Whisper review

Q: How do I install Whisper?

Run pip install -U openai-whisper. The openai-whisper package needs Python 3.8 or newer. The install section above has the other options from the project's README.

Whisper is OpenAI's general-purpose speech recognition model for multilingual transcription, speech translation into English, and language identification, usable from the command line or Python.

Updated Nov 1, 2026. Review written Oct 22, 2026 from the project's own sources; usage data refreshed every Monday.

TL;DR

Whisper is a Transformer sequence-to-sequence speech recognition model from OpenAI, trained on a large dataset of diverse audio. It is for Python developers who need transcription, translation into English, or language detection. Six model sizes trade speed for accuracy, and the code and model weights are released under the MIT License.

111.7kGitHub stars
+486new stars, week of Oct 25
1.5Mdownloads a week, +4% vs 4-week avg
#8in Voice and realtime this week

Whisper at a glance

Category: Voice and realtime
Maintained by: OpenAI (organization, @openai on GitHub)
License: MIT
Language: Python
Repository: github.com/openai/whisper
PyPI package: openai-whisper 20250625
Latest release: v20250625 on GitHub, Jun 26, 2025
First published: Sep 16, 2022

What Whisper does

Whisper transcribes speech in audio files such as FLAC, MP3, and WAV in many languages.
Whisper translates non-English speech into English with the multilingual models and the --task translate option.
Whisper detects the spoken language through whisper.detect_language() in the Python API.
Whisper offers six model sizes, from tiny to large plus turbo, with different speed, accuracy, and VRAM needs.
Whisper provides English-only .en models that tend to perform better for English, especially tiny.en and base.en.
Whisper exposes lower-level Python access through whisper.decode() and DecodingOptions for custom decoding.

How it works

Whisper runs locally as a command-line tool or a Python library built on PyTorch. It uses ffmpeg to read audio. The transcribe() method reads the entire file and processes the audio with a sliding 30-second window. It makes autoregressive sequence-to-sequence predictions on each window. A single model handles recognition, translation, and language identification. Special tokens specify the task.

How to install and start using Whisper

With pip (latest release)

pip install -U openai-whisper

From the GitHub repository (latest commit)

pip install git+https://github.com/openai/whisper.git

Install ffmpeg on macOS with Homebrew

brew install ffmpeg

Requirements

Python 3.8 to 3.11 and a recent version of PyTorch.
The ffmpeg command-line tool installed on your system.
Rust may be needed if tiktoken has no pre-built wheel for your platform.
GPU memory from about 1 GB for tiny or base up to about 10 GB for large.

First steps

Install ffmpeg with your system package manager, for example: sudo apt update && sudo apt install ffmpeg on Ubuntu or Debian.
Transcribe files with the turbo model: whisper audio.flac audio.mp3 audio.wav --model turbo
Translate non-English speech into English: whisper japanese.wav --model medium --language Japanese --task translate
Run whisper --help to view all available options.

Who Whisper is for

Python developers who want local speech-to-text transcription inside their own code.
Projects that need to translate non-English speech into English text.
Applications that must identify the spoken language in audio.
Developers who want open model weights under the MIT License.

Limitations to know

Whisper's turbo model is not trained for translation and returns the original language even with --task translate.
Whisper's accuracy varies widely depending on the language.
Whisper's larger models need significant VRAM, about 10 GB for large and about 6 GB for turbo.
Whisper has a reported issue where complete silence is hallucinated as an Arabic phrase.

Is Whisper growing? Usage data

In the week of Oct 25–31, 2026, Whisper ranked #150 on the Devtoolwatch Board and #8 in Voice and realtime, with a momentum score of 52 out of 100. Its main package had 1.5M downloads that week, +4% against its own 4-week average.

New GitHub stars per week for the last 12 weeks, from GitHub's stargazer history (today's stargazers by the week they starred).

Show the weekly numbers

New GitHub stars per week for Whisper
Week of	New stars
Aug 9, 2026	443
Aug 16, 2026	477
Aug 23, 2026	369
Aug 30, 2026	436
Sep 6, 2026	450
Sep 13, 2026	461
Sep 20, 2026	296
Sep 27, 2026	363
Oct 4, 2026	370
Oct 11, 2026	586
Oct 18, 2026	487
Oct 25, 2026	486

On Hacker News

Complete silence is always hallucinated as "ترجمة نانسي قنقر" in Arabic, 568 points and 340 comments (Jul 22, 2025)
OpenAI quietly launched Whisper V2 in a GitHub commit, 335 points and 126 comments (Dec 6, 2022)
OpenAI releases Whisper v3, new generation open source ASR model, 117 points and 58 comments (Nov 6, 2023)
Whisper v3, 46 points and 1 comments (Nov 7, 2023)
New OpenAI Whisper model: "turbo", 13 points and 1 comments (Oct 1, 2024)

The Board ranks tools by four signals each week: new stars, new stars for the repo's size, added downloads and download growth beyond the market. How the ranking works.

Whisper alternatives

Other voice and realtime AI tools on the Board in the week of Oct 25–31, 2026, by momentum:

Voice and realtime ranked by momentum, the week of Oct 25–31, 2026
Rank	Tool	Stars	Downloads/wk
#1	faster-whisperfaster-whisper is a Python reimplementation of OpenAI's Whisper speech-to-text model built on the CTranslate2 inference engine for…	26.4k	4.4M
#2	KokoroKokoro is a Python inference library for Kokoro-82M, an open-weight text-to-speech model with 82 million parameters.	9.8k	150.7k
#3	ChatterboxChatterbox is a family of open-source text-to-speech and voice cloning models by Resemble AI, with English and multilingual variants.	27.5k	73.2k
#4	VoiceMemInfrastructure for the next generation of voice agents, designed to provide universal memory. It is divided into a left brain and a…	2.8k	–
#5	PipecatPipecat is an open-source Python framework for building real-time voice and multimodal conversational agents, maintained by Daily and…	17k	348.5k
#6	WhisperXWhisperX is a Python speech recognition tool built on Whisper that adds word-level timestamps and speaker diarization.	25k	158.6k

All voice and realtime AI tools, ranked

Recent Whisper releases

v20250625, Jun 26, 2025
v20240930, Sep 30, 2024
v20240927, Sep 27, 2024
v20231117, Nov 17, 2023
v20231106, Nov 6, 2023

Whisper FAQ

Is Whisper free and open source?

Yes. Whisper's source code is on GitHub (openai/whisper) under the MIT license, so you can use it for free.

How do I install Whisper?

Run pip install -U openai-whisper. The openai-whisper package needs Python 3.8 or newer. The install section above has the other options from the project's README.

Is Whisper still maintained?

Activity has slowed: the openai/whisper repository was last updated on Aug 31, 2026, and the latest release, v20250625 on GitHub, came out on Jun 26, 2025.

How popular is Whisper?

Whisper has 111.7k GitHub stars as of Nov 1, 2026. It gained 486 new stars in the week of Oct 25–31, 2026, against a 12-week average of 435 a week. Its main package had 1.5M downloads that week, +4% against its own 4-week average. That made it #8 among voice and realtime AI tools on the Devtoolwatch Board.

Whisper vs WhisperX: how do they compare?

Whisper and WhisperX are both tracked by Devtoolwatch as voice and realtime AI tools. Whisper has 111.7k GitHub stars and WhisperX has 25k. In the week of Oct 25–31, 2026, Whisper's main package had 1.5M downloads and WhisperX's had 158.6k. WhisperX is a Python speech recognition tool built on Whisper that adds word-level timestamps and speaker diarization. WhisperX review.

What are the best alternatives to Whisper?

Other voice and realtime AI tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: faster-whisper (#1), Kokoro (#2), Chatterbox (#3), VoiceMem (#4), Pipecat (#5). The alternatives table above has their numbers.

How do I use Whisper to transcribe audio?

Whisper transcribes audio with one command: whisper audio.flac audio.mp3 audio.wav --model turbo. In Python, load a model with whisper.load_model("turbo"), call model.transcribe("audio.mp3"), and read result["text"]. For non-English speech, add the --language option, for example --language Japanese.

Which Whisper model should I use for translation?

Whisper translation needs a multilingual model: tiny, base, small, medium, or large. Do not use turbo, because it is not trained for translation. The README recommends medium or large for the best translation results.

What is the Whisper turbo model?

Whisper turbo is an optimized version of large-v3. It transcribes faster with a minimal loss in accuracy. It has 809M parameters, needs about 6 GB of VRAM, and is the default model on the command line.

Does Whisper need a GPU?

Whisper lists approximate VRAM needs for each model size. These range from about 1 GB for tiny and base to about 10 GB for large. The relative speeds in the README were measured on an A100. Real-world speed varies with language, speaking speed, and hardware.

Can Whisper detect which language is spoken?

Whisper can detect the spoken language. In Python, call model.detect_language() on a log-Mel spectrogram of the audio. It returns probabilities for each language, and the most likely one is the detected language.

Sources

Whisper README badge

Maintainers can show the tool's weekly Board rank in their README:

[![Devtoolwatch](https://index.devtoolwatch.com/badge/whisper.svg)](https://index.devtoolwatch.com/tools/whisper/)

How this page is made: Devtoolwatch writes each review from the project's README, releases, package metadata and website, with an AI model, then checks every claim against the source it cites and drops what the sources don't support. The numbers come from Devtoolwatch's daily GitHub snapshots, GitHub's stargazer history, and the npm and PyPI download APIs. Spotted an error? Email [email protected].