mirror of
https://github.com/k2-fsa/icefall.git
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b25c234c51
* 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>
104 lines
2.9 KiB
Bash
Executable File
104 lines
2.9 KiB
Bash
Executable File
#!/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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