经 AI Skill Hub 精选评估,whisper-diarization — AI 语音识别工具中文文档 获评「强烈推荐」。已获得 5.5k 颗 GitHub Star,这款AI工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 9.0 分,适合有一定技术背景的用户使用。
whisper-diarization — AI 语音识别工具中文文档 是一款基于 Jupyter Notebook 开发的开源工具,专注于 asr、speaker-diarization、speech 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
whisper-diarization — AI 语音识别工具中文文档 是一款基于 Jupyter Notebook 开发的开源工具,专注于 asr、speaker-diarization、speech 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 克隆仓库 git clone https://github.com/MahmoudAshraf97/whisper-diarization cd whisper-diarization # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 whisper-diarization --help # 基本运行 whisper-diarization [options] <input> # 详细使用说明请查阅文档 # https://github.com/MahmoudAshraf97/whisper-diarization
# whisper-diarization 配置说明 # 查看配置选项 whisper-diarization --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export WHISPER_DIARIZATION_CONFIG="/path/to/config.yml"
<p align="center"> <a href="https://github.com/MahmoudAshraf97/whisper-diarization/actions/workflows/test_run.yml"> <img src="https://github.com/MahmoudAshraf97/whisper-diarization/actions/workflows/test_run.yml/badge.svg" alt="Build Status"> </a> <a href="https://github.com/MahmoudAshraf97/whisper-diarization/stargazers"> <img src="https://img.shields.io/github/stars/MahmoudAshraf97/whisper-diarization.svg?colorA=orange&colorB=orange&logo=github" alt="GitHub stars"> </a> <a href="https://github.com/MahmoudAshraf97/whisper-diarization/issues"> <img src="https://img.shields.io/github/issues/MahmoudAshraf97/whisper-diarization.svg" alt="GitHub issues"> </a> <a href="https://github.com/MahmoudAshraf97/whisper-diarization/blob/master/LICENSE"> <img src="https://img.shields.io/github/license/MahmoudAshraf97/whisper-diarization.svg" alt="GitHub license"> </a> <a href="https://twitter.com/intent/tweet?text=&url=https%3A%2F%2Fgithub.com%2FMahmoudAshraf97%2Fwhisper-diarization"> <img src="https://img.shields.io/twitter/url/https/github.com/MahmoudAshraf97/whisper-diarization.svg?style=social" alt="Twitter"> </a> </a> <a href="https://colab.research.google.com/github/MahmoudAshraf97/whisper-diarization/blob/main/Whisper_Transcription_%2B_NeMo_Diarization.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"> </a> </p> <p align="center"> <a href="https://trendshift.io/repositories/11413" target="_blank"> <img src="https://trendshift.io/api/badge/repositories/11413" alt="MahmoudAshraf97%2Fwhisper-diarization | #9 Repository of the day" style="width: 250px; height: 55px;" width="250" height="55"/> </a> </p>
# Speaker Diarization pipeline based on OpenAI Whisper
<img src="https://github.blog/wp-content/uploads/2020/09/github-stars-logo_Color.png" alt="drawing" width="25"/> Please, star the project on github (see top-right corner) if you appreciate my contribution to the community!
Python >= 3.10 is needed, 3.9 will work but you'll need to manually install the requirements one by one.
FFMPEG and Cython are needed as prerequisites to install the requirements
pip install cython or sudo apt update && sudo apt install cython3 ```
python diarize.py -a AUDIO_FILE_NAME
If your system has enough VRAM (>=10GB), you can use diarize_parallel.py instead, the difference is that it runs NeMo in parallel with Whisper, this can be beneficial in some cases and the result is the same since the two models are nondependent on each other. This is still experimental, so expect errors and sharp edges. Your feedback is welcome.
-a AUDIO_FILE_NAME: The name of the audio file to be processed--no-stem: Disables source separation--whisper-model: The model to be used for ASR, default is medium.en--suppress_numerals: Transcribes numbers in their pronounced letters instead of digits, improves alignment accuracy--device: Choose which device to use, defaults to "cuda" if available--language: Manually select language, useful if language detection failed--batch-size: Batch size for batched inference, reduce if you run out of memory, set to 0 for non-batched inference该工具使用 BSD-2-Clause 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ BSD 2-Clause — 极度宽松,几乎可以任意使用,仅需保留版权声明。
AI Skill Hub 点评:whisper-diarization — AI 语音识别工具中文文档 的核心功能完整,质量优秀。对于AI 技术爱好者来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | whisper-diarization |
| 原始描述 | Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper |
| Topics | asrspeaker-diarizationspeechspeech-recognitionspeech-to-textwhisperstt |
| GitHub | https://github.com/MahmoudAshraf97/whisper-diarization |
| License | BSD-2-Clause |
| 语言 | Jupyter Notebook |
收录时间:2026-05-22 · 更新时间:2026-05-22 · License:BSD-2-Clause · AI Skill Hub 不对第三方内容的准确性作法律背书。