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* pruned-rnnt5-for-wenetspeech * style check * style check * add streaming conformer * add streaming decode * changes codes for fast_beam_search and export cpu jit * add modified-beam-search for streaming decoding * add modified-beam-search for streaming decoding * change for streaming_beam_search.py * add README.md and RESULTS.md * change for style_check.yml * do some changes * do some changes for export.py * add some decode commands for usage * add streaming results on README.md
166 lines
6.0 KiB
Markdown
166 lines
6.0 KiB
Markdown
## Results
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### WenetSpeech char-based training results (offline and streaming) (Pruned Transducer 5)
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#### 2022-07-22
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/447.
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When training with the L subset, the CERs are
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**Offline**:
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|decoding-method| epoch | avg | use-averaged-model | DEV | TEST-NET | TEST-MEETING|
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|-- | -- | -- | -- | -- | -- | --|
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|greedy_search | 4 | 1 | True | 8.22 | 9.03 | 14.54|
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|modified_beam_search | 4 | 1 | True | **8.17** | **9.04** | **14.44**|
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|fast_beam_search | 4 | 1 | True | 8.29 | 9.00 | 14.93|
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The offline training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
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./pruned_transducer_stateless5/train.py \
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--lang-dir data/lang_char \
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--exp-dir pruned_transducer_stateless5/exp_L_offline \
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--world-size 8 \
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--num-epochs 15 \
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--start-epoch 2 \
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--max-duration 120 \
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--valid-interval 3000 \
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--model-warm-step 3000 \
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--save-every-n 8000 \
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--average-period 1000 \
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--training-subset L
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```
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The tensorboard training log can be found at https://tensorboard.dev/experiment/SvnN2jfyTB2Hjqu22Z7ZoQ/#scalars .
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A pre-trained offline model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_offline>
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**Streaming**:
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|decoding-method| epoch | avg | use-averaged-model | DEV | TEST-NET | TEST-MEETING|
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|--|--|--|--|--|--|--|
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| greedy_search | 7| 1| True | 8.78 | 10.12 | 16.16 |
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| modified_beam_search | 7| 1| True| **8.53**| **9.95** | **15.81** |
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| fast_beam_search | 7 | 1| True | 9.01 | 10.47 | 16.28 |
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The streaming training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
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./pruned_transducer_stateless5/train.py \
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--lang-dir data/lang_char \
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--exp-dir pruned_transducer_stateless5/exp_L_streaming \
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--world-size 8 \
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--num-epochs 15 \
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--start-epoch 1 \
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--max-duration 140 \
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--valid-interval 3000 \
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--model-warm-step 3000 \
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--save-every-n 8000 \
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--average-period 1000 \
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--training-subset L \
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--dynamic-chunk-training True \
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--causal-convolution True \
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--short-chunk-size 25 \
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--num-left-chunks 4
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```
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The tensorboard training log can be found at https://tensorboard.dev/experiment/E2NXPVflSOKWepzJ1a1uDQ/#scalars .
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A pre-trained offline model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_streaming>
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### WenetSpeech char-based training results (Pruned Transducer 2)
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#### 2022-05-19
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/349.
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When training with the L subset, the CERs are
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| | dev | test-net | test-meeting | comment |
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|------------------------------------|-------|----------|--------------|------------------------------------------|
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| greedy search | 7.80 | 8.75 | 13.49 | --epoch 10, --avg 2, --max-duration 100 |
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| modified beam search (beam size 4) | 7.76 | 8.71 | 13.41 | --epoch 10, --avg 2, --max-duration 100 |
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| fast beam search (set as default) | 7.94 | 8.74 | 13.80 | --epoch 10, --avg 2, --max-duration 1500 |
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The training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
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./pruned_transducer_stateless2/train.py \
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--lang-dir data/lang_char \
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--exp-dir pruned_transducer_stateless2/exp \
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--world-size 8 \
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--num-epochs 15 \
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--start-epoch 0 \
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--max-duration 180 \
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--valid-interval 3000 \
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--model-warm-step 3000 \
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--save-every-n 8000 \
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--training-subset L
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/wM4ZUNtASRavJx79EOYYcg/#scalars
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The decoding command is:
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```
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epoch=10
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avg=2
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## greedy search
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./pruned_transducer_stateless2/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir ./pruned_transducer_stateless2/exp \
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--lang-dir data/lang_char \
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--max-duration 100 \
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--decoding-method greedy_search
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## modified beam search
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./pruned_transducer_stateless2/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir ./pruned_transducer_stateless2/exp \
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--lang-dir data/lang_char \
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--max-duration 100 \
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--decoding-method modified_beam_search \
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--beam-size 4
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## fast beam search
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./pruned_transducer_stateless2/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir ./pruned_transducer_stateless2/exp \
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--lang-dir data/lang_char \
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--max-duration 1500 \
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--decoding-method fast_beam_search \
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--beam 4 \
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--max-contexts 4 \
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--max-states 8
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```
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When training with the M subset, the CERs are
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| | dev | test-net | test-meeting | comment |
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|------------------------------------|--------|-----------|---------------|-------------------------------------------|
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| greedy search | 10.40 | 11.31 | 19.64 | --epoch 29, --avg 11, --max-duration 100 |
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| modified beam search (beam size 4) | 9.85 | 11.04 | 18.20 | --epoch 29, --avg 11, --max-duration 100 |
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| fast beam search (set as default) | 10.18 | 11.10 | 19.32 | --epoch 29, --avg 11, --max-duration 1500 |
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When training with the S subset, the CERs are
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| | dev | test-net | test-meeting | comment |
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|------------------------------------|--------|-----------|---------------|-------------------------------------------|
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| greedy search | 19.92 | 25.20 | 35.35 | --epoch 29, --avg 24, --max-duration 100 |
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| modified beam search (beam size 4) | 18.62 | 23.88 | 33.80 | --epoch 29, --avg 24, --max-duration 100 |
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| fast beam search (set as default) | 19.31 | 24.41 | 34.87 | --epoch 29, --avg 24, --max-duration 1500 |
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A pre-trained model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2>
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