115 lines
3.8 KiB
Markdown
115 lines
3.8 KiB
Markdown
# Finetune
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「[简体中文](fintune_zh.md)」|「English」
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## Requirements
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```
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pip install git+https://github.com/modelscope/FunASR
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```
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## Data Prepare
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Data examples
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```
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head -n1 data/train_example.jsonl | jq
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{
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"messages": [
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{
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"role": "system",
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"content": "You are a helpful assistant."
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},
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{
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"role": "user",
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"content": "语音转写：<|startofspeech|>!https://modelscope.cn/datasets/FunAudioLLM/funasr-demo/resolve/master/audios/IT0011W0002.wav<|endofspeech|>"
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},
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{
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"role": "assistant",
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"content": "几点了？"
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}
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],
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"speech_length": 145,
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"text_length": 3
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}
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```
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Full ref to `data/train_example.jsonl`
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Description：
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- The content of systemis fixed as `You are a helpful assistant.`
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- The content of userincludes the prompt and the path to the audio file (enclosed between `<|startofspeech|>!`and `<|endofspeech|>`).
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- The default prompts are `语音转写：`and `Speech transcription: `.
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- For corresponding languages, prompts can be combined, such as `语音转写成英文：`and `Transcribe speech into Chinese: `.
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- When the text annotation corresponding to the audio file contains no Arabic numerals or punctuation marks, you can use `语音转写，不进行文本规整：`and `Speech transcription without text normalization: `.
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- The content of assistant corresponds to the text annotation of the audio file.
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- speech_length: The number of fbank frames of the audio file (10ms per frame).
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- text_length: The number of tokens in the annotation text of the audio file (encoded using `Qwen/Qwen3-0.6B`).
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- `messages[2]["content"]`: transcription
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- `speech_length`: number of fbank frames of the audio file
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- `text_length`: number of tokens of the transcription (tokenized by `Qwen3-0.6B`)
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We provide a data format conversion tool `scp2jsonl.py`, which can convert common speech recognition training data formats such as wav scp and transcription into the ChatML format.
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`train_wav.scp`
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```
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BAC009S0764W0121 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav
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BAC009S0916W0489 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0916W0489.wav
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```
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`train_text.txt`
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```
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BAC009S0764W0121 甚至出现交易几乎停滞的情况
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BAC009S0916W0489 湖北一公司以员工名义贷款数十员工负债千万
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```
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```
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python tools/scp2jsonl.py \
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++scp+file=data/train_wav.scp \
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++transcript_file=data/train_text.txt \
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++jsonl_file=data/train_example.jsonl
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```
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## Finetune
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Modify the `audio_encoder_conf.freeze`, `audio_adaptor_conf.freeze`, and `llm_conf.freeze` in `finetune.sh`.
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Set the `freeze` parameter of the modules to be fine-tuned to false(by default, only the LLM is fine-tuned).
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For more detailed parameters, refer to: [SenseVoice Model Training and Testing](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md#Model%20Training%20and%20Testing)
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```
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bash finetune.sh
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```
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### Recommended Configuration
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- For training data less than 1000 hours, it is recommended to fine-tune the audio_adaptor.
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- For training data less than 5000 hours, it is recommended to fine-tune the audio_encoder and audio_adaptor.
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- For training data greater than 10000 hours, it is recommended to perform full-parameter fine-tuning.
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## Model Evaluation
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After model fine-tuning is completed, you can decode the model using the decode.py script:
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```
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python decode.py \
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++model_dir=/path/to/finetuned \
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++scp_file=data/val_wav.scp \
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++output_file=output.txt
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```
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After decoding is completed, text inverse normalization needs to be applied to the annotations and recognition results, and then the WER should be calculated:
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```
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python tools/whisper_mix_normalize.py data/val_text.txt data/val_norm.txt
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python tools/whisper_mix_normalize.py output.txt output_norm.txt
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compute-wer data/val_norm.txt output_norm.txt cer.txt
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tail -n8 cer.txt
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```
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