diff --git a/.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh b/.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh index bb7c7dfdc..0bec8c0c4 100755 --- a/.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh +++ b/.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh @@ -15,5 +15,5 @@ mkdir -p data cd data [ ! -e fbank ] && ln -s ~/tmp/fbank-libri fbank cd .. -./local/compute_fbank_librispeech.py +./local/compute_fbank_librispeech.py --dataset 'test-clean test-other' ls -lh data/fbank/ 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 index e70a1848d..4c393f6be 100755 --- a/.github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh +++ b/.github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh @@ -25,7 +25,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh b/.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh index df29f188e..c68ccc954 100755 --- a/.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh +++ b/.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh @@ -18,7 +18,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh b/.github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh index 91cdea01a..4cd2c4bec 100755 --- a/.github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh +++ b/.github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh @@ -20,7 +20,6 @@ abs_repo=$(realpath $repo) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh index dafea56db..6792c7088 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav for sym in 1 2 3; do diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh index c3d07dc0e..dbf678d72 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh @@ -23,7 +23,6 @@ popd log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh index 22de3b45d..b6d477afe 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh @@ -22,7 +22,6 @@ popd log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh index ceb77c7c3..efa4b53f0 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh index c6a781318..511fe0c9e 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless7-2022-11-11.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless7-2022-11-11.sh index 8e485d2e6..2bc179c86 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless7-2022-11-11.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless7-2022-11-11.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-2022-12-01.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-2022-12-01.sh index 3cbb480f6..192438353 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-2022-12-01.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-2022-12-01.sh @@ -18,7 +18,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp @@ -148,4 +147,4 @@ if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == done rm pruned_transducer_stateless7_ctc/exp/*.pt -fi \ No newline at end of file +fi diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-bs-2022-12-15.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-bs-2022-12-15.sh index ed66a728e..761eb72e2 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-bs-2022-12-15.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-bs-2022-12-15.sh @@ -10,7 +10,7 @@ log() { cd egs/librispeech/ASR -repo_url=https://huggingface.co/yfyeung/icefall-asr-librispeech-pruned_transducer_stateless7_ctc_bs-2022-12-14 +repo_url=https://huggingface.co/yfyeung/icefall-asr-librispeech-pruned_transducer_stateless7_ctc_bs-2023-01-29 log "Downloading pre-trained model from $repo_url" GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url @@ -18,7 +18,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless7-streaming-2022-12-29.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless7-streaming-2022-12-29.sh index 584f5d488..e1e4e1f10 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless7-streaming-2022-12-29.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless7-streaming-2022-12-29.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless8-2022-11-14.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless8-2022-11-14.sh index e782b8425..5d9485692 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless8-2022-11-14.sh +++ b/.github/scripts/run-librispeech-pruned-transducer-stateless8-2022-11-14.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh b/.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh index af37102d5..77cd59506 100755 --- a/.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh +++ b/.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh b/.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh index 5b8ed396b..b4aca1b6b 100755 --- a/.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh +++ b/.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav for sym in 1 2 3; do diff --git a/.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh b/.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh index 77f28b054..a58b8ec56 100755 --- a/.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh +++ b/.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh @@ -18,7 +18,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/run-pre-trained-conformer-ctc.sh b/.github/scripts/run-pre-trained-conformer-ctc.sh index 96c320616..125d1f3b1 100755 --- a/.github/scripts/run-pre-trained-conformer-ctc.sh +++ b/.github/scripts/run-pre-trained-conformer-ctc.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.flac ls -lh $repo/test_wavs/*.flac log "CTC decoding" diff --git a/.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh b/.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh index 209d4814f..89115e88d 100755 --- a/.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh +++ b/.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav for sym in 1 2 3; do diff --git a/.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh b/.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh index 34ff76fe4..85e2c89e6 100755 --- a/.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh +++ b/.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav for sym in 1 2 3; do diff --git a/.github/scripts/run-pre-trained-transducer-stateless-modified-2-aishell.sh b/.github/scripts/run-pre-trained-transducer-stateless-modified-2-aishell.sh index 75650c2d3..0644d9be0 100755 --- a/.github/scripts/run-pre-trained-transducer-stateless-modified-2-aishell.sh +++ b/.github/scripts/run-pre-trained-transducer-stateless-modified-2-aishell.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav for sym in 1 2 3; do diff --git a/.github/scripts/run-pre-trained-transducer-stateless-modified-aishell.sh b/.github/scripts/run-pre-trained-transducer-stateless-modified-aishell.sh index bcc2d74cb..79fb64311 100755 --- a/.github/scripts/run-pre-trained-transducer-stateless-modified-aishell.sh +++ b/.github/scripts/run-pre-trained-transducer-stateless-modified-aishell.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav for sym in 1 2 3; do diff --git a/.github/scripts/run-pre-trained-transducer-stateless.sh b/.github/scripts/run-pre-trained-transducer-stateless.sh index d3e40315a..41456f11b 100755 --- a/.github/scripts/run-pre-trained-transducer-stateless.sh +++ b/.github/scripts/run-pre-trained-transducer-stateless.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav for sym in 1 2 3; do diff --git a/.github/scripts/run-pre-trained-transducer.sh b/.github/scripts/run-pre-trained-transducer.sh index cfa006776..1331c966c 100755 --- a/.github/scripts/run-pre-trained-transducer.sh +++ b/.github/scripts/run-pre-trained-transducer.sh @@ -19,7 +19,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav log "Beam search decoding" diff --git a/.github/scripts/run-wenetspeech-pruned-transducer-stateless2.sh b/.github/scripts/run-wenetspeech-pruned-transducer-stateless2.sh index 2d237dcf2..90097c752 100755 --- a/.github/scripts/run-wenetspeech-pruned-transducer-stateless2.sh +++ b/.github/scripts/run-wenetspeech-pruned-transducer-stateless2.sh @@ -20,7 +20,6 @@ repo=$(basename $repo_url) log "Display test files" tree $repo/ -soxi $repo/test_wavs/*.wav ls -lh $repo/test_wavs/*.wav pushd $repo/exp diff --git a/.github/scripts/test-ncnn-export.sh b/.github/scripts/test-ncnn-export.sh index 9f5df2d58..52491d2ea 100755 --- a/.github/scripts/test-ncnn-export.sh +++ b/.github/scripts/test-ncnn-export.sh @@ -232,70 +232,3 @@ python3 ./pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \ rm -rf $repo log "--------------------------------------------------------------------------" - -# Go back to the root directory of icefall repo -popd - -pushd egs/csj/ASR - -log "==========================================================================" -repo_url=https://huggingface.co/TeoWenShen/icefall-asr-csj-pruned-transducer-stateless7-streaming-230208 -GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url -repo=$(basename $repo_url) - -pushd $repo -git lfs pull --include "exp_fluent/pretrained.pt" -git lfs pull --include "exp_disfluent/pretrained.pt" - -cd exp_fluent -ln -s pretrained.pt epoch-99.pt - -cd ../exp_disfluent -ln -s pretrained.pt epoch-99.pt - -cd ../test_wavs -git lfs pull --include "*.wav" -popd - -log "Export via torch.jit.trace()" - -for exp in exp_fluent exp_disfluent; do - ./pruned_transducer_stateless7_streaming/export-for-ncnn.py \ - --exp-dir $repo/$exp/ \ - --lang $repo/data/lang_char \ - --epoch 99 \ - --avg 1 \ - --use-averaged-model 0 \ - \ - --decode-chunk-len 32 \ - --num-left-chunks 4 \ - --num-encoder-layers "2,4,3,2,4" \ - --feedforward-dims "1024,1024,2048,2048,1024" \ - --nhead "8,8,8,8,8" \ - --encoder-dims "384,384,384,384,384" \ - --attention-dims "192,192,192,192,192" \ - --encoder-unmasked-dims "256,256,256,256,256" \ - --zipformer-downsampling-factors "1,2,4,8,2" \ - --cnn-module-kernels "31,31,31,31,31" \ - --decoder-dim 512 \ - --joiner-dim 512 - - pnnx $repo/$exp/encoder_jit_trace-pnnx.pt - pnnx $repo/$exp/decoder_jit_trace-pnnx.pt - pnnx $repo/$exp/joiner_jit_trace-pnnx.pt - - for wav in aps-smp.wav interview_aps-smp.wav reproduction-smp.wav sps-smp.wav; do - python3 ./pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \ - --tokens $repo/data/lang_char/tokens.txt \ - --encoder-param-filename $repo/$exp/encoder_jit_trace-pnnx.ncnn.param \ - --encoder-bin-filename $repo/$exp/encoder_jit_trace-pnnx.ncnn.bin \ - --decoder-param-filename $repo/$exp/decoder_jit_trace-pnnx.ncnn.param \ - --decoder-bin-filename $repo/$exp/decoder_jit_trace-pnnx.ncnn.bin \ - --joiner-param-filename $repo/$exp/joiner_jit_trace-pnnx.ncnn.param \ - --joiner-bin-filename $repo/$exp/joiner_jit_trace-pnnx.ncnn.bin \ - $repo/test_wavs/$wav - done -done - -rm -rf $repo -log "--------------------------------------------------------------------------" diff --git a/.github/workflows/run-aishell-2022-06-20.yml b/.github/workflows/run-aishell-2022-06-20.yml index 1865a0da8..f5ba73195 100644 --- a/.github/workflows/run-aishell-2022-06-20.yml +++ b/.github/workflows/run-aishell-2022-06-20.yml @@ -65,7 +65,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -87,7 +87,7 @@ jobs: GITHUB_EVENT_NAME: ${{ github.event_name }} GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }} run: | - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-gigaspeech-2022-05-13.yml b/.github/workflows/run-gigaspeech-2022-05-13.yml index e438c5dba..c7b9cc79d 100644 --- a/.github/workflows/run-gigaspeech-2022-05-13.yml +++ b/.github/workflows/run-gigaspeech-2022-05-13.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache diff --git a/.github/workflows/run-librispeech-2022-03-12.yml b/.github/workflows/run-librispeech-2022-03-12.yml index 3ba6850cd..9c7cd1228 100644 --- a/.github/workflows/run-librispeech-2022-03-12.yml +++ b/.github/workflows/run-librispeech-2022-03-12.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-2022-04-29.yml b/.github/workflows/run-librispeech-2022-04-29.yml index 595b410b8..78c9e759f 100644 --- a/.github/workflows/run-librispeech-2022-04-29.yml +++ b/.github/workflows/run-librispeech-2022-04-29.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-2022-05-13.yml b/.github/workflows/run-librispeech-2022-05-13.yml index eb0b06a2d..04799bf52 100644 --- a/.github/workflows/run-librispeech-2022-05-13.yml +++ b/.github/workflows/run-librispeech-2022-05-13.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-2022-11-11-stateless7.yml b/.github/workflows/run-librispeech-2022-11-11-stateless7.yml index 365e2761a..6dfc23920 100644 --- a/.github/workflows/run-librispeech-2022-11-11-stateless7.yml +++ b/.github/workflows/run-librispeech-2022-11-11-stateless7.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-2022-11-14-stateless8.yml b/.github/workflows/run-librispeech-2022-11-14-stateless8.yml index acb11a8f4..0544e68b3 100644 --- a/.github/workflows/run-librispeech-2022-11-14-stateless8.yml +++ b/.github/workflows/run-librispeech-2022-11-14-stateless8.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-2022-12-01-stateless7-ctc.yml b/.github/workflows/run-librispeech-2022-12-01-stateless7-ctc.yml index ccd8d50d0..62e1f2a01 100644 --- a/.github/workflows/run-librispeech-2022-12-01-stateless7-ctc.yml +++ b/.github/workflows/run-librispeech-2022-12-01-stateless7-ctc.yml @@ -60,7 +60,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -119,7 +119,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-2022-12-08-zipformer-mmi.yml b/.github/workflows/run-librispeech-2022-12-08-zipformer-mmi.yml index 5472ca59b..7dc33aaa9 100644 --- a/.github/workflows/run-librispeech-2022-12-08-zipformer-mmi.yml +++ b/.github/workflows/run-librispeech-2022-12-08-zipformer-mmi.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-2022-12-15-stateless7-ctc-bs.yml b/.github/workflows/run-librispeech-2022-12-15-stateless7-ctc-bs.yml index 6e2b40cf3..de55847ad 100644 --- a/.github/workflows/run-librispeech-2022-12-15-stateless7-ctc-bs.yml +++ b/.github/workflows/run-librispeech-2022-12-15-stateless7-ctc-bs.yml @@ -35,7 +35,7 @@ on: jobs: run_librispeech_2022_12_15_zipformer_ctc_bs: - if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event.label.name == 'blank-skip' || github.event_name == 'push' || github.event_name == 'schedule' + if: github.event.label.name == 'run-decode' || github.event.label.name == 'blank-skip' || github.event_name == 'push' || github.event_name == 'schedule' runs-on: ${{ matrix.os }} strategy: matrix: @@ -60,7 +60,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -119,7 +119,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-2022-12-29-stateless7-streaming.yml b/.github/workflows/run-librispeech-2022-12-29-stateless7-streaming.yml index 6dd93946a..feb5c6fd0 100644 --- a/.github/workflows/run-librispeech-2022-12-29-stateless7-streaming.yml +++ b/.github/workflows/run-librispeech-2022-12-29-stateless7-streaming.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-conformer-ctc3-2022-11-28.yml b/.github/workflows/run-librispeech-conformer-ctc3-2022-11-28.yml index d763fb1c5..c95ed8b9a 100644 --- a/.github/workflows/run-librispeech-conformer-ctc3-2022-11-28.yml +++ b/.github/workflows/run-librispeech-conformer-ctc3-2022-11-28.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-lstm-transducer-stateless2-2022-09-03.yml b/.github/workflows/run-librispeech-lstm-transducer-stateless2-2022-09-03.yml index f737d9a25..e14d4e92f 100644 --- a/.github/workflows/run-librispeech-lstm-transducer-stateless2-2022-09-03.yml +++ b/.github/workflows/run-librispeech-lstm-transducer-stateless2-2022-09-03.yml @@ -47,7 +47,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -106,7 +106,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml b/.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml index f67f7599b..73d91fcd4 100644 --- a/.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml +++ b/.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml b/.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml index ac7e58b20..8a690393e 100644 --- a/.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml +++ b/.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml b/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml index 575727e22..217dbdfa1 100644 --- a/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml +++ b/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml @@ -64,7 +64,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -123,7 +123,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-pretrained-conformer-ctc.yml b/.github/workflows/run-pretrained-conformer-ctc.yml index 7dbfd2bd9..4e8e7b8db 100644 --- a/.github/workflows/run-pretrained-conformer-ctc.yml +++ b/.github/workflows/run-pretrained-conformer-ctc.yml @@ -54,7 +54,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -73,7 +73,7 @@ jobs: - name: Inference with pre-trained model shell: bash run: | - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml b/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml index d6b3de8d4..ddde4f1d6 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml +++ b/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml @@ -63,7 +63,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -122,7 +122,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml b/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml index 749fb3fca..00ea97b2a 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml +++ b/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml @@ -63,7 +63,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -122,7 +122,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml b/.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml index 92bf6feb8..b3cfc9efd 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml +++ b/.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml @@ -54,7 +54,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -73,7 +73,7 @@ jobs: - name: Inference with pre-trained model shell: bash run: | - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml b/.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml index e51da8bd8..ab598541d 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml +++ b/.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml @@ -54,7 +54,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -73,7 +73,7 @@ jobs: - name: Inference with pre-trained model shell: bash run: | - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-pretrained-transducer-stateless.yml b/.github/workflows/run-pretrained-transducer-stateless.yml index 2103d0510..d663d49dd 100644 --- a/.github/workflows/run-pretrained-transducer-stateless.yml +++ b/.github/workflows/run-pretrained-transducer-stateless.yml @@ -63,7 +63,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -122,7 +122,7 @@ jobs: ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank ls -lh egs/librispeech/ASR/data/* - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-pretrained-transducer.yml b/.github/workflows/run-pretrained-transducer.yml index 902319b55..9cb9d3b59 100644 --- a/.github/workflows/run-pretrained-transducer.yml +++ b/.github/workflows/run-pretrained-transducer.yml @@ -54,7 +54,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -73,7 +73,7 @@ jobs: - name: Inference with pre-trained model shell: bash run: | - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-ptb-rnn-lm.yml b/.github/workflows/run-ptb-rnn-lm.yml index 47ed958f2..f8d9c02c5 100644 --- a/.github/workflows/run-ptb-rnn-lm.yml +++ b/.github/workflows/run-ptb-rnn-lm.yml @@ -47,7 +47,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | grep -v kaldifst | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Prepare data shell: bash diff --git a/.github/workflows/run-wenetspeech-pruned-transducer-stateless2.yml b/.github/workflows/run-wenetspeech-pruned-transducer-stateless2.yml index 8a7be0b80..14fb96ec8 100644 --- a/.github/workflows/run-wenetspeech-pruned-transducer-stateless2.yml +++ b/.github/workflows/run-wenetspeech-pruned-transducer-stateless2.yml @@ -54,7 +54,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache @@ -76,7 +76,7 @@ jobs: GITHUB_EVENT_NAME: ${{ github.event_name }} GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }} run: | - sudo apt-get -qq install git-lfs tree sox + sudo apt-get -qq install git-lfs tree export PYTHONPATH=$PWD:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH diff --git a/.github/workflows/run-yesno-recipe.yml b/.github/workflows/run-yesno-recipe.yml index ed343aee5..1187dbf38 100644 --- a/.github/workflows/run-yesno-recipe.yml +++ b/.github/workflows/run-yesno-recipe.yml @@ -67,7 +67,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | grep -v kaldifst | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Run yesno recipe shell: bash diff --git a/.github/workflows/test-ncnn-export.yml b/.github/workflows/test-ncnn-export.yml index e10cfe76b..cdea54854 100644 --- a/.github/workflows/test-ncnn-export.yml +++ b/.github/workflows/test-ncnn-export.yml @@ -46,7 +46,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache diff --git a/.github/workflows/test-onnx-export.yml b/.github/workflows/test-onnx-export.yml index c7729dedb..3dc4261ab 100644 --- a/.github/workflows/test-onnx-export.yml +++ b/.github/workflows/test-onnx-export.yml @@ -46,7 +46,7 @@ jobs: run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* - name: Cache kaldifeat id: my-cache diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index c062a2a3d..079772e97 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -56,7 +56,7 @@ jobs: run: | sudo apt update sudo apt install -q -y libsndfile1-dev libsndfile1 ffmpeg - sudo apt install -q -y --fix-missing sox libsox-dev libsox-fmt-all + sudo apt install -q -y --fix-missing libsox-dev libsox-fmt-all - name: Install Python dependencies run: | @@ -70,7 +70,7 @@ jobs: pip install git+https://github.com/lhotse-speech/lhotse # icefall requirements pip uninstall -y protobuf - pip install --no-binary protobuf protobuf + pip install --no-binary protobuf protobuf==3.20.* pip install kaldifst pip install onnxruntime @@ -119,8 +119,8 @@ jobs: cd ../transducer_stateless pytest -v -s - cd ../transducer - pytest -v -s + # cd ../transducer + # pytest -v -s cd ../transducer_stateless2 pytest -v -s @@ -157,8 +157,8 @@ jobs: cd ../transducer_stateless pytest -v -s - cd ../transducer - pytest -v -s + # cd ../transducer + # pytest -v -s cd ../transducer_stateless2 pytest -v -s diff --git a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py index d0f118959..2512f233f 100755 --- a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py @@ -391,18 +391,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -412,9 +408,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/aishell/ASR/local/prepare_char_lm_training_data.py b/egs/aishell/ASR/local/prepare_char_lm_training_data.py new file mode 100644 index 000000000..e7995680b --- /dev/null +++ b/egs/aishell/ASR/local/prepare_char_lm_training_data.py @@ -0,0 +1,164 @@ +#!/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 `tokens.txt` and a text file such as +./download/lm/aishell-transcript.txt +and outputs the LM training data to a supplied directory such +as data/lm_training_char. 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 same format with librispeech receipe +""" + +import argparse +import logging +from pathlib import Path + +import k2 +import torch + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-char", + type=str, + help="""Lang dir of asr model, e.g. data/lang_char""", + ) + parser.add_argument( + "--lm-data", + type=str, + help="""Input LM training data as text, e.g. + download/lm/aishell-train-word.txt""", + ) + parser.add_argument( + "--lm-archive", + type=str, + help="""Path to output archive, e.g. data/lm_training_char/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 + + # make token_dict from tokens.txt in order to map characters to tokens. + token_dict = {} + token_file = args.lang_char + "/tokens.txt" + + with open(token_file, "r") as f: + for line in f.readlines(): + line_list = line.split() + token_dict[line_list[0]] = int(line_list[1]) + + # 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 tokens. + word2index = dict() + + word2token = [] # Will be a list-of-list-of-int, representing tokens. + sentences = [] # Will be a list-of-list-of-int, representing word-ids. + + if "aishell-lm" in args.lm_data: + num_lines_in_total = 120098.0 + step = 50000 + elif "valid" in args.lm_data: + num_lines_in_total = 14326.0 + step = 3000 + elif "test" in args.lm_data: + num_lines_in_total = 7176.0 + 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_token = [] + for t in w: + if t in token_dict: + w_token.append(token_dict[t]) + else: + w_token.append(token_dict[""]) + word2index[w] = len(word2token) + word2token.append(w_token) + sentences.append([word2index[w] for w in line_words]) + + logging.info("Constructing ragged tensors") + words = k2.ragged.RaggedTensor(word2token) + 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} ({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/aishell/ASR/prepare.sh b/egs/aishell/ASR/prepare.sh index 5917668a1..cf4ee7818 100755 --- a/egs/aishell/ASR/prepare.sh +++ b/egs/aishell/ASR/prepare.sh @@ -7,7 +7,7 @@ set -eou pipefail nj=15 stage=-1 -stop_stage=10 +stop_stage=11 # We assume dl_dir (download dir) contains the following # directories and files. If not, they will be downloaded @@ -219,3 +219,93 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then ./local/compile_hlg.py --lang-dir $lang_phone_dir ./local/compile_hlg.py --lang-dir $lang_char_dir fi + +if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then + log "Stage 9: Generate LM training data" + + log "Processing char based data" + out_dir=data/lm_training_char + mkdir -p $out_dir $dl_dir/lm + + if [ ! -f $dl_dir/lm/aishell-train-word.txt ]; then + cp $lang_phone_dir/transcript_words.txt $dl_dir/lm/aishell-train-word.txt + fi + + ./local/prepare_char_lm_training_data.py \ + --lang-char data/lang_char \ + --lm-data $dl_dir/lm/aishell-train-word.txt \ + --lm-archive $out_dir/lm_data.pt + + if [ ! -f $dl_dir/lm/aishell-valid-word.txt ]; then + aishell_text=$dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt + aishell_valid_uid=$dl_dir/aishell/data_aishell/transcript/aishell_valid_uid + find $dl_dir/aishell/data_aishell/wav/dev -name "*.wav" | sed 's/\.wav//g' | awk -F '/' '{print $NF}' > $aishell_valid_uid + awk 'NR==FNR{uid[$1]=$1} NR!=FNR{if($1 in uid) print $0}' $aishell_valid_uid $aishell_text | + cut -d " " -f 2- > $dl_dir/lm/aishell-valid-word.txt + fi + + ./local/prepare_char_lm_training_data.py \ + --lang-char data/lang_char \ + --lm-data $dl_dir/lm/aishell-valid-word.txt \ + --lm-archive $out_dir/lm_data_valid.pt + + if [ ! -f $dl_dir/lm/aishell-test-word.txt ]; then + aishell_text=$dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt + aishell_test_uid=$dl_dir/aishell/data_aishell/transcript/aishell_test_uid + find $dl_dir/aishell/data_aishell/wav/test -name "*.wav" | sed 's/\.wav//g' | awk -F '/' '{print $NF}' > $aishell_test_uid + awk 'NR==FNR{uid[$1]=$1} NR!=FNR{if($1 in uid) print $0}' $aishell_test_uid $aishell_text | + cut -d " " -f 2- > $dl_dir/lm/aishell-test-word.txt + fi + + ./local/prepare_char_lm_training_data.py \ + --lang-char data/lang_char \ + --lm-data $dl_dir/lm/aishell-test-word.txt \ + --lm-archive $out_dir/lm_data_test.pt +fi + + +if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then + log "Stage 10: 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 tokens + # in a sentence. + + out_dir=data/lm_training_char + mkdir -p $out_dir + ln -snf ../../../librispeech/ASR/local/sort_lm_training_data.py local/ + + ./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 +fi + +if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then + log "Stage 11: Train RNN LM model" + python ../../../icefall/rnn_lm/train.py \ + --start-epoch 0 \ + --world-size 1 \ + --num-epochs 20 \ + --use-fp16 0 \ + --embedding-dim 512 \ + --hidden-dim 512 \ + --num-layers 2 \ + --batch-size 400 \ + --exp-dir rnnlm_char/exp \ + --lm-data data/lm_training_char/sorted_lm_data.pt \ + --lm-data-valid data/lm_training_char/sorted_lm_data-valid.pt \ + --vocab-size 4336 \ + --master-port 12345 +fi diff --git a/egs/aishell/ASR/pruned_transducer_stateless2/decode.py b/egs/aishell/ASR/pruned_transducer_stateless2/decode.py index 20a4f21c7..fb6c7c481 100755 --- a/egs/aishell/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/aishell/ASR/pruned_transducer_stateless2/decode.py @@ -388,18 +388,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" # we compute CER for aishell dataset. results_char = [] for res in results: @@ -413,9 +409,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/decode.py b/egs/aishell/ASR/pruned_transducer_stateless3/decode.py index bac829ae1..954d9dc7e 100755 --- a/egs/aishell/ASR/pruned_transducer_stateless3/decode.py +++ b/egs/aishell/ASR/pruned_transducer_stateless3/decode.py @@ -406,18 +406,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" # we compute CER for aishell dataset. results_char = [] for res in results: @@ -431,9 +427,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tCER", file=f) for key, val in test_set_wers: diff --git a/egs/aishell/ASR/transducer_stateless/decode.py b/egs/aishell/ASR/transducer_stateless/decode.py index e019d2329..d23f4f883 100755 --- a/egs/aishell/ASR/transducer_stateless/decode.py +++ b/egs/aishell/ASR/transducer_stateless/decode.py @@ -325,17 +325,13 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" results = sorted(results) store_transcripts(filename=recog_path, texts=results) # 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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" # we compute CER for aishell dataset. results_char = [] for res in results: @@ -349,9 +345,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tCER", file=f) for key, val in test_set_wers: diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/decode.py b/egs/aishell/ASR/transducer_stateless_modified-2/decode.py index 41cc1c01c..d164b6890 100755 --- a/egs/aishell/ASR/transducer_stateless_modified-2/decode.py +++ b/egs/aishell/ASR/transducer_stateless_modified-2/decode.py @@ -370,18 +370,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" # we compute CER for aishell dataset. results_char = [] for res in results: @@ -395,9 +391,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tCER", file=f) for key, val in test_set_wers: diff --git a/egs/aishell/ASR/transducer_stateless_modified/decode.py b/egs/aishell/ASR/transducer_stateless_modified/decode.py index 7c06e6e51..0a7d87fe8 100755 --- a/egs/aishell/ASR/transducer_stateless_modified/decode.py +++ b/egs/aishell/ASR/transducer_stateless_modified/decode.py @@ -374,18 +374,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" # we compute CER for aishell dataset. results_char = [] for res in results: @@ -399,9 +395,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tCER", file=f) for key, val in test_set_wers: diff --git a/egs/aishell2/ASR/pruned_transducer_stateless5/decode.py b/egs/aishell2/ASR/pruned_transducer_stateless5/decode.py index b5da0959b..9e44b4e34 100755 --- a/egs/aishell2/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/aishell2/ASR/pruned_transducer_stateless5/decode.py @@ -543,18 +543,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -564,9 +560,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/decode.py b/egs/aishell4/ASR/pruned_transducer_stateless5/decode.py index 37d766ec8..068e2749a 100755 --- a/egs/aishell4/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/aishell4/ASR/pruned_transducer_stateless5/decode.py @@ -406,18 +406,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -427,9 +423,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py index e4a90ef71..6c170c392 100755 --- a/egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py @@ -391,18 +391,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -412,9 +408,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/alimeeting/ASR_v2/pruned_transducer_stateless7/decode.py b/egs/alimeeting/ASR_v2/pruned_transducer_stateless7/decode.py index 53381c1f4..2741e0eeb 100755 --- a/egs/alimeeting/ASR_v2/pruned_transducer_stateless7/decode.py +++ b/egs/alimeeting/ASR_v2/pruned_transducer_stateless7/decode.py @@ -462,18 +462,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -483,9 +479,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/ami/ASR/pruned_transducer_stateless7/decode.py b/egs/ami/ASR/pruned_transducer_stateless7/decode.py index f47228fbe..9999894d1 100755 --- a/egs/ami/ASR/pruned_transducer_stateless7/decode.py +++ b/egs/ami/ASR/pruned_transducer_stateless7/decode.py @@ -478,17 +478,13 @@ def save_results( test_set_wers = dict() test_set_cers = dict() for key, results in results_dict.items(): - recog_path = ( - params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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. - wers_filename = ( - params.res_dir / f"wers-{test_set_name}-{key}-{params.suffix}.txt" - ) + wers_filename = params.res_dir / f"wers-{test_set_name}-{params.suffix}.txt" with open(wers_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -499,9 +495,7 @@ def save_results( results_char = [] for res in results: results_char.append((res[0], list("".join(res[1])), list("".join(res[2])))) - cers_filename = ( - params.res_dir / f"cers-{test_set_name}-{key}-{params.suffix}.txt" - ) + cers_filename = params.res_dir / f"cers-{test_set_name}-{params.suffix}.txt" with open(cers_filename, "w") as f: cer = write_error_stats( f, f"{test_set_name}-{key}", results_char, enable_log=True @@ -512,9 +506,7 @@ def save_results( test_set_wers = {k: v for k, v in sorted(test_set_wers.items(), key=lambda x: x[1])} test_set_cers = {k: v for k, v in sorted(test_set_cers.items(), key=lambda x: x[1])} - errs_info = ( - params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER\tCER", file=f) for key in test_set_wers: diff --git a/egs/csj/ASR/pruned_transducer_stateless7_streaming/decode.py b/egs/csj/ASR/pruned_transducer_stateless7_streaming/decode.py index 19d3c79c8..f5a1d750d 100755 --- a/egs/csj/ASR/pruned_transducer_stateless7_streaming/decode.py +++ b/egs/csj/ASR/pruned_transducer_stateless7_streaming/decode.py @@ -599,9 +599,7 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" results = sorted(results) store_transcripts(filename=recog_path, texts=results) @@ -609,9 +607,7 @@ def save_results( # 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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -621,9 +617,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py index 8595c27bd..ee694a9e0 100755 --- a/egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py @@ -399,9 +399,7 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" results = post_processing(results) results = sorted(results) store_transcripts(filename=recog_path, texts=results) @@ -409,9 +407,7 @@ def save_results( # 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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -421,9 +417,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/RESULTS.md b/egs/librispeech/ASR/RESULTS.md index ecb84eb01..9ca7a19b8 100644 --- a/egs/librispeech/ASR/RESULTS.md +++ b/egs/librispeech/ASR/RESULTS.md @@ -540,6 +540,10 @@ for m in greedy_search fast_beam_search modified_beam_search ; do done ``` +Note that a small change is made to the `pruned_transducer_stateless7/decoder.py` in +this [PR](/ceph-data4/yangxiaoyu/softwares/icefall_development/icefall_random_padding/egs/librispeech/ASR/pruned_transducer_stateless7/exp_960h_no_paddingidx_ngpu4/tensorboard) to address the +problem of emitting the first symbol at the very beginning. If you need a +model without this issue, please download the model from here: ### LibriSpeech BPE training results (Pruned Stateless LSTM RNN-T + gradient filter) diff --git a/egs/librispeech/ASR/conformer_ctc3/decode.py b/egs/librispeech/ASR/conformer_ctc3/decode.py index 6fbf9d674..e6327bb5e 100755 --- a/egs/librispeech/ASR/conformer_ctc3/decode.py +++ b/egs/librispeech/ASR/conformer_ctc3/decode.py @@ -728,18 +728,14 @@ def save_results( test_set_wers = dict() test_set_delays = dict() for key, results in results_dict.items(): - recog_path = ( - params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" results = sorted(results) store_transcripts_and_timestamps(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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer, mean_delay, var_delay = write_error_stats_with_timestamps( f, @@ -754,9 +750,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: @@ -765,8 +759,7 @@ def save_results( # sort according to the mean start symbol delay test_set_delays = sorted(test_set_delays.items(), key=lambda x: x[1][0][0]) delays_info = ( - params.res_dir - / f"symbol-delay-summary-{test_set_name}-{key}-{params.suffix}.txt" + params.res_dir / f"symbol-delay-summary-{test_set_name}-{params.suffix}.txt" ) with open(delays_info, "w") as f: print("settings\t(start, end) symbol-delay (s) (start, end)", file=f) diff --git a/egs/librispeech/ASR/conv_emformer_transducer_stateless/decode.py b/egs/librispeech/ASR/conv_emformer_transducer_stateless/decode.py index 365e8b8a7..7be3299f3 100755 --- a/egs/librispeech/ASR/conv_emformer_transducer_stateless/decode.py +++ b/egs/librispeech/ASR/conv_emformer_transducer_stateless/decode.py @@ -432,18 +432,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -453,9 +449,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/conv_emformer_transducer_stateless/streaming_decode.py b/egs/librispeech/ASR/conv_emformer_transducer_stateless/streaming_decode.py index c93125c80..e5a7c7116 100755 --- a/egs/librispeech/ASR/conv_emformer_transducer_stateless/streaming_decode.py +++ b/egs/librispeech/ASR/conv_emformer_transducer_stateless/streaming_decode.py @@ -750,17 +750,13 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" store_transcripts(filename=recog_path, texts=sorted(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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -770,9 +766,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/conv_emformer_transducer_stateless2/decode.py b/egs/librispeech/ASR/conv_emformer_transducer_stateless2/decode.py index 78e1f4096..d022d463e 100755 --- a/egs/librispeech/ASR/conv_emformer_transducer_stateless2/decode.py +++ b/egs/librispeech/ASR/conv_emformer_transducer_stateless2/decode.py @@ -432,18 +432,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -453,9 +449,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming_decode.py b/egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming_decode.py index b2cb2c96b..f5d894a7b 100755 --- a/egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming_decode.py +++ b/egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming_decode.py @@ -750,17 +750,13 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" store_transcripts(filename=recog_path, texts=sorted(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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -770,9 +766,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/finetune.sh b/egs/librispeech/ASR/finetune.sh new file mode 100755 index 000000000..63d0966ed --- /dev/null +++ b/egs/librispeech/ASR/finetune.sh @@ -0,0 +1,85 @@ +#!/usr/bin/env bash + +# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674 +export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + +set -eou pipefail + +stage=-1 +stop_stage=100 + +# This is an example script for fine-tuning. Here, we fine-tune a model trained +# on Librispeech on GigaSpeech. The model used for fine-tuning is +# pruned_transducer_stateless7 (zipformer). If you want to fine-tune model +# from another recipe, you can adapt ./pruned_transducer_stateless7/finetune.py +# for that recipe. If you have any problem, please open up an issue in https://github.com/k2-fsa/icefall/issues. + +# We assume that you have already prepared the GigaSpeech manfiest&features under ./data. +# If you haven't done that, please see https://github.com/k2-fsa/icefall/blob/master/egs/gigaspeech/ASR/prepare.sh. + +dl_dir=$PWD/download + +. shared/parse_options.sh || exit 1 + +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 -1 ] && [ $stop_stage -ge -1 ]; then + log "Stage -1: Download Pre-trained model" + + # clone from huggingface + git lfs install + git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11 + +fi + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "Stage 0: Start fine-tuning" + + # The following configuration of lr schedule should work well + # You may also tune the following parameters to adjust learning rate schedule + base_lr=0.005 + lr_epochs=100 + lr_batches=100000 + + # We recommend to start from an averaged model + finetune_ckpt=icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11/exp/pretrained.pt + export CUDA_VISIBLE_DEVICES="0,1" + + ./pruned_transducer_stateless7/finetune.py \ + --world-size 2 \ + --master-port 18180 \ + --num-epochs 20 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless7/exp_giga_finetune \ + --subset S \ + --use-fp16 1 \ + --base-lr $base_lr \ + --lr-epochs $lr_epochs \ + --lr-batches $lr_batches \ + --bpe-model icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11/data/lang_bpe_500/bpe.model \ + --do-finetune True \ + --finetune-ckpt $finetune_ckpt \ + --max-duration 500 +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Decoding" + + epoch=15 + avg=10 + + for m in greedy_search modified_beam_search; do + python pruned_transducer_stateless7/decode_gigaspeech.py \ + --epoch $epoch \ + --avg $avg \ + --use-averaged-model True \ + --beam-size 4 \ + --exp-dir pruned_transducer_stateless7/exp_giga_finetune \ + --max-duration 400 \ + --decoding-method $m + done +fi diff --git a/egs/librispeech/ASR/local/compute_fbank_librispeech.py b/egs/librispeech/ASR/local/compute_fbank_librispeech.py index 9f8503814..745eaf1e8 100755 --- a/egs/librispeech/ASR/local/compute_fbank_librispeech.py +++ b/egs/librispeech/ASR/local/compute_fbank_librispeech.py @@ -54,10 +54,20 @@ def get_args(): help="""Path to the bpe.model. If not None, we will remove short and long utterances before extracting features""", ) + + parser.add_argument( + "--dataset", + type=str, + help="""Dataset parts to compute fbank. If None, we will use all""", + ) + return parser.parse_args() -def compute_fbank_librispeech(bpe_model: Optional[str] = None): +def compute_fbank_librispeech( + bpe_model: Optional[str] = None, + dataset: Optional[str] = None, +): src_dir = Path("data/manifests") output_dir = Path("data/fbank") num_jobs = min(15, os.cpu_count()) @@ -68,15 +78,19 @@ def compute_fbank_librispeech(bpe_model: Optional[str] = None): sp = spm.SentencePieceProcessor() sp.load(bpe_model) - dataset_parts = ( - "dev-clean", - "dev-other", - "test-clean", - "test-other", - "train-clean-100", - "train-clean-360", - "train-other-500", - ) + if dataset is None: + dataset_parts = ( + "dev-clean", + "dev-other", + "test-clean", + "test-other", + "train-clean-100", + "train-clean-360", + "train-other-500", + ) + else: + dataset_parts = dataset.split(" ", -1) + prefix = "librispeech" suffix = "jsonl.gz" manifests = read_manifests_if_cached( @@ -131,4 +145,4 @@ if __name__ == "__main__": logging.basicConfig(format=formatter, level=logging.INFO) args = get_args() logging.info(vars(args)) - compute_fbank_librispeech(bpe_model=args.bpe_model) + compute_fbank_librispeech(bpe_model=args.bpe_model, dataset=args.dataset) diff --git a/egs/librispeech/ASR/lstm_transducer_stateless/decode.py b/egs/librispeech/ASR/lstm_transducer_stateless/decode.py index 3ad08f56a..856c9d945 100755 --- a/egs/librispeech/ASR/lstm_transducer_stateless/decode.py +++ b/egs/librispeech/ASR/lstm_transducer_stateless/decode.py @@ -566,18 +566,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -587,9 +583,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py b/egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py index 961d8ddfb..f989d9bc0 100755 --- a/egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py +++ b/egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py @@ -742,17 +742,13 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" store_transcripts(filename=recog_path, texts=sorted(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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -762,9 +758,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/lstm_transducer_stateless2/decode.py b/egs/librispeech/ASR/lstm_transducer_stateless2/decode.py index 78be9c01f..6c58a57e1 100755 --- a/egs/librispeech/ASR/lstm_transducer_stateless2/decode.py +++ b/egs/librispeech/ASR/lstm_transducer_stateless2/decode.py @@ -702,18 +702,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -723,9 +719,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/decode.py b/egs/librispeech/ASR/lstm_transducer_stateless3/decode.py index a380bc470..a2b4f9e1a 100755 --- a/egs/librispeech/ASR/lstm_transducer_stateless3/decode.py +++ b/egs/librispeech/ASR/lstm_transducer_stateless3/decode.py @@ -611,18 +611,14 @@ def save_results( test_set_wers = dict() test_set_delays = dict() for key, results in results_dict.items(): - recog_path = ( - params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" results = sorted(results) store_transcripts_and_timestamps(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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer, mean_delay, var_delay = write_error_stats_with_timestamps( f, f"{test_set_name}-{key}", results, enable_log=True @@ -633,9 +629,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: @@ -643,8 +637,7 @@ def save_results( test_set_delays = sorted(test_set_delays.items(), key=lambda x: x[1][0]) delays_info = ( - params.res_dir - / f"symbol-delay-summary-{test_set_name}-{key}-{params.suffix}.txt" + params.res_dir / f"symbol-delay-summary-{test_set_name}-{params.suffix}.txt" ) with open(delays_info, "w") as f: print("settings\tsymbol-delay", file=f) diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/streaming_decode.py b/egs/librispeech/ASR/lstm_transducer_stateless3/streaming_decode.py index 109746ed5..c737e3611 100755 --- a/egs/librispeech/ASR/lstm_transducer_stateless3/streaming_decode.py +++ b/egs/librispeech/ASR/lstm_transducer_stateless3/streaming_decode.py @@ -742,17 +742,13 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" store_transcripts(filename=recog_path, texts=sorted(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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -762,9 +758,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned2_knowledge/decode.py b/egs/librispeech/ASR/pruned2_knowledge/decode.py index 40d14bb5a..82fd103ea 100755 --- a/egs/librispeech/ASR/pruned2_knowledge/decode.py +++ b/egs/librispeech/ASR/pruned2_knowledge/decode.py @@ -386,17 +386,13 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -406,9 +402,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py index 0e3b7ff74..072d49d9c 100755 --- a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py @@ -420,18 +420,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -441,9 +437,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless/decode.py index 0444afe40..6dfe11cee 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/decode.py @@ -585,18 +585,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -606,9 +602,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/decoder.py b/egs/librispeech/ASR/pruned_transducer_stateless/decoder.py index 72593173c..49b82c433 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless/decoder.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/decoder.py @@ -58,7 +58,6 @@ class Decoder(nn.Module): self.embedding = nn.Embedding( num_embeddings=vocab_size, embedding_dim=embedding_dim, - padding_idx=blank_id, ) self.blank_id = blank_id self.unk_id = unk_id diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py index fbc39fb65..f4b01fd06 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py @@ -423,9 +423,7 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" # sort results so we can easily compare the difference between two # recognition results results = sorted(results) @@ -434,9 +432,7 @@ def save_results( # 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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -446,9 +442,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py index 5f135f219..172c9ab7c 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py @@ -609,18 +609,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -630,9 +626,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py b/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py index b59928103..d44ed6f81 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py @@ -59,7 +59,6 @@ class Decoder(nn.Module): self.embedding = ScaledEmbedding( num_embeddings=vocab_size, embedding_dim=decoder_dim, - padding_idx=blank_id, ) self.blank_id = blank_id diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py index bb08246d9..9c4a13606 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py @@ -425,9 +425,7 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" # sort results so we can easily compare the difference between two # recognition results results = sorted(results) @@ -436,9 +434,7 @@ def save_results( # 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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -448,9 +444,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py index 109a94a69..aa055049e 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py @@ -869,18 +869,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -890,9 +886,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py index 0e5111f33..3a1ecb7ed 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py @@ -426,18 +426,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -447,9 +443,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py index afd3a9e0e..ea1ae49cd 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py @@ -676,18 +676,14 @@ def save_results( test_set_wers = dict() test_set_delays = dict() for key, results in results_dict.items(): - recog_path = ( - params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" results = sorted(results) store_transcripts_and_timestamps(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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer, mean_delay, var_delay = write_error_stats_with_timestamps( f, f"{test_set_name}-{key}", results, enable_log=True @@ -698,9 +694,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: @@ -708,8 +702,7 @@ def save_results( test_set_delays = sorted(test_set_delays.items(), key=lambda x: x[1][0]) delays_info = ( - params.res_dir - / f"symbol-delay-summary-{test_set_name}-{key}-{params.suffix}.txt" + params.res_dir / f"symbol-delay-summary-{test_set_name}-{params.suffix}.txt" ) with open(delays_info, "w") as f: print("settings\tsymbol-delay", file=f) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py index c4e3cef16..ca3a023ce 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py @@ -442,18 +442,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -463,9 +459,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py index 90b0fcf4b..2be895feb 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py @@ -735,18 +735,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -756,9 +752,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless5/streaming_decode.py index 064811f1c..5b15dcee7 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless5/streaming_decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless5/streaming_decode.py @@ -442,18 +442,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -463,9 +459,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless6/decode.py index fd9de052a..95534efef 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless6/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/decode.py @@ -416,18 +416,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -437,9 +433,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7/decode.py index b9bce465f..32b3134b9 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/decode.py @@ -722,18 +722,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -743,9 +739,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py b/egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py new file mode 100644 index 000000000..4f64850b6 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py @@ -0,0 +1,861 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao, +# Xiaoyu Yang) +# +# 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_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search (one best) +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/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_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/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_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/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_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/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 + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn + +# from asr_datamodule import LibriSpeechAsrDataModule +from gigaspeech import GigaSpeechAsrDataModule +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 gigaspeech_scoring import asr_text_post_processing +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, +) + +LOG_EPS = math.log(1e-10) + + +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=9, + 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_stateless7/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( + "--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, + default="greedy_search", + help="""Possible values are: + - greedy_search + - 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`. + """, + ) + + 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=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, + 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=8, + help="""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( + "--max-states", + type=int, + default=64, + help="""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( + "--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""", + ) + + 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""", + ) + + 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 + + +def post_processing( + results: List[Tuple[str, List[str], List[str]]], +) -> List[Tuple[str, List[str], List[str]]]: + new_results = [] + for key, ref, hyp in results: + new_ref = asr_text_post_processing(" ".join(ref)).split() + new_hyp = asr_text_post_processing(" ".join(hyp)).split() + new_results.append((key, new_ref, new_hyp)) + return new_results + + +def decode_one_batch( + params: AttributeDict, + 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 + 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. + sp: + The BPE model. + 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, 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. + """ + 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) + + if params.simulate_streaming: + feature_lens += params.left_context + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, params.left_context), + value=LOG_EPS, + ) + 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": + 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 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: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + 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 hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + 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(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": 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} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + word_table: Optional[k2.SymbolTable] = None, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[str, 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. + 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, 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. + 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"] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + word_table=word_table, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + ref_words = ref_text.split() + this_batch.append((cut_id, 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[str, 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 = post_processing(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(): + """ + This scripts test a libri model with libri BPE + on Gigaspeech. + """ + parser = get_parser() + GigaSpeechAsrDataModule.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", + "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 + "_gigaspeech") + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + 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}" + 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}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are 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.simulate_streaming: + assert ( + params.causal_convolution + ), "Decoding in streaming requires causal convolution" + + 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 "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}") + + # we need cut ids to display recognition results. + args.return_cuts = True + gigaspeech = GigaSpeechAsrDataModule(args) + + dev_cuts = gigaspeech.dev_cuts() + test_cuts = gigaspeech.test_cuts() + + dev_dl = gigaspeech.test_dataloaders(dev_cuts) + test_dl = gigaspeech.test_dataloaders(test_cuts) + + test_sets = ["dev", "test"] + test_dls = [dev_dl, test_dl] + + for test_set, test_dl in zip(test_sets, test_dls): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + word_table=word_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/librispeech/ASR/pruned_transducer_stateless7/decoder.py b/egs/librispeech/ASR/pruned_transducer_stateless7/decoder.py index 384b78524..b085a1817 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless7/decoder.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/decoder.py @@ -56,7 +56,6 @@ class Decoder(nn.Module): self.embedding = nn.Embedding( num_embeddings=vocab_size, embedding_dim=decoder_dim, - padding_idx=blank_id, ) self.blank_id = blank_id diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py b/egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py new file mode 100755 index 000000000..726a24809 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py @@ -0,0 +1,1342 @@ +#!/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_stateless7/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless7/exp \ + --full-libri 1 \ + --max-duration 300 + +# For mix precision training: + +./pruned_transducer_stateless7/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless7/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, List, Optional, Tuple, Union + +import k2 +import optim +import sentencepiece as spm +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from decoder import Decoder +from gigaspeech import GigaSpeechAsrDataModule +from joiner import Joiner +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, ScaledAdam +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 zipformer import Zipformer + +from icefall import diagnostics +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.hooks import register_inf_check_hooks +from icefall.utils import ( + AttributeDict, + MetricsTracker, + filter_uneven_sized_batch, + setup_logger, + str2bool, +) + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None: + if isinstance(model, DDP): + # get underlying nn.Module + model = model.module + for module in model.modules(): + if hasattr(module, "batch_count"): + module.batch_count = batch_count + + +def add_finetune_arguments(parser: argparse.ArgumentParser): + parser.add_argument("--do-finetune", type=str2bool, default=False) + + parser.add_argument( + "--init-modules", + type=str, + default=None, + help=""" + Modules to be initialized. It matches all parameters starting with + a specific key. The keys are given with Comma seperated. If None, + all modules will be initialised. For example, if you only want to + initialise all parameters staring with "encoder", use "encoder"; + if you want to initialise parameters starting with encoder or decoder, + use "encoder,joiner". + """, + ) + + parser.add_argument( + "--finetune-ckpt", + type=str, + default=None, + help="Fine-tuning from which checkpoint (a path to a .pt file)", + ) + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=str, + default="2,4,3,2,4", + help="Number of zipformer encoder layers, comma separated.", + ) + + parser.add_argument( + "--feedforward-dims", + type=str, + default="1024,1024,2048,2048,1024", + help="Feedforward dimension of the zipformer encoder layers, comma separated.", + ) + + parser.add_argument( + "--nhead", + type=str, + default="8,8,8,8,8", + help="Number of attention heads in the zipformer encoder layers.", + ) + + parser.add_argument( + "--encoder-dims", + type=str, + default="384,384,384,384,384", + help="""Embedding dimension in the 2 blocks of zipformer encoder + layers, comma separated + """, + ) + + parser.add_argument( + "--attention-dims", + type=str, + default="192,192,192,192,192", + help="""Attention dimension in the 2 blocks of zipformer encoder layers,\ + comma separated; not the same as embedding dimension. + """, + ) + + parser.add_argument( + "--encoder-unmasked-dims", + type=str, + default="256,256,256,256,256", + help="""Unmasked dimensions in the encoders, relates to augmentation + during training. Must be <= each of encoder_dims. Empirically, less + than 256 seems to make performance worse. + """, + ) + + parser.add_argument( + "--zipformer-downsampling-factors", + type=str, + default="1,2,4,8,2", + help="Downsampling factor for each stack of encoder layers.", + ) + + parser.add_argument( + "--cnn-module-kernels", + type=str, + default="31,31,31,31,31", + help="Sizes of kernels in convolution modules", + ) + + 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_stateless7/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="""Path to the BPE model. + This should be the bpe model of the original model + """, + ) + + parser.add_argument( + "--base-lr", type=float, default=0.005, help="The base learning rate." + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=100000, + help="""Number of steps that affects how rapidly the learning rate + decreases. During fine-tuning, we set this very large so that the + learning rate slowly decays with number of batches. You may tune + its value by yourself. + """, + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=100, + help="""Number of epochs that affects how rapidly the learning rate + decreases. During fine-tuning, we set this very large so that the + learning rate slowly decays with number of batches. You may tune + its value by yourself. + """, + ) + + 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( + "--inf-check", + type=str2bool, + default=False, + help="Add hooks to check for infinite module outputs and gradients.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=2000, + 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=200, + 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) + add_finetune_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 warmup period that dictates the decay of the + scale on "simple" (un-pruned) loss. + """ + params = AttributeDict( + { + "frame_shift_ms": 10.0, + "allowed_excess_duration_ratio": 0.1, + "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": 3000, # For the 100h subset, use 800 + # parameters for zipformer + "feature_dim": 80, + "subsampling_factor": 4, # not passed in, this is fixed. + "warm_step": 2000, + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Zipformer and Transformer + def to_int_tuple(s: str): + return tuple(map(int, s.split(","))) + + encoder = Zipformer( + num_features=params.feature_dim, + output_downsampling_factor=2, + zipformer_downsampling_factors=to_int_tuple( + params.zipformer_downsampling_factors + ), + encoder_dims=to_int_tuple(params.encoder_dims), + attention_dim=to_int_tuple(params.attention_dims), + encoder_unmasked_dims=to_int_tuple(params.encoder_unmasked_dims), + nhead=to_int_tuple(params.nhead), + feedforward_dim=to_int_tuple(params.feedforward_dims), + cnn_module_kernels=to_int_tuple(params.cnn_module_kernels), + num_encoder_layers=to_int_tuple(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=int(params.encoder_dims.split(",")[-1]), + 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=int(params.encoder_dims.split(",")[-1]), + 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 load_model_params( + ckpt: str, model: nn.Module, init_modules: List[str] = None, strict: bool = True +): + """Load model params from checkpoint + + Args: + ckpt (str): Path to the checkpoint + model (nn.Module): model to be loaded + + """ + logging.info(f"Loading checkpoint from {ckpt}") + checkpoint = torch.load(ckpt, map_location="cpu") + + # if module list is empty, load the whole model from ckpt + if not init_modules: + if next(iter(checkpoint["model"])).startswith("module."): + logging.info("Loading checkpoint saved by DDP") + + dst_state_dict = model.state_dict() + src_state_dict = checkpoint["model"] + for key in dst_state_dict.keys(): + src_key = "{}.{}".format("module", key) + dst_state_dict[key] = src_state_dict.pop(src_key) + assert len(src_state_dict) == 0 + model.load_state_dict(dst_state_dict, strict=strict) + else: + model.load_state_dict(checkpoint["model"], strict=strict) + else: + src_state_dict = checkpoint["model"] + dst_state_dict = model.state_dict() + for module in init_modules: + logging.info(f"Loading parameters starting with prefix {module}") + src_keys = [k for k in src_state_dict.keys() if k.startswith(module)] + dst_keys = [k for k in dst_state_dict.keys() if k.startswith(module)] + assert set(src_keys) == set(dst_keys) # two sets should match exactly + for key in src_keys: + dst_state_dict[key] = src_state_dict.pop(key) + + model.load_state_dict(dst_state_dict, strict=strict) + + return None + + +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], + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute transducer 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 Zipformer 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. + """ + # For the uneven-sized batch, the total duration after padding would possibly + # cause OOM. Hence, for each batch, which is sorted descendingly by length, + # we simply drop the last few shortest samples, so that the retained total frames + # (after padding) would not exceed `allowed_max_frames`: + # `allowed_max_frames = int(max_frames * (1.0 + allowed_excess_duration_ratio))`, + # where `max_frames = max_duration * 1000 // frame_shift_ms`. + # We set allowed_excess_duration_ratio=0.1. + max_frames = params.max_duration * 1000 // params.frame_shift_ms + allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio)) + batch = filter_uneven_sized_batch(batch, allowed_max_frames) + + 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) + + batch_idx_train = params.batch_idx_train + warm_step = params.warm_step + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + 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, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + ) + + s = params.simple_loss_scale + # take down the scale on the simple loss from 1.0 at the start + # to params.simple_loss scale by warm_step. + simple_loss_scale = ( + s + if batch_idx_train >= warm_step + else 1.0 - (batch_idx_train / warm_step) * (1.0 - s) + ) + pruned_loss_scale = ( + 1.0 + if batch_idx_train >= warm_step + else 0.1 + 0.9 * (batch_idx_train / warm_step) + ) + + loss = 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], + sp: spm.SentencePieceProcessor, + 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, + sp=sp, + 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, + sp: spm.SentencePieceProcessor, + 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"]) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + # 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() + set_batch_count(model, params.batch_idx_train) + scheduler.step_batch(params.batch_idx_train) + + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + except: # noqa + display_and_save_batch(batch, params=params, sp=sp) + 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 + ): + 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 % 100 == 0 and params.use_fp16: + # If the grad scale was less than 1, try increasing it. The _growth_interval + # of the grad scaler is configurable, but we can't configure it to have + # different behavior depending on the current grad scale. + cur_grad_scale = scaler._scale.item() + if cur_grad_scale < 1.0 or (cur_grad_scale < 8.0 and batch_idx % 400 == 0): + scaler.update(cur_grad_scale * 2.0) + if cur_grad_scale < 0.01: + logging.warning(f"Grad scale is small: {cur_grad_scale}") + if cur_grad_scale < 1.0e-05: + raise RuntimeError( + f"grad_scale is too small, exiting: {cur_grad_scale}" + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0 + + 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}, " + + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "") + ) + + 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 params.use_fp16: + tb_writer.add_scalar( + "train/grad_scale", + cur_grad_scale, + params.batch_idx_train, + ) + + if batch_idx % params.valid_interval == 0 and not params.print_diagnostics: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + 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}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + 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).to(torch.float64) + + # load model parameters for model fine-tuning + if params.do_finetune: + modules = params.init_modules.split(",") if params.init_modules else None + checkpoints = load_model_params( + ckpt=params.finetune_ckpt, model=model, init_modules=modules + ) + else: + 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) + + parameters_names = [] + parameters_names.append( + [name_param_pair[0] for name_param_pair in model.named_parameters()] + ) + optimizer = ScaledAdam( + model.parameters(), + lr=params.base_lr, + clipping_scale=2.0, + parameters_names=parameters_names, + ) + + 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) + + if params.inf_check: + register_inf_check_hooks(model) + + gigaspeech = GigaSpeechAsrDataModule(args) + + train_cuts = gigaspeech.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 + if c.duration < 1.0 or c.duration > 20.0: + logging.warning( + f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + ) + return False + + # In pruned RNN-T, we require that T >= S + # where T is the number of feature frames after subsampling + # and S is the number of tokens in the utterance + + # In ./zipformer.py, the conv module uses the following expression + # for subsampling + T = ((c.num_frames - 7) // 2 + 1) // 2 + tokens = sp.encode(c.supervisions[0].text, out_type=str) + + if T < len(tokens): + logging.warning( + f"Exclude cut with ID {c.id} from training. " + f"Number of frames (before subsampling): {c.num_frames}. " + f"Number of frames (after subsampling): {T}. " + f"Text: {c.supervisions[0].text}. " + f"Tokens: {tokens}. " + f"Number of tokens: {len(tokens)}" + ) + return False + + return True + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + 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 = gigaspeech.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = gigaspeech.dev_cuts() + valid_dl = gigaspeech.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0) + 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, + sp=sp, + 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 display_and_save_batch( + batch: dict, + params: AttributeDict, + sp: spm.SentencePieceProcessor, +) -> 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`. + sp: + The BPE model. + """ + 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 = sp.encode(supervisions["text"], out_type=int) + 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, + sp: spm.SentencePieceProcessor, + 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: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + loss.backward() + 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, sp=sp) + raise + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + + +def main(): + parser = get_parser() + GigaSpeechAsrDataModule.add_arguments( + parser + ) # you may replace this with your own dataset + 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/librispeech/ASR/pruned_transducer_stateless7/gigaspeech.py b/egs/librispeech/ASR/pruned_transducer_stateless7/gigaspeech.py new file mode 100644 index 000000000..5c01d7190 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/gigaspeech.py @@ -0,0 +1,406 @@ +# 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, Optional + +import torch +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy +from lhotse.dataset import ( + CutConcatenate, + CutMix, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import 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 GigaSpeechAsrDataModule: + """ + 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=30, + 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( + "--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. ", + ) + + # GigaSpeech specific arguments + group.add_argument( + "--subset", + type=str, + default="XL", + help="Select the GigaSpeech subset (XS|S|M|L|XL)", + ) + group.add_argument( + "--small-dev", + type=str2bool, + default=False, + help="Should we use only 1000 utterances for dev (speeds up training)", + ) + + 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. + """ + + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + logging.info("About to get Musan cuts") + cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz") + 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( + 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, + drop_last=True, + ) + 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") + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_sampler.load_state_dict(sampler_state_dict) + + # '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, + ) + + 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, + shuffle=False, + ) + logging.info("About to create dev dataloader") + valid_dl = DataLoader( + validate, + sampler=valid_sampler, + batch_size=None, + num_workers=2, + 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 PrecomputedFeatures(), + return_cuts=self.args.return_cuts, + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.debug("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(f"About to get train_{self.args.subset} cuts") + path = self.args.manifest_dir / f"cuts_{self.args.subset}.jsonl.gz" + cuts_train = CutSet.from_jsonl_lazy(path) + return cuts_train + + @lru_cache() + def dev_cuts(self) -> CutSet: + logging.info("About to get dev cuts") + cuts_valid = load_manifest_lazy(self.args.manifest_dir / "cuts_DEV.jsonl.gz") + if self.args.small_dev: + return cuts_valid.subset(first=1000) + else: + return cuts_valid + + @lru_cache() + def test_cuts(self) -> CutSet: + logging.info("About to get test cuts") + return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST.jsonl.gz") diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/gigaspeech_scoring.py b/egs/librispeech/ASR/pruned_transducer_stateless7/gigaspeech_scoring.py new file mode 120000 index 000000000..fdfa6ce4b --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/gigaspeech_scoring.py @@ -0,0 +1 @@ +../../../gigaspeech/ASR/pruned_transducer_stateless2/gigaspeech_scoring.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py b/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py index 374b78cb3..b84e518d0 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py @@ -799,6 +799,47 @@ def _test_eden(): logging.info(f"state dict = {scheduler.state_dict()}") +def _plot_eden_lr(): + import matplotlib.pyplot as plt + + m = torch.nn.Linear(100, 100) + parameters_names = [] + parameters_names.append( + [name_param_pair[0] for name_param_pair in m.named_parameters()] + ) + + for lr_epoch in [4, 10, 100]: + for lr_batch in [100, 400]: + optim = ScaledAdam( + m.parameters(), lr=0.03, parameters_names=parameters_names + ) + scheduler = Eden( + optim, lr_batches=lr_batch, lr_epochs=lr_epoch, verbose=True + ) + lr = [] + + for epoch in range(10): + scheduler.step_epoch(epoch) # sets epoch to `epoch` + + for step in range(500): + lr.append(scheduler.get_lr()) + + x = torch.randn(200, 100).detach() + x.requires_grad = True + y = m(x) + dy = torch.randn(200, 100).detach() + f = (y * dy).sum() + f.backward() + + optim.step() + scheduler.step_batch() + optim.zero_grad() + plt.plot(lr, label=f"lr_epoch:{lr_epoch}, lr_batch:{lr_batch}") + + plt.legend() + plt.savefig("lr.png") + + # This is included mostly as a baseline for ScaledAdam. class Eve(Optimizer): """ @@ -1057,5 +1098,6 @@ if __name__ == "__main__": else: hidden_dim = 200 - _test_scaled_adam(hidden_dim) - _test_eden() + # _test_scaled_adam(hidden_dim) + # _test_eden() + _plot_eden_lr() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/ctc_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/ctc_decode.py index 4b373e4c7..629bec058 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/ctc_decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/ctc_decode.py @@ -541,18 +541,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats(f, f"{test_set_name}-{key}", results) test_set_wers[key] = wer @@ -560,9 +556,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/decode.py index 32a9b6bb2..7641fa5af 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/decode.py @@ -593,18 +593,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -614,9 +610,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_decode.py index f137485b2..fa7144f0f 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_decode.py @@ -532,18 +532,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats(f, f"{test_set_name}-{key}", results) test_set_wers[key] = wer @@ -551,9 +547,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/decode.py index ce45a4beb..e497787d3 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/decode.py @@ -593,18 +593,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -614,9 +610,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py index aebe2b94b..e7616fbc5 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py @@ -568,18 +568,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -589,9 +585,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming_decode.py index 7a349ecb2..c272ed641 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming_decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming_decode.py @@ -409,18 +409,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -430,9 +426,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless8/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless8/decode.py index e61367134..7b651a632 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless8/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless8/decode.py @@ -594,18 +594,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -615,9 +611,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/transducer/decode.py b/egs/librispeech/ASR/transducer/decode.py index 804713a20..8d379d1fa 100755 --- a/egs/librispeech/ASR/transducer/decode.py +++ b/egs/librispeech/ASR/transducer/decode.py @@ -325,18 +325,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -346,9 +342,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/transducer_lstm/decode.py b/egs/librispeech/ASR/transducer_lstm/decode.py index 9511ca6d7..806b68f40 100755 --- a/egs/librispeech/ASR/transducer_lstm/decode.py +++ b/egs/librispeech/ASR/transducer_lstm/decode.py @@ -322,18 +322,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -343,9 +339,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/transducer_stateless/decode.py b/egs/librispeech/ASR/transducer_stateless/decode.py index 643238f1b..42125e19f 100755 --- a/egs/librispeech/ASR/transducer_stateless/decode.py +++ b/egs/librispeech/ASR/transducer_stateless/decode.py @@ -379,18 +379,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -400,9 +396,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/transducer_stateless2/decode.py b/egs/librispeech/ASR/transducer_stateless2/decode.py index 9a6363629..b05fe2a4d 100755 --- a/egs/librispeech/ASR/transducer_stateless2/decode.py +++ b/egs/librispeech/ASR/transducer_stateless2/decode.py @@ -379,18 +379,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -400,9 +396,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py index 56ad558c6..5570b30ae 100755 --- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py +++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py @@ -380,18 +380,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -401,9 +397,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/librispeech/ASR/zipformer_mmi/decode.py b/egs/librispeech/ASR/zipformer_mmi/decode.py index 7d0ea78bb..33c0bf199 100755 --- a/egs/librispeech/ASR/zipformer_mmi/decode.py +++ b/egs/librispeech/ASR/zipformer_mmi/decode.py @@ -471,18 +471,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats(f, f"{test_set_name}-{key}", results) test_set_wers[key] = wer @@ -490,9 +486,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/mgb2/ASR/pruned_transducer_stateless5/decode.py b/egs/mgb2/ASR/pruned_transducer_stateless5/decode.py index 1463f8f67..72338bade 100755 --- a/egs/mgb2/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/mgb2/ASR/pruned_transducer_stateless5/decode.py @@ -410,17 +410,13 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -430,9 +426,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py index 219c96d60..4434aae62 100755 --- a/egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py @@ -391,18 +391,14 @@ def save_results( test_set_wers = dict() test_set_cers = dict() for key, results in results_dict.items(): - recog_path = ( - params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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. - wers_filename = ( - params.res_dir / f"wers-{test_set_name}-{key}-{params.suffix}.txt" - ) + wers_filename = params.res_dir / f"wers-{test_set_name}-{params.suffix}.txt" with open(wers_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -413,9 +409,7 @@ def save_results( results_char = [] for res in results: results_char.append((res[0], list("".join(res[1])), list("".join(res[2])))) - cers_filename = ( - params.res_dir / f"cers-{test_set_name}-{key}-{params.suffix}.txt" - ) + cers_filename = params.res_dir / f"cers-{test_set_name}-{params.suffix}.txt" with open(cers_filename, "w") as f: cer = write_error_stats( f, f"{test_set_name}-{key}", results_char, enable_log=True @@ -426,9 +420,7 @@ def save_results( test_set_wers = {k: v for k, v in sorted(test_set_wers.items(), key=lambda x: x[1])} test_set_cers = {k: v for k, v in sorted(test_set_cers.items(), key=lambda x: x[1])} - errs_info = ( - params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER\tCER", file=f) for key in test_set_wers: diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/decode.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/decode.py index bf91fef7e..3bfb832fb 100755 --- a/egs/tal_csasr/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/decode.py @@ -509,18 +509,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -530,9 +526,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless/decode.py b/egs/tedlium3/ASR/pruned_transducer_stateless/decode.py index 38f2ae83c..abba9d403 100755 --- a/egs/tedlium3/ASR/pruned_transducer_stateless/decode.py +++ b/egs/tedlium3/ASR/pruned_transducer_stateless/decode.py @@ -379,18 +379,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -400,9 +396,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/tedlium3/ASR/transducer_stateless/decode.py b/egs/tedlium3/ASR/transducer_stateless/decode.py index 01f08ce59..fb0e3116b 100755 --- a/egs/tedlium3/ASR/transducer_stateless/decode.py +++ b/egs/tedlium3/ASR/transducer_stateless/decode.py @@ -354,18 +354,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -375,9 +371,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: 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 bd73e520e..20d7341db 100755 --- a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_dev_test.py +++ b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_dev_test.py @@ -20,7 +20,7 @@ import logging from pathlib import Path import torch -from lhotse import CutSet, KaldifeatFbank, KaldifeatFbankConfig, LilcomHdf5Writer +from lhotse import CutSet, KaldifeatFbank, KaldifeatFbankConfig, LilcomChunkyWriter # Torch's multithreaded behavior needs to be disabled or # it wastes a lot of CPU and slow things down. @@ -69,7 +69,7 @@ def compute_fbank_wenetspeech_dev_test(): storage_path=f"{in_out_dir}/feats_{partition}", num_workers=num_workers, batch_duration=batch_duration, - storage_type=LilcomHdf5Writer, + storage_type=LilcomChunkyWriter, overwrite=True, ) diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py index fc8039fe3..8c18e94f9 100644 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -46,9 +46,6 @@ from torch.utils.data import DataLoader from icefall.utils import str2bool -# set_caching_enabled(False) -# torch.set_num_threads(1) - class _SeedWorkers: def __init__(self, seed: int): @@ -348,24 +345,18 @@ class WenetSpeechAsrDataModule: 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") - from lhotse.dataset.iterable_dataset import IterableDatasetWrapper - - dev_iter_dataset = IterableDatasetWrapper( - dataset=validate, - sampler=valid_sampler, - ) valid_dl = DataLoader( - dev_iter_dataset, + validate, batch_size=None, + sampler=valid_sampler, num_workers=self.args.num_workers, persistent_workers=False, ) @@ -383,19 +374,10 @@ class WenetSpeechAsrDataModule: sampler = DynamicBucketingSampler( cuts, max_duration=self.args.max_duration, - buffer_size=10000, - # rank=0, - # world_size=1, shuffle=False, ) - # from lhotse.dataset.iterable_dataset import IterableDatasetWrapper - # test_iter_dataset = IterableDatasetWrapper( - # dataset=test, - # sampler=sampler, - # ) test_dl = DataLoader( - # test_iter_dataset, test, batch_size=None, sampler=sampler, @@ -424,7 +406,9 @@ class WenetSpeechAsrDataModule: @lru_cache() def test_meeting_cuts(self) -> List[CutSet]: logging.info("About to get TEST_MEETING cuts") - return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_MEETING2.jsonl.gz") + return load_manifest_lazy( + self.args.manifest_dir / "cuts_TEST_MEETING2.jsonl.gz" + ) @lru_cache() def test_car_cuts(self) -> List[CutSet]: diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py index 04602ea2e..bdd1f27bc 100755 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py @@ -516,18 +516,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -537,9 +533,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: @@ -657,83 +651,18 @@ def main(): num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") - # Note: Please use "pip install webdataset==0.1.103" - # for installing the webdataset. - import glob - import os - - from lhotse import CutSet - from lhotse.dataset.webdataset import export_to_webdataset - # we need cut ids to display recognition results. args.return_cuts = True wenetspeech = WenetSpeechAsrDataModule(args) - dev = "dev" - test_net = "test_net" - test_meeting = "test_meeting" + dev_cuts = wenetspeech.valid_cuts() + dev_dl = wenetspeech.valid_dataloaders(dev_cuts) - if not os.path.exists(f"{dev}/shared-0.tar"): - os.makedirs(dev) - dev_cuts = wenetspeech.valid_cuts() - export_to_webdataset( - dev_cuts, - output_path=f"{dev}/shared-%d.tar", - shard_size=300, - ) + test_net_cuts = wenetspeech.test_net_cuts() + test_net_dl = wenetspeech.test_dataloaders(test_net_cuts) - if not os.path.exists(f"{test_net}/shared-0.tar"): - os.makedirs(test_net) - test_net_cuts = wenetspeech.test_net_cuts() - export_to_webdataset( - test_net_cuts, - output_path=f"{test_net}/shared-%d.tar", - shard_size=300, - ) - - if not os.path.exists(f"{test_meeting}/shared-0.tar"): - os.makedirs(test_meeting) - test_meeting_cuts = wenetspeech.test_meeting_cuts() - export_to_webdataset( - test_meeting_cuts, - output_path=f"{test_meeting}/shared-%d.tar", - shard_size=300, - ) - - dev_shards = [ - str(path) for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar"))) - ] - cuts_dev_webdataset = CutSet.from_webdataset( - dev_shards, - split_by_worker=True, - split_by_node=True, - shuffle_shards=True, - ) - - test_net_shards = [ - str(path) for path in sorted(glob.glob(os.path.join(test_net, "shared-*.tar"))) - ] - cuts_test_net_webdataset = CutSet.from_webdataset( - test_net_shards, - split_by_worker=True, - split_by_node=True, - shuffle_shards=True, - ) - - test_meeting_shards = [ - str(path) - for path in sorted(glob.glob(os.path.join(test_meeting, "shared-*.tar"))) - ] - cuts_test_meeting_webdataset = CutSet.from_webdataset( - test_meeting_shards, - split_by_worker=True, - split_by_node=True, - shuffle_shards=True, - ) - - dev_dl = wenetspeech.valid_dataloaders(cuts_dev_webdataset) - test_net_dl = wenetspeech.test_dataloaders(cuts_test_net_webdataset) - test_meeting_dl = wenetspeech.test_dataloaders(cuts_test_meeting_webdataset) + test_meeting_cuts = wenetspeech.test_meeting_cuts() + test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts) test_sets = ["DEV", "TEST_NET", "TEST_MEETING"] test_dl = [dev_dl, test_net_dl, test_meeting_dl] diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py b/egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py index 6a036a709..f7cd4cbef 100755 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py @@ -388,7 +388,7 @@ def decode_one_batch( encoder_out=encoder_out, beam=params.beam_size, encoder_out_lens=encoder_out_lens, - context_graph=context_graph + context_graph=context_graph, ) for i in range(encoder_out.size(0)): hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) @@ -428,7 +428,9 @@ def decode_one_batch( ): hyps } else: - return {f"beam_size_{params.beam_size}_context_score_{params.context_score}": hyps} + return { + f"beam_size_{params.beam_size}_context_score_{params.context_score}": hyps + } def decode_dataset( @@ -509,18 +511,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -530,9 +528,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: @@ -700,82 +696,10 @@ def main(): num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") - # Note: Please use "pip install webdataset==0.1.103" - # for installing the webdataset. - - from lhotse import CutSet - from lhotse.dataset.webdataset import export_to_webdataset - # we need cut ids to display recognition results. args.return_cuts = True wenetspeech = WenetSpeechAsrDataModule(args) - #dev = "dev" - #test_net = "test_net" - #test_meeting = "test_meeting" - - #if not os.path.exists(f"{dev}/shared-0.tar"): - # os.makedirs(dev) - # dev_cuts = wenetspeech.valid_cuts() - # export_to_webdataset( - # dev_cuts, - # output_path=f"{dev}/shared-%d.tar", - # shard_size=300, - # ) - - #if not os.path.exists(f"{test_net}/shared-0.tar"): - # os.makedirs(test_net) - # test_net_cuts = wenetspeech.test_net_cuts() - # export_to_webdataset( - # test_net_cuts, - # output_path=f"{test_net}/shared-%d.tar", - # shard_size=300, - # ) - - #if not os.path.exists(f"{test_meeting}/shared-0.tar"): - # os.makedirs(test_meeting) - # test_meeting_cuts = wenetspeech.test_meeting_cuts() - # export_to_webdataset( - # test_meeting_cuts, - # output_path=f"{test_meeting}/shared-%d.tar", - # shard_size=300, - # ) - - #dev_shards = [ - # str(path) for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar"))) - #] - #cuts_dev_webdataset = CutSet.from_webdataset( - # dev_shards, - # split_by_worker=True, - # split_by_node=True, - # shuffle_shards=True, - #) - - #test_net_shards = [ - # str(path) for path in sorted(glob.glob(os.path.join(test_net, "shared-*.tar"))) - #] - #cuts_test_net_webdataset = CutSet.from_webdataset( - # test_net_shards, - # split_by_worker=True, - # split_by_node=True, - # shuffle_shards=True, - #) - - #test_meeting_shards = [ - # str(path) - # for path in sorted(glob.glob(os.path.join(test_meeting, "shared-*.tar"))) - #] - #cuts_test_meeting_webdataset = CutSet.from_webdataset( - # test_meeting_shards, - # split_by_worker=True, - # split_by_node=True, - # shuffle_shards=True, - #) - - #dev_dl = wenetspeech.valid_dataloaders(cuts_dev_webdataset) - #test_net_dl = wenetspeech.test_dataloaders(cuts_test_net_webdataset) - #test_meeting_dl = wenetspeech.test_dataloaders(cuts_test_meeting_webdataset) - dev_cuts = wenetspeech.valid_cuts() dev_dl = wenetspeech.valid_dataloaders(dev_cuts) @@ -788,11 +712,8 @@ def main(): test_car_cuts = wenetspeech.test_car_cuts() test_car_dl = wenetspeech.test_dataloaders(test_car_cuts) - # test_sets = ["CAR", "TEST_NET", "DEV", "TEST_MEETING"] - # test_dls = [test_car_dl, test_net_dl, dev_dl, test_meeting_dl] - - test_sets = ["CAR", "TEST_NET", "TEST_MEETING"] - test_dls = [test_car_dl, test_net_dl, test_meeting_dl] + test_sets = ["CAR", "TEST_NET", "DEV", "TEST_MEETING"] + test_dls = [test_car_dl, test_net_dl, dev_dl, test_meeting_dl] for test_set, test_dl in zip(test_sets, test_dls): results_dict = decode_dataset( diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless5/streaming_decode.py b/egs/wenetspeech/ASR/pruned_transducer_stateless5/streaming_decode.py index c7863415b..3a4dc3cb8 100644 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless5/streaming_decode.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless5/streaming_decode.py @@ -466,9 +466,7 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" # sort results so we can easily compare the difference between two # recognition results results = sorted(results) @@ -477,9 +475,7 @@ def save_results( # 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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -489,9 +485,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/xbmu_amdo31/ASR/pruned_transducer_stateless5/decode.py b/egs/xbmu_amdo31/ASR/pruned_transducer_stateless5/decode.py index 6a67e26f8..b77f734e3 100755 --- a/egs/xbmu_amdo31/ASR/pruned_transducer_stateless5/decode.py +++ b/egs/xbmu_amdo31/ASR/pruned_transducer_stateless5/decode.py @@ -701,18 +701,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -722,9 +718,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/egs/xbmu_amdo31/ASR/pruned_transducer_stateless7/decode.py b/egs/xbmu_amdo31/ASR/pruned_transducer_stateless7/decode.py index ace792e13..e334e690a 100755 --- a/egs/xbmu_amdo31/ASR/pruned_transducer_stateless7/decode.py +++ b/egs/xbmu_amdo31/ASR/pruned_transducer_stateless7/decode.py @@ -593,18 +593,14 @@ def save_results( ): 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" - ) + recog_path = params.res_dir / f"recogs-{test_set_name}-{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" - ) + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" with open(errs_filename, "w") as f: wer = write_error_stats( f, f"{test_set_name}-{key}", results, enable_log=True @@ -614,9 +610,7 @@ def save_results( 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" - ) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" with open(errs_info, "w") as f: print("settings\tWER", file=f) for key, val in test_set_wers: diff --git a/icefall/utils.py b/icefall/utils.py index 2358ed02f..5d86472b5 100644 --- a/icefall/utils.py +++ b/icefall/utils.py @@ -1095,10 +1095,10 @@ def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: assert lengths.ndim == 1, lengths.ndim max_len = max(max_len, lengths.max()) n = lengths.size(0) + seq_range = torch.arange(0, max_len, device=lengths.device) + expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len) - expaned_lengths = torch.arange(max_len).expand(n, max_len).to(lengths) - - return expaned_lengths >= lengths.unsqueeze(1) + return expaned_lengths >= lengths.unsqueeze(-1) # Copied and modified from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/mask.py