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FunASR — AI 语音识别工具中文文档

基于 Python · 开源免费，本地部署，数据完全自主可控
英文名：FunASR
⭐ 16.2k Stars 🍴 1.7k Forks 💻 Python 📄 MIT 🏷 AI 9.4分
9.4AI 综合评分
audio-visual-speech-recognitionconformerdfsmnparaformerpretrained-modelpunctuationstt
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AI Skill Hub 强烈推荐：FunASR — AI 语音识别工具中文文档 是一款优质的AI工具。在 GitHub 上收获超过 16.2k 颗 Star，AI 综合评分 9.4 分，在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案，这是一个值得深入了解的选择。

📚 深度解析
FunASR — AI 语音识别工具中文文档 是一款基于 Python 的开源工具，在 GitHub 上收获 16k+ Star，是audio-visual-speech-recognition、conformer、dfsmn、paraformer领域中的优质开源项目。开源工具的最大优势在于代码完全透明，你可以审计每一行代码的安全性，也可以根据自身需求进行二次开发和定制。

**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。

**安装与环境准备**
FunASR — AI 语音识别工具中文文档 依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。

**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 FunASR — AI 语音识别工具中文文档 的版本更新，及时通知重要功能变化。
📋 工具概览

FunASR — AI 语音识别工具中文文档 是一款基于 Python 开发的开源工具，专注于 audio-visual-speech-recognition、conformer、dfsmn 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。

GitHub Stars
⭐ 16.2k
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
活跃维护，更新频繁
开源协议
MIT
AI 综合评分
9.4 分
工具类型
AI工具
Forks
1.7k
📖 中文文档
以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。

FunASR — AI 语音识别工具中文文档 是一款基于 Python 开发的开源工具，专注于 audio-visual-speech-recognition、conformer、dfsmn 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。

📌 核心特色
  • 开源免费，支持本地部署，数据完全自主可控
  • 活跃的 GitHub 开源社区，持续迭代更新
  • 提供详细文档和使用示例，新手友好
  • 支持自定义配置，灵活适配不同使用环境
  • 可作为基础组件集成进现有技术栈或进行二次开发
🎯 主要使用场景
  • 本地部署运行，保护数据隐私，满足合规要求
  • 自定义集成到现有系统，扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发
以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。
安装命令
# 方式一：pip 安装（推荐）
pip install funasr

# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install funasr

# 方式三：从源码安装（获取最新功能）
git clone https://github.com/modelscope/FunASR
cd FunASR
pip install -e .

# 验证安装
python -c "import funasr; print('安装成功')"
📋 安装步骤说明
  1. 访问 GitHub 仓库页面
  2. 按照 README 文档完成依赖安装
  3. 根据系统环境完成初始化配置
  4. 参考官方示例或文档开始使用
  5. 遇到问题可在 GitHub Issues 中查找解答
以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
funasr --help

# 基本用法
funasr input_file -o output_file

# Python 代码中调用
import funasr

# 示例
result = funasr.process("input")
print(result)
以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。
配置示例
# funasr 配置文件示例（config.yml）
app:
  name: "funasr"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
funasr --config config.yml

# 或通过环境变量配置
export FUNASR_API_KEY="your-key"
export FUNASR_OUTPUT_DIR="./output"
📑 README 深度解析 真实文档 完整度 44/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。

简介

[//]: # '<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div>'

(简体中文|English)

[//]: # "# FunASR: A Fundamental End-to-End Speech Recognition Toolkit"

SVG Banners

PyPI

<p align="center"> <a href="https://trendshift.io/repositories/3839" target="_blank"><img src="https://trendshift.io/api/badge/repositories/3839" alt="modelscope%2FFunASR | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </p>

<strong>FunASR</strong> hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model, researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！

Highlights | News | Installation | Quick Start | Tutorial | Runtime | Model Zoo | Contact

<a name="highlights"></a>

Highlights

  • FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker Diarization and multi-talker ASR. FunASR provides convenient scripts and tutorials, supporting inference and fine-tuning of pre-trained models.
  • We have released a vast collection of academic and industrial pretrained models on the ModelScope and huggingface, which can be accessed through our Model Zoo. The representative Paraformer-large, a non-autoregressive end-to-end speech recognition model, has the advantages of high accuracy, high efficiency, and convenient deployment, supporting the rapid construction of speech recognition services. For more details on service deployment, please refer to the service deployment document.

<a name="whats-new"></a>

What's new:

  • 2026/05/20: Added Qwen3-ASR (0.6B/1.7B) multi-language speech recognition models, supporting 52 languages with auto language detection. usage.
  • 2026/05/20: Added GLM-ASR-Nano (1.5B) robust speech recognition model, supporting 17 languages with dialect and low-volume speech optimization. usage.
  • 2026/05/19: Fun-ASR-Nano and SenseVoice now support speaker diarization. Use with vad_model + spk_model + punc_model to get per-sentence speaker labels. See Fun-ASR-Nano demo, SenseVoice demo.
  • 2025/12/15: Fun-ASR-Nano-2512 is an end-to-end speech recognition large model trained on tens of millions of hours real speech data. It supports low-latency real-time transcription and covers 31 languages.
  • 2024/10/29: Real-time Transcription Service 1.12 released, The 2pass-offline mode supports the SensevoiceSmal model；(docs);
  • 2024/10/10：Added support for the Whisper-large-v3-turbo model, a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. It can be downloaded from the modelscope, and openai.
  • 2024/09/26: Offline File Transcription Service 4.6, Offline File Transcription Service of English 1.7, Real-time Transcription Service 1.11 released, fix memory leak & Support the SensevoiceSmall onnx model；File Transcription Service 2.0 GPU released, Fix GPU memory leak; (docs);
  • 2024/09/25：keyword spotting models are new supported. Supports fine-tuning and inference for four models: fsmn_kws, fsmn_kws_mt, sanm_kws, sanm_kws_streaming.
  • 2024/07/04：SenseVoice is a speech foundation model with multiple speech understanding capabilities, including ASR, LID, SER, and AED.

<details><summary>Full Changelog</summary> - 2024/07/01: Offline File Transcription Service GPU 1.1 released, optimize BladeDISC model compatibility issues; ref to (docs) - 2024/06/27: Offline File Transcription Service GPU 1.0 released, supporting dynamic batch processing and multi-threading concurrency. In the long audio test set, the single-thread RTF is 0.0076, and multi-threads' speedup is 1200+ (compared to 330+ on CPU); ref to (docs) - 2024/05/15：emotion recognition models are new supported. emotion2vec+large，emotion2vec+base，emotion2vec+seed. currently supports the following categories: 0: angry 1: happy 2: neutral 3: sad 4: unknown. - 2024/05/15: Offline File Transcription Service 4.5, Offline File Transcription Service of English 1.6, Real-time Transcription Service 1.10 released, adapting to FunASR 1.0 model structure；(docs) - 2024/03/05：Added the Qwen-Audio and Qwen-Audio-Chat large-scale audio-text multimodal models, which have topped multiple audio domain leaderboards. These models support speech dialogue, usage. - 2024/03/05：Added support for the Whisper-large-v3 model, a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. It can be downloaded from themodelscope, and openai. - 2024/03/05: Offline File Transcription Service 4.4, Offline File Transcription Service of English 1.5，Real-time Transcription Service 1.9 released，docker image supports ARM64 platform, update modelscope；(docs) - 2024/01/30：funasr-1.0 has been released (docs) - 2024/01/30：emotion recognition models are new supported. model link, modified from repo. - 2024/01/25: Offline File Transcription Service 4.2, Offline File Transcription Service of English 1.3 released，optimized the VAD (Voice Activity Detection) data processing method, significantly reducing peak memory usage, memory leak optimization; Real-time Transcription Service 1.7 released，optimizatized the client-side；(docs) - 2024/01/09: The Funasr SDK for Windows version 2.0 has been released, featuring support for The offline file transcription service (CPU) of Mandarin 4.1, The offline file transcription service (CPU) of English 1.2, The real-time transcription service (CPU) of Mandarin 1.6. For more details, please refer to the official documentation or release notes(FunASR-Runtime-Windows) - 2024/01/03: File Transcription Service 4.0 released, Added support for 8k models, optimized timestamp mismatch issues and added sentence-level timestamps, improved the effectiveness of English word FST hotwords, supported automated configuration of thread parameters, and fixed known crash issues as well as memory leak problems, refer to (docs). - 2024/01/03: Real-time Transcription Service 1.6 released，The 2pass-offline mode supports Ngram language model decoding and WFST hotwords, while also addressing known crash issues and memory leak problems, (docs) - 2024/01/03: Fixed known crash issues as well as memory leak problems, (docs). - 2023/12/04: The Funasr SDK for Windows version 1.0 has been released, featuring support for The offline file transcription service (CPU) of Mandarin, The offline file transcription service (CPU) of English, The real-time transcription service (CPU) of Mandarin. For more details, please refer to the official documentation or release notes(FunASR-Runtime-Windows) - 2023/11/08: The offline file transcription service 3.0 (CPU) of Mandarin has been released, adding punctuation large model, Ngram language model, and wfst hot words. For detailed information, please refer to docs. - 2023/10/17: The offline file transcription service (CPU) of English has been released. For more details, please refer to (docs). - 2023/10/13: SlideSpeech: A large scale multi-modal audio-visual corpus with a significant amount of real-time synchronized slides. - 2023/10/10: The ASR-SpeakersDiarization combined pipeline Paraformer-VAD-SPK is now released. Experience the model to get recognition results with speaker information. - 2023/10/07: FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec. - 2023/09/01: The offline file transcription service 2.0 (CPU) of Mandarin has been released, with added support for ffmpeg, timestamp, and hotword models. For more details, please refer to (docs). - 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to (docs). - 2023/07/17: BAT is released, which is a low-latency and low-memory-consumption RNN-T model. For more details, please refer to (BAT). - 2023/06/26: ASRU2023 Multi-Channel Multi-Party Meeting Transcription Challenge 2.0 completed the competition and announced the results. For more details, please refer to (M2MeT2.0).

</details>

<a name="Installation"></a>

Installation

  • Requirements
python>=3.8
torch>=1.13
torchaudio
  • Install for pypi
pip3 install -U funasr
  • Or install from source code
git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip3 install -e ./
  • Install modelscope or huggingface_hub for the pretrained models (Optional)
pip3 install -U modelscope huggingface_hub

pip3 install -U funasr-onnx

from pathlib import Path from runtime.python.onnxruntime.funasr_onnx.paraformer_bin import Paraformer

home_dir = Path.home()

model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" model = Paraformer(model_dir, batch_size=1, quantize=True)

wav_path = [f"{home_dir}/.cache/modelscope/hub/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav"]

result = model(wav_path) print(result) ```

More examples ref to demo

Deployment Service

FunASR supports deploying pre-trained or further fine-tuned models for service. Currently, it supports the following types of service deployment:

  • File transcription service, Mandarin, CPU version, done
  • The real-time transcription service, Mandarin (CPU), done
  • File transcription service, English, CPU version, done
  • File transcription service, Mandarin, GPU version, in progress
  • and more.

For more detailed information, please refer to the service deployment documentation.

<a name="contact"></a>

Quick Start

Below is a quick start tutorial. Test audio files (Mandarin, English).

Command-line usage

funasr ++model=paraformer-zh ++vad_model="fsmn-vad" ++punc_model="ct-punc" ++input=asr_example_zh.wav

Notes: Support recognition of single audio file, as well as file list in Kaldi-style wav.scp format: wav_id wav_pat

Command-line usage

funasr-export ++model=paraformer ++quantize=false ++device=cpu
📚 实用指南（长尾问题）
适合谁
  • 跨境业务、多语言内容运营团队
  • 做语音类 AI 产品的开发者
最佳实践
  • 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
常见错误
  • API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • Python 依赖冲突：建议用 venv / uv 隔离环境
部署方案
  • Docker：FunASR 提供官方镜像，docker compose up 一键启动
  • CLI：直接 npm install -g / pip install，命令行调用
  • 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
FunASR 中文教程FunASR 安装报错怎么办FunASR Docker 部署FunASR 与同类工具对比FunASR 最佳实践FunASR 适合谁用
⚡ 核心功能
👥 适合人群
AI 技术爱好者研究人员和学生开发者和工程师技术创业者
🎯 使用场景
  • 本地部署运行，保护数据隐私，满足合规要求
  • 自定义集成到现有系统，扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发
⚖️ 优点与不足
✅ 优点
  • +GitHub 16.2k Star，社区高度认可
  • +MIT 协议，可免费商用
  • +AI Skill Hub 精选推荐
  • +完全开源免费，无授权费用
  • +本地部署，数据完全自主可控
⚠️ 不足
  • −安装和初始配置可能需要一定技术基础
  • −功能完整性通常不如成熟商业产品
  • −技术支持主要依赖开源社区，响应速度不稳定
⚠️ 使用须知

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。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一，可自由商用、修改、分发，仅需保留版权声明。

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❓ 常见问题 FAQ
FunASR 是一款Python开发的AI辅助工具。A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.
💡 AI Skill Hub 点评

总体来看，FunASR — AI 语音识别工具中文文档 是一款质量优秀的AI工具，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。

📚 深入学习 FunASR — AI 语音识别工具中文文档
查看分步骤安装教程和完整使用指南，快速上手这款工具
⚙️ 安装教程 📚 使用教程
🌐 原始信息
原始名称 FunASR
原始描述 A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.
Topics audio-visual-speech-recognitionconformerdfsmnparaformerpretrained-modelpunctuationstt
GitHub https://github.com/modelscope/FunASR
License MIT
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/modelscope/FunASR 🌐 官方网站  https://modelscope.github.io/FunASR

收录时间：2026-05-22 · 更新时间：2026-05-22 · License：MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。