Whisper-tiny.en
| Property | Value |
|---|---|
| Parameter Count | 39M |
| License | Apache 2.0 |
| Paper | Robust Speech Recognition via Large-Scale Weak Supervision |
| Task | Automatic Speech Recognition (English) |
| WER (LibriSpeech Clean) | 8.44% |
What is whisper-tiny.en?
Whisper-tiny.en is a compact English-specific automatic speech recognition (ASR) model that represents the smallest variant in OpenAI's Whisper model family. Built on a Transformer-based encoder-decoder architecture, it's specifically optimized for English speech recognition tasks, offering an excellent balance between model size and performance.
Implementation Details
The model utilizes a sequence-to-sequence architecture trained on 680,000 hours of labeled speech data. As an English-only variant, it focuses specifically on English ASR tasks, which allows for optimized performance while maintaining a small footprint of only 39M parameters.
- Transformer-based encoder-decoder architecture
- Optimized for English speech recognition
- Supports audio chunks up to 30 seconds
- Implements efficient batch processing
Core Capabilities
- English speech transcription with competitive accuracy
- Support for long-form transcription through chunking
- Timestamp prediction capability
- Robust performance across different accents and background noise
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its excellent performance-to-size ratio, offering competitive English ASR capabilities in a compact 39M parameter package. It's particularly suited for applications where computational resources are limited but English-specific ASR accuracy is crucial.
Q: What are the recommended use cases?
The model is ideal for English speech transcription tasks, particularly in scenarios requiring efficient processing of audio content. It's well-suited for batch processing, content accessibility tools, and applications requiring timestamp prediction. However, it's not recommended for real-time transcription out of the box or for high-stakes decision-making contexts.
