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118 lines
5.3 KiB
Markdown
118 lines
5.3 KiB
Markdown
## Results
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### Multi Chinese datasets (without datatang 200h) finetuning results on Whisper-large-v2
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#### Whisper
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[./whisper](./whisper)
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Character Error Rates (CERs) listed below are produced by the checkpoint of the second epoch using greedy search.
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|Model| Datasets | alimeeting | alimeeting | aishell-1 | aishell-1 | aishell-2 | aishell-2 | aishell-4 | magicdata | magicdata | kespeech-asr | kespeech-asr | kespeech-asr | WenetSpeech |
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|-|--------------------------------|-------------------|--------------|----------------|-------------|------------------|-------------|------------------|------------------|-------------|-----------------------|-----------------------|-------------|-------------------|
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| | Split | eval| test | dev | test | dev| test | test | dev| test | dev phase1 | dev phase2 | test | test meeting |
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|whisper-large-v2-ft |Greedy Search | 23.22 | 28.24 | 0.61 | 0.66 | 2.67 | 2.80 | 16.61 | 2.56 | 2.21 | 4.73 | 1.90 | 5.98 | 8.13 |
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|whisper-large-v2-ft-distill |Greedy Search | 24.91 | 26.73 | 0.91 | 0.94 | 2.71 | 2.98 | 17.65 | 2.81 | 2.47 | 5.16 | 2.10 | 6.27 | 8.34 |
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Command for training is:
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```bash
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pip install -r whisper/requirements.txt
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# We updated the label of wenetspeech to remove OCR deletion errors, see https://github.com/wenet-e2e/WenetSpeech/discussions/54
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torchrun --nproc-per-node 8 ./whisper/train.py \
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--max-duration 200 \
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--exp-dir whisper/exp_large_v2 \
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--model-name large-v2 \
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--deepspeed \
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--deepspeed_config ./whisper/ds_config_zero1.json
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```
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Command for decoding using fine-tuned models:
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```bash
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git lfs install
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git clone https://huggingface.co/yuekai/icefall_asr_multi-hans-zh_whisper
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ln -s icefall_asr_multi-hans-zh_whisper/v1.1/epoch-3-avg-10.pt whisper/exp_large_v2/epoch-999.pt
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python3 ./whisper/decode.py \
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--exp-dir whisper/exp_large_v2 \
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--model-name large-v2 \
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--epoch 999 --avg 1 \
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--beam-size 10 --max-duration 50
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```
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Fine-tuned models, training logs, decoding logs, tensorboard and decoding results
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are available at
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<https://huggingface.co/yuekai/icefall_asr_multi-hans-zh_whisper>
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### Multi Chinese datasets char-based training results (Non-streaming) on zipformer model
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This is the [pull request #1238](https://github.com/k2-fsa/icefall/pull/1238) in icefall.
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#### Non-streaming (with CTC head)
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Best results (num of params : ~69M):
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The training command:
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```
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./zipformer/train.py \
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--world-size 4 \
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--num-epochs 20 \
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--use-fp16 1 \
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--max-duration 600 \
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--num-workers 8 \
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--use-ctc 1
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```
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The decoding command:
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```
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./zipformer/decode.py \
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--epoch 20 \
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--avg 1 \
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--use-ctc 1
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```
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Character Error Rates (CERs) listed below are produced by the checkpoint of the 20th epoch using BPE model ( # tokens is 2000, byte fallback enabled).
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| Datasets | aidatatang _200zh | aidatatang _200zh | alimeeting | alimeeting | aishell-1 | aishell-1 | aishell-2 | aishell-2 | aishell-4 | magicdata | magicdata | kespeech-asr | kespeech-asr | kespeech-asr | WenetSpeech | WenetSpeech | WenetSpeech |
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|--------------------------------|------------------------------|-------------|-------------------|--------------|----------------|-------------|------------------|-------------|------------------|------------------|-------------|-----------------------|-----------------------|-------------|--------------------|-------------------------|---------------------|
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| Zipformer CER (%) | dev | test | eval | test | dev | test | dev | test | test | dev | test | dev phase1 | dev phase2 | test | dev | test meeting | test net |
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| CTC Decoding | 2.86 | 3.36 | 22.93 | 24.28 | 2.05 | 2.27 | 3.33 | 3.82 | 15.45 | 3.49 | 2.77 | 6.90 | 2.85 | 8.29 | 9.41 | 6.92 | 8.57 |
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| Greedy Search | 3.36 | 3.83 | 23.90 | 25.18 | 2.77 | 3.08 | 3.70 | 4.04 | 16.13 | 3.77 | 3.15 | 6.88 | 3.14 | 8.08 | 9.04 | 7.19 | 8.17 |
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Pre-trained model can be found here : https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-ctc-2023-10-24/
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#### Non-streaming
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Best results (num of params : ~69M):
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The training command:
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```
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./zipformer/train.py \
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--world-size 4 \
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--num-epochs 20 \
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--use-fp16 1 \
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--max-duration 600 \
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--num-workers 8
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```
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The decoding command:
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```
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./zipformer/decode.py \
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--epoch 20 \
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--avg 1
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```
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Character Error Rates (CERs) listed below are produced by the checkpoint of the 20th epoch using greedy search and BPE model ( # tokens is 2000, byte fallback enabled).
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| Datasets | aidatatang _200zh | aidatatang _200zh | alimeeting | alimeeting | aishell-1 | aishell-1 | aishell-2 | aishell-2 | aishell-4 | magicdata | magicdata | kespeech-asr | kespeech-asr | kespeech-asr | WenetSpeech | WenetSpeech | WenetSpeech |
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|--------------------------------|------------------------------|-------------|-------------------|--------------|----------------|-------------|------------------|-------------|------------------|------------------|-------------|-----------------------|-----------------------|-------------|--------------------|-------------------------|---------------------|
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| Zipformer CER (%) | dev | test | eval| test | dev | test | dev| test | test | dev| test | dev phase1 | dev phase2 | test | dev | test meeting | test net |
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| Greedy Search | 3.2 | 3.67 | 23.15 | 24.78 | 2.91 | 3.04 | 3.59 | 4.03 | 15.68 | 3.68 | 3.12 | 6.69 | 3.19 | 8.01 | 9.32 | 7.05 | 8.78 |
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Pre-trained model can be found here : https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-2023-9-2/
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