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  1. Home
  2. AI Models
  3. Speech & Audio
  4. Distil-Whisper
open sourcespeech

Distil-Whisper

Whisper-quality speech-to-text — 6x faster, free, MIT license

Developed by Hugging Face

Try Model
166M – 756M (varies by size)Params
YesAPI
stableStability
distil-large-v3Version
MITLicense
PyTorchFramework
YesRuns Local

Playground

Implementation Example

Example Prompt

user input
Audio: 60-minute podcast episode (interview about AI ethics in mp3 format)

Model Output

model response
Returns full transcript with word-level timestamps as JSON; 60-min audio processed in ~5 min on laptop CPU. Output includes speaker text segments ready for SRT or VTT subtitle export.

Examples

Real-World Applications

  • Podcast transcription
  • video subtitles
  • meeting notes
  • voicemail
  • lecture transcription
  • accessibility captioning
  • call analytics
  • dictation apps.

Docs

Model Intelligence & Architecture

What is Distil-Whisper?

Distil-Whisper is a distilled (compressed) version of OpenAI's Whisper speech-recognition model, created by Hugging Face's research team and released in November 2023. Through knowledge distillation, the team produced models that are up to 6× faster, 49% smaller, and only 1% less accurate than the original Whisper-Large-v3.

It's released under the MIT license, making it 100% free for commercial use — and unlike many models, it runs comfortably on CPU only.

Why Distil-Whisper Is Trending in 2026

With AI transcription demand exploding (podcasts, video subtitles, meeting notes), Distil-Whisper has become the top open-source speech-to-text choice when you need speed and want to avoid OpenAI's per-minute API fees.

It powers transcription features in apps like Whisper.cpp, Vibe, MacWhisper, and many open-source meeting tools.

Key Features and Capabilities

Distil-Whisper supports automatic speech recognition (ASR), automatic translation, and word-level timestamps. It works on audio in multiple sample rates and produces accurate transcripts even for noisy environments, accented speech, and technical vocabulary.

The latest distil-large-v3 matches Whisper-large-v3 quality on long-form English audio with dramatically lower compute requirements.

Who Should Use Distil-Whisper?

Distil-Whisper is built for podcasters, video editors, journalists, accessibility tool developers, meeting-note app builders, and anyone transcribing audio at scale.

It's also a top choice for privacy-sensitive transcription (legal, medical, journalist source recordings) where uploading to cloud APIs isn't acceptable.

Top Use Cases

Real-world applications include podcast transcription, video subtitle generation, meeting notes, voicemail transcription, lecture transcription for students, accessibility captioning, customer call analysis, and dictation apps.

Many indie creators use it locally to transcribe hours of content per day at zero cost.

Where Can You Run It?

Distil-Whisper runs locally on CPU, GPU, Apple Silicon (via MLX), and even mobile. It's available on Hugging Face, integrated into Whisper.cpp, Faster-Whisper, MLX-Whisper, and WhisperX — all of which support batch processing and are dramatically faster than the official OpenAI implementation.

For browser-based use, it's also available via Transformers.js running entirely in the browser via WebGPU.

How to Use Distil-Whisper (Quick Start)

Install: pip install transformers. Load and transcribe: pipe = pipeline('automatic-speech-recognition', model='distil-whisper/distil-large-v3'), then pipe('audio.mp3').

For maximum speed, use Faster-Whisper or Whisper.cpp with the distilled GGUF/CT2 weights — 1-hour audio transcribed in under 5 minutes on a laptop.

When Should You Choose Distil-Whisper?

Choose Distil-Whisper when you need fast, free, accurate transcription at scale with no per-minute fees. It's the top free choice in 2026 for English transcription.

For non-English transcription with maximum accuracy, use the full Whisper-large-v3. For real-time streaming transcription, use Whisper.cpp's streaming mode.

Pricing

Distil-Whisper is completely free under MIT license. No API fees if you self-host. For comparison, OpenAI's Whisper API charges $0.006 per minute of audio.

Pros and Cons

Pros: ✔ MIT license ✔ 6× faster than Whisper ✔ 49% smaller ✔ Runs on CPU ✔ Word-level timestamps ✔ Browser-compatible

Cons: ✘ Mainly English (multilingual is limited) ✘ Slightly less accurate on rare accents ✘ No diarization (use WhisperX for that)

Final Verdict

Distil-Whisper is the smartest free transcription AI of 2026 — fast, accurate, and runs anywhere. Discover more audio AI on FreeAPIHub.com.

Evaluation

Advantages & Limitations

Advantages
  • ✓ MIT license
  • ✓ 6x faster than Whisper
  • ✓ 49% smaller
  • ✓ Runs on CPU
  • ✓ Word-level timestamps
  • ✓ Browser compatible (WebGPU)
Limitations
  • ✗ Mostly English-focused
  • ✗ Less accurate on rare accents
  • ✗ No built-in speaker diarization

Important Notice

Verify Before You Decide

Last verified · Apr 29, 2026

The details on this page — including pricing, features, and availability — are based on our last review and may not reflect the provider's current offering. Providers update their products frequently, sometimes without prior notice.

What may have changed

Pricing Plans
Features & Limits
Availability
Terms & Policies

Always visit the official provider website to confirm the latest pricing, terms, and feature availability before subscribing or integrating.

Check official site

External Resources

Try the Model Official Website Source Code

Technical Details

Architecture
Distilled Encoder-Decoder Transformer
Stability
stable
Framework
PyTorch
License
MIT
Release Date
2023-11-02
Signup Required
No
API Available
Yes
Runs Locally
Yes

Rate Limits

No limits self-hosted

Pricing

Completely free under MIT — no API fees

Best For

Creators and businesses needing fast, free, private audio transcription at scale

Alternative To

OpenAI Whisper API, Otter.ai, Rev.com, Deepgram

Compare With

distil-whisper vs whisperdistil-whisper vs faster-whisperfree transcription aibest speech to text free

Tags

#Audio AI#Whisper#Huggingface#Transcription#Open Source AI#speech-to-text

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