Whisper Large-V3-Turbo
| Property | Value |
|---|---|
| Parameter Count | 809M |
| Model Type | Speech Recognition |
| License | Apache 2.0 |
| Paper | Research Paper |
What is whisper-large-v3-turbo?
Whisper-large-v3-turbo is a specialized distilled version of the original Whisper large-v3 model, designed for efficient automatic speech recognition (ASR) and translation. The key innovation lies in reducing the number of decoding layers from 32 to 4, resulting in significantly faster inference while maintaining high accuracy. This model supports 99 languages and leverages advanced weak supervision techniques trained on over 5M hours of labeled data.
Implementation Details
The model utilizes a Transformer-based encoder-decoder architecture optimized for speech processing tasks. It supports FP16 precision and includes safetensors implementation for improved memory efficiency.
- Supports both sequential and chunked processing for long-form audio
- Compatible with Flash Attention 2 and PyTorch SDPA for acceleration
- Includes temperature fallback and timestamp generation capabilities
- Optimized for 30-second audio segments with batching support
Core Capabilities
- Multilingual speech recognition across 99 languages
- Speech-to-text translation to English
- Word and sentence-level timestamp generation
- Robust performance with different accents and background noise
- Automatic language detection
Frequently Asked Questions
Q: What makes this model unique?
The model's key advantage is its optimized architecture that reduces computation overhead while maintaining quality. With only 4 decoding layers instead of 32, it achieves significantly faster inference speeds compared to the original large-v3 model.
Q: What are the recommended use cases?
The model is ideal for production environments requiring efficient speech recognition and translation, particularly for applications needing real-time or near-real-time processing. It's especially suitable for transcription services, accessibility tools, and multilingual content processing.
