mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
Add Zipformer-MMI (#746)
* Minor fix to conformer-mmi * Minor fixes * Fix decode.py * add training files * train with ctc warmup * add pruned_transducer_stateless7_mmi * add zipformer_mmi/mmi_decode.py, using HP as decoding graph * add mmi_decode.py * remove pruned_transducer_stateless7_mmi * rename zipformer_mmi/train_with_ctc.py as zipformer_mmi/train.py * remove unused method * rename mmi_decode.py * add export.py pretrained.py jit_pretrained.py ... * add RESULTS.md * add CI test * add docs * add README.md Co-authored-by: pkufool <wkang.pku@gmail.com>
This commit is contained in:
Zengwei Yao
GitHub
parent
e83409cbe5
commit
b25c234c51
3
.flake8
3
.flake8
@ -1,7 +1,7 @@
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[flake8]
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show-source=true
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statistics=true
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max-line-length = 80
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max-line-length = 88
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per-file-ignores =
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# line too long
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icefall/diagnostics.py: E501,
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@ -12,6 +12,7 @@ per-file-ignores =
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egs/librispeech/ASR/lstm_transducer_stateless*/*.py: E501, E203
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egs/librispeech/ASR/conv_emformer_transducer_stateless*/*.py: E501, E203
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egs/librispeech/ASR/conformer_ctc*/*py: E501,
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egs/librispeech/ASR/zipformer_mmi/*.py: E501, E203
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egs/librispeech/ASR/RESULTS.md: E999,
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# invalid escape sequence (cause by tex formular), W605
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@ -13,7 +13,6 @@ cd egs/librispeech/ASR
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repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-conformer-ctc3-2022-11-27
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log "Downloading pre-trained model from $repo_url"
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git lfs install
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GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
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repo=$(basename $repo_url)
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@ -23,7 +22,12 @@ soxi $repo/test_wavs/*.wav
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ls -lh $repo/test_wavs/*.wav
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pushd $repo/exp
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git lfs pull --include "data/*"
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git lfs pull --include "data/lang_bpe_500/HLG.pt"
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git lfs pull --include "data/lang_bpe_500/L.pt"
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git lfs pull --include "data/lang_bpe_500/LG.pt"
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git lfs pull --include "data/lang_bpe_500/Linv.pt"
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git lfs pull --include "data/lang_bpe_500/bpe.model"
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git lfs pull --include "data/lm/G_4_gram.pt"
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git lfs pull --include "exp/jit_trace.pt"
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git lfs pull --include "exp/pretrained.pt"
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ln -s pretrained.pt epoch-99.pt
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@ -13,7 +13,6 @@ cd egs/librispeech/ASR
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repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01
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log "Downloading pre-trained model from $repo_url"
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git lfs install
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GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
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repo=$(basename $repo_url)
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@ -23,7 +22,12 @@ soxi $repo/test_wavs/*.wav
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ls -lh $repo/test_wavs/*.wav
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pushd $repo/exp
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git lfs pull --include "data/*"
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git lfs pull --include "data/lang_bpe_500/HLG.pt"
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git lfs pull --include "data/lang_bpe_500/L.pt"
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git lfs pull --include "data/lang_bpe_500/LG.pt"
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git lfs pull --include "data/lang_bpe_500/Linv.pt"
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git lfs pull --include "data/lang_bpe_500/bpe.model"
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git lfs pull --include "data/lm/G_4_gram.pt"
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git lfs pull --include "exp/cpu_jit.pt"
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git lfs pull --include "exp/pretrained.pt"
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ln -s pretrained.pt epoch-99.pt
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103
.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh
vendored
Executable file
103
.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh
vendored
Executable file
@ -0,0 +1,103 @@
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#!/usr/bin/env bash
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set -e
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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cd egs/librispeech/ASR
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repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-mmi-2022-12-08
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log "Downloading pre-trained model from $repo_url"
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GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
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repo=$(basename $repo_url)
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log "Display test files"
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tree $repo/
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soxi $repo/test_wavs/*.wav
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ls -lh $repo/test_wavs/*.wav
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pushd $repo/exp
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git lfs pull --include "data/lang_bpe_500/3gram.pt"
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git lfs pull --include "data/lang_bpe_500/4gram.pt"
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git lfs pull --include "data/lang_bpe_500/L.pt"
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git lfs pull --include "data/lang_bpe_500/LG.pt"
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git lfs pull --include "data/lang_bpe_500/Linv.pt"
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git lfs pull --include "data/lang_bpe_500/bpe.model"
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git lfs pull --include "exp/cpu_jit.pt"
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git lfs pull --include "exp/pretrained.pt"
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ln -s pretrained.pt epoch-99.pt
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ls -lh *.pt
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popd
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log "Export to torchscript model"
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./zipformer_mmi/export.py \
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--exp-dir $repo/exp \
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--use-averaged-model false \
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--bpe-model $repo/data/lang_bpe_500/bpe.model \
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--epoch 99 \
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--avg 1 \
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--jit 1
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ls -lh $repo/exp/*.pt
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log "Decode with models exported by torch.jit.script()"
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./zipformer_mmi/jit_pretrained.py \
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--bpe-model $repo/data/lang_bpe_500/bpe.model \
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--nn-model-filename $repo/exp/cpu_jit.pt \
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--lang-dir $repo/data/lang_bpe_500 \
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$repo/test_wavs/1089-134686-0001.wav \
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$repo/test_wavs/1221-135766-0001.wav \
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$repo/test_wavs/1221-135766-0002.wav
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for method in 1best nbest nbest-rescoring-LG nbest-rescoring-3-gram nbest-rescoring-4-gram; do
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log "$method"
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./zipformer_mmi/pretrained.py \
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--method $method \
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--checkpoint $repo/exp/pretrained.pt \
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--lang-dir $repo/data/lang_bpe_500 \
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--bpe-model $repo/data/lang_bpe_500/bpe.model \
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$repo/test_wavs/1089-134686-0001.wav \
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$repo/test_wavs/1221-135766-0001.wav \
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$repo/test_wavs/1221-135766-0002.wav
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done
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echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
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echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}"
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if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode" ]]; then
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mkdir -p zipformer_mmi/exp
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ln -s $PWD/$repo/exp/pretrained.pt zipformer_mmi/exp/epoch-999.pt
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ln -s $PWD/$repo/data/lang_bpe_500 data/
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ls -lh data
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ls -lh zipformer_mmi/exp
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log "Decoding test-clean and test-other"
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# use a small value for decoding with CPU
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max_duration=100
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for method in 1best nbest nbest-rescoring-LG nbest-rescoring-3-gram nbest-rescoring-4-gram; do
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log "Decoding with $method"
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./zipformer_mmi/decode.py \
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--decoding-method $method \
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--epoch 999 \
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--avg 1 \
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--use-averaged-model 0 \
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--nbest-scale 1.2 \
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--hp-scale 1.0 \
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--max-duration $max_duration \
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--lang-dir $repo/data/lang_bpe_500 \
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--exp-dir zipformer_mmi/exp
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done
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rm zipformer_mmi/exp/*.pt
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fi
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167
.github/workflows/run-librispeech-2022-12-08-zipformer-mmi.yml
vendored
Normal file
167
.github/workflows/run-librispeech-2022-12-08-zipformer-mmi.yml
vendored
Normal file
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# Copyright 2022 Zengwei Yao
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# See ../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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name: run-librispeech-2022-12-08-zipformer-mmi
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# zipformer
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on:
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push:
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branches:
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- master
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pull_request:
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types: [labeled]
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schedule:
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# minute (0-59)
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# hour (0-23)
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# day of the month (1-31)
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# month (1-12)
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# day of the week (0-6)
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# nightly build at 15:50 UTC time every day
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- cron: "50 15 * * *"
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concurrency:
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group: run_librispeech_2022_12_08_zipformer-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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run_librispeech_2022_12_08_zipformer:
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if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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os: [ubuntu-latest]
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python-version: [3.8]
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fail-fast: false
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steps:
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- uses: actions/checkout@v2
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with:
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fetch-depth: 0
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- name: Setup Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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cache: 'pip'
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cache-dependency-path: '**/requirements-ci.txt'
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- name: Install Python dependencies
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run: |
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grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install
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pip uninstall -y protobuf
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pip install --no-binary protobuf protobuf
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- name: Cache kaldifeat
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id: my-cache
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uses: actions/cache@v2
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with:
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path: |
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~/tmp/kaldifeat
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key: cache-tmp-${{ matrix.python-version }}-2022-09-25
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- name: Install kaldifeat
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if: steps.my-cache.outputs.cache-hit != 'true'
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shell: bash
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run: |
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.github/scripts/install-kaldifeat.sh
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- name: Cache LibriSpeech test-clean and test-other datasets
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id: libri-test-clean-and-test-other-data
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uses: actions/cache@v2
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with:
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path: |
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~/tmp/download
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key: cache-libri-test-clean-and-test-other
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- name: Download LibriSpeech test-clean and test-other
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if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
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shell: bash
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run: |
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.github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
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- name: Prepare manifests for LibriSpeech test-clean and test-other
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shell: bash
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run: |
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.github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
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- name: Cache LibriSpeech test-clean and test-other fbank features
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id: libri-test-clean-and-test-other-fbank
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uses: actions/cache@v2
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with:
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path: |
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~/tmp/fbank-libri
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key: cache-libri-fbank-test-clean-and-test-other-v2
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- name: Compute fbank for LibriSpeech test-clean and test-other
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if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true'
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shell: bash
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run: |
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.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh
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- name: Inference with pre-trained model
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shell: bash
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env:
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GITHUB_EVENT_NAME: ${{ github.event_name }}
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GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
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run: |
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mkdir -p egs/librispeech/ASR/data
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ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
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ls -lh egs/librispeech/ASR/data/*
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sudo apt-get -qq install git-lfs tree sox
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export PYTHONPATH=$PWD:$PYTHONPATH
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export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
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export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
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.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh
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- name: Display decoding results for librispeech zipformer-mmi
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if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
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shell: bash
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run: |
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cd egs/librispeech/ASR/
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tree ./zipformer-mmi/exp
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cd zipformer-mmi
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echo "results for zipformer-mmi"
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echo "===1best==="
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find exp/1best -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
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find exp/1best -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
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echo "===nbest==="
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find exp/nbest -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
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find exp/nbest -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
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echo "===nbest-rescoring-LG==="
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find exp/nbest-rescoring-LG -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
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find exp/nbest-rescoring-LG -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
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echo "===nbest-rescoring-3-gram==="
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find exp/nbest-rescoring-3-gram -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
|
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find exp/nbest-rescoring-3-gram -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
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|
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echo "===nbest-rescoring-4-gram==="
|
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find exp/nbest-rescoring-4-gram -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
|
||||
find exp/nbest-rescoring-4-gram -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
|
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|
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- name: Upload decoding results for librispeech zipformer-mmi
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uses: actions/upload-artifact@v2
|
||||
if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
|
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with:
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name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-zipformer_mmi-2022-12-08
|
||||
path: egs/librispeech/ASR/zipformer_mmi/exp/
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@ -7,3 +7,4 @@ LibriSpeech
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tdnn_lstm_ctc
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conformer_ctc
|
||||
lstm_pruned_stateless_transducer
|
||||
zipformer_mmi
|
||||
|
||||
422
docs/source/recipes/librispeech/zipformer_mmi.rst
Normal file
422
docs/source/recipes/librispeech/zipformer_mmi.rst
Normal file
@ -0,0 +1,422 @@
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Zipformer MMI
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===============
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.. hint::
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||||
|
||||
Please scroll down to the bottom of this page to find download links
|
||||
for pretrained models if you don't want to train a model from scratch.
|
||||
|
||||
|
||||
This tutorial shows you how to train an Zipformer MMI model
|
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with the `LibriSpeech <https://www.openslr.org/12>`_ dataset.
|
||||
|
||||
We use LF-MMI to compute the loss.
|
||||
|
||||
.. note::
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||||
|
||||
You can find the document about LF-MMI training at the following address:
|
||||
|
||||
`<https://github.com/k2-fsa/next-gen-kaldi-wechat/blob/master/pdf/LF-MMI-training-and-decoding-in-k2-Part-I.pdf>`_
|
||||
|
||||
|
||||
Data preparation
|
||||
----------------
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/librispeech/ASR
|
||||
$ ./prepare.sh
|
||||
|
||||
The script ``./prepare.sh`` handles the data preparation for you, **automagically**.
|
||||
All you need to do is to run it.
|
||||
|
||||
.. note::
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||||
|
||||
We encourage you to read ``./prepare.sh``.
|
||||
|
||||
The data preparation contains several stages. You can use the following two
|
||||
options:
|
||||
|
||||
- ``--stage``
|
||||
- ``--stop-stage``
|
||||
|
||||
to control which stage(s) should be run. By default, all stages are executed.
|
||||
|
||||
|
||||
For example,
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/librispeech/ASR
|
||||
$ ./prepare.sh --stage 0 --stop-stage 0
|
||||
|
||||
means to run only stage 0.
|
||||
|
||||
To run stage 2 to stage 5, use:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ ./prepare.sh --stage 2 --stop-stage 5
|
||||
|
||||
.. hint::
|
||||
|
||||
If you have pre-downloaded the `LibriSpeech <https://www.openslr.org/12>`_
|
||||
dataset and the `musan <http://www.openslr.org/17/>`_ dataset, say,
|
||||
they are saved in ``/tmp/LibriSpeech`` and ``/tmp/musan``, you can modify
|
||||
the ``dl_dir`` variable in ``./prepare.sh`` to point to ``/tmp`` so that
|
||||
``./prepare.sh`` won't re-download them.
|
||||
|
||||
.. note::
|
||||
|
||||
All generated files by ``./prepare.sh``, e.g., features, lexicon, etc,
|
||||
are saved in ``./data`` directory.
|
||||
|
||||
We provide the following YouTube video showing how to run ``./prepare.sh``.
|
||||
|
||||
.. note::
|
||||
|
||||
To get the latest news of `next-gen Kaldi <https://github.com/k2-fsa>`_, please subscribe
|
||||
the following YouTube channel by `Nadira Povey <https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_:
|
||||
|
||||
`<https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_
|
||||
|
||||
.. youtube:: ofEIoJL-mGM
|
||||
|
||||
Training
|
||||
--------
|
||||
|
||||
For stability, it uses CTC loss for model warm-up and then switches to MMI loss.
|
||||
|
||||
Configurable options
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/librispeech/ASR
|
||||
$ ./zipformer_mmi/train.py --help
|
||||
|
||||
shows you the training options that can be passed from the commandline.
|
||||
The following options are used quite often:
|
||||
|
||||
- ``--full-libri``
|
||||
|
||||
If it's True, the training part uses all the training data, i.e.,
|
||||
960 hours. Otherwise, the training part uses only the subset
|
||||
``train-clean-100``, which has 100 hours of training data.
|
||||
|
||||
.. CAUTION::
|
||||
|
||||
The training set is perturbed by speed with two factors: 0.9 and 1.1.
|
||||
If ``--full-libri`` is True, each epoch actually processes
|
||||
``3x960 == 2880`` hours of data.
|
||||
|
||||
- ``--num-epochs``
|
||||
|
||||
It is the number of epochs to train. For instance,
|
||||
``./zipformer_mmi/train.py --num-epochs 30`` trains for 30 epochs
|
||||
and generates ``epoch-1.pt``, ``epoch-2.pt``, ..., ``epoch-30.pt``
|
||||
in the folder ``./zipformer_mmi/exp``.
|
||||
|
||||
- ``--start-epoch``
|
||||
|
||||
It's used to resume training.
|
||||
``./zipformer_mmi/train.py --start-epoch 10`` loads the
|
||||
checkpoint ``./zipformer_mmi/exp/epoch-9.pt`` and starts
|
||||
training from epoch 10, based on the state from epoch 9.
|
||||
|
||||
- ``--world-size``
|
||||
|
||||
It is used for multi-GPU single-machine DDP training.
|
||||
|
||||
- (a) If it is 1, then no DDP training is used.
|
||||
|
||||
- (b) If it is 2, then GPU 0 and GPU 1 are used for DDP training.
|
||||
|
||||
The following shows some use cases with it.
|
||||
|
||||
**Use case 1**: You have 4 GPUs, but you only want to use GPU 0 and
|
||||
GPU 2 for training. You can do the following:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/librispeech/ASR
|
||||
$ export CUDA_VISIBLE_DEVICES="0,2"
|
||||
$ ./zipformer_mmi/train.py --world-size 2
|
||||
|
||||
**Use case 2**: You have 4 GPUs and you want to use all of them
|
||||
for training. You can do the following:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/librispeech/ASR
|
||||
$ ./zipformer_mmi/train.py --world-size 4
|
||||
|
||||
**Use case 3**: You have 4 GPUs but you only want to use GPU 3
|
||||
for training. You can do the following:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/librispeech/ASR
|
||||
$ export CUDA_VISIBLE_DEVICES="3"
|
||||
$ ./zipformer_mmi/train.py --world-size 1
|
||||
|
||||
.. caution::
|
||||
|
||||
Only multi-GPU single-machine DDP training is implemented at present.
|
||||
Multi-GPU multi-machine DDP training will be added later.
|
||||
|
||||
- ``--max-duration``
|
||||
|
||||
It specifies the number of seconds over all utterances in a
|
||||
batch, before **padding**.
|
||||
If you encounter CUDA OOM, please reduce it.
|
||||
|
||||
.. HINT::
|
||||
|
||||
Due to padding, the number of seconds of all utterances in a
|
||||
batch will usually be larger than ``--max-duration``.
|
||||
|
||||
A larger value for ``--max-duration`` may cause OOM during training,
|
||||
while a smaller value may increase the training time. You have to
|
||||
tune it.
|
||||
|
||||
|
||||
Pre-configured options
|
||||
~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
There are some training options, e.g., weight decay,
|
||||
number of warmup steps, results dir, etc,
|
||||
that are not passed from the commandline.
|
||||
They are pre-configured by the function ``get_params()`` in
|
||||
`zipformer_mmi/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/zipformer_mmi/train.py>`_
|
||||
|
||||
You don't need to change these pre-configured parameters. If you really need to change
|
||||
them, please modify ``./zipformer_mmi/train.py`` directly.
|
||||
|
||||
Training logs
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
Training logs and checkpoints are saved in ``zipformer_mmi/exp``.
|
||||
You will find the following files in that directory:
|
||||
|
||||
- ``epoch-1.pt``, ``epoch-2.pt``, ...
|
||||
|
||||
These are checkpoint files saved at the end of each epoch, containing model
|
||||
``state_dict`` and optimizer ``state_dict``.
|
||||
To resume training from some checkpoint, say ``epoch-10.pt``, you can use:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ ./zipformer_mmi/train.py --start-epoch 11
|
||||
|
||||
- ``checkpoint-436000.pt``, ``checkpoint-438000.pt``, ...
|
||||
|
||||
These are checkpoint files saved every ``--save-every-n`` batches,
|
||||
containing model ``state_dict`` and optimizer ``state_dict``.
|
||||
To resume training from some checkpoint, say ``checkpoint-436000``, you can use:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ ./zipformer_mmi/train.py --start-batch 436000
|
||||
|
||||
- ``tensorboard/``
|
||||
|
||||
This folder contains tensorBoard logs. Training loss, validation loss, learning
|
||||
rate, etc, are recorded in these logs. You can visualize them by:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd zipformer_mmi/exp/tensorboard
|
||||
$ tensorboard dev upload --logdir . --description "Zipformer MMI training for LibriSpeech with icefall"
|
||||
|
||||
It will print something like below:
|
||||
|
||||
.. code-block::
|
||||
|
||||
TensorFlow installation not found - running with reduced feature set.
|
||||
Upload started and will continue reading any new data as it's added to the logdir.
|
||||
|
||||
To stop uploading, press Ctrl-C.
|
||||
|
||||
New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/xyOZUKpEQm62HBIlUD4uPA/
|
||||
|
||||
Note there is a URL in the above output. Click it and you will see
|
||||
tensorboard.
|
||||
|
||||
.. hint::
|
||||
|
||||
If you don't have access to google, you can use the following command
|
||||
to view the tensorboard log locally:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd zipformer_mmi/exp/tensorboard
|
||||
tensorboard --logdir . --port 6008
|
||||
|
||||
It will print the following message:
|
||||
|
||||
.. code-block::
|
||||
|
||||
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
|
||||
TensorBoard 2.8.0 at http://localhost:6008/ (Press CTRL+C to quit)
|
||||
|
||||
Now start your browser and go to `<http://localhost:6008>`_ to view the tensorboard
|
||||
logs.
|
||||
|
||||
|
||||
- ``log/log-train-xxxx``
|
||||
|
||||
It is the detailed training log in text format, same as the one
|
||||
you saw printed to the console during training.
|
||||
|
||||
Usage example
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
You can use the following command to start the training using 8 GPUs:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
export CUDA_VISIBLE_DEVICES="0,1,2,3"
|
||||
./zipformer_mmi/train.py \
|
||||
--world-size 4 \
|
||||
--num-epochs 30 \
|
||||
--start-epoch 1 \
|
||||
--full-libri 1 \
|
||||
--exp-dir zipformer_mmi/exp \
|
||||
--max-duration 500 \
|
||||
--use-fp16 1 \
|
||||
--num-workers 2
|
||||
|
||||
Decoding
|
||||
--------
|
||||
|
||||
The decoding part uses checkpoints saved by the training part, so you have
|
||||
to run the training part first.
|
||||
|
||||
.. hint::
|
||||
|
||||
There are two kinds of checkpoints:
|
||||
|
||||
- (1) ``epoch-1.pt``, ``epoch-2.pt``, ..., which are saved at the end
|
||||
of each epoch. You can pass ``--epoch`` to
|
||||
``zipformer_mmi/decode.py`` to use them.
|
||||
|
||||
- (2) ``checkpoints-436000.pt``, ``epoch-438000.pt``, ..., which are saved
|
||||
every ``--save-every-n`` batches. You can pass ``--iter`` to
|
||||
``zipformer_mmi/decode.py`` to use them.
|
||||
|
||||
We suggest that you try both types of checkpoints and choose the one
|
||||
that produces the lowest WERs.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/librispeech/ASR
|
||||
$ ./zipformer_mmi/decode.py --help
|
||||
|
||||
shows the options for decoding.
|
||||
|
||||
The following shows the example using ``epoch-*.pt``:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
for m in nbest nbest-rescoring-LG nbest-rescoring-3-gram nbest-rescoring-4-gram; do
|
||||
./zipformer_mmi/decode.py \
|
||||
--epoch 30 \
|
||||
--avg 10 \
|
||||
--exp-dir ./zipformer_mmi/exp/ \
|
||||
--max-duration 100 \
|
||||
--lang-dir data/lang_bpe_500 \
|
||||
--nbest-scale 1.2 \
|
||||
--hp-scale 1.0 \
|
||||
--decoding-method $m
|
||||
done
|
||||
|
||||
|
||||
Export models
|
||||
-------------
|
||||
|
||||
`zipformer_mmi/export.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/zipformer_mmi/export.py>`_ supports exporting checkpoints from ``zipformer_mmi/exp`` in the following ways.
|
||||
|
||||
Export ``model.state_dict()``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Checkpoints saved by ``zipformer_mmi/train.py`` also include
|
||||
``optimizer.state_dict()``. It is useful for resuming training. But after training,
|
||||
we are interested only in ``model.state_dict()``. You can use the following
|
||||
command to extract ``model.state_dict()``.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./zipformer_mmi/export.py \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 30 \
|
||||
--avg 9 \
|
||||
--jit 0
|
||||
|
||||
It will generate a file ``./zipformer_mmi/exp/pretrained.pt``.
|
||||
|
||||
.. hint::
|
||||
|
||||
To use the generated ``pretrained.pt`` for ``zipformer_mmi/decode.py``,
|
||||
you can run:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd zipformer_mmi/exp
|
||||
ln -s pretrained epoch-9999.pt
|
||||
|
||||
And then pass ``--epoch 9999 --avg 1 --use-averaged-model 0`` to
|
||||
``./zipformer_mmi/decode.py``.
|
||||
|
||||
To use the exported model with ``./zipformer_mmi/pretrained.py``, you
|
||||
can run:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./zipformer_mmi/pretrained.py \
|
||||
--checkpoint ./zipformer_mmi/exp/pretrained.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--method 1best \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
Export model using ``torch.jit.script()``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./zipformer_mmi/export.py \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 30 \
|
||||
--avg 9 \
|
||||
--jit 1
|
||||
|
||||
It will generate a file ``cpu_jit.pt`` in the given ``exp_dir``. You can later
|
||||
load it by ``torch.jit.load("cpu_jit.pt")``.
|
||||
|
||||
Note ``cpu`` in the name ``cpu_jit.pt`` means the parameters when loaded into Python
|
||||
are on CPU. You can use ``to("cuda")`` to move them to a CUDA device.
|
||||
|
||||
To use the generated files with ``./zipformer_mmi/jit_pretrained.py``:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./zipformer_mmi/jit_pretrained.py \
|
||||
--nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--method 1best \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
Download pretrained models
|
||||
--------------------------
|
||||
|
||||
If you don't want to train from scratch, you can download the pretrained models
|
||||
by visiting the following links:
|
||||
|
||||
- `<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-mmi-2022-12-08>`_
|
||||
|
||||
See `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>`_
|
||||
for the details of the above pretrained models
|
||||
@ -1,5 +1,62 @@
|
||||
## Results
|
||||
|
||||
### zipformer_mmi (zipformer with mmi loss)
|
||||
|
||||
See <https://github.com/k2-fsa/icefall/pull/746> for more details.
|
||||
|
||||
[zipformer_mmi](./zipformer_mmi)
|
||||
|
||||
The tensorboard log can be found at
|
||||
<https://tensorboard.dev/experiment/xyOZUKpEQm62HBIlUD4uPA/>
|
||||
|
||||
You can find a pretrained model, training logs, decoding logs, and decoding
|
||||
results at:
|
||||
<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-mmi-2022-12-08>
|
||||
|
||||
Number of model parameters: 69136519, i.e., 69.14 M
|
||||
|
||||
| | test-clean | test-other | comment |
|
||||
|--------------------------|------------|-------------|---------------------|
|
||||
| 1best | 2.54 | 5.65 | --epoch 30 --avg 10 |
|
||||
| nbest | 2.54 | 5.66 | --epoch 30 --avg 10 |
|
||||
| nbest-rescoring-LG | 2.49 | 5.42 | --epoch 30 --avg 10 |
|
||||
| nbest-rescoring-3-gram | 2.52 | 5.62 | --epoch 30 --avg 10 |
|
||||
| nbest-rescoring-4-gram | 2.5 | 5.51 | --epoch 30 --avg 10 |
|
||||
|
||||
The training commands are:
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES="0,1,2,3"
|
||||
|
||||
./zipformer_mmi/train.py \
|
||||
--world-size 4 \
|
||||
--master-port 12345 \
|
||||
--num-epochs 30 \
|
||||
--start-epoch 1 \
|
||||
--lang-dir data/lang_bpe_500 \
|
||||
--max-duration 500 \
|
||||
--full-libri 1 \
|
||||
--use-fp16 1 \
|
||||
--exp-dir zipformer_mmi/exp
|
||||
```
|
||||
|
||||
The decoding commands for the transducer branch are:
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES="5"
|
||||
|
||||
for m in nbest nbest-rescoring-LG nbest-rescoring-3-gram nbest-rescoring-4-gram; do
|
||||
./zipformer_mmi/decode.py \
|
||||
--epoch 30 \
|
||||
--avg 10 \
|
||||
--exp-dir ./zipformer_mmi/exp/ \
|
||||
--max-duration 100 \
|
||||
--lang-dir data/lang_bpe_500 \
|
||||
--nbest-scale 1.2 \
|
||||
--hp-scale 1.0 \
|
||||
--decoding-method $m
|
||||
done
|
||||
```
|
||||
|
||||
|
||||
### pruned_transducer_stateless7_ctc (zipformer with transducer loss and ctc loss)
|
||||
|
||||
See <https://github.com/k2-fsa/icefall/pull/683> for more details.
|
||||
|
||||
@ -291,7 +291,10 @@ def main():
|
||||
|
||||
batch_size = nnet_output.shape[0]
|
||||
supervision_segments = torch.tensor(
|
||||
[[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
|
||||
[
|
||||
[i, 0, feature_lengths[i] // params.subsampling_factor]
|
||||
for i in range(batch_size)
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
|
||||
@ -339,7 +339,10 @@ def main():
|
||||
|
||||
batch_size = nnet_output.shape[0]
|
||||
supervision_segments = torch.tensor(
|
||||
[[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
|
||||
[
|
||||
[i, 0, feature_lengths[i] // params.subsampling_factor]
|
||||
for i in range(batch_size)
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
|
||||
@ -660,14 +660,22 @@ def main():
|
||||
# we need cut ids to display recognition results.
|
||||
args.return_cuts = True
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
test_clean_cuts = librispeech.test_clean_cuts()
|
||||
test_other_cuts = librispeech.test_other_cuts()
|
||||
|
||||
test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
|
||||
test_other_dl = librispeech.test_dataloaders(test_other_cuts)
|
||||
|
||||
# CAUTION: `test_sets` is for displaying only.
|
||||
# If you want to skip test-clean, you have to skip
|
||||
# it inside the for loop. That is, use
|
||||
#
|
||||
# if test_set == 'test-clean': continue
|
||||
#
|
||||
test_sets = ["test-clean", "test-other"]
|
||||
for test_set, test_dl in zip(test_sets, librispeech.test_dataloaders()):
|
||||
test_dls = [test_clean_dl, test_other_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dls):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
|
||||
@ -30,6 +30,8 @@ import torch.multiprocessing as mp
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import LibriSpeechAsrDataModule
|
||||
from conformer import Conformer
|
||||
from lhotse.cut import Cut
|
||||
from lhotse.dataset.sampling.base import CutSampler
|
||||
from lhotse.utils import fix_random_seed
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.nn.utils import clip_grad_norm_
|
||||
@ -100,6 +102,41 @@ def get_parser():
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="conformer_mmi/exp-attn",
|
||||
help="""The experiment dir.
|
||||
It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=str,
|
||||
default="data/lang_bpe_500",
|
||||
help="""The lang dir
|
||||
It contains language related input files such as
|
||||
"lexicon.txt"
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
default=42,
|
||||
help="The seed for random generators intended for reproducibility",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-pruned-intersect",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="""Whether to use `intersect_dense_pruned` to get denominator
|
||||
lattice.""",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
@ -114,12 +151,6 @@ def get_params() -> AttributeDict:
|
||||
|
||||
Explanation of options saved in `params`:
|
||||
|
||||
- exp_dir: It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
|
||||
- lang_dir: It contains language related input files such as
|
||||
"lexicon.txt"
|
||||
|
||||
- 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.
|
||||
@ -164,8 +195,6 @@ def get_params() -> AttributeDict:
|
||||
"""
|
||||
params = AttributeDict(
|
||||
{
|
||||
"exp_dir": Path("conformer_mmi/exp_500_with_attention"),
|
||||
"lang_dir": Path("data/lang_bpe_500"),
|
||||
"best_train_loss": float("inf"),
|
||||
"best_valid_loss": float("inf"),
|
||||
"best_train_epoch": -1,
|
||||
@ -184,15 +213,12 @@ def get_params() -> AttributeDict:
|
||||
"beam_size": 6, # will change it to 8 after some batches (see code)
|
||||
"reduction": "sum",
|
||||
"use_double_scores": True,
|
||||
# "att_rate": 0.0,
|
||||
# "num_decoder_layers": 0,
|
||||
"att_rate": 0.7,
|
||||
"num_decoder_layers": 6,
|
||||
# parameters for Noam
|
||||
"weight_decay": 1e-6,
|
||||
"lr_factor": 5.0,
|
||||
"warm_step": 80000,
|
||||
"use_pruned_intersect": False,
|
||||
"den_scale": 1.0,
|
||||
# use alignments before this number of batches
|
||||
"use_ali_until": 13000,
|
||||
@ -661,7 +687,7 @@ def run(rank, world_size, args):
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
fix_random_seed(42)
|
||||
fix_random_seed(params.seed)
|
||||
if world_size > 1:
|
||||
setup_dist(rank, world_size, params.master_port)
|
||||
|
||||
@ -745,8 +771,29 @@ def run(rank, world_size, args):
|
||||
valid_ali = None
|
||||
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
train_dl = librispeech.train_dataloaders()
|
||||
valid_dl = librispeech.valid_dataloaders()
|
||||
train_cuts = librispeech.train_clean_100_cuts()
|
||||
if params.full_libri:
|
||||
train_cuts += librispeech.train_clean_360_cuts()
|
||||
train_cuts += librispeech.train_other_500_cuts()
|
||||
|
||||
def remove_short_and_long_utt(c: Cut):
|
||||
# Keep only utterances with duration between 1 second and 20 seconds
|
||||
#
|
||||
# Caution: There is a reason to select 20.0 here. Please see
|
||||
# ../local/display_manifest_statistics.py
|
||||
#
|
||||
# You should use ../local/display_manifest_statistics.py to get
|
||||
# an utterance duration distribution for your dataset to select
|
||||
# the threshold
|
||||
return 1.0 <= c.duration <= 20.0
|
||||
|
||||
train_cuts = train_cuts.filter(remove_short_and_long_utt)
|
||||
|
||||
train_dl = librispeech.train_dataloaders(train_cuts)
|
||||
|
||||
valid_cuts = librispeech.dev_clean_cuts()
|
||||
valid_cuts += librispeech.dev_other_cuts()
|
||||
valid_dl = librispeech.valid_dataloaders(valid_cuts)
|
||||
|
||||
for epoch in range(params.start_epoch, params.num_epochs):
|
||||
train_dl.sampler.set_epoch(epoch)
|
||||
@ -796,6 +843,7 @@ def main():
|
||||
parser = get_parser()
|
||||
LibriSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
world_size = args.world_size
|
||||
assert world_size >= 1
|
||||
|
||||
@ -30,6 +30,8 @@ import torch.multiprocessing as mp
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import LibriSpeechAsrDataModule
|
||||
from conformer import Conformer
|
||||
from lhotse.cut import Cut
|
||||
from lhotse.dataset.sampling.base import CutSampler
|
||||
from lhotse.utils import fix_random_seed
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.nn.utils import clip_grad_norm_
|
||||
@ -100,6 +102,26 @@ def get_parser():
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="conformer_mmi/exp",
|
||||
help="""The experiment dir.
|
||||
It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=str,
|
||||
default="data/lang_bpe_500",
|
||||
help="""The lang dir
|
||||
It contains language related input files such as
|
||||
"lexicon.txt"
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
@ -107,6 +129,14 @@ def get_parser():
|
||||
help="The seed for random generators intended for reproducibility",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-pruned-intersect",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="""Whether to use `intersect_dense_pruned` to get denominator
|
||||
lattice.""",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
@ -121,12 +151,6 @@ def get_params() -> AttributeDict:
|
||||
|
||||
Explanation of options saved in `params`:
|
||||
|
||||
- exp_dir: It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
|
||||
- lang_dir: It contains language related input files such as
|
||||
"lexicon.txt"
|
||||
|
||||
- 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.
|
||||
@ -171,8 +195,6 @@ def get_params() -> AttributeDict:
|
||||
"""
|
||||
params = AttributeDict(
|
||||
{
|
||||
"exp_dir": Path("conformer_mmi/exp_500"),
|
||||
"lang_dir": Path("data/lang_bpe_500"),
|
||||
"best_train_loss": float("inf"),
|
||||
"best_valid_loss": float("inf"),
|
||||
"best_train_epoch": -1,
|
||||
@ -193,13 +215,10 @@ def get_params() -> AttributeDict:
|
||||
"use_double_scores": True,
|
||||
"att_rate": 0.0,
|
||||
"num_decoder_layers": 0,
|
||||
# "att_rate": 0.7,
|
||||
# "num_decoder_layers": 6,
|
||||
# parameters for Noam
|
||||
"weight_decay": 1e-6,
|
||||
"lr_factor": 5.0,
|
||||
"warm_step": 80000,
|
||||
"use_pruned_intersect": False,
|
||||
"den_scale": 1.0,
|
||||
# use alignments before this number of batches
|
||||
"use_ali_until": 13000,
|
||||
@ -752,8 +771,29 @@ def run(rank, world_size, args):
|
||||
valid_ali = None
|
||||
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
train_dl = librispeech.train_dataloaders()
|
||||
valid_dl = librispeech.valid_dataloaders()
|
||||
train_cuts = librispeech.train_clean_100_cuts()
|
||||
if params.full_libri:
|
||||
train_cuts += librispeech.train_clean_360_cuts()
|
||||
train_cuts += librispeech.train_other_500_cuts()
|
||||
|
||||
def remove_short_and_long_utt(c: Cut):
|
||||
# Keep only utterances with duration between 1 second and 20 seconds
|
||||
#
|
||||
# Caution: There is a reason to select 20.0 here. Please see
|
||||
# ../local/display_manifest_statistics.py
|
||||
#
|
||||
# You should use ../local/display_manifest_statistics.py to get
|
||||
# an utterance duration distribution for your dataset to select
|
||||
# the threshold
|
||||
return 1.0 <= c.duration <= 20.0
|
||||
|
||||
train_cuts = train_cuts.filter(remove_short_and_long_utt)
|
||||
|
||||
train_dl = librispeech.train_dataloaders(train_cuts)
|
||||
|
||||
valid_cuts = librispeech.dev_clean_cuts()
|
||||
valid_cuts += librispeech.dev_other_cuts()
|
||||
valid_dl = librispeech.valid_dataloaders(valid_cuts)
|
||||
|
||||
for epoch in range(params.start_epoch, params.num_epochs):
|
||||
fix_random_seed(params.seed + epoch)
|
||||
@ -804,6 +844,7 @@ def main():
|
||||
parser = get_parser()
|
||||
LibriSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
world_size = args.world_size
|
||||
assert world_size >= 1
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
|
||||
lang_dir=data/lang_bpe_500
|
||||
|
||||
for ngram in 2 3 5; do
|
||||
for ngram in 2 3 4 5; do
|
||||
if [ ! -f $lang_dir/${ngram}gram.arpa ]; then
|
||||
./shared/make_kn_lm.py \
|
||||
-ngram-order ${ngram} \
|
||||
|
||||
@ -72,14 +72,14 @@ Check ./pretrained.py for its usage.
|
||||
Note: If you don't want to train a model from scratch, we have
|
||||
provided one for you. You can get it at
|
||||
|
||||
https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
|
||||
https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01
|
||||
|
||||
with the following commands:
|
||||
|
||||
sudo apt-get install git-lfs
|
||||
git lfs install
|
||||
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
|
||||
# You will find the pre-trained model in icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11/exp
|
||||
git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01
|
||||
# You will find the pre-trained model in icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01/exp
|
||||
"""
|
||||
|
||||
import argparse
|
||||
|
||||
@ -304,7 +304,10 @@ def main():
|
||||
|
||||
batch_size = nnet_output.shape[0]
|
||||
supervision_segments = torch.tensor(
|
||||
[[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
|
||||
[
|
||||
[i, 0, feature_lengths[i] // params.subsampling_factor]
|
||||
for i in range(batch_size)
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
|
||||
@ -322,7 +322,10 @@ def main():
|
||||
|
||||
batch_size = nnet_output.shape[0]
|
||||
supervision_segments = torch.tensor(
|
||||
[[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
|
||||
[
|
||||
[i, 0, feature_lengths[i] // params.subsampling_factor]
|
||||
for i in range(batch_size)
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
|
||||
26
egs/librispeech/ASR/zipformer_mmi/README.md
Normal file
26
egs/librispeech/ASR/zipformer_mmi/README.md
Normal file
@ -0,0 +1,26 @@
|
||||
This recipe implements Zipformer-MMI model.
|
||||
|
||||
See https://k2-fsa.github.io/icefall/recipes/librispeech/zipformer_mmi.html for detailed tutorials.
|
||||
|
||||
It uses **CTC loss for warm-up** and then switches to MMI loss during training.
|
||||
|
||||
For decoding, it uses HP (H is ctc_topo, P is token-level bi-gram) as decoding graph. Supported decoding methods are:
|
||||
- **1best**. Extract the best path from the decoding lattice as the decoding result.
|
||||
- **nbest**. Extract n paths from the decoding lattice; the path with the highest score is the decoding result.
|
||||
- **nbest-rescoring-LG**. Extract n paths from the decoding lattice, rescore them with an word-level 3-gram LM, the path with the highest score is the decoding result.
|
||||
- **nbest-rescoring-3-gram**. Extract n paths from the decoding lattice, rescore them with an token-level 3-gram LM, the path with the highest score is the decoding result.
|
||||
- **nbest-rescoring-4-gram**. Extract n paths from the decoding lattice, rescore them with an token-level 4-gram LM, the path with the highest score is the decoding result.
|
||||
|
||||
Experimental results training on train-clean-100 (epoch-30-avg-10):
|
||||
- 1best. 6.43 & 17.44
|
||||
- nbest, nbest-scale=1.2, 6.43 & 17.45
|
||||
- nbest-rescoring-LG, nbest-scale=1.2, 5.87 & 16.35
|
||||
- nbest-rescoring-3-gram, nbest-scale=1.2, 6.19 & 16.57
|
||||
- nbest-rescoring-4-gram, nbest-scale=1.2, 5.87 & 16.07
|
||||
|
||||
Experimental results training on full librispeech (epoch-30-avg-10):
|
||||
- 1best. 2.54 & 5.65
|
||||
- nbest, nbest-scale=1.2, 2.54 & 5.66
|
||||
- nbest-rescoring-LG, nbest-scale=1.2, 2.49 & 5.42
|
||||
- nbest-rescoring-3-gram, nbest-scale=1.2, 2.52 & 5.62
|
||||
- nbest-rescoring-4-gram, nbest-scale=1.2, 2.5 & 5.51
|
||||
0
egs/librispeech/ASR/zipformer_mmi/__init__.py
Normal file
0
egs/librispeech/ASR/zipformer_mmi/__init__.py
Normal file
1
egs/librispeech/ASR/zipformer_mmi/asr_datamodule.py
Symbolic link
1
egs/librispeech/ASR/zipformer_mmi/asr_datamodule.py
Symbolic link
@ -0,0 +1 @@
|
||||
../pruned_transducer_stateless2/asr_datamodule.py
|
||||
736
egs/librispeech/ASR/zipformer_mmi/decode.py
Executable file
736
egs/librispeech/ASR/zipformer_mmi/decode.py
Executable file
@ -0,0 +1,736 @@
|
||||
#!/usr/bin/env python3
|
||||
#
|
||||
# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
|
||||
# Liyong Guo,
|
||||
# Zengwei Yao)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Usage:
|
||||
(1) 1best
|
||||
./zipformer_mmi/mmi_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--max-duration 100 \
|
||||
--decoding-method 1best
|
||||
(2) nbest
|
||||
./zipformer_mmi/mmi_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--max-duration 100 \
|
||||
--nbest-scale 1.0 \
|
||||
--decoding-method nbest
|
||||
(3) nbest-rescoring-LG
|
||||
./zipformer_mmi/mmi_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--max-duration 100 \
|
||||
--nbest-scale 1.0 \
|
||||
--decoding-method nbest-rescoring-LG
|
||||
(4) nbest-rescoring-3-gram
|
||||
./zipformer_mmi/mmi_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--max-duration 100 \
|
||||
--nbest-scale 1.0 \
|
||||
--decoding-method nbest-rescoring-3-gram
|
||||
(5) nbest-rescoring-4-gram
|
||||
./zipformer_mmi/mmi_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--max-duration 100 \
|
||||
--nbest-scale 1.0 \
|
||||
--decoding-method nbest-rescoring-4-gram
|
||||
"""
|
||||
|
||||
|
||||
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 train import add_model_arguments, get_ctc_model, get_params
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
average_checkpoints_with_averaged_model,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.decode import (
|
||||
get_lattice,
|
||||
nbest_decoding,
|
||||
nbest_rescore_with_LM,
|
||||
one_best_decoding,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
get_texts,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
|
||||
LOG_EPS = math.log(1e-10)
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=30,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 1.
|
||||
You can specify --avg to use more checkpoints for model averaging.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--iter",
|
||||
type=int,
|
||||
default=0,
|
||||
help="""If positive, --epoch is ignored and it
|
||||
will use the checkpoint exp_dir/checkpoint-iter.pt.
|
||||
You can specify --avg to use more checkpoints for model averaging.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--avg",
|
||||
type=int,
|
||||
default=15,
|
||||
help="Number of checkpoints to average. Automatically select "
|
||||
"consecutive checkpoints before the checkpoint specified by "
|
||||
"'--epoch' and '--iter'",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-averaged-model",
|
||||
type=str2bool,
|
||||
default=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="zipformer_mmi/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="1best",
|
||||
help="""Decoding method. Use HP as decoding graph, where H is
|
||||
ctc_topo and P is token-level bi-gram lm.
|
||||
Supported values are:
|
||||
- (1) 1best. Extract the best path from the decoding lattice as the
|
||||
decoding result.
|
||||
- (2) nbest. Extract n paths from the decoding lattice; the path
|
||||
with the highest score is the decoding result.
|
||||
- (4) nbest-rescoring-LG. Extract n paths from the decoding lattice,
|
||||
rescore them with an word-level 3-gram LM, the path with the
|
||||
highest score is the decoding result.
|
||||
- (5) nbest-rescoring-3-gram. Extract n paths from the decoding
|
||||
lattice, rescore them with an token-level 3-gram LM, the path with
|
||||
the highest score is the decoding result.
|
||||
- (6) nbest-rescoring-4-gram. Extract n paths from the decoding
|
||||
lattice, rescore them with an token-level 4-gram LM, the path with
|
||||
the highest score is the decoding result.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=100,
|
||||
help="""Number of paths for n-best based decoding method.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="""The scale to be applied to `lattice.scores`.
|
||||
It's needed if you use any kinds of n-best based rescoring.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
A smaller value results in more unique paths.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--hp-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="""The scale to be applied to `ctc_topo_P.scores`.
|
||||
""",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def get_decoding_params() -> AttributeDict:
|
||||
"""Parameters for decoding."""
|
||||
params = AttributeDict(
|
||||
{
|
||||
"frame_shift_ms": 10,
|
||||
"search_beam": 20,
|
||||
"output_beam": 8,
|
||||
"min_active_states": 30,
|
||||
"max_active_states": 10000,
|
||||
"use_double_scores": True,
|
||||
}
|
||||
)
|
||||
return params
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
HP: Optional[k2.Fsa],
|
||||
bpe_model: Optional[spm.SentencePieceProcessor],
|
||||
batch: dict,
|
||||
G: Optional[k2.Fsa] = None,
|
||||
LG: 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 no rescoring is used, the key is the string `no_rescore`.
|
||||
If LM rescoring is used, the key is the string `lm_scale_xxx`,
|
||||
where `xxx` is the value of `lm_scale`. An example key is
|
||||
`lm_scale_0.7`
|
||||
- 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`.
|
||||
|
||||
- params.decoding_method is "1best", it uses 1best decoding without LM rescoring.
|
||||
- params.decoding_method is "nbest", it uses nbest decoding without LM rescoring.
|
||||
- params.decoding_method is "nbest-rescoring-LG", it uses nbest rescoring with word-level 3-gram LM.
|
||||
- params.decoding_method is "nbest-rescoring-3-gram", it uses nbest rescoring with token-level 3-gram LM.
|
||||
- params.decoding_method is "nbest-rescoring-4-gram", it uses nbest rescoring with token-level 4-gram LM.
|
||||
|
||||
model:
|
||||
The neural model.
|
||||
HP:
|
||||
The decoding graph. H is ctc_topo, P is token-level bi-gram LM.
|
||||
bpe_model:
|
||||
The BPE model.
|
||||
batch:
|
||||
It is the return value from iterating
|
||||
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
|
||||
for the format of the `batch`.
|
||||
LG:
|
||||
An LM. L is the lexicon, G is a word-level 3-gram LM.
|
||||
It is used when params.decoding_method is "nbest-rescoring-LG".
|
||||
G:
|
||||
An LM. L is the lexicon, G is a token-level 3-gram or 4-gram LM.
|
||||
It is used when params.decoding_method is "nbest-rescoring-3-gram"
|
||||
or "nbest-rescoring-4-gram".
|
||||
Returns:
|
||||
Return the decoding result. See above description for the format of
|
||||
the returned dict. Note: If it decodes to nothing, then return None.
|
||||
"""
|
||||
device = HP.device
|
||||
feature = batch["inputs"]
|
||||
assert feature.ndim == 3, feature.shape
|
||||
feature = feature.to(device)
|
||||
|
||||
# at entry, feature is (N, T, C)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
feature_lens = supervisions["num_frames"].to(device)
|
||||
|
||||
nnet_output, encoder_out_lens = model(x=feature, x_lens=feature_lens)
|
||||
# nnet_output is (N, T, C)
|
||||
|
||||
supervision_segments = torch.stack(
|
||||
(
|
||||
supervisions["sequence_idx"],
|
||||
supervisions["start_frame"] // params.subsampling_factor,
|
||||
supervisions["num_frames"] // params.subsampling_factor,
|
||||
),
|
||||
1,
|
||||
).to(torch.int32)
|
||||
|
||||
lattice = get_lattice(
|
||||
nnet_output=nnet_output,
|
||||
decoding_graph=HP,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
method = params.decoding_method
|
||||
|
||||
if method in ["1best", "nbest"]:
|
||||
if method == "1best":
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
key = "no_rescore"
|
||||
else:
|
||||
best_path = nbest_decoding(
|
||||
lattice=lattice,
|
||||
num_paths=params.num_paths,
|
||||
use_double_scores=params.use_double_scores,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
key = f"no_rescore-nbest-scale-{params.nbest_scale}-{params.num_paths}" # noqa
|
||||
|
||||
# Note: `best_path.aux_labels` contains token IDs, not word IDs
|
||||
# since we are using HP, not HLG here.
|
||||
#
|
||||
# token_ids is a lit-of-list of IDs
|
||||
token_ids = get_texts(best_path)
|
||||
# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
|
||||
hyps = bpe_model.decode(token_ids)
|
||||
# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
|
||||
hyps = [s.split() for s in hyps]
|
||||
return {key: hyps}
|
||||
|
||||
assert method in [
|
||||
"nbest-rescoring-LG", # word-level 3-gram lm
|
||||
"nbest-rescoring-3-gram", # token-level 3-gram lm
|
||||
"nbest-rescoring-4-gram", # token-level 4-gram lm
|
||||
]
|
||||
|
||||
lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
|
||||
lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
|
||||
lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
|
||||
|
||||
if method == "nbest-rescoring-LG":
|
||||
assert LG is not None
|
||||
LM = LG
|
||||
else:
|
||||
assert G is not None
|
||||
LM = G
|
||||
best_path_dict = nbest_rescore_with_LM(
|
||||
lattice=lattice,
|
||||
LM=LM,
|
||||
num_paths=params.num_paths,
|
||||
lm_scale_list=lm_scale_list,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
|
||||
ans = dict()
|
||||
suffix = f"-nbest-scale-{params.nbest_scale}-{params.num_paths}"
|
||||
for lm_scale_str, best_path in best_path_dict.items():
|
||||
token_ids = get_texts(best_path)
|
||||
# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
|
||||
hyps = bpe_model.decode(token_ids)
|
||||
# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
|
||||
hyps = [s.split() for s in hyps]
|
||||
ans[lm_scale_str + suffix] = hyps
|
||||
return ans
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
HP: k2.Fsa,
|
||||
bpe_model: spm.SentencePieceProcessor,
|
||||
G: Optional[k2.Fsa] = None,
|
||||
LG: 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.
|
||||
HP:
|
||||
The decoding graph. H is ctc_topo, P is token-level bi-gram LM.
|
||||
bpe_model:
|
||||
The BPE model.
|
||||
LG:
|
||||
An LM. L is the lexicon, G is a word-level 3-gram LM.
|
||||
It is used when params.decoding_method is "nbest-rescoring-LG".
|
||||
G:
|
||||
An LM. L is the lexicon, G is a token-level 3-gram or 4-gram LM.
|
||||
It is used when params.decoding_method is "nbest-rescoring-3-gram"
|
||||
or "nbest-rescoring-4-gram".
|
||||
|
||||
Returns:
|
||||
Return a dict, whose key may be "no-rescore" if no LM rescoring
|
||||
is used, or it may be "lm_scale_0.7" if LM rescoring 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 = "?"
|
||||
|
||||
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,
|
||||
HP=HP,
|
||||
bpe_model=bpe_model,
|
||||
batch=batch,
|
||||
G=G,
|
||||
LG=LG,
|
||||
)
|
||||
|
||||
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 % 100 == 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 = 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)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
errs_info = (
|
||||
params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_info, "w") as f:
|
||||
print("settings\tWER", file=f)
|
||||
for key, val in test_set_wers:
|
||||
print("{}\t{}".format(key, val), file=f)
|
||||
|
||||
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
|
||||
note = "\tbest for {}".format(test_set_name)
|
||||
for key, val in test_set_wers:
|
||||
s += "{}\t{}{}\n".format(key, val, note)
|
||||
note = ""
|
||||
logging.info(s)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
LibriSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
args.lang_dir = Path(args.lang_dir)
|
||||
|
||||
params = get_params()
|
||||
# add decoding params
|
||||
params.update(get_decoding_params())
|
||||
params.update(vars(args))
|
||||
|
||||
assert params.decoding_method in (
|
||||
"1best",
|
||||
"nbest",
|
||||
"nbest-rescoring-LG", # word-level 3-gram lm
|
||||
"nbest-rescoring-3-gram", # token-level 3-gram lm
|
||||
"nbest-rescoring-4-gram", # token-level 4-gram lm
|
||||
), params.decoding_method
|
||||
params.res_dir = params.exp_dir / params.decoding_method
|
||||
|
||||
if params.iter > 0:
|
||||
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
|
||||
else:
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
|
||||
if 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}")
|
||||
logging.info(params)
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
max_token_id = max(lexicon.tokens)
|
||||
num_classes = max_token_id + 1 # +1 for the blank
|
||||
|
||||
params.vocab_size = num_classes
|
||||
# <blk> and <unk> are defined in local/train_bpe_model.py
|
||||
params.blank_id = 0
|
||||
|
||||
bpe_model = spm.SentencePieceProcessor()
|
||||
bpe_model.load(str(params.lang_dir / "bpe.model"))
|
||||
mmi_graph_compiler = MmiTrainingGraphCompiler(
|
||||
params.lang_dir,
|
||||
uniq_filename="lexicon.txt",
|
||||
device=device,
|
||||
oov="<UNK>",
|
||||
sos_id=1,
|
||||
eos_id=1,
|
||||
)
|
||||
HP = mmi_graph_compiler.ctc_topo_P
|
||||
HP.scores *= params.hp_scale
|
||||
if not hasattr(HP, "lm_scores"):
|
||||
HP.lm_scores = HP.scores.clone()
|
||||
|
||||
LG = None
|
||||
G = None
|
||||
|
||||
if params.decoding_method == "nbest-rescoring-LG":
|
||||
lg_filename = params.lang_dir / "LG.pt"
|
||||
logging.info(f"Loading {lg_filename}")
|
||||
LG = k2.Fsa.from_dict(torch.load(lg_filename, map_location=device))
|
||||
LG = k2.Fsa.from_fsas([LG]).to(device)
|
||||
LG.lm_scores = LG.scores.clone()
|
||||
|
||||
elif params.decoding_method in ["nbest-rescoring-3-gram", "nbest-rescoring-4-gram"]:
|
||||
order = params.decoding_method[-6]
|
||||
assert order in ("3", "4"), (params.decoding_method, order)
|
||||
order = int(order)
|
||||
if not (params.lang_dir / f"{order}gram.pt").is_file():
|
||||
logging.info(f"Loading {order}gram.fst.txt")
|
||||
logging.warning("It may take a few minutes.")
|
||||
with open(params.lang_dir / f"{order}gram.fst.txt") as f:
|
||||
first_token_disambig_id = lexicon.token_table["#0"]
|
||||
|
||||
G = k2.Fsa.from_openfst(f.read(), acceptor=False)
|
||||
# G.aux_labels is not needed in later computations, so
|
||||
# remove it here.
|
||||
del G.aux_labels
|
||||
# CAUTION: The following line is crucial.
|
||||
# Arcs entering the back-off state have label equal to #0.
|
||||
# We have to change it to 0 here.
|
||||
G.labels[G.labels >= first_token_disambig_id] = 0
|
||||
G = k2.Fsa.from_fsas([G]).to(device)
|
||||
# G = k2.remove_epsilon(G)
|
||||
G = k2.arc_sort(G)
|
||||
# Save a dummy value so that it can be loaded in C++.
|
||||
# See https://github.com/pytorch/pytorch/issues/67902
|
||||
# for why we need to do this.
|
||||
G.dummy = 1
|
||||
|
||||
torch.save(G.as_dict(), params.lang_dir / f"{order}gram.pt")
|
||||
else:
|
||||
logging.info(f"Loading pre-compiled {order}gram.pt")
|
||||
d = torch.load(params.lang_dir / f"{order}gram.pt", map_location=device)
|
||||
G = k2.Fsa.from_dict(d)
|
||||
|
||||
G.lm_scores = G.scores.clone()
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_ctc_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()
|
||||
|
||||
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
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
test_clean_cuts = librispeech.test_clean_cuts()
|
||||
test_other_cuts = librispeech.test_other_cuts()
|
||||
|
||||
test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
|
||||
test_other_dl = librispeech.test_dataloaders(test_other_cuts)
|
||||
|
||||
test_sets = ["test-clean", "test-other"]
|
||||
test_dl = [test_clean_dl, test_other_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dl):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
HP=HP,
|
||||
bpe_model=bpe_model,
|
||||
G=G,
|
||||
LG=LG,
|
||||
)
|
||||
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1
egs/librispeech/ASR/zipformer_mmi/encoder_interface.py
Symbolic link
1
egs/librispeech/ASR/zipformer_mmi/encoder_interface.py
Symbolic link
@ -0,0 +1 @@
|
||||
../pruned_transducer_stateless2/encoder_interface.py
|
||||
307
egs/librispeech/ASR/zipformer_mmi/export.py
Executable file
307
egs/librispeech/ASR/zipformer_mmi/export.py
Executable file
@ -0,0 +1,307 @@
|
||||
#!/usr/bin/env python3
|
||||
#
|
||||
# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# This script converts several saved checkpoints
|
||||
# to a single one using model averaging.
|
||||
"""
|
||||
|
||||
Usage:
|
||||
|
||||
(1) Export to torchscript model using torch.jit.script()
|
||||
|
||||
./zipformer_mmi/export.py \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 30 \
|
||||
--avg 9 \
|
||||
--jit 1
|
||||
|
||||
It will generate a file `cpu_jit.pt` in the given `exp_dir`. You can later
|
||||
load it by `torch.jit.load("cpu_jit.pt")`.
|
||||
|
||||
Note `cpu` in the name `cpu_jit.pt` means the parameters when loaded into Python
|
||||
are on CPU. You can use `to("cuda")` to move them to a CUDA device.
|
||||
|
||||
Check
|
||||
https://github.com/k2-fsa/sherpa
|
||||
for how to use the exported models outside of icefall.
|
||||
|
||||
(2) Export `model.state_dict()`
|
||||
|
||||
./zipformer_mmi/export.py \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 20 \
|
||||
--avg 10
|
||||
|
||||
It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
|
||||
load it by `icefall.checkpoint.load_checkpoint()`.
|
||||
|
||||
To use the generated file with `zipformer_mmi/decode.py`,
|
||||
you can do:
|
||||
|
||||
cd /path/to/exp_dir
|
||||
ln -s pretrained.pt epoch-9999.pt
|
||||
|
||||
cd /path/to/egs/librispeech/ASR
|
||||
./zipformer_mmi/decode.py \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--epoch 9999 \
|
||||
--avg 1 \
|
||||
--max-duration 600 \
|
||||
--decoding-method greedy_search \
|
||||
--bpe-model data/lang_bpe_500/bpe.model
|
||||
|
||||
Check ./pretrained.py for its usage.
|
||||
|
||||
Note: If you don't want to train a model from scratch, we have
|
||||
provided one for you. You can get it at
|
||||
|
||||
https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-mmi-2022-12-08
|
||||
|
||||
with the following commands:
|
||||
|
||||
sudo apt-get install git-lfs
|
||||
git lfs install
|
||||
git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-mmi-2022-12-08
|
||||
# You will find the pre-trained model in icefall-asr-librispeech-zipformer-mmi-2022-12-08/exp
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
from scaling_converter import convert_scaled_to_non_scaled
|
||||
from train import add_model_arguments, get_ctc_model, get_params
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
average_checkpoints_with_averaged_model,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.utils import str2bool
|
||||
|
||||
|
||||
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="zipformer_mmi/exp",
|
||||
help="""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",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--jit",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="""True to save a model after applying torch.jit.script.
|
||||
It will generate a file named cpu_jit.pt
|
||||
|
||||
Check ./jit_pretrained.py for how to use it.
|
||||
""",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
args = get_parser().parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
# <blk> is defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_ctc_model(params)
|
||||
|
||||
model.to(device)
|
||||
|
||||
if not params.use_averaged_model:
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
: params.avg
|
||||
]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
elif params.avg == 1:
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
else:
|
||||
start = params.epoch - params.avg + 1
|
||||
filenames = []
|
||||
for i in range(start, params.epoch + 1):
|
||||
if i >= 1:
|
||||
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
else:
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
: params.avg + 1
|
||||
]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg + 1:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
filename_start = filenames[-1]
|
||||
filename_end = filenames[0]
|
||||
logging.info(
|
||||
"Calculating the averaged model over iteration checkpoints"
|
||||
f" from {filename_start} (excluded) to {filename_end}"
|
||||
)
|
||||
model.to(device)
|
||||
model.load_state_dict(
|
||||
average_checkpoints_with_averaged_model(
|
||||
filename_start=filename_start,
|
||||
filename_end=filename_end,
|
||||
device=device,
|
||||
)
|
||||
)
|
||||
else:
|
||||
assert params.avg > 0, params.avg
|
||||
start = params.epoch - params.avg
|
||||
assert start >= 1, start
|
||||
filename_start = f"{params.exp_dir}/epoch-{start}.pt"
|
||||
filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
|
||||
logging.info(
|
||||
f"Calculating the averaged model over epoch range from "
|
||||
f"{start} (excluded) to {params.epoch}"
|
||||
)
|
||||
model.to(device)
|
||||
model.load_state_dict(
|
||||
average_checkpoints_with_averaged_model(
|
||||
filename_start=filename_start,
|
||||
filename_end=filename_end,
|
||||
device=device,
|
||||
)
|
||||
)
|
||||
|
||||
model.to("cpu")
|
||||
model.eval()
|
||||
|
||||
if params.jit is True:
|
||||
convert_scaled_to_non_scaled(model, inplace=True)
|
||||
logging.info("Using torch.jit.script()")
|
||||
model = torch.jit.script(model)
|
||||
filename = params.exp_dir / "cpu_jit.pt"
|
||||
model.save(str(filename))
|
||||
logging.info(f"Saved to {filename}")
|
||||
else:
|
||||
logging.info("Not using torchscript. Export model.state_dict()")
|
||||
# Save it using a format so that it can be loaded
|
||||
# by :func:`load_checkpoint`
|
||||
filename = params.exp_dir / "pretrained.pt"
|
||||
torch.save({"model": model.state_dict()}, str(filename))
|
||||
logging.info(f"Saved to {filename}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
391
egs/librispeech/ASR/zipformer_mmi/jit_pretrained.py
Executable file
391
egs/librispeech/ASR/zipformer_mmi/jit_pretrained.py
Executable file
@ -0,0 +1,391 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang,
|
||||
# Zengwei)
|
||||
#
|
||||
# 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 loads torchscript models, exported by `torch.jit.script()`
|
||||
and uses them to decode waves.
|
||||
You can use the following command to get the exported models:
|
||||
|
||||
./zipformer_mmi/export.py \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 20 \
|
||||
--avg 10 \
|
||||
--jit 1
|
||||
|
||||
Usage of this script:
|
||||
|
||||
(1) 1best
|
||||
./zipformer_mmi/jit_pretrained.py \
|
||||
--nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--method 1best \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(2) nbest
|
||||
./zipformer_mmi/jit_pretrained.py \
|
||||
--nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--nbest-scale 1.2 \
|
||||
--method nbest \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(3) nbest-rescoring-LG
|
||||
./zipformer_mmi/jit_pretrained.py \
|
||||
--nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--nbest-scale 1.2 \
|
||||
--method nbest-rescoring-LG \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(4) nbest-rescoring-3-gram
|
||||
./zipformer_mmi/jit_pretrained.py \
|
||||
--nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--nbest-scale 1.2 \
|
||||
--method nbest-rescoring-3-gram \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(5) nbest-rescoring-4-gram
|
||||
./zipformer_mmi/jit_pretrained.py \
|
||||
--nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--nbest-scale 1.2 \
|
||||
--method nbest-rescoring-4-gram \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from pathlib import Path
|
||||
from typing import List
|
||||
|
||||
import k2
|
||||
import kaldifeat
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torchaudio
|
||||
from decode import get_decoding_params
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import get_params
|
||||
|
||||
from icefall.decode import (
|
||||
get_lattice,
|
||||
nbest_decoding,
|
||||
nbest_rescore_with_LM,
|
||||
one_best_decoding,
|
||||
)
|
||||
from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler
|
||||
from icefall.utils import get_texts
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nn-model-filename",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the torchscript model cpu_jit.pt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
help="""Path to bpe.model.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--method",
|
||||
type=str,
|
||||
default="1best",
|
||||
help="""Decoding method. Use HP as decoding graph, where H is
|
||||
ctc_topo and P is token-level bi-gram lm.
|
||||
Supported values are:
|
||||
- (1) 1best. Extract the best path from the decoding lattice as the
|
||||
decoding result.
|
||||
- (2) nbest. Extract n paths from the decoding lattice; the path
|
||||
with the highest score is the decoding result.
|
||||
- (4) nbest-rescoring-LG. Extract n paths from the decoding lattice,
|
||||
rescore them with an word-level 3-gram LM, the path with the
|
||||
highest score is the decoding result.
|
||||
- (5) nbest-rescoring-3-gram. Extract n paths from the decoding
|
||||
lattice, rescore them with an token-level 3-gram LM, the path with
|
||||
the highest score is the decoding result.
|
||||
- (6) nbest-rescoring-4-gram. Extract n paths from the decoding
|
||||
lattice, rescore them with an token-level 4-gram LM, the path with
|
||||
the highest score is the decoding result.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--sample-rate",
|
||||
type=int,
|
||||
default=16000,
|
||||
help="The sample rate of the input sound file",
|
||||
)
|
||||
|
||||
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(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=100,
|
||||
help="""Number of paths for n-best based decoding method.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=1.2,
|
||||
help="""The scale to be applied to `lattice.scores`.
|
||||
It's needed if you use any kinds of n-best based rescoring.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
A smaller value results in more unique paths.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ngram-lm-scale",
|
||||
type=float,
|
||||
default=0.1,
|
||||
help="""
|
||||
Used when method is nbest-rescoring-LG, nbest-rescoring-3-gram,
|
||||
and nbest-rescoring-4-gram.
|
||||
It specifies the scale for n-gram LM scores.
|
||||
(Note: You need to tune it on a dataset.)
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--hp-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="""The scale to be applied to `ctc_topo_P.scores`.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"sound_files",
|
||||
type=str,
|
||||
nargs="+",
|
||||
help="The input sound file(s) to transcribe. "
|
||||
"Supported formats are those supported by torchaudio.load(). "
|
||||
"For example, wav and flac are supported. "
|
||||
"The sample rate has to be 16kHz.",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def read_sound_files(
|
||||
filenames: List[str], expected_sample_rate: float = 16000
|
||||
) -> List[torch.Tensor]:
|
||||
"""Read a list of sound files into a list 1-D float32 torch tensors.
|
||||
Args:
|
||||
filenames:
|
||||
A list of sound filenames.
|
||||
expected_sample_rate:
|
||||
The expected sample rate of the sound files.
|
||||
Returns:
|
||||
Return a list of 1-D float32 torch tensors.
|
||||
"""
|
||||
ans = []
|
||||
for f in filenames:
|
||||
wave, sample_rate = torchaudio.load(f)
|
||||
assert (
|
||||
sample_rate == expected_sample_rate
|
||||
), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
|
||||
# We use only the first channel
|
||||
ans.append(wave[0])
|
||||
return ans
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
logging.info(vars(args))
|
||||
|
||||
params = get_params()
|
||||
# add decoding params
|
||||
params.update(get_decoding_params())
|
||||
params.update(vars(args))
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
model = torch.jit.load(params.nn_model_filename)
|
||||
model.eval()
|
||||
model.to(device)
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(args.bpe_model)
|
||||
|
||||
logging.info("Constructing Fbank computer")
|
||||
opts = kaldifeat.FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = 16000
|
||||
opts.mel_opts.num_bins = 80
|
||||
|
||||
fbank = kaldifeat.Fbank(opts)
|
||||
|
||||
logging.info(f"Reading sound files: {args.sound_files}")
|
||||
waves = read_sound_files(
|
||||
filenames=params.sound_files, expected_sample_rate=params.sample_rate
|
||||
)
|
||||
waves = [w.to(device) for w in waves]
|
||||
|
||||
logging.info("Decoding started")
|
||||
features = fbank(waves)
|
||||
feature_lengths = [f.size(0) for f in features]
|
||||
|
||||
features = pad_sequence(
|
||||
features,
|
||||
batch_first=True,
|
||||
padding_value=math.log(1e-10),
|
||||
)
|
||||
feature_lengths = torch.tensor(feature_lengths, device=device)
|
||||
|
||||
bpe_model = spm.SentencePieceProcessor()
|
||||
bpe_model.load(str(params.lang_dir / "bpe.model"))
|
||||
mmi_graph_compiler = MmiTrainingGraphCompiler(
|
||||
params.lang_dir,
|
||||
uniq_filename="lexicon.txt",
|
||||
device=device,
|
||||
oov="<UNK>",
|
||||
sos_id=1,
|
||||
eos_id=1,
|
||||
)
|
||||
HP = mmi_graph_compiler.ctc_topo_P
|
||||
HP.scores *= params.hp_scale
|
||||
if not hasattr(HP, "lm_scores"):
|
||||
HP.lm_scores = HP.scores.clone()
|
||||
|
||||
method = params.method
|
||||
assert method in (
|
||||
"1best",
|
||||
"nbest",
|
||||
"nbest-rescoring-LG", # word-level 3-gram lm
|
||||
"nbest-rescoring-3-gram", # token-level 3-gram lm
|
||||
"nbest-rescoring-4-gram", # token-level 4-gram lm
|
||||
)
|
||||
# loading language model for rescoring
|
||||
LM = None
|
||||
if method == "nbest-rescoring-LG":
|
||||
lg_filename = params.lang_dir / "LG.pt"
|
||||
logging.info(f"Loading {lg_filename}")
|
||||
LG = k2.Fsa.from_dict(torch.load(lg_filename, map_location=device))
|
||||
LG = k2.Fsa.from_fsas([LG]).to(device)
|
||||
LG.lm_scores = LG.scores.clone()
|
||||
LM = LG
|
||||
elif method in ["nbest-rescoring-3-gram", "nbest-rescoring-4-gram"]:
|
||||
order = method[-6]
|
||||
assert order in ("3", "4")
|
||||
order = int(order)
|
||||
logging.info(f"Loading pre-compiled {order}gram.pt")
|
||||
d = torch.load(params.lang_dir / f"{order}gram.pt", map_location=device)
|
||||
G = k2.Fsa.from_dict(d)
|
||||
G.lm_scores = G.scores.clone()
|
||||
LM = G
|
||||
|
||||
# Encoder forward
|
||||
nnet_output, encoder_out_lens = model(x=features, x_lens=feature_lengths)
|
||||
|
||||
batch_size = nnet_output.shape[0]
|
||||
supervision_segments = torch.tensor(
|
||||
[
|
||||
[i, 0, feature_lengths[i] // params.subsampling_factor]
|
||||
for i in range(batch_size)
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
lattice = get_lattice(
|
||||
nnet_output=nnet_output,
|
||||
decoding_graph=HP,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
if method in ["1best", "nbest"]:
|
||||
if method == "1best":
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
else:
|
||||
best_path = nbest_decoding(
|
||||
lattice=lattice,
|
||||
num_paths=params.num_paths,
|
||||
use_double_scores=params.use_double_scores,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
else:
|
||||
best_path_dict = nbest_rescore_with_LM(
|
||||
lattice=lattice,
|
||||
LM=LM,
|
||||
num_paths=params.num_paths,
|
||||
lm_scale_list=[params.ngram_lm_scale],
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
best_path = next(iter(best_path_dict.values()))
|
||||
|
||||
# Note: `best_path.aux_labels` contains token IDs, not word IDs
|
||||
# since we are using HP, not HLG here.
|
||||
#
|
||||
# token_ids is a lit-of-list of IDs
|
||||
token_ids = get_texts(best_path)
|
||||
# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
|
||||
hyps = bpe_model.decode(token_ids)
|
||||
# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
|
||||
hyps = [s.split() for s in hyps]
|
||||
s = "\n"
|
||||
for filename, hyp in zip(params.sound_files, hyps):
|
||||
words = " ".join(hyp)
|
||||
s += f"{filename}:\n{words}\n\n"
|
||||
logging.info(s)
|
||||
|
||||
logging.info("Decoding Done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
75
egs/librispeech/ASR/zipformer_mmi/model.py
Normal file
75
egs/librispeech/ASR/zipformer_mmi/model.py
Normal file
@ -0,0 +1,75 @@
|
||||
# Copyright 2022 Xiaomi Corp. (authors: 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.
|
||||
|
||||
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from encoder_interface import EncoderInterface
|
||||
|
||||
|
||||
class CTCModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
encoder: EncoderInterface,
|
||||
encoder_dim: int,
|
||||
vocab_size: int,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
encoder:
|
||||
It is the transcription network in the paper. Its accepts
|
||||
two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
|
||||
It returns two tensors: `logits` of shape (N, T, encoder_dm) and
|
||||
`logit_lens` of shape (N,).
|
||||
"""
|
||||
super().__init__()
|
||||
assert isinstance(encoder, EncoderInterface), type(encoder)
|
||||
|
||||
self.encoder = encoder
|
||||
|
||||
self.ctc_output = nn.Sequential(
|
||||
nn.Dropout(p=0.1),
|
||||
nn.Linear(encoder_dim, vocab_size),
|
||||
nn.LogSoftmax(dim=-1),
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_lens: torch.Tensor,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Args:
|
||||
x:
|
||||
A 3-D tensor of shape (N, T, C).
|
||||
x_lens:
|
||||
A 1-D tensor of shape (N,). It contains the number of frames in `x`
|
||||
before padding.
|
||||
Returns:
|
||||
Return the ctc outputs and encoder output lengths.
|
||||
"""
|
||||
assert x.ndim == 3, x.shape
|
||||
assert x_lens.ndim == 1, x_lens.shape
|
||||
|
||||
encoder_out, x_lens = self.encoder(x, x_lens)
|
||||
assert torch.all(x_lens > 0)
|
||||
|
||||
# compute ctc log-probs
|
||||
ctc_output = self.ctc_output(encoder_out)
|
||||
|
||||
return ctc_output, x_lens
|
||||
1
egs/librispeech/ASR/zipformer_mmi/optim.py
Symbolic link
1
egs/librispeech/ASR/zipformer_mmi/optim.py
Symbolic link
@ -0,0 +1 @@
|
||||
../pruned_transducer_stateless7/optim.py
|
||||
410
egs/librispeech/ASR/zipformer_mmi/pretrained.py
Executable file
410
egs/librispeech/ASR/zipformer_mmi/pretrained.py
Executable file
@ -0,0 +1,410 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang,
|
||||
# Zengwei)
|
||||
#
|
||||
# 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 loads a checkpoint and uses it to decode waves.
|
||||
You can generate the checkpoint with the following command:
|
||||
|
||||
./zipformer_mmi/export.py \
|
||||
--exp-dir ./zipformer_mmi/exp \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 20 \
|
||||
--avg 10
|
||||
|
||||
Usage of this script:
|
||||
|
||||
(1) 1best
|
||||
./zipformer_mmi/pretrained.py \
|
||||
--checkpoint ./zipformer_mmi/exp/pretrained.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--method 1best \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(2) nbest
|
||||
./zipformer_mmi/pretrained.py \
|
||||
--checkpoint ./zipformer_mmi/exp/pretrained.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--nbest-scale 1.2 \
|
||||
--method nbest \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(3) nbest-rescoring-LG
|
||||
./zipformer_mmi/pretrained.py \
|
||||
--checkpoint ./zipformer_mmi/exp/pretrained.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--nbest-scale 1.2 \
|
||||
--method nbest-rescoring-LG \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(4) nbest-rescoring-3-gram
|
||||
./zipformer_mmi/pretrained.py \
|
||||
--checkpoint ./zipformer_mmi/exp/pretrained.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--nbest-scale 1.2 \
|
||||
--method nbest-rescoring-3-gram \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(5) nbest-rescoring-4-gram
|
||||
./zipformer_mmi/pretrained.py \
|
||||
--checkpoint ./zipformer_mmi/exp/pretrained.pt \
|
||||
--bpe-model ./data/lang_bpe_500/bpe.model \
|
||||
--nbest-scale 1.2 \
|
||||
--method nbest-rescoring-4-gram \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
|
||||
You can also use `./zipformer_mmi/exp/epoch-xx.pt`.
|
||||
|
||||
Note: ./zipformer_mmi/exp/pretrained.pt is generated by
|
||||
./zipformer_mmi/export.py
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from pathlib import Path
|
||||
from typing import List
|
||||
|
||||
import k2
|
||||
import kaldifeat
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torchaudio
|
||||
from decode import get_decoding_params
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import add_model_arguments, get_ctc_model, get_params
|
||||
|
||||
from icefall.decode import (
|
||||
get_lattice,
|
||||
nbest_decoding,
|
||||
nbest_rescore_with_LM,
|
||||
one_best_decoding,
|
||||
)
|
||||
from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler
|
||||
from icefall.utils import get_texts
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--checkpoint",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the checkpoint. "
|
||||
"The checkpoint is assumed to be saved by "
|
||||
"icefall.checkpoint.save_checkpoint().",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
help="""Path to bpe.model.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--method",
|
||||
type=str,
|
||||
default="1best",
|
||||
help="""Decoding method. Use HP as decoding graph, where H is
|
||||
ctc_topo and P is token-level bi-gram lm.
|
||||
Supported values are:
|
||||
- (1) 1best. Extract the best path from the decoding lattice as the
|
||||
decoding result.
|
||||
- (2) nbest. Extract n paths from the decoding lattice; the path
|
||||
with the highest score is the decoding result.
|
||||
- (4) nbest-rescoring-LG. Extract n paths from the decoding lattice,
|
||||
rescore them with an word-level 3-gram LM, the path with the
|
||||
highest score is the decoding result.
|
||||
- (5) nbest-rescoring-3-gram. Extract n paths from the decoding
|
||||
lattice, rescore them with an token-level 3-gram LM, the path with
|
||||
the highest score is the decoding result.
|
||||
- (6) nbest-rescoring-4-gram. Extract n paths from the decoding
|
||||
lattice, rescore them with an token-level 4-gram LM, the path with
|
||||
the highest score is the decoding result.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--sample-rate",
|
||||
type=int,
|
||||
default=16000,
|
||||
help="The sample rate of the input sound file",
|
||||
)
|
||||
|
||||
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(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=100,
|
||||
help="""Number of paths for n-best based decoding method.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=1.2,
|
||||
help="""The scale to be applied to `lattice.scores`.
|
||||
It's needed if you use any kinds of n-best based rescoring.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
A smaller value results in more unique paths.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ngram-lm-scale",
|
||||
type=float,
|
||||
default=0.1,
|
||||
help="""
|
||||
Used when method is nbest-rescoring-LG, nbest-rescoring-3-gram,
|
||||
and nbest-rescoring-4-gram.
|
||||
It specifies the scale for n-gram LM scores.
|
||||
(Note: You need to tune it on a dataset.)
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--hp-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="""The scale to be applied to `ctc_topo_P.scores`.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"sound_files",
|
||||
type=str,
|
||||
nargs="+",
|
||||
help="The input sound file(s) to transcribe. "
|
||||
"Supported formats are those supported by torchaudio.load(). "
|
||||
"For example, wav and flac are supported. "
|
||||
"The sample rate has to be 16kHz.",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def read_sound_files(
|
||||
filenames: List[str], expected_sample_rate: float
|
||||
) -> List[torch.Tensor]:
|
||||
"""Read a list of sound files into a list 1-D float32 torch tensors.
|
||||
Args:
|
||||
filenames:
|
||||
A list of sound filenames.
|
||||
expected_sample_rate:
|
||||
The expected sample rate of the sound files.
|
||||
Returns:
|
||||
Return a list of 1-D float32 torch tensors.
|
||||
"""
|
||||
ans = []
|
||||
for f in filenames:
|
||||
wave, sample_rate = torchaudio.load(f)
|
||||
assert (
|
||||
sample_rate == expected_sample_rate
|
||||
), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
|
||||
# We use only the first channel
|
||||
ans.append(wave[0])
|
||||
return ans
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
params = get_params()
|
||||
# add decoding params
|
||||
params.update(get_decoding_params())
|
||||
params.update(vars(args))
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
# <blk> is defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
logging.info(f"{params}")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
logging.info("Creating model")
|
||||
model = get_ctc_model(params)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
checkpoint = torch.load(args.checkpoint, map_location="cpu")
|
||||
model.load_state_dict(checkpoint["model"], strict=False)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
model.device = device
|
||||
|
||||
logging.info("Constructing Fbank computer")
|
||||
opts = kaldifeat.FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = params.sample_rate
|
||||
opts.mel_opts.num_bins = params.feature_dim
|
||||
|
||||
fbank = kaldifeat.Fbank(opts)
|
||||
|
||||
logging.info(f"Reading sound files: {params.sound_files}")
|
||||
waves = read_sound_files(
|
||||
filenames=params.sound_files, expected_sample_rate=params.sample_rate
|
||||
)
|
||||
waves = [w.to(device) for w in waves]
|
||||
|
||||
logging.info("Decoding started")
|
||||
features = fbank(waves)
|
||||
feature_lengths = [f.size(0) for f in features]
|
||||
|
||||
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
|
||||
feature_lengths = torch.tensor(feature_lengths, device=device)
|
||||
|
||||
bpe_model = spm.SentencePieceProcessor()
|
||||
bpe_model.load(str(params.lang_dir / "bpe.model"))
|
||||
mmi_graph_compiler = MmiTrainingGraphCompiler(
|
||||
params.lang_dir,
|
||||
uniq_filename="lexicon.txt",
|
||||
device=device,
|
||||
oov="<UNK>",
|
||||
sos_id=1,
|
||||
eos_id=1,
|
||||
)
|
||||
HP = mmi_graph_compiler.ctc_topo_P
|
||||
HP.scores *= params.hp_scale
|
||||
if not hasattr(HP, "lm_scores"):
|
||||
HP.lm_scores = HP.scores.clone()
|
||||
|
||||
method = params.method
|
||||
assert method in (
|
||||
"1best",
|
||||
"nbest",
|
||||
"nbest-rescoring-LG", # word-level 3-gram lm
|
||||
"nbest-rescoring-3-gram", # token-level 3-gram lm
|
||||
"nbest-rescoring-4-gram", # token-level 4-gram lm
|
||||
)
|
||||
# loading language model for rescoring
|
||||
LM = None
|
||||
if method == "nbest-rescoring-LG":
|
||||
lg_filename = params.lang_dir / "LG.pt"
|
||||
logging.info(f"Loading {lg_filename}")
|
||||
LG = k2.Fsa.from_dict(torch.load(lg_filename, map_location=device))
|
||||
LG = k2.Fsa.from_fsas([LG]).to(device)
|
||||
LG.lm_scores = LG.scores.clone()
|
||||
LM = LG
|
||||
elif method in ["nbest-rescoring-3-gram", "nbest-rescoring-4-gram"]:
|
||||
order = method[-6]
|
||||
assert order in ("3", "4")
|
||||
order = int(order)
|
||||
logging.info(f"Loading pre-compiled {order}gram.pt")
|
||||
d = torch.load(params.lang_dir / f"{order}gram.pt", map_location=device)
|
||||
G = k2.Fsa.from_dict(d)
|
||||
G.lm_scores = G.scores.clone()
|
||||
LM = G
|
||||
|
||||
# Encoder forward
|
||||
nnet_output, encoder_out_lens = model(x=features, x_lens=feature_lengths)
|
||||
|
||||
batch_size = nnet_output.shape[0]
|
||||
supervision_segments = torch.tensor(
|
||||
[
|
||||
[i, 0, feature_lengths[i] // params.subsampling_factor]
|
||||
for i in range(batch_size)
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
lattice = get_lattice(
|
||||
nnet_output=nnet_output,
|
||||
decoding_graph=HP,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
if method in ["1best", "nbest"]:
|
||||
if method == "1best":
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
else:
|
||||
best_path = nbest_decoding(
|
||||
lattice=lattice,
|
||||
num_paths=params.num_paths,
|
||||
use_double_scores=params.use_double_scores,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
else:
|
||||
best_path_dict = nbest_rescore_with_LM(
|
||||
lattice=lattice,
|
||||
LM=LM,
|
||||
num_paths=params.num_paths,
|
||||
lm_scale_list=[params.ngram_lm_scale],
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
best_path = next(iter(best_path_dict.values()))
|
||||
|
||||
# Note: `best_path.aux_labels` contains token IDs, not word IDs
|
||||
# since we are using HP, not HLG here.
|
||||
#
|
||||
# token_ids is a lit-of-list of IDs
|
||||
token_ids = get_texts(best_path)
|
||||
# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
|
||||
hyps = bpe_model.decode(token_ids)
|
||||
# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
|
||||
hyps = [s.split() for s in hyps]
|
||||
s = "\n"
|
||||
for filename, hyp in zip(params.sound_files, hyps):
|
||||
words = " ".join(hyp)
|
||||
s += f"{filename}:\n{words}\n\n"
|
||||
logging.info(s)
|
||||
|
||||
logging.info("Decoding Done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
1
egs/librispeech/ASR/zipformer_mmi/scaling.py
Symbolic link
1
egs/librispeech/ASR/zipformer_mmi/scaling.py
Symbolic link
@ -0,0 +1 @@
|
||||
../pruned_transducer_stateless7/scaling.py
|
||||
1
egs/librispeech/ASR/zipformer_mmi/scaling_converter.py
Symbolic link
1
egs/librispeech/ASR/zipformer_mmi/scaling_converter.py
Symbolic link
@ -0,0 +1 @@
|
||||
../pruned_transducer_stateless7/scaling_converter.py
|
||||
57
egs/librispeech/ASR/zipformer_mmi/test_model.py
Executable file
57
egs/librispeech/ASR/zipformer_mmi/test_model.py
Executable file
@ -0,0 +1,57 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
"""
|
||||
To run this file, do:
|
||||
|
||||
cd icefall/egs/librispeech/ASR
|
||||
python ./zipformer_mmi/test_model.py
|
||||
"""
|
||||
|
||||
import torch
|
||||
from train import get_ctc_model, get_params
|
||||
|
||||
|
||||
def test_model():
|
||||
params = get_params()
|
||||
params.vocab_size = 500
|
||||
params.num_encoder_layers = "2,4,3,2,4"
|
||||
# params.feedforward_dims = "1024,1024,1536,1536,1024"
|
||||
params.feedforward_dims = "1024,1024,2048,2048,1024"
|
||||
params.nhead = "8,8,8,8,8"
|
||||
params.encoder_dims = "384,384,384,384,384"
|
||||
params.attention_dims = "192,192,192,192,192"
|
||||
params.encoder_unmasked_dims = "256,256,256,256,256"
|
||||
params.zipformer_downsampling_factors = "1,2,4,8,2"
|
||||
params.cnn_module_kernels = "31,31,31,31,31"
|
||||
model = get_ctc_model(params)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
print(f"Number of model parameters: {num_param}")
|
||||
|
||||
features = torch.randn(2, 100, 80)
|
||||
feature_lengths = torch.full((2,), 100)
|
||||
model(x=features, x_lens=feature_lengths)
|
||||
|
||||
|
||||
def main():
|
||||
test_model()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1198
egs/librispeech/ASR/zipformer_mmi/train.py
Executable file
1198
egs/librispeech/ASR/zipformer_mmi/train.py
Executable file
File diff suppressed because it is too large
Load Diff
1
egs/librispeech/ASR/zipformer_mmi/zipformer.py
Symbolic link
1
egs/librispeech/ASR/zipformer_mmi/zipformer.py
Symbolic link
@ -0,0 +1 @@
|
||||
../pruned_transducer_stateless7/zipformer.py
|
||||
@ -717,6 +717,107 @@ def rescore_with_n_best_list(
|
||||
return ans
|
||||
|
||||
|
||||
def nbest_rescore_with_LM(
|
||||
lattice: k2.Fsa,
|
||||
LM: k2.Fsa,
|
||||
num_paths: int,
|
||||
lm_scale_list: List[float],
|
||||
nbest_scale: float = 1.0,
|
||||
use_double_scores: bool = True,
|
||||
) -> Dict[str, k2.Fsa]:
|
||||
"""Rescore an n-best list with an n-gram LM.
|
||||
The path with the maximum score is used as the decoding output.
|
||||
|
||||
Args:
|
||||
lattice:
|
||||
An FsaVec with axes [utt][state][arc]. It must have the following
|
||||
attributes: ``aux_labels`` and ``lm_scores``. They are both token
|
||||
IDs.
|
||||
LM:
|
||||
An FsaVec containing only a single FSA. It is one of follows:
|
||||
- LG, L is lexicon and G is word-level n-gram LM.
|
||||
- G, token-level n-gram LM.
|
||||
num_paths:
|
||||
Size of nbest list.
|
||||
lm_scale_list:
|
||||
A list of floats representing LM score scales.
|
||||
nbest_scale:
|
||||
Scale to be applied to ``lattice.score`` when sampling paths
|
||||
using ``k2.random_paths``.
|
||||
use_double_scores:
|
||||
True to use double precision during computation. False to use
|
||||
single precision.
|
||||
Returns:
|
||||
A dict of FsaVec, whose key is an lm_scale and the value is the
|
||||
best decoding path for each utterance in the lattice.
|
||||
"""
|
||||
device = lattice.device
|
||||
|
||||
assert len(lattice.shape) == 3
|
||||
assert hasattr(lattice, "aux_labels")
|
||||
assert hasattr(lattice, "lm_scores")
|
||||
|
||||
assert LM.shape == (1, None, None)
|
||||
assert LM.device == device
|
||||
|
||||
nbest = Nbest.from_lattice(
|
||||
lattice=lattice,
|
||||
num_paths=num_paths,
|
||||
use_double_scores=use_double_scores,
|
||||
nbest_scale=nbest_scale,
|
||||
)
|
||||
# nbest.fsa.scores contains 0s
|
||||
|
||||
nbest = nbest.intersect(lattice)
|
||||
|
||||
# Now nbest.fsa has its scores set
|
||||
assert hasattr(nbest.fsa, "lm_scores")
|
||||
|
||||
# am scores + bi-gram scores
|
||||
hp_scores = nbest.tot_scores()
|
||||
|
||||
# Now start to intersect nbest with LG or G
|
||||
inv_fsa = k2.invert(nbest.fsa)
|
||||
if hasattr(LM, "aux_labels"):
|
||||
# LM is LG here
|
||||
# delete token IDs as it is not needed
|
||||
del inv_fsa.aux_labels
|
||||
inv_fsa.scores.zero_()
|
||||
inv_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(inv_fsa)
|
||||
path_to_utt_map = nbest.shape.row_ids(1)
|
||||
|
||||
LM = k2.arc_sort(LM)
|
||||
path_lattice = k2.intersect_device(
|
||||
LM,
|
||||
inv_fsa_with_epsilon_loops,
|
||||
b_to_a_map=torch.zeros_like(path_to_utt_map),
|
||||
sorted_match_a=True,
|
||||
)
|
||||
|
||||
# Its labels are token IDs.
|
||||
# If LM is G, its aux_labels are tokens IDs;
|
||||
# If LM is LG, its aux_labels are words IDs.
|
||||
path_lattice = k2.top_sort(k2.connect(path_lattice))
|
||||
one_best = k2.shortest_path(path_lattice, use_double_scores=use_double_scores)
|
||||
|
||||
lm_scores = one_best.get_tot_scores(
|
||||
use_double_scores=use_double_scores,
|
||||
log_semiring=True, # Note: we always use True
|
||||
)
|
||||
# If LM is LG, we might get empty paths
|
||||
lm_scores[lm_scores == float("-inf")] = -1e9
|
||||
|
||||
ans = dict()
|
||||
for lm_scale in lm_scale_list:
|
||||
tot_scores = hp_scores.values / lm_scale + lm_scores
|
||||
tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
|
||||
max_indexes = tot_scores.argmax()
|
||||
best_path = k2.index_fsa(nbest.fsa, max_indexes)
|
||||
key = f"lm_scale_{lm_scale}"
|
||||
ans[key] = best_path
|
||||
return ans
|
||||
|
||||
|
||||
def rescore_with_whole_lattice(
|
||||
lattice: k2.Fsa,
|
||||
G_with_epsilon_loops: k2.Fsa,
|
||||
|
||||
@ -112,8 +112,12 @@ def _compute_mmi_loss_exact_non_optimized(
|
||||
num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=True)
|
||||
|
||||
# TODO: pass output_beam as function argument
|
||||
num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=beam_size)
|
||||
den_lats = k2.intersect_dense(den_graphs, dense_fsa_vec, output_beam=beam_size)
|
||||
num_lats = k2.intersect_dense(
|
||||
num_graphs, dense_fsa_vec, output_beam=beam_size, max_arcs=2147483600
|
||||
)
|
||||
den_lats = k2.intersect_dense(
|
||||
den_graphs, dense_fsa_vec, output_beam=beam_size, max_arcs=2147483600
|
||||
)
|
||||
|
||||
num_tot_scores = num_lats.get_tot_scores(log_semiring=True, use_double_scores=True)
|
||||
|
||||
@ -144,7 +148,7 @@ def _compute_mmi_loss_pruned(
|
||||
"""
|
||||
num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)
|
||||
|
||||
num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=10.0)
|
||||
num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=8.0)
|
||||
|
||||
# the values for search_beam/output_beam/min_active_states/max_active_states
|
||||
# are not tuned. You may want to tune them.
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user