Wav2vec2 Large Vi Vlsp2020

🚀 Vietnamese ASR Wav2Vec2 Models

These models are designed for Vietnamese automatic speech recognition, leveraging the wav2vec2 architecture and trained on extensive Vietnamese audio data.

🚀 Quick Start

You can quickly start using these models by referring to the usage examples below or by clicking the Colab link to run the code online.

✨ Features

• Advanced Architecture: Utilize the wav2vec2 architecture, which is effective for speech recognition tasks.
• Extensive Training: Pre - trained on 13k hours of un - labeled Vietnamese YouTube audio and fine - tuned on 250 hours of labeled VLSP ASR dataset.
• Low WER: Achieve low Word Error Rate (WER) on the VLSP T1 testset, with and without Language Model (LM).

📦 Installation

#pytorch
#!pip install transformers==4.20.0
#!pip install https://github.com/kpu/kenlm/archive/master.zip
#!pip install pyctcdecode==0.4.0
#!pip install huggingface_hub==0.10.0

💻 Usage Examples

Basic Usage

from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
from transformers import Wav2Vec2ProcessorWithLM
from IPython.lib.display import Audio
import torchaudio
import torch

# Load model & processor
model_name = "nguyenvulebinh/wav2vec2-large-vi-vlsp2020"
model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)

# Load an example audio (16k)
audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="t2_0000006682.wav")))
input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')

# Infer
output = model(**input_data)

# Output transcript without LM
print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))

# Output transcript with LM
print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)

📚 Documentation

Model description

Our models use wav2vec2 architecture, pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio. You can find more description here

Benchmark WER result on VLSP T1 testset:

Model Parameters License

The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode

Contact

nguyenvulebinh@gmail.com

📄 License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. For more information, please visit here.