---
license: cc-by-4.0
language:
- hak
pipeline_tag: automatic-speech-recognition
---
# Model Card for whisper-large-v3-taiwanese-hakka

<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of the Taiwanese Hakka [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3), which uses the ids of each dialect as prompts during training, to experiment whether the addition of prompts to the finetune of whisper when using multiple dialects will give better results.

## Dialect and Id
- 四縣: htia_sixian
- 海陸: htia_hailu
- 大埔: htia_dapu
- 饒平: htia_raoping
- 詔安: htia_zhaoan
- 南四縣: htia_nansixian

### Training process
The training of the model was performed with the following hyperparameters

- Batch size: 32
- Epochs: 3
- Warmup Steps: 50
- Total Steps: 42549
- Learning rate: 7e-5
- Data augmentation: No


### How to use

```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "formospeech/whisper-large-v3-taiwanese-hakka"
dialect_id = "htia_sixian"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=30,
    batch_size=16,
    torch_dtype=torch_dtype,
    device=device,
)
generate_kwargs = {"language": "Chinese", "prompt_ids": torch.from_numpy(processor.get_prompt_ids(dialect_id)).to(device)}
transcription = pipe("path/to/my_audio.wav", generate_kwargs=generate_kwargs)
print(transcription.replace(f" {dialect_id}", ""))
```