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https://github.com/fengredrum/finetune-whisper-lora

Fine-Tune Whisper with Transformers and PEFT
https://github.com/fengredrum/finetune-whisper-lora

asr cantonese lora whisper

Last synced: 2 months ago
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Fine-Tune Whisper with Transformers and PEFT

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README

        

# Finetune Whisper using LoRA for Cantonese and Mandarin


🤗 HF Repo •🐱 Github Repo

## Get Started

### 1. Setup Docker Environment

Switch to the docker folder and build Docker GPU image for training:

```bash
cd docker
docker compose build
```

Onece the building process complete, run the following command to start a Docker container and attach to it:

```bash
docker compose up -d
docker exec -it asr bash
```

### 2. Prepare Training Data

See detail in [dataset_scripts](dataset_scripts/README.md) folder.

### 3. Finetune Pretrained Model

```bash
# Finetuning
python finetune.py --model_id base --streaming True --train_batch_size 64 --gradient_accumulation_steps 2 --fp16 True
```

```bash
# LoRA Finetuning
python finetune_lora.py --model_id large-v2 --streaming True --train_batch_size 64 --gradient_accumulation_steps 2
```

### 4. Evaluate Performance

```bash
# Evaluation
python eval.py --model_name_or_path Oblivion208/whisper-tiny-cantonese --streaming True --batch_size 64
```

```bash
# LoRA Evaluation
python eval_lora.py --peft_model_id Oblivion208/whisper-large-v2-lora-mix --streaming True --batch_size 64
```

**Note:** Setting `--streaming` to `False` will cache acoustic features on local disk, which speeds up finetuning processes, but it increases the disk usage dramatically (almost three times of raw audio files size).

## Approximate Performance Evaluation

The following models are all trained and evaluated on a single RTX 3090 GPU via [Vast.ai](https://cloud.vast.ai/?ref_id=78038 "Vast.ai").

### Cantonese Test Results Comparison

#### MDCC

| Model name | Parameters | Finetune Steps | Time Spend | Training Loss | Validation Loss | CER % | Finetuned Model |
| ------------------------------- | ---------- | -------------- | ---------- | ------------- | --------------- | ----- | ------------------------------------------------------------------------------------------------------------------------ |
| whisper-tiny-cantonese | 39 M | 3200 | 4h 34m | 0.0485 | 0.771 | 11.10 | [Link](https://huggingface.co/Oblivion208/whisper-tiny-cantonese "Oblivion208/whisper-tiny-cantonese") |
| whisper-base-cantonese | 74 M | 7200 | 13h 32m | 0.0186 | 0.477 | 7.66 | [Link](https://huggingface.co/Oblivion208/whisper-base-cantonese "Oblivion208/whisper-base-cantonese") |
| whisper-small-cantonese | 244 M | 3600 | 6h 38m | 0.0266 | 0.137 | 6.16 | [Link](https://huggingface.co/Oblivion208/whisper-small-cantonese "Oblivion208/whisper-small-cantonese") |
| whisper-small-lora-cantonese | 3.5 M | 8000 | 21h 27m | 0.0687 | 0.382 | 7.40 | [Link](https://huggingface.co/Oblivion208/whisper-small-lora-cantonese "Oblivion208/whisper-small-lora-cantonese") |
| whisper-large-v2-lora-cantonese | 15 M | 10000 | 33h 40m | 0.0046 | 0.277 | 3.77 | [Link](https://huggingface.co/Oblivion208/whisper-large-v2-lora-cantonese "Oblivion208/whisper-large-v2-lora-cantonese") |

#### Common Voice Corpus 11.0

| Model name | Original CER % | w/o Finetune CER % | Jointly Finetune CER % |
| ------------------------------- | -------------- | ------------------ | ---------------------- |
| whisper-tiny-cantonese | 124.03 | 66.85 | 35.87 |
| whisper-base-cantonese | 78.24 | 61.42 | 16.73 |
| whisper-small-cantonese | 52.83 | 31.23 | / |
| whisper-small-lora-cantonese | 37.53 | 19.38 | 14.73 |
| whisper-large-v2-lora-cantonese | 37.53 | 19.38 | 9.63 |

## Requirements

- Transformers
- Accelerate
- Datasets
- PEFT
- bitsandbytes
- librosa

## References

1. https://github.com/openai/whisper
2. https://huggingface.co/blog/fine-tune-whisper
3. https://huggingface.co/docs/peft/task_guides/int8-asr
4. https://huggingface.co/alvanlii/whisper-largev2-cantonese-peft-lora