Industrial-grade speech recognition toolkit: 170x realtime, 50+ languages, speaker diarization, emotion detection, streaming, and OpenAI-compatible API.
Quick Start
No local setup? Open the Colab quickstart to transcribe a public sample or upload your own audio in a browser.
pip install torch torchaudio
pip install funasrfrom funasr import AutoModel
model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", spk_model="cam++", device="cuda")
result = model.generate(input="meeting.wav")Output — structured text with speaker labels, timestamps, and punctuation:
[00:00.4 → 00:03.8] Speaker 0: Let's discuss the Q3 plan.
[00:04.2 → 00:07.1] Speaker 1: Sounds good. I have three points.
[00:07.5 → 00:12.3] Speaker 0: Go ahead. We have 30 minutes.That's it. One model, one call — VAD segmentation, speech recognition, punctuation, speaker diarization all happen automatically.
LLM-powered ASR: Fun-ASR-Nano
For highest accuracy across 31 languages (including Chinese dialects), use Fun-ASR-Nano — an LLM-based ASR combining SenseVoice encoder with Qwen3-0.6B decoder:
from funasr import AutoModel
model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", vad_model="fsmn-vad", device="cuda")
result = model.generate(input="meeting.wav")With vLLM acceleration (16x faster, batch processing):
from funasr.auto.auto_model_vllm import AutoModelVLLM
model = AutoModelVLLM(model="FunAudioLLM/Fun-ASR-Nano-2512", tensor_parallel_size=1)
results = model.generate(["audio1.wav", "audio2.wav"], language="auto")Deploy as API server:
funasr-server --device cuda→ OpenAI-compatible endpoint at localhost:8000Use with AI agents: MCP Server for Claude/Cursor · OpenAI API for LangChain/Dify/AutoGen
Why FunASR?
| FunASR | Whisper | Cloud APIs | |
|---|---|---|---|
| Speed | 170x realtime | 13x realtime | ~1x realtime |
| Speaker ID | ✅ Built-in | ❌ Needs pyannote | ✅ Extra cost |
| Emotion | ✅ Happy/Sad/Angry | ❌ | ❌ |
| Languages | 50+ | 57 | Varies |
| Streaming | ✅ WebSocket | ❌ | ✅ |
| vLLM Acceleration | ✅ 2-3x faster | ❌ | N/A |
| Self-hosted | ✅ MIT license | ✅ MIT license | ❌ Cloud only |
| Cost | Free | Free | $0.006/min+ |
| CPU viable | ✅ 17x realtime | ❌ Too slow | N/A |
Trying FunASR for the first time? Use the Colab quickstart before setting up a local environment. Choosing a first model? Start with the model selection guide. Planning a switch from Whisper or a cloud ASR provider? Use the migration guide and benchmark example to test representative audio, map features, and roll out safely.
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Benchmark
184 long-form audio files (192 min). Full report →
| Model | GPU Speed | CPU Speed | vs Whisper-large-v3 |
|---|---|---|---|
| SenseVoice-Small | 170x realtime | 17x realtime | 🚀 13x faster |
| Paraformer-Large | 120x realtime | 15x realtime | 🚀 9x faster |
| Whisper-large-v3-turbo | 46x realtime | ❌ | 3.4x faster |
| Fun-ASR-Nano | 17x realtime | 3.6x realtime | 1.3x faster |
| Whisper-large-v3 | 13x realtime | ❌ | baseline |
Key takeaway: FunASR models run on CPU faster than Whisper runs on GPU.
What's new
- 2026/05/24: vLLM Inference Engine — 2-3x faster LLM decoding for Fun-ASR-Nano. Streaming WebSocket service with VAD + Speaker Diarization. Guide →
- 2026/05/24: Dynamic VAD — adaptive silence threshold (default on). Short sentences stay intact, long segments get auto-split. Details →
- 2026/05/24: v1.3.3 —
funasr-serverCLI, OpenAI-compatible API, MCP Server for AI agents.pip install --upgrade funasr - 2026/05/20: Added Qwen3-ASR (0.6B/1.7B) — 52 languages, auto detection. usage
- 2026/05/20: Added GLM-ASR-Nano (1.5B) — 17 languages, dialect support. usage
- 2026/05/19: Fun-ASR-Nano and SenseVoice now support speaker diarization.
- 2025/12/15: Fun-ASR-Nano-2512 — 31 languages, tens of millions of hours training.
- 2024/10/10: Whisper-large-v3-turbo support added.
- 2024/07/04: SenseVoice — ASR + emotion + audio events.
- 2024/01/30: FunASR 1.0 released.
Installation
pip install funasrgit clone https://github.com/modelscope/FunASR.git && cd FunASR
pip install -e ./Requirements: Python ≥ 3.8. Install PyTorch + torchaudio first (pytorch.org), then pip install funasr.
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Model Zoo
| Model | Task | Languages | Params | Links |
|---|---|---|---|---|
| Fun-ASR-Nano | ASR + timestamps | 31 languages | 800M | ⭐ 🤗 |
| SenseVoiceSmall | ASR + emotion + events | zh/en/ja/ko/yue | 234M | ⭐ 🤗 |
| Paraformer-zh | ASR + timestamps | zh/en | 220M | ⭐ 🤗 |
| Paraformer-zh-streaming | Streaming ASR | zh/en | 220M | ⭐ 🤗 |
| Qwen3-ASR | ASR, 52 languages | multilingual | 1.7B | usage |
| GLM-ASR-Nano | ASR, 17 languages | multilingual | 1.5B | usage |
| Whisper-large-v3 | ASR + translation | multilingual | 1550M | usage |
| Whisper-large-v3-turbo | ASR + translation | multilingual | 809M | usage |
| ct-punc | Punctuation | zh/en | 290M | ⭐ 🤗 |
| fsmn-vad | VAD | zh/en | 0.4M | ⭐ 🤗 |
| cam++ | Speaker diarization | — | 7.2M | ⭐ 🤗 |
| emotion2vec+large | Emotion recognition | — | 300M | ⭐ 🤗 |
Usage
Full examples with parameter docs: Tutorial →
from funasr import AutoModel
# Chinese production (VAD + ASR + punctuation + speaker)
model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc", spk_model="cam++", device="cuda")
result = model.generate(input="meeting.wav", hotword="关键词 20")
# 31 languages with timestamps
model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", hub="hf", trust_remote_code=True,
vad_model="fsmn-vad", vad_kwargs={"max_single_segment_time": 30000}, device="cuda")
result = model.generate(input="audio.wav", batch_size=1)
# Streaming real-time
model = AutoModel(model="paraformer-zh-streaming", device="cuda")
result = model.generate(input="chunk.wav", cache={}, chunk_size=[0, 10, 5])
# Emotion recognition
model = AutoModel(model="emotion2vec_plus_large", device="cuda")
result = model.generate(input="audio.wav", granularity="utterance")CLI (Agent-Friendly)
# Transcribe audio (simplest)
funasr audio.wav
# JSON output (for AI agents)
funasr audio.wav --output-format json
# SRT subtitles
funasr audio.wav --output-format srt --output-dir ./subs
# Speaker diarization + timestamps
funasr audio.wav --spk --timestamps -f json
# Choose model and language
funasr audio.wav --model paraformer --language zh
# Batch transcribe
funasr *.wav --output-format srt --output-dir ./outputAvailable models: sensevoice (default), paraformer, paraformer-en, fun-asr-nano
Deploy
# OpenAI-compatible API (recommended)
pip install torch torchaudio
pip install funasr vllm fastapi uvicorn python-multipart
funasr-server --device cuda
# → POST /v1/audio/transcriptions at localhost:8000Verify it with a public sample:
curl -L https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav -o sample.wav
curl http://localhost:8000/v1/audio/transcriptions \
-F file=@sample.wav \
-F model=sensevoice \
-F response_format=verbose_json# Docker streaming service
docker pull registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-online-cpu-0.1.12OpenAI API example → · Gradio demo → · Client recipes → · JavaScript/TypeScript recipes → · Kubernetes template → · Workflow recipes → · Postman collection → · OpenAPI spec → · Security guide → · Deployment matrix → · Deployment docs → · Agent integration →
Community
| 📖 Documentation | 🐛 Issues |
| 💬 Discussions | 🤗 HuggingFace |
| 🤝 Contributing | 📈 20k growth plan |
Star History
<a href="https://star-history.com/#modelscope/FunASR&Date"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=modelscope/FunASR&type=Date&theme=dark" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=modelscope/FunASR&type=Date" /> <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=modelscope/FunASR&type=Date" width="600" /> </picture> </a>License
Citations
@inproceedings{gao2023funasr,
author={Zhifu Gao and others},
title={FunASR: A Fundamental End-to-End Speech Recognition Toolkit},
booktitle={INTERSPEECH},
year={2023}
}Repository
modelscope/FunASR
Created
November 24, 2022
Updated
June 15, 2026
Language
Python