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thaiphonghuan/
wav2vec2-large-vietnamese-vivos

Name: wav2vec2-large-vietnamese-vivos
Availability: InStock

safetensors

wav2vec2

apache-2.0

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Wav2Vec2-Large-Vietnamese-VIVOS (Fine-tuned)

This repository contains a fine-tuned version of the nguyenvulebinh/wav2vec2-large-vi-vlsp2020 model on the VIVOS: Vietnamese Speech Corpus for ASR.
The model is designed for Automatic Speech Recognition (ASR) in Vietnamese.

📖 Dataset: VIVOS

Name: VIVOS - Vietnamese Speech Corpus for ASR
Size: ~15 hours of recording speech
Description:
VIVOS is a free Vietnamese speech corpus prepared for Automatic Speech Recognition (ASR) tasks.
It consists of audio recordings paired with corresponding transcriptions.
More information: Kaggle dataset link

🧩 Base Model

This model is fine-tuned from nguyenvulebinh/wav2vec2-large-vi-vlsp2020,
which is a large-scale pretrained Wav2Vec2 model for Vietnamese ASR.

🚀 Usage

You can use this model directly with the Hugging Face transformers library:

python

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
import soundfile as sf

# Load processor and model
processor = Wav2Vec2Processor.from_pretrained("your-username/your-model-repo")
model = Wav2Vec2ForCTC.from_pretrained("your-username/your-model-repo")

# Load audio file
speech, rate = sf.read("path_to_your_audio.wav")

# Preprocess
input_values = processor(speech, sampling_rate=rate, return_tensors="pt", padding=True).input_values

# Perform inference
with torch.no_grad():
    logits = model(input_values).logits

# Decode prediction
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0])

print("Transcription:", transcription)

📌 Acknowledgements

Base model: nguyenvulebinh/wav2vec2-large-vi-vlsp2020
Dataset: VIVOS - Vietnamese Speech Corpus for ASR

thaiphonghuan/wav2vec2-large-vietnamese-vivos