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* a copy of stateless4 as base * distillation with hubert * fix typo * example usage * usage * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * fix comment * add results of 100hours * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * check fairseq and quantization * a short intro to distillation framework * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * add intro of statless6 in README * fix type error of dst_manifest_dir * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * make export.py call stateless6/train.py instead of stateless2/train.py * update results by stateless6 * adjust results format * fix typo Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
103 lines
3.8 KiB
Markdown
103 lines
3.8 KiB
Markdown
# Results for train-clean-100
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This page shows the WERs for test-clean/test-other using only
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train-clean-100 subset as training data.
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## Distillation with hubert
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### 2022-05-27
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Related models/log/tensorboard:
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https://huggingface.co/GuoLiyong/stateless6_baseline_vs_disstillation
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Following results are obtained by ./distillation_with_hubert.sh
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The only differences is in pruned_transducer_stateless6/train.py.
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For baseline: set enable_distillation=False
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For distillation: set enable_distillation=True (the default)
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Decoding method is modified beam search.
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| | test-clean | test-other | comment |
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|-------------------------------------|------------|------------|------------------------------------------|
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| baseline no vq distillation | 7.09 | 18.88 | --epoch 20, --avg 10, --max-duration 200 |
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| baseline no vq distillation | 6.83 | 18.19 | --epoch 30, --avg 10, --max-duration 200 |
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| baseline no vq distillation | 6.73 | 17.79 | --epoch 40, --avg 10, --max-duration 200 |
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| baseline no vq distillation | 6.75 | 17.68 | --epoch 50, --avg 10, --max-duration 200 |
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| distillation with hubert | 5.82 | 15.98 | --epoch 20, --avg 10, --max-duration 200 |
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| distillation with hubert | 5.52 | 15.15 | --epoch 30, --avg 10, --max-duration 200 |
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| distillation with hubert | 5.45 | 14.94 | --epoch 40, --avg 10, --max-duration 200 |
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| distillation with hubert | 5.50 | 14.77 | --epoch 50, --avg 10, --max-duration 200 |
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## Conformer encoder + embedding decoder
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### 2022-02-21
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Using commit `2332ba312d7ce72f08c7bac1e3312f7e3dd722dc`.
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| | test-clean | test-other | comment |
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|-------------------------------------|------------|------------|------------------------------------------|
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| greedy search (max sym per frame 1) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
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| greedy search (max sym per frame 2) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
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| greedy search (max sym per frame 3) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
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| modified beam search (beam size 4) | 6.31 | 16.3 | --epoch 57, --avg 17, --max-duration 100 |
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The training command for reproducing is given below:
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```bash
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cd egs/librispeech/ASR/
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./prepare.sh
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./prepare_giga_speech.sh
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export CUDA_VISIBLE_DEVICES="0,1"
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./transducer_stateless_multi_datasets/train.py \
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--world-size 2 \
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--num-epochs 60 \
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--start-epoch 0 \
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--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
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--full-libri 0 \
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--max-duration 300 \
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--lr-factor 1 \
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--bpe-model data/lang_bpe_500/bpe.model \
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--modified-transducer-prob 0.25
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--giga-prob 0.2
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```
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The decoding command is given below:
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```bash
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for epoch in 57; do
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for avg in 17; do
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for sym in 1 2 3; do
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./transducer_stateless_multi_datasets/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100 \
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--context-size 2 \
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--max-sym-per-frame $sym
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done
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done
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done
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epoch=57
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avg=17
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./transducer_stateless_multi_datasets/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100 \
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--context-size 2 \
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--decoding-method modified_beam_search \
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--beam-size 4
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```
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The tensorboard log is available at
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<https://tensorboard.dev/experiment/qUEKzMnrTZmOz1EXPda9RA/>
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A pre-trained model and decoding logs can be found at
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<https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21>
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