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vi-whisper-large-v3-turbo-v1

Automatic Speech Recognition
Transformers
Safetensors
Vietnamese
whisper
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metadata
datasets:
  - capleaf/viVoice
  - NhutP/VSV-1100
  - doof-ferb/fpt_fosd
  - doof-ferb/infore1_25hours
  - google/fleurs
  - doof-ferb/LSVSC
  - quocanh34/viet_vlsp
  - linhtran92/viet_youtube_asr_corpus_v2
  - doof-ferb/infore2_audiobooks
  - linhtran92/viet_bud500
language:
  - vi
metrics:
  - wer
base_model:
  - openai/whisper-large-v3-turbo
new_version: suzii/vi-whisper-large-v3-turbo
library_name: transformers

Fine-tuned Whisper-V3-Turbo for Vietnamese ASR

This project involves fine-tuning the Whisper-V3-Turbo model to improve its performance for Automatic Speech Recognition (ASR) in the Vietnamese language. The model was trained for 240 hours using a single Nvidia A6000 GPU.

Data Sources

The training data comes from various Vietnamese speech corpora. Below is a list of datasets used for training:

  1. capleaf/viVoice
  2. NhutP/VSV-1100
  3. doof-ferb/fpt_fosd
  4. doof-ferb/infore1_25hours
  5. google/fleurs (vi_vn)
  6. doof-ferb/LSVSC
  7. quocanh34/viet_vlsp
  8. linhtran92/viet_youtube_asr_corpus_v2
  9. doof-ferb/infore2_audiobooks
  10. linhtran92/viet_bud500

Model

The model used in this project is the Whisper-V3-Turbo. Whisper is a multilingual ASR model trained on a large and diverse dataset. The version used here has been fine-tuned specifically for the Vietnamese language.

Training Configuration

  • GPU Used: Nvidia A6000
  • Training Time: 240 hours

Usage

To use the fine-tuned model, follow the steps below:

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 = "suzii/vi-whisper-large-v3-turbo-v1"

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,
    torch_dtype=torch_dtype,
    device=device,
)
result = pipe("your-audio.mp3", return_timestamps=True)

Acknowledgements

This project would not be possible without the following datasets: