([简体中文](./README_zh.md)|English|[日本語](./README_ja.md)|[한국어](./README_ko.md))
Industrial speech recognition. 170x faster than Whisper. 50+ languages.
Speaker diarization · Emotion detection · Streaming · One API call
Quick Start · Colab · Benchmark · Model selection · Migration guide · Use cases · Deployment matrix · Models · Agent Integration · Docs · Contribute
---
## Quick Start
[](https://colab.research.google.com/github/modelscope/FunASR/blob/main/examples/colab/funasr_quickstart.ipynb)
No local setup? Open the [Colab quickstart](./examples/colab/) to transcribe a public sample or upload your own audio in a browser.
```bash
pip install torch torchaudio
pip install funasr
```
```python
from 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](https://github.com/FunAudioLLM/Fun-ASR) — an LLM-based ASR combining SenseVoice encoder with Qwen3-0.6B decoder:
```python
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):
```python
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:8000
>
> **Use with AI agents:** [MCP Server](examples/mcp_server/) for Claude/Cursor · [OpenAI API](examples/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](./examples/colab/) before setting up a local environment. Choosing a first model? Start with the [model selection guide](./docs/model_selection.md). Planning a switch from Whisper or a cloud ASR provider? Use the [migration guide](./docs/migration_from_whisper.md) and [benchmark example](./examples/migration/) to test representative audio, map features, and roll out safely.
---
## Benchmark
> 184 long-form audio files (192 min). [Full report →](https://modelscope.github.io/FunASR/benchmark.html)
| 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 →](docs/vllm_guide.md)
- 2026/05/24: **Dynamic VAD** — adaptive silence threshold (default on). Short sentences stay intact, long segments get auto-split. [Details →](docs/vllm_guide.md#附录dynamicstreamingvad)
- 2026/05/24: **v1.3.3** — `funasr-server` CLI, 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](examples/industrial_data_pretraining/qwen3_asr)
- 2026/05/20: Added GLM-ASR-Nano (1.5B) — 17 languages, dialect support. [usage](examples/industrial_data_pretraining/glm_asr)
- 2026/05/19: Fun-ASR-Nano and SenseVoice now support speaker diarization.
- 2025/12/15: [Fun-ASR-Nano-2512](https://github.com/FunAudioLLM/Fun-ASR) — 31 languages, tens of millions of hours training.
Older
- 2024/10/10: Whisper-large-v3-turbo support added.
- 2024/07/04: [SenseVoice](https://github.com/FunAudioLLM/SenseVoice) — ASR + emotion + audio events.
- 2024/01/30: FunASR 1.0 released.
From source / Requirements
```bash
git clone https://github.com/modelscope/FunASR.git && cd FunASR
pip install -e ./
```
Requirements: Python ≥ 3.8. Install PyTorch + torchaudio first ([pytorch.org](https://pytorch.org/get-started/locally/)), then `pip install funasr`.
## License
[MIT License](./LICENSE)
## Citations
```bibtex
@inproceedings{gao2023funasr,
author={Zhifu Gao and others},
title={FunASR: A Fundamental End-to-End Speech Recognition Toolkit},
booktitle={INTERSPEECH},
year={2023}
}
```