open sourcespeech

Distil-Whisper

Fast, efficient, and accurate real-time transcription.

Developed by Hugging Face

Official Site
60MParams
YesAPI Available
stableStability
1.0Version
MITLicense
PyTorchFramework
NoRuns Locally
Real-World Applications
  • Real-time transcriptionOptimized Capability
  • Voice command recognitionOptimized Capability
  • Subtitle generationOptimized Capability
  • Audio content analysisOptimized Capability
Implementation Example
Example Prompt
Transcribe the following audio snippet into text using Distil-Whisper.
Model Output
"The quick brown fox jumps over the lazy dog."
Advantages
  • ✓ Achieves up to 6x faster inference compared to the original Whisper model.
  • ✓ Maintains a low ≤ 1% word error rate in English speech tasks.
  • ✓ Consumes under 50% of the parameters, making it suitable for resource-constrained environments.
Limitations
  • ✗ May not perform as well on non-English languages compared to the original model.
  • ✗ Limited support for advanced features available in full Whisper.
  • ✗ Potentially less robust in noisy environments than larger models.
Model Intelligence & Architecture

Technical Documentation

Distil-Whisper is a distilled version of OpenAI's Whisper model developed by Hugging Face. Optimized for speed and efficiency, it delivers real-time transcription with up to six times faster inference while using less than half the parameters of the original model. Despite its smaller size, Distil-Whisper maintains a low word error rate, making it a top choice for developers focused on speech-to-text applications requiring rapid and reliable audio processing.

Technical Overview

Distil-Whisper is designed to balance performance and computational efficiency. By distilling knowledge from the full Whisper model, it reduces complexity while retaining high-quality transcription output. This makes it particularly suitable for deployment in real-time or resource-constrained environments where latency and throughput are critical.

Framework & Architecture

  • Framework: PyTorch
  • Architecture: Distilled Transformer-based speech recognition model
  • Parameters: Reduced size relative to original Whisper (exact parameter count optimized for efficiency)
  • Latest Version: 1.0

The model architecture focuses on leveraging transformer layers optimized for speech recognition tasks. The use of PyTorch ensures strong community support, ease of fine-tuning, and integration flexibility with existing ML pipelines.

Key Features / Capabilities

  • Up to 6x faster inference compared to the original Whisper model
  • Uses less than half the parameters, reducing memory and compute requirements
  • Maintains low word error rate (WER) for accurate transcription
  • Ideal for real-time transcription applications
  • Supports multiple languages and audio types inherent to the Whisper architecture
  • Open-source with easy access to source code and pretrained weights

Use Cases

  • Real-time transcription services for live audio streams
  • Voice command recognition for interactive applications
  • Subtitle generation for videos and multimedia content
  • Audio content analysis for indexing and searching spoken content

Access & Licensing

Distil-Whisper is open source under the MIT License, enabling developers to freely access, modify, and deploy the model in commercial and non-commercial projects. The source code is available on GitHub: https://github.com/huggingface/distil-whisper. Official model details and documentation can be found on Hugging Face: https://huggingface.co/distil-whisper. This open accessibility empowers developers to build cutting-edge speech recognition applications with high efficiency and accuracy.

Technical Specification Sheet

FAQs

Technical Details
Architecture
Transformer-based Encoder-Decoder
Stability
stable
Framework
PyTorch
Signup Required
No
API Available
Yes
Runs Locally
No
Release Date
2023-11-30

Best For

Real-time applications requiring fast and accurate transcription.

Alternatives

OpenAI Whisper, Google Speech Recognition, Amazon Transcribe

Pricing Summary

Available under an MIT license, facilitating both free and commercial use.

Compare With

Distil-Whisper vs WhisperDistil-Whisper vs Google Speech-to-TextDistil-Whisper vs Microsoft Azure SpeechDistil-Whisper vs Mozilla DeepSpeech

Explore Tags

#asr#speech-recognition

Explore Related AI Models

Discover similar models to Distil-Whisper

View All Models
OPEN SOURCE

SpeechT5

SpeechT5 is a versatile speech processing model developed by Microsoft, designed to handle speech recognition, speech synthesis, and speech translation tasks within a unified framework.

Speech & AudioView Details
OPEN SOURCE

wav2vec 2.0

wav2vec 2.0 is a self-supervised speech representation learning model developed by Meta AI, revolutionizing automatic speech recognition (ASR) by significantly decreasing the need for labeled data.

Speech & AudioView Details
OPEN SOURCE

OpenVoice

OpenVoice V2 is a cutting-edge open-source voice cloning and speech synthesis model focused on delivering high-fidelity voice outputs with emotional and stylistic flexibility.

Speech & AudioView Details