Whisper Small English Model
| Property | Value |
|---|---|
| Parameter Count | 242M parameters |
| Model Type | Automatic Speech Recognition |
| License | Apache 2.0 |
| Paper | Robust Speech Recognition via Large-Scale Weak Supervision |
What is whisper-small.en?
Whisper-small.en is a specialized English-only variant of OpenAI's Whisper speech recognition model. It's a transformer-based encoder-decoder architecture specifically optimized for English ASR tasks, offering an excellent balance between model size and performance. With 242M parameters, it represents a middle-ground option in the Whisper model family, providing robust speech recognition capabilities while maintaining reasonable computational requirements.
Implementation Details
The model is implemented as a sequence-to-sequence transformer that processes audio input as log-Mel spectrograms. It can handle audio chunks of up to 30 seconds in length, with built-in support for longer audio through automatic chunking.
- Trained on 680,000 hours of labeled speech data
- Supports F32 tensor operations
- Implements automatic chunking for long-form transcription
- Includes integrated timestamp prediction capabilities
Core Capabilities
- High-accuracy English speech recognition
- Robust performance across different accents and background noise
- Support for batch processing and GPU acceleration
- Zero-shot adaptation to various domains
- Optional timestamp generation for word-level timing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on English ASR, offering better performance on English tasks compared to multilingual variants while maintaining a relatively compact size. It's particularly notable for its robustness to different accents and noise conditions.
Q: What are the recommended use cases?
The model is ideal for English speech transcription tasks, particularly in scenarios requiring batch processing of audio files, development of accessibility tools, or research applications. It's well-suited for both short-form and long-form transcription tasks, though real-time transcription may require additional optimization.
