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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: 17 days ago
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Fine-Tune Whisper with Transformers and PEFT
- Host: GitHub
- URL: https://github.com/fengredrum/finetune-whisper-lora
- Owner: fengredrum
- License: mit
- Created: 2023-08-01T13:56:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-04T05:18:52.000Z (about 1 year ago)
- Last Synced: 2024-07-30T21:03:16.610Z (3 months ago)
- Topics: asr, cantonese, lora, whisper
- Language: Python
- Homepage: https://huggingface.co/spaces/Oblivion208/Oblivion208-whisper-base-cantonese
- Size: 117 MB
- Stars: 18
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
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