metadata

license: cc-by-nc-4.0
pipeline_tag: automatic-speech-recognition
base_model:
  - openai/whisper-small
library_name: transformers
language:
  - ami
  - trv
  - bnn
  - pwn
  - tay
  - tsu
  - tao
  - dru
  - xsy
  - pyu
  - szy
  - ckv
  - sxr
  - ssf
  - xnb

Model Card for whisper-small-formosan-all

This model is a fine-tuned version of the Taiwanese indigenous openai/whisper-small.
Note: we use indonesian as whisper language id

Training process

The training of the model was performed with the following hyperparameters

Batch size: 32*4 (on 4 L40s GPU)
Gradient accumulation steps: 8
Total steps: 1600
Learning rate: 1.25e-5
Data augmentation: No
Optimizer: schedule_free_adamw
LR scheduler type: constant

How to use

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-small-formosan-all"
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": "id"}
transcription = pipe("path/to/my_audio.wav", generate_kwargs=generate_kwargs)
print(transcription)