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是开源的本地优先 AI 音频与音乐创作工作室，在 macOS 与 Windows 上离线完成语音合成、声音克隆、带歌词的歌曲生成与多轨乐谱，零云 API 费用。",{"owner":490,"repo":491,"repoId":492,"category":64,"subgroup":65,"language":493,"tags":494,"starCount":139,"ownerUid":495,"latestTag":496,"platforms":497,"lastReleaseTime":498,"downloadCount":95,"description":499},"Gary23333","opentts",1356280885,"Swift",[],120565299,"v5.0.3",[444],"2026-09-03T18:33:50Z","锻造回声 ForgedEcho 是一款 macOS 原生的声音克隆与 AI 语音生产工作台，把多种开源语音大模型封装为图形界面，支持声音克隆、情绪控制、字幕批量配音、有声书生成与本地 LoRA 微调，全程离线运行。",{"description":501,"readmeHtml":502},"FunASR 是阿里巴巴开源的工业级语音识别工具包（Python），覆盖离线与流式转写、端点检测、标点恢复与说话人分离，支持 CPU\u002FGPU 部署并提供 OpenAI 兼容 API 与 MCP Server。","\u003Ch2 id=\"项目概述\">项目概述\u003C\u002Fh2>\u003Cp>FunASR 是阿里巴巴达摩院开源的工业级语音识别工具包，覆盖离线转写、流式识别与边缘部署三类场景。它把 ASR、端点检测、标点恢复、说话人分离等能力做成可自由组合的模型单元，内置中文识别能力突出的 Paraformer 等模型，并提供 OpenAI 兼容 API 与 MCP Server，方便接入现有 Agent 工作流。\u003C\u002Fp>\n\u003Cblockquote>\n\u003Cp>任务、模型、运行时三者解耦，按场景自由组合成完整语音流水线。\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>项目\u003C\u002Fth>\n\u003Cth>内容\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>平台\u003C\u002Ftd>\n\u003Ctd>Linux、macOS、Windows（CPU 与 GPU）\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>语言\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>许可\u003C\u002Ftd>\n\u003Ctd>MIT\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>服务形态\u003C\u002Ftd>\n\u003Ctd>本地 SDK、OpenAI 兼容 API、MCP Server\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"主要功能\">主要功能\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>语音识别\u003C\u002Fstrong>：离线与流式转写，中文表现突出，支持多语种与方言\u003C\u002Fli>\n\u003Cli>\u003Cstrong>端点检测\u003C\u002Fstrong>：FSMN-VAD 切分语音起止，长音频自动分段\u003C\u002Fli>\n\u003Cli>\u003Cstrong>标点恢复\u003C\u002Fstrong>：转写文本自动补全标点\u003C\u002Fli>\n\u003Cli>\u003Cstrong>说话人分离\u003C\u002Fstrong>：CAM++ 区分多说话人并输出时间戳\u003C\u002Fli>\n\u003Cli>\u003Cstrong>情感识别\u003C\u002Fstrong>：SenseVoice 同时输出情绪与音频事件标签\u003C\u002Fli>\n\u003Cli>\u003Cstrong>模型组合\u003C\u002Fstrong>：ASR、VAD、说话人模型按需拼接成 pipeline\u003C\u002Fli>\n\u003Cli>\u003Cstrong>服务化部署\u003C\u002Fstrong>：OpenAI 兼容 API、MCP Server 与 vLLM 批量加速\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"适用人群\">适用人群\u003C\u002Fh2>\u003Cul>\n\u003Cli>需要本地转写会议录音与播客的开发者\u003C\u002Fli>\n\u003Cli>构建实时语音助手的工程师\u003C\u002Fli>\n\u003Cli>做中文语音识别的算法工程师\u003C\u002Fli>\n\u003Cli>接入 Agent 工作流的语音应用团队\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"快速上手\">快速上手\u003C\u002Fh2>\u003Col>\n\u003Cli>安装：\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">pip install torch torchaudio\npip install funasr\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>加载模型转写音频：\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-python\">from funasr import AutoModel\n\nmodel = AutoModel(model='FunAudioLLM\u002FFun-ASR-Nano-2512', device='cuda')\nresult = model.generate(input='asr_example_zh.wav')\nprint(result[0]['text'])\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>CPU 场景可选 SenseVoiceSmall 模型，GPU 部署参照官方 vLLM 指南\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"核心优势\">核心优势\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>中文领先\u003C\u002Fstrong>：Paraformer 系列对中文与方言识别效果好\u003C\u002Fli>\n\u003Cli>\u003Cstrong>部署灵活\u003C\u002Fstrong>：同一套工具包覆盖 CPU、GPU 到边缘设备\u003C\u002Fli>\n\u003Cli>\u003Cstrong>易集成\u003C\u002Fstrong>：OpenAI 兼容 API 与 MCP Server 现成可用\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"系统要求\">系统要求\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>项目\u003C\u002Fth>\n\u003Cth>要求\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>操作系统\u003C\u002Ftd>\n\u003Ctd>Linux、macOS、Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>语言环境\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>硬件\u003C\u002Ftd>\n\u003Ctd>CPU 可运行，GPU 需匹配 CUDA 的 PyTorch\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"注意事项\">注意事项\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>GPU 检测\u003C\u002Fstrong>：设 \u003Ccode>device=cuda\u003C\u002Fcode> 前先确认 \u003Ccode>torch.cuda.is_available()\u003C\u002Fcode> 返回 True，否则改用 CPU 或重装匹配 CUDA 的 PyTorch\u003C\u002Fli>\n\u003Cli>\u003Cstrong>说话人编号\u003C\u002Fstrong>：返回的说话人序号仅在本条录音内有效，不代表真实身份\u003C\u002Fli>\n\u003Cli>\u003Cstrong>模型差异\u003C\u002Fstrong>：Fun-ASR-Nano 与 MLT-Nano 语言覆盖不同，按 checkpoint 单独选择\u003C\u002Fli>\n\u003C\u002Ful>\n",1793197907773]