LibreAI / Audio / Whisper
// audio · openai/whisper

Whisper

Robust Speech Recognition via Large-Scale Weak Supervision

★ 109.9k stars⑂ 13.3k forksMITupdated 2026-08
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

Whisper is an open-source audio project by openai (openai/whisper on GitHub). It has 109,909 GitHub stars and 13,321 forks, is released under the MIT license, and was last updated on GitHub on 31 August 2026.

Repository
openai/whisper
GitHub stars
109,909
Forks
13,321
License
MIT
Last updated on GitHub
31 August 2026
Category
Audio
Listing
Full install guide
Official site
Read the docs
// how it works

How it works

  1. Pick your direction

    Speech-to-text, text-to-speech, voice cloning, or music/SFX generation — each project here has a lane.

  2. Install the runtime

    Python packages, Docker images, and C++ builds cover servers, desktops, and edge devices.

  3. Load a model or voice

    Download the weights or pick a voice; many tools bundle a strong default so you can start immediately.

  4. Process your audio

    Transcribe files, synthesize scripts, or generate music — then export standard audio formats.

// features

Why people love it

State-of-the-art transcription

Open speech recognition now rivals paid APIs on accuracy across languages.

Natural neural voices

Modern TTS is strikingly human — good enough for narration and voiceovers.

Voice cloning

Clone a voice from a short sample for consistent characters and branding.

Music & SFX generation

Compose backing tracks and sound effects from text descriptions.

Runs offline

Entire pipelines work without internet — ideal for private or field use.

API-compatible servers

Drop-in endpoints that mimic commercial APIs, so existing apps switch easily.

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// run it

Run it yourself

  1. Step 1 — Clone the repo

    Download the source and move into the project folder.

    git clone https://github.com/openai/whisper.git
    cd whisper
  2. Step 2 — Create an environment

    Isolate dependencies so nothing clashes with your other projects.

    python -m venv .venv
    source .venv/bin/activate  # Windows: .venv\Scripts\activate
  3. Step 3 — Install and launch

    Install the requirements, then follow the README's launch command (it varies per project).

    pip install -r requirements.txt
// use cases

What you can build

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// faq

Questions, answered

How accurate is open-source transcription?

Excellent — the leading open models match commercial services on clean audio, in many languages.

Can I clone a voice ethically?

The tech clones from short samples; only ever clone voices you own or have explicit permission to use.

Who maintains Whisper?

It is an open-source project at openai/whisper with 13,321 forks. Check the repo's contributors and recent commits to see how active maintenance is.

Do I need a GPU?

Transcription and TTS run on CPU acceptably; GPUs speed up batch jobs and music generation a lot.

Which languages are supported?

Coverage varies — transcription is broadest, TTS quality is best in major languages.

Can I use outputs commercially?

Usually yes, but check both the code license and the model license.

// keep exploring

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// alternatives

Alternatives in Audio

The most-starred other Audio projects in the LibreAI catalogue — the realistic shortlist if Whisper is not the right fit.

Get Whisper

Free and open — grab the source, star the repo, and start creating.