Whisper Small Tamil Model
| Property | Value |
|---|---|
| Parameter Count | 242M |
| License | Apache 2.0 |
| Base Model | openai/whisper-small |
| Word Error Rate | 43.32% |
| Tensor Type | F32 |
What is whisper-small-ta?
Whisper-small-ta is a specialized automatic speech recognition (ASR) model fine-tuned specifically for the Tamil language. Built upon OpenAI's whisper-small architecture, this model represents a significant step forward in Tamil language processing, offering robust speech-to-text capabilities with 242M parameters.
Implementation Details
The model was trained using a carefully curated approach with the following specifications: learning rate of 1e-05, batch size of 16, and 4000 training steps. The training utilized the Adam optimizer with linear learning rate scheduling and 500 warmup steps. Native AMP was employed for mixed precision training.
- Fine-tuned on Mozilla Common Voice 11.0 Tamil dataset
- Implements Transformer-based architecture
- Uses Native AMP for optimized training
- Achieved progressive WER improvement from 51.02% to 43.32%
Core Capabilities
- Tamil speech-to-text transcription
- Optimized for various Tamil dialects and accents
- Suitable for both academic and production environments
- Handles diverse speaker variations
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Tamil language processing, with extensive fine-tuning on the Common Voice dataset. Its progressive training results show significant improvement in accuracy, making it particularly effective for Tamil speech recognition tasks.
Q: What are the recommended use cases?
The model is ideal for Tamil speech-to-text applications, including transcription services, voice assistants, and content accessibility tools. However, users should note its limitations in noisy environments or with heavily accented speech not represented in the training data.
