# 使用 Homebrew, Nix, MacPorts 安装 openai-whisper

查看 openai-whisper 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。

## 安装

```sh
sudo av install brew:openai-whisper
```

其他安装命令:

### macOS

- Homebrew (100%):

```sh
brew install openai-whisper
```

  证据: local Homebrew formula metadata

- MacPorts (94%):

```sh
sudo port install whisper
```

  证据: MacPorts ports tree: audio/whisper/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1

### Linux

- Nix (92%):

```sh
nix profile install nixpkgs#openai-whisper
```

  证据: nixpkgs package indexes: openai-whisper from https://raw.githubusercontent.com/NixOS/nixpkgs/master/pkgs/top-level/all-packages.nix

## 软件包事实

- **软件包键:** brew:openai-whisper
- **软件包管理器:** Homebrew
- **版本:** 20250625
- **来源摘要:** General-purpose speech recognition model
- **主页:** <https://github.com/openai/whisper>
- **仓库:** <https://github.com/openai/whisper>
- **最后更新:** 2026-07-05T21:07:55Z
- **已生成:** 2026-08-03T19:37:03+00:00

## 可执行文件

- whisper (别名)

## 安装行为

- Bottle: 不可用

## 版本和新鲜度

- 页面生成时间: 2026-08-03
- 管理器版本: 20250625
## 项目历史与用法

Whisper is OpenAI's open-source automatic speech recognition package and command-line tool. It wraps a family of sequence-to-sequence Transformer models trained for transcription, language identification, and speech translation, exposing them through both a Python API and the `whisper` executable.

### 项目历史

The public repository was created on September 16, 2022, around OpenAI's release of Whisper as code plus model weights under the MIT license. The accompanying paper, submitted to arXiv on December 6, 2022, framed Whisper as a robustness-first speech-recognition system trained at web scale rather than a narrowly benchmark-tuned ASR model.

Whisper's design used a single multitask token interface for speech recognition, speech translation, spoken-language identification, and voice activity detection. That made the package unusually self-contained for an ASR release: users could install the Python package, ensure ffmpeg was available, choose a model size, and transcribe local audio without training a model or calling a hosted API.

### 采用历史

The project became a major reference point for local and open speech transcription because OpenAI released both inference code and model weights. The model family also fed a wider ecosystem of ports, front ends, batch transcribers, and integrations, including downstream implementations optimized for smaller devices or different runtimes.

Homebrew, MacPorts, and Nix packaging made the command-line workflow convenient for Unix-like systems. In package-nerd terms, `openai-whisper` sits at the intersection of Python packaging, system multimedia dependencies through ffmpeg, and model artifact distribution.

### 使用方式

Developers use the `whisper` command to transcribe audio files, specify model sizes, set input languages, and request translation into English. Python users load a model with `whisper.load_model()` and call `transcribe()` for scripts, pipelines, notebooks, and media-processing jobs.

The README documents six model-size families plus English-only variants for some sizes, with memory and speed tradeoffs. That packaging shape matters because installing the package is only one part of operating it; users also choose model weights, hardware, ffmpeg availability, and task settings.

### 为什么软件包爱好者会关心

Whisper is a rare package-manager entry that installs a small CLI front end for very large model artifacts. It demonstrates how ML tools blur the usual package boundary: the executable is ordinary Python software, while most practical value comes from downloaded weights and GPU/CPU runtime behavior.

It also made speech recognition feel like a normal developer dependency. For many users, `brew install openai-whisper` or `pip install openai-whisper` turned multilingual ASR from a cloud service integration into a local command-line primitive.

### 时间线

- September 16, 2022: the GitHub repository was created. December 6, 2022: the Whisper paper was submitted to arXiv. Later releases added model updates such as large-v3 and turbo, while keeping the package centered on the same CLI and Python API.

### Related projects

- Whisper depends on PyTorch and ffmpeg in normal use and uses OpenAI's tiktoken tokenizer. Its adoption also encouraged alternate runtimes and ports, most famously C/C++ implementations that target smaller machines and offline workflows.

### 来源

- <https://api.github.com/repos/openai/whisper>
- <https://arxiv.org/abs/2212.04356>
- <https://cdn.openai.com/papers/whisper.pdf>
- <https://formulae.brew.sh/formula/openai-whisper>
- <https://github.com/openai/whisper>


## 安全说明

没有找到 openai-whisper 的匹配本地密钥处理 manifest。Nucleus 软件包元数据仍在此发布，以便未来覆盖拥有稳定的软件包 URL。


## 其他软件包管理器记录

- Nix - openai-whisper: normalized package name match | nixpkgs package indexes: openai-whisper from https://raw.githubusercontent.com/NixOS/nixpkgs/master/pkgs/top-level/all-packages.nix
- MacPorts - whisper: installed executable or alias match | MacPorts ports tree: audio/whisper/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1


## Combined YAML source

View the package source record on GitHub. [combined/openai-whisper.yml](https://github.com/mxcl/pkgdb/blob/main/combined/openai-whisper.yml)


## 来源

- pkg.so package database
- Geiger risk classifier
- curated package history
- pkgdb category and tag curation
- external package-manager database matches
- cross-ecosystem install command graph