wav2vec2-xls-r-300m-timit-phoneme
| Property | Value |
|---|---|
| Author | vitouphy |
| Base Model | facebook/wav2vec2-xls-r-300m |
| Task | Phoneme Recognition |
| Test Error Rate | 7.996% |
| Framework | PyTorch 1.10.2 |
What is wav2vec2-xls-r-300m-timit-phoneme?
This is a specialized speech recognition model fine-tuned for phoneme recognition using the DARPA TIMIT dataset. Built upon Facebook's wav2vec2-xls-r-300m architecture, it's specifically optimized for converting speech audio into phonetic transcriptions. The model demonstrates strong performance with a 7.996% error rate on the test set.
Implementation Details
The model utilizes the Wav2Vec2 architecture with CTC (Connectionist Temporal Classification) for phoneme recognition. It was trained using a carefully curated split of the TIMIT dataset (80/10/10 for train/validation/test), representing approximately 137/17/17 minutes of audio data respectively. The training process employed mixed precision training with Native AMP and utilized the Adam optimizer with specific hyperparameters.
- Learning rate: 3e-05 with linear scheduler and 2000 warmup steps
- Batch size: 32 (8 per batch with 4 gradient accumulation steps)
- Training steps: 10000
- Advanced mixed precision training implementation
Core Capabilities
- Direct audio-to-phoneme transcription
- Support for both pipeline and custom implementation approaches
- Efficient processing of audio chunks with configurable stride lengths
- Batch processing capability with attention mask support
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in phoneme recognition, differentiating it from standard speech-to-text models. It's particularly valuable for linguistic research and applications requiring phonetic analysis of speech.
Q: What are the recommended use cases?
The model is ideal for phonetic transcription tasks, linguistic research, accent analysis, and speech therapy applications. It's particularly suited for applications requiring detailed phonetic analysis of English speech.
