([简体中文](./README_zh.md)|English|[日本語](./README_ja.md)|[한국어](./README_ko.md))
Industrial speech recognition. Up to 340x realtime, 26x 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
```
**Flagship model — Fun-ASR-Nano** (LLM-ASR, 31 languages; the default recommendation, needs a GPU):
```python
from funasr import AutoModel
model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", device="cuda")
result = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(result[0]["text"])
# 欢迎大家来体验达摩院推出的语音识别模型。
```
On CPU (or for multilingual + emotion in one pass), use **SenseVoice** — which also returns speaker diarization and timestamps:
```python
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", spk_model="cam++", device="cuda") # use device="cpu" if you don't have a GPU
result = model.generate(
input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
batch_size_s=300,
)
# One call returns VAD segments with speaker id + timestamps — render them however you like:
for seg in result[0]["sentence_info"]:
print(f"[{seg['start']/1000:.1f}s] Speaker {seg['spk']}: {rich_transcription_postprocess(seg['sentence'])}")
```
**Output** — structured text with speaker labels, timestamps, and punctuation:
```
[0.6s] Speaker 0: 欢迎大家来体验达摩院推出的语音识别模型
```
That's it. **One model, one call** — VAD segmentation, speech recognition, punctuation, speaker diarization all happen automatically.
### Scale & deploy the flagship
At scale, accelerate Fun-ASR-Nano with vLLM (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?
Whisper is a single model; **FunASR is a toolkit** — you pick the right model per job: **Fun-ASR-Nano** (flagship LLM-ASR, GPU, 340x realtime with vLLM, 31 languages), **SenseVoice** (CPU-friendly, + emotion & audio events), **Paraformer** (low-latency streaming). The table shows what the toolkit delivers vs one Whisper model — each capability is labelled with the model that provides it:
| | FunASR (toolkit) | Whisper | Cloud APIs |
|---|---|---|---|
| Top speed | **340x realtime** (Fun-ASR-Nano + vLLM) | 13x realtime | ~1x realtime |
| Speaker ID | ✅ Built-in | ❌ Needs pyannote | ✅ Extra cost |
| Emotion | ✅ via SenseVoice | ❌ | ❌ |
| Languages | 50+ (Qwen3-ASR 52, Nano 31) | 57 | Varies |
| Streaming | ✅ WebSocket (Paraformer) | ❌ | ✅ |
| CPU viable | ✅ 17x realtime (SenseVoice) | ❌ Too slow | N/A |
| Self-hosted | ✅ MIT license | ✅ MIT license | ❌ Cloud only |
| Cost | Free | Free | $0.006/min+ |
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.
---
## Installation
```bash
pip install funasr
```
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`.
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.
## 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}
}
```
