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https://github.com/k2-fsa/icefall.git
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5df24c1685
* add whisper fbank for wenetspeech * add whisper fbank for other dataset * add str to bool * add decode for wenetspeech * add requirments.txt * add original model decode with 30s * test feature extractor speed * add aishell2 feat * change compute feature batch * fix overwrite * fix executor * regression * add kaldifeatwhisper fbank * fix io issue * parallel jobs * use multi machines * add wenetspeech fine-tune scripts * add monkey patch codes * remove useless file * fix subsampling factor * fix too long audios * add remove long short * fix whisper version to support multi batch beam * decode all wav files * remove utterance more than 30s in test_net * only test net * using soft links * add kespeech whisper feats * fix index error * add manifests for whisper * change to licomchunky writer * add missing option * decrease cpu usage * add speed perturb for kespeech * fix kespeech speed perturb * add dataset * load checkpoint from specific path * add speechio * add speechio results --------- Co-authored-by: zr_jin <peter.jin.cn@gmail.com>
88 lines
3.5 KiB
Markdown
88 lines
3.5 KiB
Markdown
## Results
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### Aishell2 char-based training results
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#### Pruned transducer stateless 5
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Using the codes from this commit https://github.com/k2-fsa/icefall/pull/465.
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When training with context size equals to 1, the WERs are
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| | dev-ios | test-ios | comment |
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|------------------------------------|-------|----------|----------------------------------|
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| greedy search | 5.57 | 5.89 | --epoch 25, --avg 5, --max-duration 600 |
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| modified beam search (beam size 4) | 5.32 | 5.56 | --epoch 25, --avg 5, --max-duration 600 |
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| fast beam search (set as default) | 5.5 | 5.78 | --epoch 25, --avg 5, --max-duration 600 |
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| fast beam search nbest | 5.46 | 5.74 | --epoch 25, --avg 5, --max-duration 600 |
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| fast beam search oracle | 1.92 | 2.2 | --epoch 25, --avg 5, --max-duration 600 |
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| fast beam search nbest LG | 5.59 | 5.93 | --epoch 25, --avg 5, --max-duration 600 |
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The training command for reproducing is given below:
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```bash
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./pruned_transducer_stateless5/train.py \
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--world-size 4 \
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--lang-dir data/lang_char \
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--num-epochs 40 \
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--start-epoch 1 \
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--exp-dir /result \
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--max-duration 300 \
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--use-fp16 0 \
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--num-encoder-layers 24 \
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--dim-feedforward 1536 \
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--nhead 8 \
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--encoder-dim 384 \
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--decoder-dim 512 \
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--joiner-dim 512 \
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--context-size 1
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```
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The decoding command is:
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```bash
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for method in greedy_search modified_beam_search fast_beam_search fast_beam_search_nbest fast_beam_search_nbest_oracle fast_beam_search_nbest_LG; do
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./pruned_transducer_stateless5/decode.py \
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--epoch 25 \
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--avg 5 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method $method \
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--max-sym-per-frame 1 \
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--num-encoder-layers 24 \
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--dim-feedforward 1536 \
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--nhead 8 \
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--encoder-dim 384 \
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--decoder-dim 512 \
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--joiner-dim 512 \
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--context-size 1 \
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--beam 20.0 \
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--max-contexts 8 \
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--max-states 64 \
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--num-paths 200 \
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--nbest-scale 0.5 \
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--context-size 1 \
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--use-averaged-model True
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done
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/RXyX4QjQQVKjBS2eQ2Qajg/#scalars
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A pre-trained model and decoding logs can be found at <https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12>
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When training with context size equals to 2, the WERs are
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| | dev-ios | test-ios | comment |
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|------------------------------------|-------|----------|----------------------------------|
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| greedy search | 5.47 | 5.81 | --epoch 25, --avg 5, --max-duration 600 |
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| modified beam search (beam size 4) | 5.38 | 5.61 | --epoch 25, --avg 5, --max-duration 600 |
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| fast beam search (set as default) | 5.36 | 5.61 | --epoch 25, --avg 5, --max-duration 600 |
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| fast beam search nbest | 5.37 | 5.6 | --epoch 25, --avg 5, --max-duration 600 |
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| fast beam search oracle | 2.04 | 2.2 | --epoch 25, --avg 5, --max-duration 600 |
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| fast beam search nbest LG | 5.59 | 5.82 | --epoch 25, --avg 5, --max-duration 600 |
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/5AxJ8LHoSre8kDAuLp4L7Q/#scalars
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A pre-trained model and decoding logs can be found at <https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12>
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