new_lines = [] + for i in tqdm(range(len(lines))): + x = lines[i].rstrip() + seg_list = jieba.cut(x, use_paddle=True) + new_line = " ".join(seg_list) + new_lines.append(new_line) + + f_new = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_new.write(line) + f_new.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/local/text2token.py b/egs/tal_csasr/ASR/local/text2token.py new file mode 100755 index 000000000..71be2a613 --- /dev/null +++ b/egs/tal_csasr/ASR/local/text2token.py @@ -0,0 +1,195 @@ +#!/usr/bin/env python3 +# Copyright 2017 Johns Hopkins University (authors: Shinji Watanabe) +# 2022 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import codecs +import re +import sys +from typing import List + +from pypinyin import lazy_pinyin, pinyin + +is_python2 = sys.version_info[0] == 2 + + +def exist_or_not(i, match_pos): + start_pos = None + end_pos = None + for pos in match_pos: + if pos[0] <= i < pos[1]: + start_pos = pos[0] + end_pos = pos[1] + break + + return start_pos, end_pos + + +def get_parser(): + parser = argparse.ArgumentParser( + description="convert raw text to tokenized text", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--nchar", + "-n", + default=1, + type=int, + help="number of characters to split, i.e., \ + aabb -> a a b b with -n 1 and aa bb with -n 2", + ) + parser.add_argument( + "--skip-ncols", "-s", default=0, type=int, help="skip first n columns" + ) + parser.add_argument( + "--space", default="", type=str, help="space symbol" + ) + parser.add_argument( + "--non-lang-syms", + "-l", + default=None, + type=str, + help="list of non-linguistic symobles, e.g., etc.", + ) + parser.add_argument( + "text", type=str, default=False, nargs="?", help="input text" + ) + parser.add_argument( + "--trans_type", + "-t", + type=str, + default="char", + choices=["char", "pinyin", "lazy_pinyin"], + help="""Transcript type. char/pinyin/lazy_pinyin""", + ) + return parser + + +def token2id( + texts, token_table, token_type: str = "lazy_pinyin", oov: str = "" +) -> List[List[int]]: + """Convert token to id. + Args: + texts: + The input texts, it refers to the chinese text here. + token_table: + The token table is built based on "data/lang_xxx/token.txt" + token_type: + The type of token, such as "pinyin" and "lazy_pinyin". + oov: + Out of vocabulary token. When a word(token) in the transcript + does not exist in the token list, it is replaced with `oov`. + + Returns: + The list of ids for the input texts. + """ + if texts is None: + raise ValueError("texts can't be None!") + else: + oov_id = token_table[oov] + ids: List[List[int]] = [] + for text in texts: + chars_list = list(str(text)) + if token_type == "lazy_pinyin": + text = lazy_pinyin(chars_list) + sub_ids = [ + token_table[txt] if txt in token_table else oov_id + for txt in text + ] + ids.append(sub_ids) + else: # token_type = "pinyin" + text = pinyin(chars_list) + sub_ids = [ + token_table[txt[0]] if txt[0] in token_table else oov_id + for txt in text + ] + ids.append(sub_ids) + return ids + + +def main(): + parser = get_parser() + args = parser.parse_args() + + rs = [] + if args.non_lang_syms is not None: + with codecs.open(args.non_lang_syms, "r", encoding="utf-8") as f: + nls = [x.rstrip() for x in f.readlines()] + rs = [re.compile(re.escape(x)) for x in nls] + + if args.text: + f = codecs.open(args.text, encoding="utf-8") + else: + f = codecs.getreader("utf-8")( + sys.stdin if is_python2 else sys.stdin.buffer + ) + + sys.stdout = codecs.getwriter("utf-8")( + sys.stdout if is_python2 else sys.stdout.buffer + ) + line = f.readline() + n = args.nchar + while line: + x = line.split() + print(" ".join(x[: args.skip_ncols]), end=" ") + a = " ".join(x[args.skip_ncols :]) # noqa E203 + + # get all matched positions + match_pos = [] + for r in rs: + i = 0 + while i >= 0: + m = r.search(a, i) + if m: + match_pos.append([m.start(), m.end()]) + i = m.end() + else: + break + if len(match_pos) > 0: + chars = [] + i = 0 + while i < len(a): + start_pos, end_pos = exist_or_not(i, match_pos) + if start_pos is not None: + chars.append(a[start_pos:end_pos]) + i = end_pos + else: + chars.append(a[i]) + i += 1 + a = chars + + if args.trans_type == "pinyin": + a = pinyin(list(str(a))) + a = [one[0] for one in a] + + if args.trans_type == "lazy_pinyin": + a = lazy_pinyin(list(str(a))) + + a = [a[j : j + n] for j in range(0, len(a), n)] # noqa E203 + + a_flat = [] + for z in a: + a_flat.append("".join(z)) + + a_chars = "".join(a_flat) + print(a_chars) + line = f.readline() + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/local/text_normalize.py b/egs/tal_csasr/ASR/local/text_normalize.py new file mode 100755 index 000000000..e97b3a5a3 --- /dev/null +++ b/egs/tal_csasr/ASR/local/text_normalize.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright 2022 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input "text_full", which includes all transcript files +for a dataset: + - text_full +and generates the output file text_normalize which is implemented +to normalize text: + - text +""" + + +import argparse + +from tqdm import tqdm + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Normalizing for text", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input", + default="data/lang_char/text_full", + type=str, + help="the input text file", + ) + parser.add_argument( + "--output", + default="data/lang_char/text", + type=str, + help="the text implemented with normalizer", + ) + + return parser + + +def text_normalize(str_line: str): + line = str_line.strip().rstrip("\n") + line = line.replace("", "") + line = line.replace("<%>", "") + line = line.replace("<->", "") + line = line.replace("<$>", "") + line = line.replace("<#>", "") + line = line.replace("<_>", "") + line = line.replace("", "") + line = line.replace("`", "") + line = line.replace("'", "") + line = line.replace("&", "") + line = line.replace(",", "") + line = line.replace("Ａ", "A") + line = line.replace("Ｃ", "C") + line = line.replace("Ｄ", "D") + line = line.replace("Ｅ", "E") + line = line.replace("Ｇ", "G") + line = line.replace("Ｈ", "H") + line = line.replace("Ｉ", "I") + line = line.replace("Ｎ", "N") + line = line.replace("Ｕ", "U") + line = line.replace("Ｗ", "W") + line = line.replace("Ｙ", "Y") + line = line.replace("ａ", "A") + line = line.replace("ｂ", "B") + line = line.replace("ｃ", "C") + line = line.replace("ｋ", "K") + line = line.replace("ｔ", "T") + line = line.replace("，", "") + line = line.replace("丶", "") + line = line.replace("。", "") + line = line.replace("、", "") + line = line.replace("？", "") + line = line.replace("·", "") + line = line.replace("*", "") + line = line.replace("！", "") + line = line.replace("$", "") + line = line.replace("+", "") + line = line.replace("-", "") + line = line.replace("\\", "") + line = line.replace("?", "") + line = line.replace("￥", "") + line = line.replace("%", "") + line = line.replace(".", "") + line = line.replace("<", "") + line = line.replace("＆", "") + line = line.replace("~", "") + line = line.replace("=", "") + line = line.replace(":", "") + line = line.replace("!", "") + line = line.replace("/", "") + line = line.replace("‘", "") + line = line.replace("’", "") + line = line.replace("“", "") + line = line.replace("”", "") + line = line.replace("[", "") + line = line.replace("]", "") + line = line.replace("@", "") + line = line.replace("#", "") + line = line.replace("：", "") + line = line.replace("；", "") + line = line.replace("…", "") + line = line.replace("《", "") + line = line.replace("》", "") + line = line.upper() + + return line + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input + output_file = args.output + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + new_lines = [] + for i in tqdm(range(len(lines))): + new_line = text_normalize(lines[i]) + new_lines.append(new_line) + + f_new = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_new.write(line) + f_new.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/local/tokenize_with_bpe_model.py b/egs/tal_csasr/ASR/local/tokenize_with_bpe_model.py new file mode 100644 index 000000000..d7fd838f2 --- /dev/null +++ b/egs/tal_csasr/ASR/local/tokenize_with_bpe_model.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright 2021 Mobvoi Inc. (authors: Binbin Zhang) +# Copyright 2022 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script takes as input text (it includes Chinese and English): + - text +and generates the text_with_bpe. + - text_with_bpe +""" + + +import argparse +import logging + +import sentencepiece as spm +from tqdm import tqdm + +from icefall.utils import tokenize_by_bpe_model + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Prepare text_with_bpe", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input", + default="data/lang_char/text", + type=str, + help="the text includes Chinese and English words", + ) + parser.add_argument( + "--output", + default="data/lang_char/text_with_bpe", + type=str, + help="the text_with_bpe tokenized by bpe model", + ) + parser.add_argument( + "--bpe-model", + default="data/lang_char/bpe.model", + type=str, + help="the bpe model for processing the English parts", + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input + output_file = args.output + bpe_model = args.bpe_model + + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + + logging.info("Starting reading the text") + new_lines = [] + for i in tqdm(range(len(lines))): + x = lines[i] + txt_tokens = tokenize_by_bpe_model(sp, x) + new_line = txt_tokens.replace("/", " ") + new_lines.append(new_line) + + logging.info("Starting writing the text_with_bpe") + f_out = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_out.write(line) + f_out.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/prepare.sh b/egs/tal_csasr/ASR/prepare.sh new file mode 100755 index 000000000..340521ad8 --- /dev/null +++ b/egs/tal_csasr/ASR/prepare.sh @@ -0,0 +1,172 @@ +#!/usr/bin/env bash + +set -eou pipefail + +stage=-1 +stop_stage=100 + +# We assume dl_dir (download dir) contains the following +# directories and files. If not, they will be downloaded +# by this script automatically. +# +# - $dl_dir/tal_csasr +# You can find three directories:train_set, dev_set, and test_set. +# You can get it from https://ai.100tal.com/dataset +# +# - $dl_dir/musan +# This directory contains the following directories downloaded from +# http://www.openslr.org/17/ +# +# - music +# - noise +# - speech + +dl_dir=$PWD/download + +. shared/parse_options.sh || exit 1 + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +log "dl_dir: $dl_dir" + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "Stage 0: Download data" + # Before you run this script, you must get the TAL_CSASR dataset + # from https://ai.100tal.com/dataset + mv $dl_dir/TALCS_corpus $dl_dir/tal_csasr + + # If you have pre-downloaded it to /path/to/TALCS_corpus, + # you can create a symlink + # + # ln -sfv /path/to/TALCS_corpus $dl_dir/tal_csasr + + # If you have pre-downloaded it to /path/to/musan, + # you can create a symlink + # + # ln -sfv /path/to/musan $dl_dir/musan + # + if [ ! -d $dl_dir/musan ]; then + lhotse download musan $dl_dir + fi +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Prepare tal_csasr manifest" + # We assume that you have downloaded the TALCS_corpus + # to $dl_dir/tal_csasr + if [ ! -f data/manifests/tal_csasr/.manifests.done ]; then + mkdir -p data/manifests/tal_csasr + lhotse prepare tal-csasr $dl_dir/tal_csasr data/manifests/tal_csasr + touch data/manifests/tal_csasr/.manifests.done + fi +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Prepare musan manifest" + # We assume that you have downloaded the musan corpus + # to data/musan + if [ ! -f data/manifests/.musan_manifests.done ]; then + log "It may take 6 minutes" + mkdir -p data/manifests + lhotse prepare musan $dl_dir/musan data/manifests + touch data/manifests/.musan_manifests.done + fi +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Compute fbank for musan" + if [ ! -f data/fbank/.msuan.done ]; then + mkdir -p data/fbank + ./local/compute_fbank_musan.py + touch data/fbank/.msuan.done + fi +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Compute fbank for tal_csasr" + if [ ! -f data/fbank/.tal_csasr.done ]; then + mkdir -p data/fbank + ./local/compute_fbank_tal_csasr.py + touch data/fbank/.tal_csasr.done + fi +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "Stage 5: Prepare char based lang" + lang_char_dir=data/lang_char + mkdir -p $lang_char_dir + + # Download BPE models trained with LibriSpeech + # Here we use the BPE model with 5000 units trained with Librispeech. + # You can also use other BPE models if available. + if [ ! -f $lang_char_dir/bpe.model ]; then + wget -O $lang_char_dir/bpe.model \ + https://huggingface.co/luomingshuang/bpe_models_trained_with_Librispeech/resolve/main/lang_bpe_5000/bpe.model + fi + + # Prepare text. + # Note: in Linux, you can install jq with the following command: + # 1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64 + # 2. chmod +x ./jq + # 3. cp jq /usr/bin + if [ ! -f $lang_char_dir/text_full ]; then + gunzip -c data/manifests/tal_csasr/tal_csasr_supervisions_train_set.jsonl.gz \ + | jq ".text" | sed 's/"//g' \ + | ./local/text2token.py -t "char" > $lang_char_dir/text_train + + gunzip -c data/manifests/tal_csasr/tal_csasr_supervisions_dev_set.jsonl.gz \ + | jq ".text" | sed 's/"//g' \ + | ./local/text2token.py -t "char" > $lang_char_dir/text_dev + + gunzip -c data/manifests/tal_csasr/tal_csasr_supervisions_test_set.jsonl.gz \ + | jq ".text" | sed 's/"//g' \ + | ./local/text2token.py -t "char" > $lang_char_dir/text_test + + for r in text_train text_dev text_test ; do + cat $lang_char_dir/$r >> $lang_char_dir/text_full + done + fi + + # Prepare text normalize + if [ ! -f $lang_char_dir/text ]; then + python ./local/text_normalize.py \ + --input $lang_char_dir/text_full \ + --output $lang_char_dir/text + fi + + # Prepare words segments + if [ ! -f $lang_char_dir/text_words_segmentation ]; then + python ./local/text2segments.py \ + --input $lang_char_dir/text \ + --output $lang_char_dir/text_words_segmentation + + cat $lang_char_dir/text_words_segmentation | sed "s/ /\n/g" \ + | sort -u | sed "/^$/d" \ + | uniq > $lang_char_dir/words_no_ids.txt + fi + + # Prepare words.txt + if [ ! -f $lang_char_dir/words.txt ]; then + ./local/prepare_words.py \ + --input $lang_char_dir/words_no_ids.txt \ + --output $lang_char_dir/words.txt + fi + + # Tokenize text with BPE model + python ./local/tokenize_with_bpe_model.py \ + --input $lang_char_dir/text \ + --output $lang_char_dir/text_with_bpe \ + --bpe-model $lang_char_dir/bpe.model + + if [ ! -f $lang_char_dir/L_disambig.pt ]; then + python local/prepare_char.py + fi +fi diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/__init__.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/asr_datamodule.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/asr_datamodule.py new file mode 100644 index 000000000..49bfb148b --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/asr_datamodule.py @@ -0,0 +1,434 @@ +# Copyright 2021 Piotr Żelasko +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import inspect +import logging +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, List, Optional + +import torch +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy +from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures + CutConcatenate, + CutMix, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import ( # noqa F401 for AudioSamples + AudioSamples, + OnTheFlyFeatures, +) +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader + +from icefall.utils import str2bool + + +class _SeedWorkers: + def __init__(self, seed: int): + self.seed = seed + + def __call__(self, worker_id: int): + fix_random_seed(self.seed + worker_id) + + +class TAL_CSASRAsrDataModule: + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + This class should be derived for specific corpora used in ASR tasks. + """ + + def __init__(self, args: argparse.Namespace): + self.args = args + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + + group.add_argument( + "--manifest-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/valid/test cuts.", + ) + + group.add_argument( + "--max-duration", + type=int, + default=200.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + + group.add_argument( + "--num-buckets", + type=int, + default=300, + help="The number of buckets for the DynamicBucketingSampler" + "(you might want to increase it for larger datasets).", + ) + + group.add_argument( + "--concatenate-cuts", + type=str2bool, + default=False, + help="When enabled, utterances (cuts) will be concatenated " + "to minimize the amount of padding.", + ) + + group.add_argument( + "--duration-factor", + type=float, + default=1.0, + help="Determines the maximum duration of a concatenated cut " + "relative to the duration of the longest cut in a batch.", + ) + + group.add_argument( + "--gap", + type=float, + default=1.0, + help="The amount of padding (in seconds) inserted between " + "concatenated cuts. This padding is filled with noise when " + "noise augmentation is used.", + ) + + group.add_argument( + "--on-the-fly-feats", + type=str2bool, + default=False, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + + group.add_argument( + "--drop-last", + type=str2bool, + default=True, + help="Whether to drop last batch. Used by sampler.", + ) + + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + group.add_argument( + "--enable-spec-aug", + type=str2bool, + default=True, + help="When enabled, use SpecAugment for training dataset.", + ) + + group.add_argument( + "--spec-aug-time-warp-factor", + type=int, + default=80, + help="Used only when --enable-spec-aug is True. " + "It specifies the factor for time warping in SpecAugment. " + "Larger values mean more warping. " + "A value less than 1 means to disable time warp.", + ) + + group.add_argument( + "--enable-musan", + type=str2bool, + default=True, + help="When enabled, select noise from MUSAN and mix it" + "with training dataset. ", + ) + + group.add_argument( + "--input-strategy", + type=str, + default="PrecomputedFeatures", + help="AudioSamples or PrecomputedFeatures", + ) + + def train_dataloaders( + self, + cuts_train: CutSet, + sampler_state_dict: Optional[Dict[str, Any]] = None, + ) -> DataLoader: + """ + Args: + cuts_train: + CutSet for training. + sampler_state_dict: + The state dict for the training sampler. + """ + logging.info("About to get Musan cuts") + cuts_musan = load_manifest( + self.args.manifest_dir / "musan_cuts.jsonl.gz" + ) + + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + transforms.append( + CutMix( + cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True + ) + ) + else: + logging.info("Disable MUSAN") + + if self.args.concatenate_cuts: + logging.info( + f"Using cut concatenation with duration factor " + f"{self.args.duration_factor} and gap {self.args.gap}." + ) + # Cut concatenation should be the first transform in the list, + # so that if we e.g. mix noise in, it will fill the gaps between + # different utterances. + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + input_transforms = [] + if self.args.enable_spec_aug: + logging.info("Enable SpecAugment") + logging.info( + f"Time warp factor: {self.args.spec_aug_time_warp_factor}" + ) + # Set the value of num_frame_masks according to Lhotse's version. + # In different Lhotse's versions, the default of num_frame_masks is + # different. + num_frame_masks = 10 + num_frame_masks_parameter = inspect.signature( + SpecAugment.__init__ + ).parameters["num_frame_masks"] + if num_frame_masks_parameter.default == 1: + num_frame_masks = 2 + logging.info(f"Num frame mask: {num_frame_masks}") + input_transforms.append( + SpecAugment( + time_warp_factor=self.args.spec_aug_time_warp_factor, + num_frame_masks=num_frame_masks, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ) + else: + logging.info("Disable SpecAugment") + + logging.info("About to create train dataset") + train = K2SpeechRecognitionDataset( + input_strategy=eval(self.args.input_strategy)(), + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + num_cuts_for_bins_estimate=20000, + buffer_size=60000, + drop_last=self.args.drop_last, + ) + else: + logging.info("Using SingleCutSampler.") + train_sampler = SingleCutSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + ) + logging.info("About to create train dataloader") + + # 'seed' is derived from the current random state, which will have + # previously been set in the main process. + seed = torch.randint(0, 100000, ()).item() + worker_init_fn = _SeedWorkers(seed) + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + worker_init_fn=worker_init_fn, + ) + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_dl.sampler.load_state_dict(sampler_state_dict) + + return train_dl + + def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader: + transforms = [] + if self.args.concatenate_cuts: + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + logging.info("About to create dev dataset") + if self.args.on_the_fly_feats: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + return_cuts=self.args.return_cuts, + ) + else: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + return_cuts=self.args.return_cuts, + ) + valid_sampler = DynamicBucketingSampler( + cuts_valid, + max_duration=self.args.max_duration, + rank=0, + world_size=1, + shuffle=False, + ) + logging.info("About to create dev dataloader") + valid_dl = DataLoader( + validate, + sampler=valid_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + ) + + return valid_dl + + def test_dataloaders(self, cuts: CutSet) -> DataLoader: + logging.debug("About to create test dataset") + test = K2SpeechRecognitionDataset( + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) + if self.args.on_the_fly_feats + else eval(self.args.input_strategy)(), + return_cuts=self.args.return_cuts, + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + rank=0, + world_size=1, + shuffle=False, + ) + logging.info("About to create test dataloader") + test_dl = DataLoader( + test, + batch_size=None, + sampler=sampler, + num_workers=self.args.num_workers, + ) + return test_dl + + @lru_cache() + def train_cuts(self) -> CutSet: + logging.info("About to get train cuts") + return load_manifest_lazy( + self.args.manifest_dir / "tal_csasr_cuts_train_set.jsonl.gz" + ) + + @lru_cache() + def valid_cuts(self) -> CutSet: + logging.info("About to get dev cuts") + return load_manifest_lazy( + self.args.manifest_dir / "tal_csasr_cuts_dev_set.jsonl.gz" + ) + + @lru_cache() + def test_cuts(self) -> List[CutSet]: + logging.info("About to get test cuts") + return load_manifest_lazy( + self.args.manifest_dir / "tal_csasr_cuts_test_set.jsonl.gz" + ) diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/beam_search.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/beam_search.py new file mode 120000 index 000000000..ed78bd4bb --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/beam_search.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/conformer.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/conformer.py new file mode 120000 index 000000000..b2af4e1df --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/conformer.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/decode.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/decode.py new file mode 100755 index 000000000..305729a99 --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/decode.py @@ -0,0 +1,755 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./pruned_transducer_stateless5/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./pruned_transducer_stateless5/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_transducer_stateless5/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./pruned_transducer_stateless5/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" + + +import argparse +import logging +import re +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import TAL_CSASRAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from lhotse.cut import Cut +from local.text_normalize import text_normalize +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless5/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, + sp: spm.SentencePieceProcessor = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] + zh_hyps = [] + en_hyps = [] + pattern = re.compile(r"([\u4e00-\u9fff])") + en_letter = "[\u0041-\u005a|\u0061-\u007a]+" # English letters + zh_char = "[\u4e00-\u9fa5]+" # Chinese chars + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + hyps.append(chars_new) + zh_hyps.append(zh_text) + en_hyps.append(en_text) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + hyps.append(chars_new) + zh_hyps.append(zh_text) + en_hyps.append(en_text) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + hyps.append(chars_new) + zh_hyps.append(zh_text) + en_hyps.append(en_text) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode( + [lexicon.token_table[idx] for idx in hyp_tokens[i]] + ) + chars = pattern.split(hyp.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + hyps.append(chars_new) + zh_hyps.append(zh_text) + en_hyps.append(en_text) + if params.decoding_method == "greedy_search": + return {"greedy_search": (hyps, zh_hyps, en_hyps)} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): (hyps, zh_hyps, en_hyps) + } + else: + return {f"beam_size_{params.beam_size}": (hyps, zh_hyps, en_hyps)} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + decoding_graph: Optional[k2.Fsa] = None, + sp: spm.SentencePieceProcessor = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + zh_results = defaultdict(list) + en_results = defaultdict(list) + pattern = re.compile(r"([\u4e00-\u9fff])") + en_letter = "[\u0041-\u005a|\u0061-\u007a]+" # English letters + zh_char = "[\u4e00-\u9fa5]+" # Chinese chars + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + zh_texts = [] + en_texts = [] + for i in range(len(texts)): + text = texts[i] + chars = pattern.split(text.upper()) + chars_new = [] + zh_text = [] + en_text = [] + for char in chars: + if char != "": + tokens = char.strip().split(" ") + chars_new.extend(tokens) + for token in tokens: + zh_text.extend(re.findall(zh_char, token)) + en_text.extend(re.findall(en_letter, token)) + zh_texts.append(zh_text) + en_texts.append(en_text) + texts[i] = chars_new + hyps_dict = decode_one_batch( + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + batch=batch, + sp=sp, + ) + + for name, hyps_texts in hyps_dict.items(): + this_batch = [] + this_batch_zh = [] + this_batch_en = [] + # print(hyps_texts) + hyps, zh_hyps, en_hyps = hyps_texts + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + this_batch.append((ref_text, hyp_words)) + + for hyp_words, ref_text in zip(zh_hyps, zh_texts): + this_batch_zh.append((ref_text, hyp_words)) + + for hyp_words, ref_text in zip(en_hyps, en_texts): + this_batch_en.append((ref_text, hyp_words)) + + results[name].extend(this_batch) + zh_results[name + "_zh"].extend(this_batch_zh) + en_results[name + "_en"].extend(this_batch_en) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results, zh_results, en_results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + TAL_CSASRAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + bpe_model = params.lang_dir + "/bpe.model" + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + def text_normalize_for_cut(c: Cut): + # Text normalize for each sample + text = c.supervisions[0].text + text = text.strip("\n").strip("\t") + c.supervisions[0].text = text_normalize(text) + return c + + tal_csasr = TAL_CSASRAsrDataModule(args) + + dev_cuts = tal_csasr.valid_cuts() + dev_cuts = dev_cuts.map(text_normalize_for_cut) + dev_dl = tal_csasr.valid_dataloaders(dev_cuts) + + test_cuts = tal_csasr.test_cuts() + test_cuts = test_cuts.map(text_normalize_for_cut) + test_dl = tal_csasr.test_dataloaders(test_cuts) + + test_sets = ["dev", "test"] + test_dl = [dev_dl, test_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict, zh_results_dict, en_results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + sp=sp, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=zh_results_dict, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=en_results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/decoder.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/decoder.py new file mode 120000 index 000000000..8a5e07bd5 --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/decoder.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/encoder_interface.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/encoder_interface.py new file mode 120000 index 000000000..2fc10439b --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/encoder_interface.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/encoder_interface.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/export.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/export.py new file mode 100755 index 000000000..8f900208a --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/export.py @@ -0,0 +1,285 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# 2022 Xiaomi Corporation (Author: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./pruned_transducer_stateless5/export.py \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --lang-dir ./data/lang_char \ + --epoch 30 \ + --avg 24 \ + --use-averaged-model True + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `pruned_transducer_stateless5/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/tal_csasr/ASR + ./pruned_transducer_stateless5/decode.py \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --epoch 30 \ + --avg 24 \ + --max-duration 800 \ + --decoding-method greedy_search \ + --lang-dir ./data/lang_char +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless5/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + add_model_arguments(parser) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + bpe_model = params.lang_dir + "/bpe.model" + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.eval() + + model.to("cpu") + model.eval() + + if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/joiner.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/joiner.py new file mode 120000 index 000000000..f31b5fd9b --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/joiner.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/model.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/model.py new file mode 120000 index 000000000..be059ba7c --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/model.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/model.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/optim.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/optim.py new file mode 120000 index 000000000..661206562 --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/optim.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/optim.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/pretrained.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/pretrained.py new file mode 100755 index 000000000..dbe213b24 --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/pretrained.py @@ -0,0 +1,375 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# 2022 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +(1) greedy search +./pruned_transducer_stateless5/pretrained.py \ + --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \ + --lang-dir ./data/lang_char \ + --decoding-method greedy_search \ + /path/to/foo.wav \ + /path/to/bar.wav + +(2) modified beam search +./pruned_transducer_stateless5/pretrained.py \ + --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \ + --lang-dir ./data/lang_char \ + --decoding-method modified_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(3) fast beam search +./pruned_transducer_stateless5/pretrained.py \ + --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \ + --lang-dir ./data/lang_char \ + --decoding-method fast_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +You can also use `./pruned_transducer_stateless5/exp/epoch-xx.pt`. + +Note: ./pruned_transducer_stateless5/exp/pretrained.pt is generated by +./pruned_transducer_stateless5/export.py +""" + + +import argparse +import logging +import math +import re +from typing import List + +import k2 +import kaldifeat +import sentencepiece as spm +import torch +import torchaudio +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.lexicon import Lexicon + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "sound_files", + type=str, + nargs="+", + help="The input sound file(s) to transcribe. " + "Supported formats are those supported by torchaudio.load(). " + "For example, wav and flac are supported. " + "The sample rate has to be 16kHz.", + ) + + parser.add_argument( + "--sample-rate", + type=int, + default=16000, + help="The sample rate of the input sound file", + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. Used only when + --method is greedy_search. + """, + ) + + add_model_arguments(parser) + + return parser + + +def read_sound_files( + filenames: List[str], expected_sample_rate: float +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + assert sample_rate == expected_sample_rate, ( + f"expected sample rate: {expected_sample_rate}. " + f"Given: {sample_rate}" + ) + # We use only the first channel + ans.append(wave[0]) + return ans + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + + params.update(vars(args)) + + bpe_model = params.lang_dir + "/bpe.model" + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + lexicon = Lexicon(params.lang_dir) + params.blank_di = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(f"{params}") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + logging.info("Creating model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"], strict=False) + model.to(device) + model.eval() + model.device = device + + logging.info("Constructing Fbank computer") + opts = kaldifeat.FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = params.sample_rate + opts.mel_opts.num_bins = params.feature_dim + + fbank = kaldifeat.Fbank(opts) + + logging.info(f"Reading sound files: {params.sound_files}") + waves = read_sound_files( + filenames=params.sound_files, expected_sample_rate=params.sample_rate + ) + waves = [w.to(device) for w in waves] + + logging.info("Decoding started") + features = fbank(waves) + feature_lengths = [f.size(0) for f in features] + + features = pad_sequence( + features, batch_first=True, padding_value=math.log(1e-10) + ) + + feature_lengths = torch.tensor(feature_lengths, device=device) + + encoder_out, encoder_out_lens = model.encoder( + x=features, x_lens=feature_lengths + ) + + num_waves = encoder_out.size(0) + hyps = [] + msg = f"Using {params.method}" + if params.method == "beam_search": + msg += f" with beam size {params.beam_size}" + logging.info(msg) + + pattern = re.compile(r"([\u4e00-\u9fff])") + if params.method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + for char in chars: + if char != "": + chars_new.extend(char.strip().split(" ")) + hyps.append(chars_new) + elif params.method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + for char in chars: + if char != "": + chars_new.extend(char.strip().split(" ")) + hyps.append(chars_new) + elif params.method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for i in range(encoder_out.size(0)): + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + chars = pattern.split(hyp.upper()) + chars_new = [] + for char in chars: + if char != "": + chars_new.extend(char.strip().split(" ")) + hyps.append(chars_new) + else: + for i in range(num_waves): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError(f"Unsupported method: {params.method}") + + hyp = sp.decode([lexicon.token_table[idx] for idx in hyp]) + chars = pattern.split(hyp.upper()) + chars_new = [] + for char in chars: + if char != "": + chars_new.extend(char.strip().split(" ")) + hyps.append(chars_new) + + s = "\n" + for filename, hyp in zip(params.sound_files, hyps): + words = " ".join(hyp) + s += f"{filename}:\n{words}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/scaling.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/scaling.py new file mode 120000 index 000000000..be7b111c6 --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/scaling.py @@ -0,0 +1 @@ +../../../../egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py \ No newline at end of file diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/test_model.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/test_model.py new file mode 100755 index 000000000..9aad32014 --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/test_model.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +To run this file, do: + + cd icefall/egs/librispeech/ASR + python ./pruned_transducer_stateless4/test_model.py +""" + +from train import get_params, get_transducer_model + + +def test_model_1(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.num_encoder_layers = 24 + params.dim_feedforward = 1536 # 384 * 4 + params.encoder_dim = 384 + model = get_transducer_model(params) + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + + +# See Table 1 from https://arxiv.org/pdf/2005.08100.pdf +def test_model_M(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.num_encoder_layers = 18 + params.dim_feedforward = 1024 + params.encoder_dim = 256 + params.nhead = 4 + params.decoder_dim = 512 + params.joiner_dim = 512 + model = get_transducer_model(params) + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + + +def main(): + # test_model_1() + test_model_M() + + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py new file mode 100755 index 000000000..ca35eba45 --- /dev/null +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py @@ -0,0 +1,1115 @@ +#!/usr/bin/env python3 +# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo, +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless5/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless5/exp \ + --full-libri 1 \ + --max-duration 300 + +# For mix precision training: + +./pruned_transducer_stateless5/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless5/exp \ + --full-libri 1 \ + --max-duration 550 + +""" + + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import TAL_CSASRAsrDataModule +from conformer import Conformer +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from local.text_normalize import text_normalize +from local.tokenize_with_bpe_model import tokenize_by_bpe_model +from model import Transducer +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[ + torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler +] + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=int, + default=24, + help="Number of conformer encoder layers..", + ) + + parser.add_argument( + "--dim-feedforward", + type=int, + default=1536, + help="Feedforward dimension of the conformer encoder layer.", + ) + + parser.add_argument( + "--nhead", + type=int, + default=8, + help="Number of attention heads in the conformer encoder layer.", + ) + + parser.add_argument( + "--encoder-dim", + type=int, + default=384, + help="Attention dimension in the conformer encoder layer.", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Embedding dimension in the decoder model.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="""Dimension used in the joiner model. + Outputs from the encoder and decoder model are projected + to this dimension before adding. + """, + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless5/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="The initial learning rate. This value should not need " + "to be changed.", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate + decreases. We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=6, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=4000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=30, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=100, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 100, + "valid_interval": 2000, + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + # parameters for Noam + "model_warm_step": 1000, # arg given to model, not for lrate + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Conformer and Transformer + encoder = Conformer( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + batch: dict, + is_training: bool, + warmup: float = 1.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = ( + model.device + if isinstance(model, DDP) + else next(model.parameters()).device + ) + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = graph_compiler.texts_to_ids_with_bpe(texts) + if type(y) == list: + y = k2.RaggedTensor(y).to(device) + else: + y = y.to(device) + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + warmup=warmup, + ) + # after the main warmup step, we keep pruned_loss_scale small + # for the same amount of time (model_warm_step), to avoid + # overwhelming the simple_loss and causing it to diverge, + # in case it had not fully learned the alignment yet. + pruned_loss_scale = ( + 0.0 + if warmup < 1.0 + else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) + ) + loss = ( + params.simple_loss_scale * simple_loss + + pruned_loss_scale * pruned_loss + ) + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: CharCtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + graph_compiler: CharCtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + # print(batch["supervisions"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + bpe_model = params.lang_dir + "/bpe.model" + import sentencepiece as spm + + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + lexicon = Lexicon(params.lang_dir) + graph_compiler = CharCtcTrainingGraphCompiler( + lexicon=lexicon, + device=device, + ) + + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2 ** 22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + tal_csasr = TAL_CSASRAsrDataModule(args) + train_cuts = tal_csasr.train_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + def text_normalize_for_cut(c: Cut): + # Text normalize for each sample + text = c.supervisions[0].text + text = text.strip("\n").strip("\t") + text = text_normalize(text) + text = tokenize_by_bpe_model(sp, text) + c.supervisions[0].text = text + return c + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + train_cuts = train_cuts.map(text_normalize_for_cut) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = tal_csasr.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = tal_csasr.valid_cuts() + valid_cuts = valid_cuts.map(text_normalize_for_cut) + valid_dl = tal_csasr.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + graph_compiler=graph_compiler, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + graph_compiler=graph_compiler, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + graph_compiler: CharCtcTrainingGraphCompiler, + params: AttributeDict, +): + return + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + # warmup = 0.0 is so that the derivs for the pruned loss stay zero + # (i.e. are not remembered by the decaying-average in adam), because + # we want to avoid these params being subject to shrinkage in adam. + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=0.0, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except Exception as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + TAL_CSASRAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/tal_csasr/ASR/shared b/egs/tal_csasr/ASR/shared new file mode 120000 index 000000000..e9461a6d7 --- /dev/null +++ b/egs/tal_csasr/ASR/shared @@ -0,0 +1 @@ +../../librispeech/ASR/shared \ No newline at end of file diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless/export.py b/egs/tedlium3/ASR/pruned_transducer_stateless/export.py index 1e6edbb99..a1c3bcea3 100644 --- a/egs/tedlium3/ASR/pruned_transducer_stateless/export.py +++ b/egs/tedlium3/ASR/pruned_transducer_stateless/export.py @@ -117,8 +117,6 @@ def main(): args = get_parser().parse_args() args.exp_dir = Path(args.exp_dir) - assert args.jit is False, "Support torchscript will be added later" - params = get_params() params.update(vars(args)) @@ -161,6 +159,11 @@ def main(): model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/egs/tedlium3/ASR/transducer_stateless/export.py b/egs/tedlium3/ASR/transducer_stateless/export.py index f2bfa2ec9..c32b1d002 100644 --- a/egs/tedlium3/ASR/transducer_stateless/export.py +++ b/egs/tedlium3/ASR/transducer_stateless/export.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 # # Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang -# Mingshuang Luo) +# Mingshuang Luo) # # See ../../../../LICENSE for clarification regarding multiple authors # @@ -185,8 +185,6 @@ def main(): args = get_parser().parse_args() args.exp_dir = Path(args.exp_dir) - assert args.jit is False, "Support torchscript will be added later" - params = get_params() params.update(vars(args)) @@ -229,6 +227,11 @@ def main(): model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/egs/timit/ASR/local/prepare_lexicon.py b/egs/timit/ASR/local/prepare_lexicon.py index f0168ebd6..04023a9ab 100644 --- a/egs/timit/ASR/local/prepare_lexicon.py +++ b/egs/timit/ASR/local/prepare_lexicon.py @@ -58,15 +58,19 @@ def prepare_lexicon(manifests_dir: str, lang_dir: str): Return: The lexicon.txt file and the train.text in lang_dir. """ + import gzip + phones = set() - supervisions_train = Path(manifests_dir) / "supervisions_TRAIN.json" + supervisions_train = ( + Path(manifests_dir) / "timit_supervisions_TRAIN.jsonl.gz" + ) lexicon = Path(lang_dir) / "lexicon.txt" logging.info(f"Loading {supervisions_train}!") - with open(supervisions_train, "r") as load_f: - load_dicts = json.load(load_f) - for load_dict in load_dicts: + with gzip.open(supervisions_train, "r") as load_f: + for line in load_f.readlines(): + load_dict = json.loads(line) text = load_dict["text"] # list the phone units and filter the empty item phones_list = list(filter(None, text.split())) diff --git a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_dev_test.py b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_dev_test.py index 11aaef5c5..c10a84d21 100755 --- a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_dev_test.py +++ b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_dev_test.py @@ -43,7 +43,7 @@ def compute_fbank_wenetspeech_dev_test(): # number of seconds in a batch batch_duration = 600 - subsets = ("S", "M", "DEV", "TEST_NET", "TEST_MEETING") + subsets = ("DEV", "TEST_NET", "TEST_MEETING") device = torch.device("cpu") if torch.cuda.is_available(): @@ -63,8 +63,12 @@ def compute_fbank_wenetspeech_dev_test(): logging.info(f"Loading {raw_cuts_path}") cut_set = CutSet.from_file(raw_cuts_path) - logging.info("Computing features") + logging.info("Splitting cuts into smaller chunks") + cut_set = cut_set.trim_to_supervisions( + keep_overlapping=False, min_duration=None + ) + logging.info("Computing features") cut_set = cut_set.compute_and_store_features_batch( extractor=extractor, storage_path=f"{in_out_dir}/feats_{partition}", @@ -72,9 +76,6 @@ def compute_fbank_wenetspeech_dev_test(): batch_duration=batch_duration, storage_type=LilcomHdf5Writer, ) - cut_set = cut_set.trim_to_supervisions( - keep_overlapping=False, min_duration=None - ) logging.info(f"Saving to {cuts_path}") cut_set.to_file(cuts_path) diff --git a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py index 4622bdb55..bf9a03b20 100755 --- a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py +++ b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py @@ -128,8 +128,12 @@ def compute_fbank_wenetspeech_splits(args): logging.info(f"Loading {raw_cuts_path}") cut_set = CutSet.from_file(raw_cuts_path) - logging.info("Computing features") + logging.info("Splitting cuts into smaller chunks.") + cut_set = cut_set.trim_to_supervisions( + keep_overlapping=False, min_duration=None + ) + logging.info("Computing features") cut_set = cut_set.compute_and_store_features_batch( extractor=extractor, storage_path=f"{output_dir}/feats_{subset}_{idx}", @@ -138,14 +142,8 @@ def compute_fbank_wenetspeech_splits(args): storage_type=LilcomChunkyWriter, ) - logging.info("About to split cuts into smaller chunks.") - cut_set = cut_set.trim_to_supervisions( - keep_overlapping=False, min_duration=None - ) - logging.info(f"Saving to {cuts_path}") cut_set.to_file(cuts_path) - logging.info(f"Saved to {cuts_path}") def main(): diff --git a/egs/wenetspeech/ASR/prepare.sh b/egs/wenetspeech/ASR/prepare.sh index d4e550d81..6ce4734a7 100755 --- a/egs/wenetspeech/ASR/prepare.sh +++ b/egs/wenetspeech/ASR/prepare.sh @@ -54,7 +54,7 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then # ln -sfv /path/to/WenetSpeech $dl_dir/WenetSpeech # if [ ! -d $dl_dir/WenetSpeech/wenet_speech ] && [ ! -f $dl_dir/WenetSpeech/metadata/v1.list ]; then - log "Stage 0: should download WenetSpeech first" + log "Stage 0: You should download WenetSpeech first" exit 1; fi @@ -191,7 +191,9 @@ if [ $stage -le 15 ] && [ $stop_stage -ge 15 ]; then # Prepare text. # Note: in Linux, you can install jq with the following command: - # wget -O jq https://github.com/stedolan/jq/release/download/jq-1.6/jq-linux64 + # 1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64 + # 2. chmod +x ./jq + # 3. cp jq /usr/bin if [ ! -f $lang_char_dir/text ]; then gunzip -c data/manifests/supervisions_L.jsonl.gz \ | jq 'text' | sed 's/"//g' \ diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py index 200a694d6..10c953e3b 100644 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -192,13 +192,6 @@ class WenetSpeechAsrDataModule: "with training dataset. ", ) - group.add_argument( - "--lazy-load", - type=str2bool, - default=True, - help="lazily open CutSets to avoid OOM (for L|XL subset)", - ) - group.add_argument( "--training-subset", type=str, @@ -420,17 +413,10 @@ class WenetSpeechAsrDataModule: @lru_cache() def train_cuts(self) -> CutSet: logging.info("About to get train cuts") - if self.args.lazy_load: - logging.info("use lazy cuts") - cuts_train = CutSet.from_jsonl_lazy( - self.args.manifest_dir - / f"cuts_{self.args.training_subset}.jsonl.gz" - ) - else: - cuts_train = CutSet.from_file( - self.args.manifest_dir - / f"cuts_{self.args.training_subset}.jsonl.gz" - ) + cuts_train = load_manifest_lazy( + self.args.manifest_dir + / f"cuts_{self.args.training_subset}.jsonl.gz" + ) return cuts_train @lru_cache() diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/export.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/export.py index 8c4f92c81..345792a3c 100644 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/export.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/export.py @@ -114,8 +114,6 @@ def main(): args = get_parser().parse_args() args.exp_dir = Path(args.exp_dir) - assert args.jit is False, "Support torchscript will be added later" - params = get_params() params.update(vars(args)) @@ -155,6 +153,11 @@ def main(): model.eval() if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) logging.info("Using torch.jit.script") model = torch.jit.script(model) filename = params.exp_dir / "cpu_jit.pt" diff --git a/icefall/__init__.py b/icefall/__init__.py index ec77e89b5..52d551c6a 100644 --- a/icefall/__init__.py +++ b/icefall/__init__.py @@ -61,5 +61,6 @@ from .utils import ( setup_logger, store_transcripts, str2bool, + subsequent_chunk_mask, write_error_stats, ) diff --git a/icefall/char_graph_compiler.py b/icefall/char_graph_compiler.py index a50b57d40..235160e14 100644 --- a/icefall/char_graph_compiler.py +++ b/icefall/char_graph_compiler.py @@ -79,6 +79,31 @@ class CharCtcTrainingGraphCompiler(object): ids.append(sub_ids) return ids + def texts_to_ids_with_bpe(self, texts: List[str]) -> List[List[int]]: + """Convert a list of texts (which include chars and bpes) + to a list-of-list of token IDs. + + Args: + texts: + It is a list of strings. + An example containing two strings is given below: + + [['你', '好', '▁C', 'hina'], ['北','京', '▁', 'welcome', '您'] + Returns: + Return a list-of-list of token IDs. + """ + ids: List[List[int]] = [] + for text in texts: + text = text.split("/") + sub_ids = [ + self.token_table[txt] + if txt in self.token_table + else self.oov_id + for txt in text + ] + ids.append(sub_ids) + return ids + def compile( self, token_ids: List[List[int]], diff --git a/icefall/decode.py b/icefall/decode.py index 94f3e88ba..680e29619 100644 --- a/icefall/decode.py +++ b/icefall/decode.py @@ -20,7 +20,34 @@ from typing import Dict, List, Optional, Union import k2 import torch -from icefall.utils import get_texts +from icefall.utils import add_eos, add_sos, get_texts + +DEFAULT_LM_SCALE = [ + 0.01, + 0.05, + 0.08, + 0.1, + 0.3, + 0.5, + 0.6, + 0.7, + 0.9, + 1.0, + 1.1, + 1.2, + 1.3, + 1.5, + 1.7, + 1.9, + 2.0, + 2.1, + 2.2, + 2.3, + 2.5, + 3.0, + 4.0, + 5.0, +] def _intersect_device( @@ -308,9 +335,7 @@ class Nbest(object): del word_fsa.aux_labels word_fsa.scores.zero_() - word_fsa_with_epsilon_loops = k2.remove_epsilon_and_add_self_loops( - word_fsa - ) + word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa) path_to_utt_map = self.shape.row_ids(1) @@ -609,7 +634,7 @@ def rescore_with_n_best_list( num_paths: Size of nbest list. lm_scale_list: - A list of float representing LM score scales. + A list of floats representing LM score scales. nbest_scale: Scale to be applied to ``lattice.score`` when sampling paths using ``k2.random_paths``. @@ -954,3 +979,161 @@ def rescore_with_attention_decoder( key = f"ngram_lm_scale_{n_scale}_attention_scale_{a_scale}" ans[key] = best_path return ans + + +def rescore_with_rnn_lm( + lattice: k2.Fsa, + num_paths: int, + rnn_lm_model: torch.nn.Module, + model: torch.nn.Module, + memory: torch.Tensor, + memory_key_padding_mask: Optional[torch.Tensor], + sos_id: int, + eos_id: int, + blank_id: int, + nbest_scale: float = 1.0, + ngram_lm_scale: Optional[float] = None, + attention_scale: Optional[float] = None, + rnn_lm_scale: Optional[float] = None, + use_double_scores: bool = True, +) -> Dict[str, k2.Fsa]: + """This function extracts `num_paths` paths from the given lattice and uses + an attention decoder to rescore them. The path with the highest score is + the decoding output. + + Args: + lattice: + An FsaVec with axes [utt][state][arc]. + num_paths: + Number of paths to extract from the given lattice for rescoring. + model: + A transformer model. See the class "Transformer" in + conformer_ctc/transformer.py for its interface. + memory: + The encoder memory of the given model. It is the output of + the last torch.nn.TransformerEncoder layer in the given model. + Its shape is `(T, N, C)`. + memory_key_padding_mask: + The padding mask for memory with shape `(N, T)`. + sos_id: + The token ID for SOS. + eos_id: + The token ID for EOS. + nbest_scale: + It's the scale applied to `lattice.scores`. A smaller value + leads to more unique paths at the risk of missing the correct path. + ngram_lm_scale: + Optional. It specifies the scale for n-gram LM scores. + attention_scale: + Optional. It specifies the scale for attention decoder scores. + rnn_lm_scale: + Optional. It specifies the scale for RNN LM scores. + Returns: + A dict of FsaVec, whose key contains a string + ngram_lm_scale_attention_scale and the value is the + best decoding path for each utterance in the lattice. + """ + nbest = Nbest.from_lattice( + lattice=lattice, + num_paths=num_paths, + use_double_scores=use_double_scores, + nbest_scale=nbest_scale, + ) + # nbest.fsa.scores are all 0s at this point + + nbest = nbest.intersect(lattice) + # Now nbest.fsa has its scores set. + # Also, nbest.fsa inherits the attributes from `lattice`. + assert hasattr(nbest.fsa, "lm_scores") + + am_scores = nbest.compute_am_scores() + ngram_lm_scores = nbest.compute_lm_scores() + + # The `tokens` attribute is set inside `compile_hlg.py` + assert hasattr(nbest.fsa, "tokens") + assert isinstance(nbest.fsa.tokens, torch.Tensor) + + path_to_utt_map = nbest.shape.row_ids(1).to(torch.long) + # the shape of memory is (T, N, C), so we use axis=1 here + expanded_memory = memory.index_select(1, path_to_utt_map) + + if memory_key_padding_mask is not None: + # The shape of memory_key_padding_mask is (N, T), so we + # use axis=0 here. + expanded_memory_key_padding_mask = memory_key_padding_mask.index_select( + 0, path_to_utt_map + ) + else: + expanded_memory_key_padding_mask = None + + # remove axis corresponding to states. + tokens_shape = nbest.fsa.arcs.shape().remove_axis(1) + tokens = k2.RaggedTensor(tokens_shape, nbest.fsa.tokens) + tokens = tokens.remove_values_leq(0) + token_ids = tokens.tolist() + + if len(token_ids) == 0: + print("Warning: rescore_with_attention_decoder(): empty token-ids") + return None + + nll = model.decoder_nll( + memory=expanded_memory, + memory_key_padding_mask=expanded_memory_key_padding_mask, + token_ids=token_ids, + sos_id=sos_id, + eos_id=eos_id, + ) + assert nll.ndim == 2 + assert nll.shape[0] == len(token_ids) + + attention_scores = -nll.sum(dim=1) + + # Now for RNN LM + sos_tokens = add_sos(tokens, sos_id) + tokens_eos = add_eos(tokens, eos_id) + sos_tokens_row_splits = sos_tokens.shape.row_splits(1) + sentence_lengths = sos_tokens_row_splits[1:] - sos_tokens_row_splits[:-1] + + x_tokens = sos_tokens.pad(mode="constant", padding_value=blank_id) + y_tokens = tokens_eos.pad(mode="constant", padding_value=blank_id) + + x_tokens = x_tokens.to(torch.int64) + y_tokens = y_tokens.to(torch.int64) + sentence_lengths = sentence_lengths.to(torch.int64) + + rnn_lm_nll = rnn_lm_model(x=x_tokens, y=y_tokens, lengths=sentence_lengths) + assert rnn_lm_nll.ndim == 2 + assert rnn_lm_nll.shape[0] == len(token_ids) + + rnn_lm_scores = -1 * rnn_lm_nll.sum(dim=1) + + ngram_lm_scale_list = DEFAULT_LM_SCALE + attention_scale_list = DEFAULT_LM_SCALE + rnn_lm_scale_list = DEFAULT_LM_SCALE + + if ngram_lm_scale: + ngram_lm_scale_list = [ngram_lm_scale] + + if attention_scale: + attention_scale_list = [attention_scale] + + if rnn_lm_scale: + rnn_lm_scale_list = [rnn_lm_scale] + + ans = dict() + for n_scale in ngram_lm_scale_list: + for a_scale in attention_scale_list: + for r_scale in rnn_lm_scale_list: + tot_scores = ( + am_scores.values + + n_scale * ngram_lm_scores.values + + a_scale * attention_scores + + r_scale * rnn_lm_scores + ) + ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) + max_indexes = ragged_tot_scores.argmax() + best_path = k2.index_fsa(nbest.fsa, max_indexes) + + key = f"ngram_lm_scale_{n_scale}_attention_scale_{a_scale}_rnn_lm_scale_{r_scale}" # noqa + ans[key] = best_path + return ans diff --git a/icefall/dist.py b/icefall/dist.py index 203c7c563..6334f9c13 100644 --- a/icefall/dist.py +++ b/icefall/dist.py @@ -21,14 +21,46 @@ import torch from torch import distributed as dist -def setup_dist(rank, world_size, master_port=None): - os.environ["MASTER_ADDR"] = "localhost" - os.environ["MASTER_PORT"] = ( - "12354" if master_port is None else str(master_port) - ) - dist.init_process_group("nccl", rank=rank, world_size=world_size) - torch.cuda.set_device(rank) +def setup_dist(rank, world_size, master_port=None, use_ddp_launch=False): + """ + rank and world_size are used only if use_ddp_launch is False. + """ + if "MASTER_ADDR" not in os.environ: + os.environ["MASTER_ADDR"] = "localhost" + + if "MASTER_PORT" not in os.environ: + os.environ["MASTER_PORT"] = ( + "12354" if master_port is None else str(master_port) + ) + + if use_ddp_launch is False: + dist.init_process_group("nccl", rank=rank, world_size=world_size) + torch.cuda.set_device(rank) + else: + dist.init_process_group("nccl") def cleanup_dist(): dist.destroy_process_group() + + +def get_world_size(): + if "WORLD_SIZE" in os.environ: + return int(os.environ["WORLD_SIZE"]) + if dist.is_available() and dist.is_initialized(): + return dist.get_world_size() + else: + return 1 + + +def get_rank(): + if "RANK" in os.environ: + return int(os.environ["RANK"]) + elif dist.is_available() and dist.is_initialized(): + return dist.rank() + else: + return 1 + + +def get_local_rank(): + return int(os.environ.get("LOCAL_RANK", 0)) diff --git a/icefall/rnn_lm/compute_perplexity.py b/icefall/rnn_lm/compute_perplexity.py new file mode 100755 index 000000000..550801a8f --- /dev/null +++ b/icefall/rnn_lm/compute_perplexity.py @@ -0,0 +1,237 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: + ./rnn_lm/compute_perplexity.py \ + --epoch 4 \ + --avg 2 \ + --lm-data ./data/bpe_500/sorted_lm_data-test.pt + +""" + +import argparse +import logging +import math +from pathlib import Path + +import torch +from dataset import get_dataloader +from model import RnnLmModel + +from icefall.checkpoint import average_checkpoints, load_checkpoint +from icefall.utils import AttributeDict, setup_logger, str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=49, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + parser.add_argument( + "--avg", + type=int, + default=20, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="rnn_lm/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lm-data", + type=str, + help="Path to the LM test data for computing perplexity", + ) + + parser.add_argument( + "--vocab-size", + type=int, + default=500, + help="Vocabulary size of the model", + ) + + parser.add_argument( + "--embedding-dim", + type=int, + default=2048, + help="Embedding dim of the model", + ) + + parser.add_argument( + "--hidden-dim", + type=int, + default=2048, + help="Hidden dim of the model", + ) + + parser.add_argument( + "--num-layers", + type=int, + default=3, + help="Number of RNN layers the model", + ) + + parser.add_argument( + "--tie-weights", + type=str2bool, + default=False, + help="""True to share the weights between the input embedding layer and the + last output linear layer + """, + ) + + parser.add_argument( + "--batch-size", + type=int, + default=50, + help="Number of RNN layers the model", + ) + + parser.add_argument( + "--max-sent-len", + type=int, + default=100, + help="Number of RNN layers the model", + ) + + parser.add_argument( + "--sos-id", + type=int, + default=1, + help="SOS ID", + ) + + parser.add_argument( + "--eos-id", + type=int, + default=1, + help="EOS ID", + ) + + parser.add_argument( + "--blank-id", + type=int, + default=0, + help="Blank ID", + ) + return parser + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + args.lm_data = Path(args.lm_data) + + params = AttributeDict(vars(args)) + + setup_logger(f"{params.exp_dir}/log-ppl/") + logging.info("Computing perplexity started") + logging.info(params) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + logging.info("About to create model") + model = RnnLmModel( + vocab_size=params.vocab_size, + embedding_dim=params.embedding_dim, + hidden_dim=params.hidden_dim, + num_layers=params.num_layers, + tie_weights=params.tie_weights, + ) + + if params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + model.to(device) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.eval() + num_param = sum([p.numel() for p in model.parameters()]) + num_param_requires_grad = sum( + [p.numel() for p in model.parameters() if p.requires_grad] + ) + + logging.info(f"Number of model parameters: {num_param}") + logging.info( + f"Number of model parameters (requires_grad): " + f"{num_param_requires_grad} " + f"({num_param_requires_grad/num_param_requires_grad*100}%)" + ) + + logging.info(f"Loading LM test data from {params.lm_data}") + test_dl = get_dataloader( + filename=params.lm_data, + is_distributed=False, + params=params, + ) + + tot_loss = 0.0 + num_tokens = 0 + num_sentences = 0 + for batch_idx, batch in enumerate(test_dl): + x, y, sentence_lengths = batch + x = x.to(device) + y = y.to(device) + sentence_lengths = sentence_lengths.to(device) + + nll = model(x, y, sentence_lengths) + loss = nll.sum().cpu().item() + + tot_loss += loss + num_tokens += sentence_lengths.sum().cpu().item() + num_sentences += x.size(0) + + ppl = math.exp(tot_loss / num_tokens) + logging.info( + f"total nll: {tot_loss}, num tokens: {num_tokens}, " + f"num sentences: {num_sentences}, ppl: {ppl:.3f}" + ) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +if __name__ == "__main__": + main() diff --git a/icefall/rnn_lm/dataset.py b/icefall/rnn_lm/dataset.py new file mode 100644 index 000000000..598e329c4 --- /dev/null +++ b/icefall/rnn_lm/dataset.py @@ -0,0 +1,218 @@ +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Tuple + +import k2 +import torch +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from icefall.utils import AttributeDict, add_eos, add_sos + + +class LmDataset(torch.utils.data.Dataset): + def __init__( + self, + sentences: k2.RaggedTensor, + words: k2.RaggedTensor, + sentence_lengths: torch.Tensor, + max_sent_len: int, + batch_size: int, + ): + """ + Args: + sentences: + A ragged tensor of dtype torch.int32 with 2 axes [sentence][word]. + words: + A ragged tensor of dtype torch.int32 with 2 axes [word][token]. + sentence_lengths: + A 1-D tensor of dtype torch.int32 containing number of tokens + of each sentence. + max_sent_len: + Maximum sentence length. It is used to change the batch size + dynamically. In general, we try to keep the product of + "max_sent_len in a batch" and "num_of_sent in a batch" being + a constant. + batch_size: + The expected batch size. It is changed dynamically according + to the "max_sent_len". + + See `../local/prepare_lm_training_data.py` for how `sentences` and + `words` are generated. We assume that `sentences` are sorted by length. + See `../local/sort_lm_training_data.py`. + """ + super().__init__() + self.sentences = sentences + self.words = words + + sentence_lengths = sentence_lengths.tolist() + + assert batch_size > 0, batch_size + assert max_sent_len > 1, max_sent_len + batch_indexes = [] + num_sentences = sentences.dim0 + cur = 0 + while cur < num_sentences: + sz = sentence_lengths[cur] // max_sent_len + 1 + # Assume the current sentence has 3 * max_sent_len tokens, + # in the worst case, the subsequent sentences also have + # this number of tokens, we should reduce the batch size + # so that this batch will not contain too many tokens + actual_batch_size = batch_size // sz + 1 + actual_batch_size = min(actual_batch_size, batch_size) + end = cur + actual_batch_size + end = min(end, num_sentences) + this_batch_indexes = torch.arange(cur, end).tolist() + batch_indexes.append(this_batch_indexes) + cur = end + assert batch_indexes[-1][-1] == num_sentences - 1 + + self.batch_indexes = k2.RaggedTensor(batch_indexes) + + def __len__(self) -> int: + """Return number of batches in this dataset""" + return self.batch_indexes.dim0 + + def __getitem__(self, i: int) -> k2.RaggedTensor: + """Get the i'th batch in this dataset + Return a ragged tensor with 2 axes [sentence][token]. + """ + assert 0 <= i < len(self), i + + # indexes is a 1-D tensor containing sentence indexes + indexes = self.batch_indexes[i] + + # sentence_words is a ragged tensor with 2 axes + # [sentence][word] + sentence_words = self.sentences[indexes] + + # in case indexes contains only 1 entry, the returned + # sentence_words is a 1-D tensor, we have to convert + # it to a ragged tensor + if isinstance(sentence_words, torch.Tensor): + sentence_words = k2.RaggedTensor(sentence_words.unsqueeze(0)) + + # sentence_word_tokens is a ragged tensor with 3 axes + # [sentence][word][token] + sentence_word_tokens = self.words.index(sentence_words) + assert sentence_word_tokens.num_axes == 3 + + sentence_tokens = sentence_word_tokens.remove_axis(1) + return sentence_tokens + + +class LmDatasetCollate: + def __init__(self, sos_id: int, eos_id: int, blank_id: int): + """ + Args: + sos_id: + Token ID of the SOS symbol. + eos_id: + Token ID of the EOS symbol. + blank_id: + Token ID of the blank symbol. + """ + self.sos_id = sos_id + self.eos_id = eos_id + self.blank_id = blank_id + + def __call__( + self, batch: List[k2.RaggedTensor] + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Return a tuple containing 3 tensors: + + - x, a 2-D tensor of dtype torch.int32; each row contains tokens + for a sentence starting with `self.sos_id`. It is padded to + the max sentence length with `self.blank_id`. + + - y, a 2-D tensor of dtype torch.int32; each row contains tokens + for a sentence ending with `self.eos_id` before padding. + Then it is padded to the max sentence length with + `self.blank_id`. + + - lengths, a 2-D tensor of dtype torch.int32, containing the number of + tokens of each sentence before padding. + """ + # The batching stuff has already been done in LmDataset + assert len(batch) == 1 + sentence_tokens = batch[0] + row_splits = sentence_tokens.shape.row_splits(1) + sentence_token_lengths = row_splits[1:] - row_splits[:-1] + sentence_tokens_with_sos = add_sos(sentence_tokens, self.sos_id) + sentence_tokens_with_eos = add_eos(sentence_tokens, self.eos_id) + + x = sentence_tokens_with_sos.pad( + mode="constant", padding_value=self.blank_id + ) + y = sentence_tokens_with_eos.pad( + mode="constant", padding_value=self.blank_id + ) + sentence_token_lengths += 1 # plus 1 since we added a SOS + + return x.to(torch.int64), y.to(torch.int64), sentence_token_lengths + + +def get_dataloader( + filename: str, + is_distributed: bool, + params: AttributeDict, +) -> torch.utils.data.DataLoader: + """Get dataloader for LM training. + + Args: + filename: + Path to the file containing LM data. The file is assumed to + be generated by `../local/sort_lm_training_data.py`. + is_distributed: + True if using DDP training. False otherwise. + params: + Set `get_params()` from `rnn_lm/train.py` + Returns: + Return a dataloader containing the LM data. + """ + lm_data = torch.load(filename) + + words = lm_data["words"] + sentences = lm_data["sentences"] + sentence_lengths = lm_data["sentence_lengths"] + + dataset = LmDataset( + sentences=sentences, + words=words, + sentence_lengths=sentence_lengths, + max_sent_len=params.max_sent_len, + batch_size=params.batch_size, + ) + if is_distributed: + sampler = DistributedSampler(dataset, shuffle=True, drop_last=False) + else: + sampler = None + + collate_fn = LmDatasetCollate( + sos_id=params.sos_id, + eos_id=params.eos_id, + blank_id=params.blank_id, + ) + + dataloader = DataLoader( + dataset, + batch_size=1, + collate_fn=collate_fn, + sampler=sampler, + shuffle=sampler is None, + ) + return dataloader diff --git a/icefall/rnn_lm/export.py b/icefall/rnn_lm/export.py new file mode 100644 index 000000000..094035fce --- /dev/null +++ b/icefall/rnn_lm/export.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python3 +# +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. + +import argparse +import logging +from pathlib import Path + +import torch +from model import RnnLmModel + +from icefall.checkpoint import load_checkpoint +from icefall.utils import AttributeDict, load_averaged_model, str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=29, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + parser.add_argument( + "--avg", + type=int, + default=5, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--vocab-size", + type=int, + default=500, + help="Vocabulary size of the model", + ) + + parser.add_argument( + "--embedding-dim", + type=int, + default=2048, + help="Embedding dim of the model", + ) + + parser.add_argument( + "--hidden-dim", + type=int, + default=2048, + help="Hidden dim of the model", + ) + + parser.add_argument( + "--num-layers", + type=int, + default=3, + help="Number of RNN layers the model", + ) + + parser.add_argument( + "--tie-weights", + type=str2bool, + default=True, + help="""True to share the weights between the input embedding layer and the + last output linear layer + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="rnn_lm/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=True, + help="""True to save a model after applying torch.jit.script. + """, + ) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = AttributeDict({}) + params.update(vars(args)) + + logging.info(params) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + model = RnnLmModel( + vocab_size=params.vocab_size, + embedding_dim=params.embedding_dim, + hidden_dim=params.hidden_dim, + num_layers=params.num_layers, + tie_weights=params.tie_weights, + ) + + model.to(device) + + if params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + model = load_averaged_model( + params.exp_dir, model, params.epoch, params.avg, device + ) + + model.to("cpu") + model.eval() + + if params.jit: + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/icefall/rnn_lm/model.py b/icefall/rnn_lm/model.py new file mode 100644 index 000000000..88b2cc41f --- /dev/null +++ b/icefall/rnn_lm/model.py @@ -0,0 +1,120 @@ +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +import torch +import torch.nn.functional as F + +from icefall.utils import make_pad_mask + + +class RnnLmModel(torch.nn.Module): + def __init__( + self, + vocab_size: int, + embedding_dim: int, + hidden_dim: int, + num_layers: int, + tie_weights: bool = False, + ): + """ + Args: + vocab_size: + Vocabulary size of BPE model. + embedding_dim: + Input embedding dimension. + hidden_dim: + Hidden dimension of RNN layers. + num_layers: + Number of RNN layers. + tie_weights: + True to share the weights between the input embedding layer and the + last output linear layer. See https://arxiv.org/abs/1608.05859 + and https://arxiv.org/abs/1611.01462 + """ + super().__init__() + + self.input_embedding = torch.nn.Embedding( + num_embeddings=vocab_size, + embedding_dim=embedding_dim, + ) + + self.rnn = torch.nn.LSTM( + input_size=embedding_dim, + hidden_size=hidden_dim, + num_layers=num_layers, + batch_first=True, + ) + + self.output_linear = torch.nn.Linear( + in_features=hidden_dim, out_features=vocab_size + ) + + self.vocab_size = vocab_size + if tie_weights: + logging.info("Tying weights") + assert embedding_dim == hidden_dim, (embedding_dim, hidden_dim) + self.output_linear.weight = self.input_embedding.weight + else: + logging.info("Not tying weights") + + def forward( + self, x: torch.Tensor, y: torch.Tensor, lengths: torch.Tensor + ) -> torch.Tensor: + """ + Args: + x: + A 2-D tensor with shape (N, L). Each row + contains token IDs for a sentence and starts with the SOS token. + y: + A shifted version of `x` and with EOS appended. + lengths: + A 1-D tensor of shape (N,). It contains the sentence lengths + before padding. + Returns: + Return a 2-D tensor of shape (N, L) containing negative log-likelihood + loss values. Note: Loss values for padding positions are set to 0. + """ + assert x.ndim == y.ndim == 2, (x.ndim, y.ndim) + assert lengths.ndim == 1, lengths.ndim + assert x.shape == y.shape, (x.shape, y.shape) + + batch_size = x.size(0) + assert lengths.size(0) == batch_size, (lengths.size(0), batch_size) + + # embedding is of shape (N, L, embedding_dim) + embedding = self.input_embedding(x) + + # Note: We use batch_first==True + rnn_out, _ = self.rnn(embedding) + logits = self.output_linear(rnn_out) + + # Note: No need to use `log_softmax()` here + # since F.cross_entropy() expects unnormalized probabilities + + # nll_loss is of shape (N*L,) + # nll -> negative log-likelihood + nll_loss = F.cross_entropy( + logits.reshape(-1, self.vocab_size), y.reshape(-1), reduction="none" + ) + # Set loss values for padding positions to 0 + mask = make_pad_mask(lengths).reshape(-1) + nll_loss.masked_fill_(mask, 0) + + nll_loss = nll_loss.reshape(batch_size, -1) + + return nll_loss diff --git a/icefall/rnn_lm/test_dataset.py b/icefall/rnn_lm/test_dataset.py new file mode 100755 index 000000000..bf961f54b --- /dev/null +++ b/icefall/rnn_lm/test_dataset.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import k2 +import torch +from rnn_lm.dataset import LmDataset, LmDatasetCollate + + +def main(): + sentences = k2.RaggedTensor( + [[0, 1, 2], [1, 0, 1], [0, 1], [1, 3, 0, 2, 0], [3], [0, 2, 1]] + ) + words = k2.RaggedTensor([[3, 6], [2, 8, 9, 3], [5], [5, 6, 7, 8, 9]]) + + num_sentences = sentences.dim0 + + sentence_lengths = [0] * num_sentences + for i in range(num_sentences): + word_ids = sentences[i] + + # NOTE: If word_ids is a tensor with only 1 entry, + # token_ids is a torch.Tensor + token_ids = words[word_ids] + if isinstance(token_ids, k2.RaggedTensor): + token_ids = token_ids.values + + # token_ids is a 1-D tensor containing the BPE tokens + # of the current sentence + + sentence_lengths[i] = token_ids.numel() + + sentence_lengths = torch.tensor(sentence_lengths, dtype=torch.int32) + + indices = torch.argsort(sentence_lengths, descending=True) + sentences = sentences[indices.to(torch.int32)] + sentence_lengths = sentence_lengths[indices] + + dataset = LmDataset( + sentences=sentences, + words=words, + sentence_lengths=sentence_lengths, + max_sent_len=3, + batch_size=4, + ) + + collate_fn = LmDatasetCollate(sos_id=1, eos_id=-1, blank_id=0) + dataloader = torch.utils.data.DataLoader( + dataset, batch_size=1, collate_fn=collate_fn + ) + + for i in dataloader: + print(i) + # I've checked the output manually; the output is as expected. + + +if __name__ == "__main__": + main() diff --git a/icefall/rnn_lm/test_dataset_ddp.py b/icefall/rnn_lm/test_dataset_ddp.py new file mode 100755 index 000000000..48fbb19cb --- /dev/null +++ b/icefall/rnn_lm/test_dataset_ddp.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python3 +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import k2 +import torch +import torch.multiprocessing as mp +from rnn_lm.dataset import LmDataset, LmDatasetCollate +from torch import distributed as dist + + +def generate_data(): + sentences = k2.RaggedTensor( + [[0, 1, 2], [1, 0, 1], [0, 1], [1, 3, 0, 2, 0], [3], [0, 2, 1]] + ) + words = k2.RaggedTensor([[3, 6], [2, 8, 9, 3], [5], [5, 6, 7, 8, 9]]) + + num_sentences = sentences.dim0 + + sentence_lengths = [0] * num_sentences + for i in range(num_sentences): + word_ids = sentences[i] + + # NOTE: If word_ids is a tensor with only 1 entry, + # token_ids is a torch.Tensor + token_ids = words[word_ids] + if isinstance(token_ids, k2.RaggedTensor): + token_ids = token_ids.values + + # token_ids is a 1-D tensor containing the BPE tokens + # of the current sentence + + sentence_lengths[i] = token_ids.numel() + + sentence_lengths = torch.tensor(sentence_lengths, dtype=torch.int32) + + indices = torch.argsort(sentence_lengths, descending=True) + sentences = sentences[indices.to(torch.int32)] + sentence_lengths = sentence_lengths[indices] + + return sentences, words, sentence_lengths + + +def run(rank, world_size): + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "12352" + + dist.init_process_group("nccl", rank=rank, world_size=world_size) + torch.cuda.set_device(rank) + + sentences, words, sentence_lengths = generate_data() + + dataset = LmDataset( + sentences=sentences, + words=words, + sentence_lengths=sentence_lengths, + max_sent_len=3, + batch_size=4, + ) + sampler = torch.utils.data.distributed.DistributedSampler( + dataset, shuffle=True, drop_last=False + ) + + collate_fn = LmDatasetCollate(sos_id=1, eos_id=-1, blank_id=0) + dataloader = torch.utils.data.DataLoader( + dataset, + batch_size=1, + collate_fn=collate_fn, + sampler=sampler, + shuffle=False, + ) + + for i in dataloader: + print(f"rank: {rank}", i) + + dist.destroy_process_group() + + +def main(): + world_size = 2 + mp.spawn(run, args=(world_size,), nprocs=world_size, join=True) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/icefall/rnn_lm/test_model.py b/icefall/rnn_lm/test_model.py new file mode 100755 index 000000000..5a216a3fb --- /dev/null +++ b/icefall/rnn_lm/test_model.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python3 +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from rnn_lm.model import RnnLmModel + + +def test_rnn_lm_model(): + vocab_size = 4 + model = RnnLmModel( + vocab_size=vocab_size, embedding_dim=10, hidden_dim=10, num_layers=2 + ) + x = torch.tensor( + [ + [1, 3, 2, 2], + [1, 2, 2, 0], + [1, 2, 0, 0], + ] + ) + y = torch.tensor( + [ + [3, 2, 2, 1], + [2, 2, 1, 0], + [2, 1, 0, 0], + ] + ) + lengths = torch.tensor([4, 3, 2]) + nll_loss = model(x, y, lengths) + print(nll_loss) + """ + tensor([[1.1180, 1.3059, 1.2426, 1.7773], + [1.4231, 1.2783, 1.7321, 0.0000], + [1.4231, 1.6752, 0.0000, 0.0000]], grad_fn=) + """ + + +def test_rnn_lm_model_tie_weights(): + model = RnnLmModel( + vocab_size=10, + embedding_dim=10, + hidden_dim=10, + num_layers=2, + tie_weights=True, + ) + assert model.input_embedding.weight is model.output_linear.weight + + +def main(): + test_rnn_lm_model() + test_rnn_lm_model_tie_weights() + + +if __name__ == "__main__": + torch.manual_seed(20211122) + main() diff --git a/icefall/rnn_lm/train.py b/icefall/rnn_lm/train.py new file mode 100755 index 000000000..bb5f03fb9 --- /dev/null +++ b/icefall/rnn_lm/train.py @@ -0,0 +1,617 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: + ./rnn_lm/train.py \ + --start-epoch 0 \ + --world-size 2 \ + --num-epochs 1 \ + --use-fp16 0 \ + --embedding-dim 800 \ + --hidden-dim 200 \ + --num-layers 2\ + --batch-size 400 + +""" + +import argparse +import logging +import math +from pathlib import Path +from shutil import copyfile +from typing import Optional, Tuple + +import torch +import torch.multiprocessing as mp +import torch.nn as nn +import torch.optim as optim +from dataset import get_dataloader +from lhotse.utils import fix_random_seed +from model import RnnLmModel +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.nn.utils import clip_grad_norm_ +from torch.utils.tensorboard import SummaryWriter + +from icefall.checkpoint import load_checkpoint +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=10, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=0, + help="""Resume training from from this epoch. + If it is positive, it will load checkpoint from + exp_dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="rnn_lm/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, logs, etc, are saved + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + parser.add_argument( + "--batch-size", + type=int, + default=50, + ) + + parser.add_argument( + "--lm-data", + type=str, + default="data/lm_training_bpe_500/sorted_lm_data.pt", + help="LM training data", + ) + + parser.add_argument( + "--lm-data-valid", + type=str, + default="data/lm_training_bpe_500/sorted_lm_data-valid.pt", + help="LM validation data", + ) + + parser.add_argument( + "--vocab-size", + type=int, + default=500, + help="Vocabulary size of the model", + ) + + parser.add_argument( + "--embedding-dim", + type=int, + default=2048, + help="Embedding dim of the model", + ) + + parser.add_argument( + "--hidden-dim", + type=int, + default=2048, + help="Hidden dim of the model", + ) + + parser.add_argument( + "--num-layers", + type=int, + default=3, + help="Number of RNN layers the model", + ) + + parser.add_argument( + "--tie-weights", + type=str2bool, + default=False, + help="""True to share the weights between the input embedding layer and the + last output linear layer + """, + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters.""" + + params = AttributeDict( + { + "max_sent_len": 200, + "sos_id": 1, + "eos_id": 1, + "blank_id": 0, + "lr": 1e-3, + "weight_decay": 1e-6, + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 200, + "reset_interval": 2000, + "valid_interval": 5000, + "env_info": get_env_info(), + } + ) + return params + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, +) -> None: + """Load checkpoint from file. + + If params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. Otherwise, this function does nothing. + + Apart from loading state dict for `model`, `optimizer` and `scheduler`, + it also updates `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + optimizer: + The optimizer that we are using. + scheduler: + The learning rate scheduler we are using. + Returns: + Return None. + """ + if params.start_epoch <= 0: + return + + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + logging.info(f"Loading checkpoint: {filename}") + saved_params = load_checkpoint( + filename, + model=model, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + model: nn.Module, + x: torch.Tensor, + y: torch.Tensor, + sentence_lengths: torch.Tensor, + is_training: bool, +) -> Tuple[torch.Tensor, MetricsTracker]: + """Compute the negative log-likelihood loss given a model and its input. + Args: + model: + The NN model, e.g., RnnLmModel. + x: + A 2-D tensor. Each row contains BPE token IDs for a sentence. Also, + each row starts with SOS ID. + y: + A 2-D tensor. Each row is a shifted version of the corresponding row + in `x` but ends with an EOS ID (before padding). + sentence_lengths: + A 1-D tensor containing number of tokens of each sentence + before padding. + is_training: + True for training. False for validation. + """ + with torch.set_grad_enabled(is_training): + device = model.device + x = x.to(device) + y = y.to(device) + sentence_lengths = sentence_lengths.to(device) + + nll = model(x, y, sentence_lengths) + loss = nll.sum() + + num_tokens = sentence_lengths.sum().item() + + loss_info = MetricsTracker() + # Note: Due to how MetricsTracker() is designed, + # we use "frames" instead of "num_tokens" as a key here + loss_info["frames"] = num_tokens + loss_info["loss"] = loss.detach().item() + return loss, loss_info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process. The validation loss + is saved in `params.valid_loss`. + """ + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + x, y, sentence_lengths = batch + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + model=model, + x=x, + y=y, + sentence_lengths=sentence_lengths, + is_training=False, + ) + + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all sentences is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + """ + model.train() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(train_dl): + params.batch_idx_train += 1 + x, y, sentence_lengths = batch + batch_size = x.size(0) + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + model=model, + x=x, + y=y, + sentence_lengths=sentence_lengths, + is_training=True, + ) + + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + optimizer.zero_grad() + loss.backward() + clip_grad_norm_(model.parameters(), 5.0, 2.0) + optimizer.step() + + if batch_idx % params.log_interval == 0: + # Note: "frames" here means "num_tokens" + this_batch_ppl = math.exp(loss_info["loss"] / loss_info["frames"]) + tot_ppl = math.exp(tot_loss["loss"] / tot_loss["frames"]) + + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}, ppl: {this_batch_ppl}] " + f"tot_loss[{tot_loss}, ppl: {tot_ppl}], " + f"batch size: {batch_size}" + ) + + if tb_writer is not None: + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + tb_writer.add_scalar( + "train/current_ppl", this_batch_ppl, params.batch_idx_train + ) + + tb_writer.add_scalar( + "train/tot_ppl", tot_ppl, params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + + valid_info = compute_validation_loss( + params=params, + model=model, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + + valid_ppl = math.exp(valid_info["loss"] / valid_info["frames"]) + logging.info( + f"Epoch {params.cur_epoch}, validation: {valid_info}, " + f"ppl: {valid_ppl}" + ) + + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + tb_writer.add_scalar( + "train/valid_ppl", valid_ppl, params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + is_distributed = world_size > 1 + + fix_random_seed(params.seed) + if is_distributed: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + logging.info(params) + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + + logging.info(f"Device: {device}") + + logging.info("About to create model") + model = RnnLmModel( + vocab_size=params.vocab_size, + embedding_dim=params.embedding_dim, + hidden_dim=params.hidden_dim, + num_layers=params.num_layers, + tie_weights=params.tie_weights, + ) + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if is_distributed: + model = DDP(model, device_ids=[rank]) + + model.device = device + + optimizer = optim.Adam( + model.parameters(), + lr=params.lr, + weight_decay=params.weight_decay, + ) + if checkpoints: + logging.info("Load optimizer state_dict from checkpoint") + optimizer.load_state_dict(checkpoints["optimizer"]) + + logging.info(f"Loading LM training data from {params.lm_data}") + train_dl = get_dataloader( + filename=params.lm_data, + is_distributed=is_distributed, + params=params, + ) + + logging.info(f"Loading LM validation data from {params.lm_data_valid}") + valid_dl = get_dataloader( + filename=params.lm_data_valid, + is_distributed=is_distributed, + params=params, + ) + + # Note: No learning rate scheduler is used here + for epoch in range(params.start_epoch, params.num_epochs): + if is_distributed: + train_dl.sampler.set_epoch(epoch) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + optimizer=optimizer, + train_dl=train_dl, + valid_dl=valid_dl, + tb_writer=tb_writer, + world_size=world_size, + ) + + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + rank=rank, + ) + + logging.info("Done!") + + if is_distributed: + torch.distributed.barrier() + cleanup_dist() + + +def main(): + parser = get_parser() + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/icefall/utils.py b/icefall/utils.py index b38574f0c..360f29588 100644 --- a/icefall/utils.py +++ b/icefall/utils.py @@ -20,6 +20,7 @@ import argparse import collections import logging import os +import re import subprocess from collections import defaultdict from contextlib import contextmanager @@ -30,11 +31,14 @@ from typing import Dict, Iterable, List, TextIO, Tuple, Union import k2 import k2.version import kaldialign +import sentencepiece as spm import torch import torch.distributed as dist import torch.nn as nn from torch.utils.tensorboard import SummaryWriter +from icefall.checkpoint import average_checkpoints + Pathlike = Union[str, Path] @@ -90,7 +94,11 @@ def str2bool(v): def setup_logger( - log_filename: Pathlike, log_level: str = "info", use_console: bool = True + log_filename: Pathlike, + log_level: str = "info", + rank: int = 0, + world_size: int = 1, + use_console: bool = True, ) -> None: """Setup log level. @@ -100,12 +108,16 @@ def setup_logger( log_level: The log level to use, e.g., "debug", "info", "warning", "error", "critical" + rank: + Rank of this node in DDP training. + world_size: + Number of nodes in DDP training. + use_console: + True to also print logs to console. """ now = datetime.now() date_time = now.strftime("%Y-%m-%d-%H-%M-%S") - if dist.is_available() and dist.is_initialized(): - world_size = dist.get_world_size() - rank = dist.get_rank() + if world_size > 1: formatter = f"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] ({rank}/{world_size}) %(message)s" # noqa log_filename = f"{log_filename}-{date_time}-{rank}" else: @@ -696,6 +708,42 @@ def make_pad_mask(lengths: torch.Tensor) -> torch.Tensor: return expaned_lengths >= lengths.unsqueeze(1) +# Copied and modified from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/mask.py +def subsequent_chunk_mask( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + Returns: + torch.Tensor: mask + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + ret = torch.zeros(size, size, device=device, dtype=torch.bool) + for i in range(size): + if num_left_chunks < 0: + start = 0 + else: + start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) + ending = min((i // chunk_size + 1) * chunk_size, size) + ret[i, start:ending] = True + return ret + + def l1_norm(x): return torch.sum(torch.abs(x)) @@ -799,3 +847,71 @@ def optim_step_and_measure_param_change( delta = l2_norm(p_orig - p_new) / l2_norm(p_orig) relative_change[n] = delta.item() return relative_change + + +def load_averaged_model( + model_dir: str, + model: torch.nn.Module, + epoch: int, + avg: int, + device: torch.device, +): + """ + Load a model which is the average of all checkpoints + + :param model_dir: a str of the experiment directory + :param model: a torch.nn.Module instance + + :param epoch: the last epoch to load from + :param avg: how many models to average from + :param device: move model to this device + + :return: A model averaged + """ + + # start cannot be negative + start = max(epoch - avg + 1, 0) + filenames = [f"{model_dir}/epoch-{i}.pt" for i in range(start, epoch + 1)] + + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + return model + + +def tokenize_by_bpe_model( + sp: spm.SentencePieceProcessor, + txt: str, +) -> str: + """ + Tokenize text with bpe model. This function is from + https://github1s.com/wenet-e2e/wenet/blob/main/wenet/dataset/processor.py#L322-L342. + Args: + sp: spm.SentencePieceProcessor. + txt: str + + Return: + A new string which includes chars and bpes. + """ + tokens = [] + # CJK(China Japan Korea) unicode range is [U+4E00, U+9FFF], ref: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + pattern = re.compile(r"([\u4e00-\u9fff])") + # Example: + # txt = "你好 ITS'S OKAY 的" + # chars = ["你", "好", " ITS'S OKAY ", "的"] + chars = pattern.split(txt.upper()) + mix_chars = [w for w in chars if len(w.strip()) > 0] + for ch_or_w in mix_chars: + # ch_or_w is a single CJK charater(i.e., "你"), do nothing. + if pattern.fullmatch(ch_or_w) is not None: + tokens.append(ch_or_w) + # ch_or_w contains non-CJK charaters(i.e., " IT'S OKAY "), + # encode ch_or_w using bpe_model. + else: + for p in sp.encode_as_pieces(ch_or_w): + tokens.append(p) + txt_with_bpe = "/".join(tokens) + + return txt_with_bpe