Whisper-Medusa-v1
| Property | Value |
|---|---|
| Parameter Count | 1.56B |
| License | MIT |
| Tensor Type | F32 |
| Training Data | LibriSpeech ASR |
What is whisper-medusa-v1?
Whisper-Medusa-v1 is an innovative enhancement to the original Whisper model, designed to accelerate speech recognition through speculative decoding. This model maintains Whisper's encoder-decoder architecture while introducing Medusa heads that can predict multiple tokens simultaneously, significantly improving inference speed with minimal impact on Word Error Rate (WER).
Implementation Details
The model leverages the LibriSpeech dataset for training and is specifically optimized for English language processing. It implements a sophisticated approach to speech recognition that balances speed and accuracy through its unique multi-token prediction capability.
- Built on Whisper's encoder-decoder architecture
- Implements speculative decoding via Medusa heads
- Optimized for 16kHz audio sampling rate
- Supports CUDA acceleration for faster processing
Core Capabilities
- Fast and accurate speech transcription
- Optimized performance for English audio
- Efficient processing through multi-token prediction
- Seamless integration with existing audio processing pipelines
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its Medusa heads architecture, which enables multiple token predictions per iteration, significantly improving processing speed compared to standard Whisper models while maintaining accuracy.
Q: What are the recommended use cases?
This model is ideal for applications requiring fast English speech transcription, particularly in scenarios where processing speed is crucial without compromising significantly on accuracy. It's best suited for clean audio input at 16kHz sampling rate.
