wav2vec2-base-vietnamese-160h
| Property | Value |
|---|---|
| Author | khanhld |
| License | CC-BY-NC-4.0 |
| Base Architecture | Wav2vec 2.0 |
| Best WER (Common Voice) | 10.78% |
| Training Data | 160 hours (VIOS, Common Voice, FOSD, VLSP) |
What is wav2vec2-base-vietnamese-160h?
This is a specialized speech recognition model designed specifically for the Vietnamese language, built on the powerful Wav2vec 2.0 architecture. The model has been fine-tuned on approximately 160 hours of Vietnamese speech data collected from various sources including VIOS, Common Voice, FOSD, and VLSP datasets. Even without language model integration, it achieves impressive word error rates of 15.05% on VIVOS and 10.78% on Common Voice 8.0.
Implementation Details
The model leverages the Wav2vec 2.0 architecture and can be easily implemented using the Hugging Face Transformers library. It processes audio at 16kHz sampling rate and outputs Vietnamese text transcriptions. The implementation supports both CPU and GPU inference, with simple integration through the Transformers pipeline.
- Built on Wav2vec 2.0 base architecture
- Fine-tuned on diverse Vietnamese speech datasets
- Operates on 16kHz audio input
- Supports batch processing capabilities
Core Capabilities
- Direct speech-to-text transcription for Vietnamese audio
- Robust performance across different Vietnamese accents and speaking styles
- Efficient processing with both CPU and GPU support
- Ready-to-use implementation with minimal setup requirements
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for being specifically optimized for Vietnamese speech recognition, achieving strong performance metrics without requiring a language model. It's particularly notable for its accessibility and ease of implementation through the Hugging Face ecosystem.
Q: What are the recommended use cases?
The model is ideal for Vietnamese speech transcription tasks, particularly in applications requiring real-time or batch processing of Vietnamese audio content. It's suitable for both research and production environments, though users should note the CC-BY-NC-4.0 license restrictions.
