Whisper Base Vi V1.1: Whisper Base for Vietnamese Fine-Tuned by Nam Phung 🚀

📝 Introduction

This is a fine-tuned version of openai/whisper-base model on 100 hours of Vietnamese speech data. The model aims to improve transcription accuracy and robustness for Vietnamese automatic speech recognition (ASR) tasks, especially in real-world scenarios.

📊 Fine-tuning Results

Word Error Rate (WER): 16.9148

Evaluation was performed on a held-out test set with diverse regional accents and speaking styles.

📝 Model Description

The Whisper Base model is a transformer-based sequence-to-sequence model designed for automatic speech recognition and translation tasks. It has been trained on over 680,000 hours of labeled audio data in multiple languages. The fine-tuned version of this model focuses on the Vietnamese language, aiming to improve transcription accuracy and handling of local dialects.

This model works with the WhisperProcessor to pre-process audio inputs into log-Mel spectrograms and decode them into text.

📁 Dataset

Total Duration: More 100 hours of high-quality Vietnamese speech data
Sources: Public Vietnamese datasets
Format: 16kHz WAV files with corresponding text transcripts
Preprocessing: Audio was normalized and segmented. Transcripts were cleaned and tokenized.

🚀 How to Use

To use the fine-tuned model, you can go to: https://github.com/namphung134/np-asr-vietnamese

Or you can follow these steps:

Install the required dependencies:

# Install required libraries
!pip install transformers torch librosa soundfile --quiet

# Import necessary libraries
import torch
import librosa
import soundfile as sf
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline

print("Environment setup completed!")

Use the model for inference:

import torch
import librosa
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# Load processor and model
model_id = "namphungdn134/whisper-base-vi"
print(f"Loading model from: {model_id}")
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device)

# config language and task
forced_decoder_ids = processor.get_decoder_prompt_ids(language="vi", task="transcribe")
model.config.forced_decoder_ids = forced_decoder_ids
print(f"Forced decoder IDs for Vietnamese: {forced_decoder_ids}")

# Preprocess
audio_path = "example.wav"  
print(f"Loading audio from: {audio_path}")
audio, sr = librosa.load(audio_path, sr=16000)  
input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features.to(device)
print(f"Input features shape: {input_features.shape}")

# Generate
print("Generating transcription...")
with torch.no_grad():
   predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)

transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print("📝 Transcription:", transcription)

# Debug: Print token to check
print("Predicted IDs:", predicted_ids[0].tolist())

⚠️ Limitations

This model is specifically fine-tuned for the Vietnamese language. It might not perform well on other languages.
Struggles with overlapping speech or noisy background.
Performance may drop with strong dialectal variations not well represented in training data.

📄 License

This model is licensed under the MIT License.

📚 Citation

If you use this model in your research or application, please cite the original Whisper model and this fine-tuning work as follows:

@article{Whisper2021,
  title={Whisper: A Multilingual Speech Recognition Model},
  author={OpenAI},
  year={2021},
  journal={arXiv:2202.12064},
  url={https://arxiv.org/abs/2202.12064}
}

@misc{title={Whisper Base Vi V1.1 - Nam Phung},
  author={Nam Phùng},
  organization={DUT},
  year={2025},
  url={https://huggingface.co/namphungdn134/whisper-base-vi}
}

📬 Contact

For questions, collaborations, or suggestions, feel free to reach out via [namphungdn134@gmail.com].

namphungdn134/whisper-base-vi