whisper is by openai — not by us.
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Indexed Aug 3, 2026 · 106,515 stars at index time. Maintainers: claiming verifies your identity and unlocks a higher assurance tier. Removal requests are honored.
Application · for humans & their agents
whisper
unclaimed listingactively maintainedFreeMIT
Robust Speech Recognition via Large-Scale Weak Supervision
by Open Source Community · New publisher
Robust Speech Recognition via Large-Scale Weak Supervision
openai/whisper is an open-source project by openai: Robust Speech Recognition via Large-Scale Weak Supervision. Indexed here so it can be found — not resold.
It is free. Get it from the upstream repository: https://github.com/openai/whisper
From the project's own README (excerpt, reproduced for discovery under its MIT license):
Whisper
[[Blog]](https://openai.com/blog/whisper) [[Paper]](https://arxiv.org/abs/2212.04356) [[Model card]](https://github.com/openai/whisper/blob/main/model-card.md) [[Colab example]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb)
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.
Approach
A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.
Setup
We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably OpenAI's tiktoken for their fast tokenizer implementation. You can download and install (or update to) the latest release of Whisper with the following command:
pip install -U openai-whisper
Alternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:
pip install git+https://github.com/openai/whisper.git
To update the package to the latest version of this repository, please run:
pip install --upgrade --no-deps…
01Capabilities
Does
- + Audio codec
- + Voice input capture
- + Media processing
Doesn’t
- No exclusions declared
02Requirements & stack
Depends on
No declared dependencies
Credentials needed
None declared
Stack
03Community
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Issues 0
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04Trust Passport
Full passport →0/0 automated components pass. An automated score is never a security guarantee.
This listing is unclaimed, so publisher identity cannot be verified and it stays below the “verified” tier by design — that is a statement about the listing, not about the project’s quality. Our automated scans still ran; a maintainer who claims it unlocks identity verification.
- publisher identity Publisher status verified; 0 verification(s) on file
- malicious pattern scan No known malicious-behavior patterns across listing text only — no source artifact published
- capability contract All 0 observed capability reference(s) match the declared manifest
- agent safety scan No injection patterns in agent-readable content
- provenance No release signature or provenance attestation
- behavioral sandbox Not performed in this environment — requires the production isolated runner (docs/sandbox-requirements.md). No untrusted code is ever executed on the application host.
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05Versions
Full history →| Version | Channel | Released | Notes |
|---|---|---|---|
| 0.0.0 | stable | Aug 3, 2026 | Indexed listing — see the upstream repository for real release history. |