wav2vec2-large-vi
| Property | Value |
|---|---|
| Parameters | 317M |
| License | CC-BY-NC-4.0 |
| Training Data | 13k hours YouTube Vietnamese audio |
| Architecture | wav2vec2 large |
What is wav2vec2-large-vi?
wav2vec2-large-vi is a self-supervised learning model designed specifically for Vietnamese speech processing. Trained on a massive dataset of 13,000 hours of diverse Vietnamese YouTube audio, it represents a significant advancement in Vietnamese speech technology. The model was trained for 20 epochs over approximately 30 days using TPU V3-8 infrastructure.
Implementation Details
The model utilizes the wav2vec2 architecture, adapted for Vietnamese language processing. It contains approximately 317M parameters and demonstrates impressive performance on speech recognition tasks, achieving a Word Error Rate (WER) of 5.32% with 5-grams Language Model on the VLSP 2020 benchmark.
- Pre-trained on diverse audio including clean speech, noise, conversations, and multiple dialects
- Implements the complete wav2vec2 architecture for feature extraction
- Supports both base and large model variants
- Compatible with HuggingFace's transformers library
Core Capabilities
- Self-supervised speech representation learning
- Robust performance across different Vietnamese dialects
- Support for downstream ASR tasks
- Integration with language models for improved accuracy
Frequently Asked Questions
Q: What makes this model unique?
The model's training on 13,000 hours of diverse Vietnamese audio content, combined with its large parameter count and specialized architecture for Vietnamese language, makes it particularly effective for Vietnamese speech processing tasks.
Q: What are the recommended use cases?
The model is ideal for Vietnamese automatic speech recognition (ASR), speech representation learning, and can be fine-tuned for specific speech processing tasks. It's particularly useful for applications requiring robust Vietnamese speech understanding across different dialects and acoustic conditions.
