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---
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_11_0
language:
- yue
metrics:
- cer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
<p align="left">
🤗 <a href="https://huggingface.co/Oblivion208" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/fengredrum/finetune-whisper-lora" target="_blank">Github Repo</a>
</p>
## Usage
```python
import torch
import librosa
from transformers import WhisperProcessor, WhisperTokenizer, WhisperForConditionalGeneration
# Setups
model_name_or_path = "Oblivion208/whisper-tiny-cantonese"
task = "transcribe"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path).to(device)
tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, task=task)
processor = WhisperProcessor.from_pretrained(model_name_or_path, task=task)
feature_extractor = processor.feature_extractor
model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
filepath = 'test.wav'
audio, sr = librosa.load(filepath, sr=16000, mono=True)
inputs = processor(audio, sample_rate=sr, return_tensors="pt").to(device)
with torch.inference_mode():
generated_tokens = model.generate(
input_features=inputs.input_features,
return_dict_in_generate=True,
max_new_tokens=255,
)
transcription = tokenizer.batch_decode(
generated_tokens.sequences, skip_special_tokens=True)
print(transcription)
```
## Approximate Performance Evaluation
The following models are all trained and evaluated on a single RTX 3090 GPU.
### 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 |
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