wav2vec2-large-xlsr-thai-demo
| Property | Value |
|---|---|
| Author | sakares |
| Base Model | facebook/wav2vec2-large-xlsr-53 |
| Model Hub | HuggingFace |
| Task | Thai Speech Recognition |
What is wav2vec2-large-xlsr-thai-demo?
This is a specialized speech recognition model fine-tuned for the Thai language, based on Facebook's wav2vec2-large-xlsr-53 architecture. The model has been specifically optimized for processing Thai speech input and converting it to text, requiring a 16kHz sampling rate for optimal performance.
Implementation Details
The model utilizes the Wav2Vec2ForCTC architecture for speech recognition, combined with Thai-specific processing using PyThaiNLP for tokenization. It achieves a Word Error Rate (WER) of 44.46% on the Common Voice Thai test set. The implementation includes built-in resampling capabilities and special character handling for improved accuracy.
- Integrates with PyThaiNLP for Thai-specific tokenization
- Supports batch processing for efficient inference
- Includes automatic audio resampling to 16kHz
- Handles special character normalization
Core Capabilities
- Direct speech-to-text transcription for Thai language
- Batch processing support for multiple audio files
- Integration with Common Voice dataset
- GPU acceleration support for faster inference
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Thai language speech recognition, utilizing the powerful wav2vec2-large-xlsr-53 architecture combined with Thai-specific tokenization and processing.
Q: What are the recommended use cases?
The model is ideal for Thai speech recognition tasks, particularly in applications requiring 16kHz audio input. It's suitable for automated transcription services, voice command systems, and research applications focusing on Thai language processing.
