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Whisper Tiny
Whisper
OpenAI's Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation. It can transcribe speech into text in the language it was spoken (ASR) or translate it into English.
Model Variants
Whisper comes in various sizes with different capabilities:
Model | Parameters | Description |
|---|---|---|
Whisper Turbo | ~1.55B | Optimized version of large-v3, faster with minimal accuracy loss |
Whisper Large-v3 | 1.55B | Most advanced version with best accuracy |
Whisper Large-v2 | 1.55B | Enhanced version with 2.5x more training epochs |
Whisper Large | 1.55B | Original large model |
Whisper Medium | 769M | Mid-sized model with good performance |
Whisper Small | 244M | Smaller model with faster inference |
Whisper Base | 74M | Basic model with lower resource requirements |
Whisper Tiny | 39M | Smallest model for lightweight applications |
Each model (except for the largest) is available in English-only and multilingual versions.
Whisper Large-v3
Overview: The most advanced version of Whisper with improved performance across a wide variety of languages.
Key Features:
- Trained on 1M hours of weakly labeled audio and 4M hours of pseudo-labeled audio
- 10-20% error reduction compared to Whisper large-v2
- 128 Mel frequency bins (improved from 80 in previous versions)
- Parameter size: 1.55B
- Robust to accents, background noise, and technical language
- Zero-shot translation from multiple languages into English
Technical Specifications:
- Maximum audio input: 30 seconds natively (longer with chunking algorithm)
- Supported file formats: mp3, mp4, mpeg, mpga, m4a, wav, webm
- Language detection capabilities for identifying spoken language
Use Cases:
- High-quality transcription
- Multilingual speech recognition
- Audio content analysis
- Captioning and accessibility
- Research applications
Whisper Turbo
Overview: An optimized version of the large-v3 model with faster transcription speed and minimal degradation in accuracy.
Key Features:
- Based on the large-v3 architecture
- Optimized for speed while maintaining high accuracy
- Excellent for English transcription
- Full multilingual capabilities
- Efficient for production applications
Use Cases:
- Production transcription systems
- Real-time applications
- Streaming applications
- Enterprise solutions
- Content moderation
Frequently Asked Questions
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