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README
Apache-2.0

whisper-api

介绍

使用winsper语音识别开源模型封装成openai chatgpt兼容接口

软件架构

使用uvicorn、fastapi、openai-whisper等开源库实现高性能接口

更多介绍 https://blog.csdn.net/weixin_40986713/article/details/138712293

使用说明

  1. 下载代码
  2. 安装 ffmpeg https://ffmpeg.org/download.html
  3. 安装依赖 项目根目录下执行命令 pip install -r requirements.txt
  4. 运行代码 项目根目录下执行命令 python main.py

这里的 http://0.0.0.0:3003 就是连接地址。

  • 首次启动会下载模型,如果下载失败,请手动下载模型,放在项目根目录下,并修改 main.py 里的 model_size 参数。

docker

  1. docker打包命令
docker build -t whisper .

2.docker命令启动

gpu显卡模式

docker run -itd --name whisper-api -p 3003:3003 --gpus all --restart=always whisper
  • 默认 ACCESS_TOKEN=sk-tarzan

cpu模式

docker run -itd --name whisper-api -p 3003:3003 --restart=always whisper
  • 默认 ACCESS_TOKEN=sk-tarzan

鉴权模式

docker run -itd --name whisper-api -p 3003:3003-e ACCESS_TOKEN=yourtoken --gpus all --restart=always whisper
docker run -itd --name whisper-api -p 3003:3003-e ACCESS_TOKEN=yourtoken --restart=always whisper
  • yourtoken 修改你设置的鉴权token,接口调用header 里传 Authorization:Bearer sk-tarzan

参数模式

docker run -itd --name whisper-api -p 3003:3003-e ACCESS_TOKEN=yourtoken MODEL_SIZE=base MODEL_SIZE=base LANGUAGE=Chinese --gpus all --restart=always whisper
docker run -itd --name whisper-api -p 3003:3003-e ACCESS_TOKEN=yourtoken MODEL_SIZE=base MODEL_SIZE=base LANGUAGE=Chinese  --restart=always whisper
  • ACCESS_TOKEN 默认是sk-tarzan,修改你设置的鉴权token,接口调用header 里传 Authorization:Bearer sk-tarzan
  • MODEL_SIZE模型类型,默认是small,可选模型Available models: ['tiny.en', 'tiny', 'base.en', 'base', 'small.en', 'small', 'medium.en', 'medium', 'large-v1', 'large-v2', 'large-v3', 'large']
  • LANGUAGE语言类型,默认根据用户语言自动选择,可选语言Available languages: ['auto','Chinese','English','French','German','Italian','Korean','Polish','Portuguese','Russian','Spanish','Japanese']
  • LANGUAGE=auto 时会再第一次调用模型时候,下载tiny模型,使用tiny模式来判断用户的语言
  • 因为whisper模型训练时,没有区分中文简体和繁体,代码里对输出中文做了格式化处理,默认中文设置为简体中文

可用型号和语言

您可能还需要安装 rust,以防 tiktoken 没有为您的平台提供预编译的轮子。如果在上述 pip install 命令过程中遇到安装错误,请按照 入门页面 的指引来安装 Rust 开发环境。此外,您可能需要配置 PATH 环境变量,例如 export PATH="$HOME/.cargo/bin:$PATH"。如果安装过程中出现 No module named 'setuptools_rust' 错误,您需要安装 setuptools_rust,可以通过运行类似命令来完成。

尺寸 参数大小 纯英文型号 多语言模型 所需显存 相对速度
tiny 39 M tiny.en tiny ~1 GB ~32x
base 74 M base.en base ~1 GB ~16x
small 244 M small.en small ~2 GB ~6x
medium 769 M medium.en medium ~5 GB ~2x
large 1550 M 不适用 large ~10 GB 1x

针对仅英文应用的.en模型往往表现更佳,尤其是对于tiny.enbase.en模型。我们观察到,对于small.enmedium.en模型,这种差异变得不那么明显。

Whisper的表现因语言而异,幅度很大。下图展示了使用WER(词错误率)或CER(字符错误率,以斜体显示)在Common Voice 15和Fleurs数据集上评估的large-v3large-v2模型按语言划分的性能分解。其他模型和数据集对应的更多WER/CER指标,以及用于翻译评估的BLEU(双语评估替代)分数,可以在论文的附录D.1、D.2和D.4中找到。 docker日志查看

docker logs -f [容器id或容器名称]

可识别语言细分

  • Dutch: 荷兰语
  • Spanish: 西班牙语
  • Korean: 韩语
  • Italian: 意大利语
  • German: 德语
  • Thai: 泰语
  • Russian: 俄语
  • Portuguese: 葡萄牙语
  • Polish: 波兰语
  • Indonesian: 印度尼西亚语
  • Mandarin (TW): 普通话(台湾)
  • Swedish: 瑞典语
  • Czech: 捷克语
  • English: 英语
  • Japanese: 日语
  • French: 法语
  • Romanian: 罗马尼亚语
  • Cantonese (CN): 粤语(中国大陆)
  • Turkish: 土耳其语
  • Mandarin (CN): 普通话(中国大陆)
  • Catalan: 加泰罗尼亚语
  • Hungarian: 匈牙利语
  • Ukrainian: 乌克兰语
  • Greek: 希腊语
  • Bulgarian: 保加利亚语
  • Arabic: 阿拉伯语
  • Serbian: 塞尔维亚语
  • Macedonian: 马其顿语
  • Cantonese (HK): 粤语(香港)
  • Latvian: 拉脱维亚语
  • Slovenian: 斯洛文尼亚语
  • Hindi: 印地语
  • Galician: 加利西亚语
  • Danish: 丹麦语
  • Urdu: 乌尔都语
  • Slovak: 斯洛伐克语
  • Hebrew: 希伯来语
  • Finnish: 芬兰语
  • Azerbaijani: 阿塞拜疆语
  • Lithuanian: 立陶宛语
  • Estonian: 爱沙尼亚语
  • Nynorsk: 新挪威语
  • Welsh: 威尔士语
  • Punjabi: 旁遮普语
  • Afrikaans: 南非荷兰语
  • Persian: 波斯语
  • Basque: 巴斯克语
  • Vietnamese: 越南语
  • Bengali: 孟加拉语
  • Nepali: 尼泊尔语
  • Marathi: 马拉地语
  • Belarusian: 白俄罗斯语
  • Kazakh: 哈萨克语
  • Armenian: 亚美尼亚语
  • Swahili: 斯瓦希里语
  • Tamil: 泰米尔语
  • Albanian: 阿尔巴尼亚语
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简介

使用winsper语音识别开源模型封装成openai chatgpt兼容接口,高性能处理 展开 收起
Python 等 2 种语言
Apache-2.0
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