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+([简体中文](./README_zh.md)|English|[日本語](./README_ja.md)|[한국어](./README_ko.md))
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+ Industrial speech recognition. Up to 340x realtime, 26x faster than Whisper. 50+ languages.
+ Speaker diarization · Emotion detection · Streaming · One API call
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+ 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}
+}
+```