FunAudioLLM/Paraformer-GGUF

Paraformer-zh · GGUF (FunASR llama.cpp runtime)

GGUF build of FunASR's Paraformer-zh (SAN-M encoder + CIF predictor + SAN-M decoder, non-autoregressive) for the zero-Python, CPU/edge [FunASR llama.cpp runtime](https://github.com/modelscope/FunASR/tree/main/runtime/llama.cpp) — fast Mandarin ASR, ~21× real-time on CPU.

Get it running (no Python, no build)

These are GGUF weights for the [FunASR llama.cpp runtime](https://github.com/modelscope/FunASR/tree/main/runtime/llama.cpp) — a whisper.cpp-style, single self-contained binary for CPU / edge. Grab a prebuilt binary, then fetch this model and run:

• —Prebuilt binaries (Linux / macOS / Windows) → [GitHub Releases](https://github.com/modelscope/FunASR/releases) (tag runtime-llamacpp-v*)
• —Deployment guide & qualified benchmarks → [funasr.com/deploy/llama-cpp](https://www.funasr.com/deploy/llama-cpp.html)

bash download-funasr-model.sh paraformer ./gguf
llama-funasr-paraformer -m ./gguf/paraformer-q8.gguf --vad ./gguf/fsmn-vad.gguf -a audio.wav

Files

Usage

The binary prints transcription text directly (no Python detok). --ids for raw ids.

llama-funasr-paraformer -m paraformer-f16.gguf -a audio.wav --vad fsmn-vad.gguf

On CPU (8 threads): 9.85 % CER on the 184-clip Mandarin benchmark (vs whisper.cpp 22–31 %).

Links

• —🧩 Runtime & build: [FunASR · runtime/llama.cpp](https://github.com/modelscope/FunASR/tree/main/runtime/llama.cpp) — ⭐ Star [FunASR](https://github.com/modelscope/FunASR)!
• —Source model: funasr/paraformer-zh