funasr 1.3.18

Industrial-grade speech recognition: 170x realtime, 50+ languages, speaker diarization, emotion detection.

(简体中文|English|日本語|한국어)

Industrial speech recognition toolkit for offline, streaming, and edge deployment.
ASR · VAD · punctuation · speaker pipelines · emotion and audio-event models · OpenAI-compatible serving

Quick Start · Colab · Benchmark · Model selection · Migration guide · Use cases · Community integrations · Deployment matrix · Models · Agent Integration · Docs · Contribute

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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 funasr

Flagship model — Fun-ASR-Nano (LLM-ASR for Chinese, English, and Japanese, plus Chinese dialect groups and regional accents; needs a GPU):

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。

For the separate 31-language checkpoint, use Fun-ASR-MLT-Nano-2512. Language coverage is checkpoint-specific, so Nano and MLT-Nano should be treated as distinct model choices.

On CPU (or for five-language ASR plus emotion and audio-event tags), use SenseVoiceSmall. The pipeline below composes SenseVoiceSmall with FSMN-VAD and CAM++; diarization is provided by the separate CAM++ model, not by the SenseVoiceSmall checkpoint: See the SenseVoice paper, Hugging Face checkpoint, and GGUF edge checkpoint.

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,
)

# The AutoModel pipeline returns VAD segments with speaker ids and timestamps:
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

One AutoModel pipeline call coordinates the configured ASR, VAD, and speaker models and returns the combined result.

Scale & deploy the flagship

At scale, accelerate Fun-ASR-Nano with vLLM (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:8000

Use with AI agents: MCP Server for Claude/Cursor · 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 (Chinese, English, Japanese, and Chinese dialects; GPU), Fun-ASR-MLT-Nano (31 languages), SenseVoiceSmall (five-language ASR plus emotion and audio events), and Paraformer (low-latency streaming). The table shows toolkit-level capabilities and names the model or pipeline that provides each one:

	FunASR (toolkit)	Whisper	Cloud APIs
Top speed	340x realtime (Fun-ASR-Nano + vLLM)	13x realtime	~1x realtime
Speaker ID	✅ via VAD + CAM++ pipeline	❌ Needs pyannote	✅ Extra cost
Emotion	✅ via SenseVoice	❌	❌
Languages	Checkpoint-specific (for example Qwen3-ASR 52, MLT-Nano 31, Nano zh/en/ja)	57	Varies
Streaming	✅ WebSocket (Paraformer)	❌	✅
CPU viable	✅ 17x realtime (SenseVoice)	❌ Too slow	N/A
Self-hosted	✅ Yes (toolkit: MIT; model licenses vary)	✅ MIT license	❌ Cloud only
Cost	Free	Free	$0.006/min+

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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Installation

pip install funasr

From source / Requirements

git 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	zh/en/ja + Chinese dialects and accents	800M	⭐ 🤗
Fun-ASR-MLT-Nano	ASR	31 languages	800M	⭐ 🤗
SenseVoiceSmall	ASR + emotion + events	zh/en/ja/ko/yue	234M	⭐ 🤗 GGUF paper
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	⭐ 🤗

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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="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", hotword="关键词 20")


# Streaming real-time (feed audio chunk by chunk)
import soundfile as sf
model = AutoModel(model="paraformer-zh-streaming", device="cuda")
audio, sr = sf.read("speech.wav", dtype="float32")   # 16 kHz mono
chunk_size = [0, 10, 5]                               # 600 ms chunks
chunk_stride = chunk_size[1] * 960
cache = {}
n_chunks = (len(audio) - 1) // chunk_stride + 1
for i in range(n_chunks):
    chunk = audio[i * chunk_stride : (i + 1) * chunk_stride]
    res = model.generate(input=chunk, cache=cache, is_final=(i == n_chunks - 1),
                         chunk_size=chunk_size, encoder_chunk_look_back=4, decoder_chunk_look_back=1)
    if res[0]["text"]:
        print(res[0]["text"], end="", flush=True)

# 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 ./output

Available models: sensevoice (default), paraformer, paraformer-en, fun-asr-nano

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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:8000

Verify 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-0.1.12

CPU / Edge — llama.cpp / GGUF (no GPU, no Python)

Run SenseVoice / Paraformer / Fun-ASR-Nano as a single self-contained binary on CPU and edge devices — this is to FunASR what whisper.cpp is to Whisper, but with ~3× lower CER than whisper.cpp on Chinese. Built-in FSMN-VAD, no Python at runtime.

# Linux / macOS: run from the extracted release directory
bash download-funasr-model.sh sensevoice ./gguf        # or: paraformer | nano
./llama-funasr-sensevoice -m ./gguf/sensevoice-small-q8.gguf --vad ./gguf/fsmn-vad.gguf -a audio.wav
# → 欢迎大家来体验达摩院推出的语音识别模型

# Windows PowerShell: run from the extracted archive root (with the `hf` CLI installed)
hf download FunAudioLLM/SenseVoiceSmall-GGUF sensevoice-small-q8.gguf --local-dir .\gguf
hf download FunAudioLLM/fsmn-vad-GGUF fsmn-vad.gguf --local-dir .\gguf
.\llama-funasr-sensevoice.exe -m .\gguf\sensevoice-small-q8.gguf --vad .\gguf\fsmn-vad.gguf -a audio.wav
# Use the windows-x64-cuda package on RTX 30-class GPUs:
.\llama-funasr-sensevoice.exe -m .\gguf\sensevoice-small-q8.gguf --vad .\gguf\fsmn-vad.gguf -a audio.wav --backend cuda

Prebuilt binaries: Releases · v0.1.7 · Windows CUDA zip · Download & quickstart: funasr.com/llama-cpp · GGUF models: Hugging Face · Docs & benchmarks: runtime/llama.cpp/

OpenAI 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 →

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Benchmark

184 long-form audio files (192 min). Full report → · RTFx and reproducibility notes →

Model	Chinese CER ↓	GPU Speed	CPU Speed	vs Whisper-large-v3
Fun-ASR-Nano (vLLM)	8.20%	340x realtime	—	🚀 26x faster
SenseVoice-Small	7.81%	170x realtime	17x realtime	🚀 13x faster
Paraformer-Large	10.18%	120x realtime	15x realtime	🚀 9x faster
Whisper-large-v3-turbo	21.71%	46x realtime	❌	3.4x faster
Whisper-large-v3	20.02%	13x realtime	❌	baseline

Key takeaway: FunASR models run on CPU faster than Whisper runs on GPU.

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What's new

• 2026/07/18: v1.3.16 on PyPI — client-driven realtime endpoints for Fun-ASR-Nano. Start one WebSocket session, stream PCM, and send COMMIT for each utterance without loading server-side VAD; short utterances finalize and timestamps remain monotonic across commits. Install with pip install --upgrade funasr, then run funasr-realtime-server --endpoint-mode client. Guide →
• 2026/07/18: llama.cpp runtime v0.1.7 — prebuilt Windows CUDA package for SenseVoiceSmall (funasr-llamacpp-windows-x64-cuda.zip) plus Linux / macOS / Windows CPU packages. Download the GGUF model, then run llama-funasr-sensevoice ... --backend cuda on supported NVIDIA GPUs. Release →
• 2026/06/20: llama.cpp / GGUF runtime — run SenseVoice / Paraformer / Fun-ASR-Nano on CPU & edge as a single self-contained binary (a whisper.cpp-style alternative), built-in FSMN-VAD, no Python at runtime. Prebuilt binaries for Linux / macOS / Windows + q8 quantized models (~half the size, same accuracy). runtime/llama.cpp/ · Releases
• 2026/06/21: v1.3.12 on PyPI — rolling fixes (qwen3-asr language codes, glm_asr, vLLM repetition_penalty). pip install --upgrade funasr
• 2026/05/24: vLLM Inference Engine — 2-3x faster LLM decoding for Fun-ASR-Nano. Streaming WebSocket service with VAD + Speaker Diarization. Guide → · Realtime WS tuning → · API stability checklist →
• 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-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
• 2026/05/20: Added GLM-ASR-Nano (1.5B) — 17 languages, dialect support. usage
• 2026/05/19: Fun-ASR-Nano and SenseVoice can be combined with VAD and CAM++ for speaker diarization.
• 2025/12/15: Fun-ASR-Nano-2512 — Chinese, English, Japanese, and Chinese dialect support; trained on tens of millions of hours.

Older

• 2024/10/10: Whisper-large-v3-turbo support added.
• 2024/07/04: SenseVoice — ASR + emotion + audio events.
• 2024/01/30: FunASR 1.0 released.

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Community

📖 Documentation	🐛 Issues
💬 Discussions	🤗 HuggingFace
🤝 Contributing	🌐 funasr.com
🗺️ Repository roles & roadmap	📈 Growth plan
🧩 Community projects	💡 Use-case showcase

Star History

License

• FunASR toolkit source code in this repository: MIT License.
• Pretrained model weights are licensed separately. Check the license shown on each model card; when a model card links to the FunASR Model Open Source License Agreement, those terms apply.

Citations

@inproceedings{gao2023funasr,
  author={Zhifu Gao and others},
  title={FunASR: A Fundamental End-to-End Speech Recognition Toolkit},
  booktitle={INTERSPEECH},
  year={2023}
}