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f2e36ec414
* added scripts for char-based lang prep training scripts * added `Zipformer` recipe for commonvoice --------- Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
152 lines
4.7 KiB
Markdown
152 lines
4.7 KiB
Markdown
## Results
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### Commonvoice Cantonese (zh-HK) Char training results (Zipformer)
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See #1546 for more details.
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Number of model parameters: 72526519, i.e., 72.53 M
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The best CER, for CommonVoice 16.1 (cv-corpus-16.1-2023-12-06/zh-HK) is below:
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| | Dev | Test | Note |
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|----------------------|-------|------|--------------------|
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| greedy_search | 1.17 | 1.22 | --epoch 24 --avg 5 |
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| modified_beam_search | 0.98 | 1.11 | --epoch 24 --avg 5 |
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| fast_beam_search | 1.08 | 1.27 | --epoch 24 --avg 5 |
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When doing the cross-corpus validation on [MDCC](https://arxiv.org/abs/2201.02419) (w/o blank penalty),
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the best CER is below:
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| | Dev | Test | Note |
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|----------------------|-------|------|--------------------|
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| greedy_search | 42.40 | 42.03| --epoch 24 --avg 5 |
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| modified_beam_search | 39.73 | 39.19| --epoch 24 --avg 5 |
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| fast_beam_search | 42.14 | 41.98| --epoch 24 --avg 5 |
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When doing the cross-corpus validation on [MDCC](https://arxiv.org/abs/2201.02419) (with blank penalty set to 2.2),
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the best CER is below:
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| | Dev | Test | Note |
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|----------------------|-------|------|----------------------------------------|
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| greedy_search | 39.19 | 39.09| --epoch 24 --avg 5 --blank-penalty 2.2 |
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| modified_beam_search | 37.73 | 37.65| --epoch 24 --avg 5 --blank-penalty 2.2 |
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| fast_beam_search | 37.73 | 37.74| --epoch 24 --avg 5 --blank-penalty 2.2 |
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To reproduce the above result, use the following commands for training:
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```bash
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export CUDA_VISIBLE_DEVICES="0,1"
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./zipformer/train_char.py \
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--world-size 2 \
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--num-epochs 30 \
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--start-epoch 1 \
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--use-fp16 1 \
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--exp-dir zipformer/exp \
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--cv-manifest-dir data/zh-HK/fbank \
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--language zh-HK \
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--use-validated-set 1 \
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--context-size 1 \
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--max-duration 1000
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```
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and the following commands for decoding:
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```bash
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for method in greedy_search modified_beam_search fast_beam_search; do
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./zipformer/decode_char.py \
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--epoch 24 \
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--avg 5 \
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--decoding-method $method \
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--exp-dir zipformer/exp \
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--cv-manifest-dir data/zh-HK/fbank \
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--context-size 1 \
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--language zh-HK
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done
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```
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Detailed experimental results and pre-trained model are available at:
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<https://huggingface.co/zrjin/icefall-asr-commonvoice-zh-HK-zipformer-2024-03-20>
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### CommonVoice English (en) BPE training results (Pruned Stateless Transducer 7)
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#### [pruned_transducer_stateless7](./pruned_transducer_stateless7)
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See #997 for more details.
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Number of model parameters: 70369391, i.e., 70.37 M
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Note that the result is obtained using GigaSpeech transcript trained BPE model
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The best WER, as of 2023-04-17, for Common Voice English 13.0 (cv-corpus-13.0-2023-03-09/en) is below:
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Results are:
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| | Dev | Test |
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|----------------------|-------|-------|
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| greedy_search | 9.96 | 12.54 |
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| modified_beam_search | 9.86 | 12.48 |
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To reproduce the above result, use the following commands for training:
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```bash
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./pruned_transducer_stateless7/train.py \
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--world-size 4 \
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--num-epochs 30 \
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--start-epoch 1 \
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--use-fp16 1 \
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--exp-dir pruned_transducer_stateless7/exp \
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--max-duration 550
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```
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and the following commands for decoding:
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```bash
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# greedy search
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./pruned_transducer_stateless7/decode.py \
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--epoch 30 \
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--avg 5 \
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--decoding-method greedy_search \
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--exp-dir pruned_transducer_stateless7/exp \
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--bpe-model data/en/lang_bpe_500/bpe.model \
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--max-duration 600
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# modified beam search
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./pruned_transducer_stateless7/decode.py \
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--epoch 30 \
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--avg 5 \
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--decoding-method modified_beam_search \
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--beam-size 4 \
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--exp-dir pruned_transducer_stateless7/exp \
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--bpe-model data/en/lang_bpe_500/bpe.model \
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--max-duration 600
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```
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Pretrained model is available at
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<https://huggingface.co/yfyeung/icefall-asr-cv-corpus-13.0-2023-03-09-en-pruned-transducer-stateless7-2023-04-17>
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### Commonvoice (fr) BPE training results (Pruned Stateless Transducer 7_streaming)
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#### [pruned_transducer_stateless7_streaming](./pruned_transducer_stateless7_streaming)
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See #1018 for more details.
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Number of model parameters: 70369391, i.e., 70.37 M
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The best WER for Common Voice French 12.0 (cv-corpus-12.0-2022-12-07/fr) is below:
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Results are:
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| decoding method | Test |
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|----------------------|-------|
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| greedy_search | 9.95 |
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| modified_beam_search | 9.57 |
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| fast_beam_search | 9.67 |
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Note: This best result is trained on the full librispeech and gigaspeech, and then fine-tuned on the full commonvoice.
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Detailed experimental results and Pretrained model are available at
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<https://huggingface.co/shaojieli/icefall-asr-commonvoice-fr-pruned-transducer-stateless7-streaming-2023-04-02>
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