FunASR README

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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 · Model selection · Models · Deployment matrix · Deployment hub · Docs · Benchmark · Contribute

Quick Start

No local setup? Open the Colab quickstart to transcribe a public sample or upload your own audio in a browser.

Found FunASR useful? Star the project so more builders can find it.

# CPU-only installs can use the default PyPI wheels.
pip install torch torchaudio
pip install funasr

For GPU quickstarts, install the PyTorch and torchaudio wheels that match your NVIDIA driver from pytorch.org before installing FunASR. After installation, confirm the GPU is visible:

python - <<'PY'
import torch
print(torch.cuda.is_available())
PY

Only use device="cuda" when this prints True; otherwise use device="cpu" or reinstall PyTorch with the correct CUDA wheel.

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"])

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.

For a CPU-first example with five-language ASR plus emotion and audio-event tags, use SenseVoiceSmall. The pipeline below combines it with FSMN-VAD and CAM++ for speaker-aware VAD segments; these are not native speaker outputs of 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="cpu")
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'])}")

This prints each returned segment's start time in seconds, anonymous speaker index, and text with SenseVoice tags removed. Text and segment boundaries depend on the audio and checkpoint; no fixed transcript is asserted here.

CAM++ extracts spk_embedding vectors. AutoModel clusters those embeddings and assigns speaker indices to VAD segments. Indices are local to a recording, not known-person identities. See the SDK contract for the component and result boundaries. Change to device="cuda" only after verifying a compatible GPU environment as described above.

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

Use with voice agents: OpenClaw realtime plugin for self-hosted Talk and Voice Call transcription

Why FunASR?

FunASR is a toolkit: choose the task, checkpoint, and runtime separately. Support in one model or adapter does not imply support in every serving backend.

See the Model Zoo and deployment matrix for checkpoint, interface, and licensing boundaries. Benchmark on your own audio and hardware before choosing a runtime.

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.

Installation

pip install funasr

git clone https://github.com/modelscope/FunASR.git && cd FunASR
pip install -e ./

Model Zoo

This list includes third-party models. OpenMOSS publishes MOSS-Transcribe-Diarize; FunASR provides an adapter, not ownership of its weights. Its unified path is offline, with anonymous labels scoped to each recording, not realtime or known-person identification. Model licenses are separate from the toolkit's MIT license.

Usage

Python tutorial · SDK parameters and outputs · Training · Model registration

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")

# Optional Silero VAD (install first: python -m pip install "funasr[silero]")
model = AutoModel(
    model="paraformer-zh", vad_model="silero-vad", device="cuda",
    vad_kwargs={"silero_threshold": 0.5, "silero_min_silence_duration_ms": 100},
)
result = model.generate(input="audio.wav")

# 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

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
# Joint long-form ASR + anonymous speaker labels (offline HTTP):
funasr-server --model moss-transcribe-diarize --device cuda:0

MOSS service, Docker, Kubernetes, vLLM, SGLang, LocalAI, and FunClip guide →

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-vulkan package with a current AMD, Intel, or NVIDIA Vulkan driver:
.\llama-funasr-sensevoice.exe -m .\gguf\sensevoice-small-q8.gguf --vad .\gguf\fsmn-vad.gguf -a audio.wav --backend vulkan
# 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

Use funasr-llamacpp-linux-x64-vulkan.tar.gz on Linux GPU systems with a working Vulkan driver/ICD:

./llama-funasr-sensevoice -m ./gguf/sensevoice-small-q8.gguf --vad ./gguf/fsmn-vad.gguf -a audio.wav --backend vulkan

The Windows Vulkan ZIP uses the system Vulkan loader supplied by the GPU driver; installing the Vulkan SDK is only necessary when building from source. Both Vulkan packages currently accelerate SenseVoiceSmall.

Tagged releases provide two Windows CUDA packages. The standard windows-x64-cuda ZIP targets CUDA architecture 86, while windows-x64-cuda-blackwell targets architecture 120 (sm_120) for RTX 50 / Blackwell GPUs. Both ZIPs bundle the required cuBLAS DLLs and use the static MSVC runtime, so users need a compatible NVIDIA driver but not a separate CUDA Toolkit installation. CI verifies the architecture and package boundary; it does not prove inference on physical Blackwell hardware.

Prebuilt binaries: Releases · v0.2.6 · Linux Vulkan tarball · Windows Vulkan zip · Windows CUDA zip · Windows Blackwell CUDA zip · Download & quickstart: funasr.com/deploy/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 →

Benchmark

The historical benchmark report and split-engine measurements retain their original results. They are separate records, not universal speed rankings or production capacity guarantees.

Use the RTFx and reproducibility notes to compare checkpoint/revision, audio set, hardware, batching, warmup, timing scope, and CER/WER. Offline throughput is not streaming latency. The migration benchmark example helps measure your own recordings with the same evaluation scope.

What's new

• MOSS-Transcribe-Diarize brings long-form ASR, timestamps, and anonymous speaker labels to FunASR services, Docker, Kubernetes, vLLM/SGLang workflows, and FunClip. Deploy MOSS ->
• FunASR 1.4.14 completes MOSS service and Model Zoo discovery, and improves realtime serving stability while retaining the NumPy ABI safeguard. Install with python -m pip install -U "funasr==1.4.14". Release ->
• Production deployment now includes faster, more resilient realtime serving and verified llama.cpp archives for ten Linux, macOS, and Windows targets. GPU services -> · CPU/edge packages ->

See GitHub Releases for the complete changelog and downloadable assets.

Community

Start with troubleshooting before reporting a problem. Include your exact model, runtime, environment and a minimal reproduction.

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}
}