wav2vec2-large-xlsr-53-tamil
| Property | Value |
|---|---|
| Author | Amrrs |
| Base Model | facebook/wav2vec2-large-xlsr-53 |
| Task | Tamil Speech Recognition |
| Model Hub | Hugging Face |
What is wav2vec2-large-xlsr-53-tamil?
This is a specialized speech recognition model fine-tuned specifically for the Tamil language. Built upon Facebook's wav2vec2-large-xlsr-53 architecture, it's designed to transcribe Tamil speech into text using the Common Voice dataset. The model operates on 16kHz audio input and employs CTC (Connectionist Temporal Classification) for direct speech-to-text conversion without requiring a separate language model.
Implementation Details
The model utilizes the Wav2Vec2ForCTC architecture and requires audio input to be sampled at 16kHz. It achieved a Word Error Rate (WER) of 82.94% on the test set, indicating areas for potential improvement. The implementation includes built-in preprocessing capabilities, including audio resampling from 48kHz to 16kHz when necessary.
- Direct integration with the Transformers library
- Built-in audio preprocessing and resampling
- Supports batch processing for multiple audio files
- CUDA-compatible for GPU acceleration
Core Capabilities
- Tamil speech recognition without language model dependency
- Automatic audio resampling to required 16kHz
- Batch processing support for efficient inference
- Character-level tokenization with special character handling
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Tamil language speech recognition, utilizing the powerful wav2vec2-large-xlsr-53 architecture. It's designed for direct use without requiring a language model, making it more accessible for immediate implementation.
Q: What are the recommended use cases?
The model is best suited for Tamil speech transcription tasks where the audio input can be provided at 16kHz sampling rate. It's particularly useful for applications requiring quick deployment without the complexity of a separate language model.
