Fun-ASR-Nano-GGUF

nile2000/Fun-ASR-Nano-GGUF

Fun-ASR-Nano-GGUF is a automatic speech recognition model from nile2000. Use it when you need speech turned into text. It is set up for gguf. The card lists the license as apache-2.0.

GGUF build of Fun-ASR-Nano (SenseVoice SAN-M encoder + adaptor + Qwen3-0.6B LLM decoder) for the zero-Python, CPU/edge FunASR llama.cpp runtime — the accuracy leader (LLM decoder), single C++ binary.

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At a glance

Task: Automatic Speech Recognition
Library: gguf
License: apache-2.0
Access: Public
Created: Jul 28, 2026
Updated: Jul 28, 2026
SHA: ab3288e7

Model card

From the Hugging Face model README

Fun-ASR-Nano · GGUF (FunASR llama.cpp runtime)

GGUF build of Fun-ASR-Nano (SenseVoice SAN-M encoder + adaptor + Qwen3-0.6B LLM decoder) for the zero-Python, CPU/edge FunASR llama.cpp runtime — the accuracy leader (LLM decoder), single C++ binary.

LLM quantization (pick by size vs accuracy)

The Fun-ASR-Nano LLM (Qwen3-0.6B) ships in three tiers — all within 0.1% CER (184-file micro-CER). Pair any with funasr-encoder-f16.gguf (470 MB).

LLM file	size	CER ↓	speed
`qwen3-0.6b-q4km.gguf`	484 MB	8.35%	6.1×
`qwen3-0.6b-q5km.gguf`	551 MB	8.25%	5.7×
`qwen3-0.6b-q8_0.gguf`	805 MB	8.30%	6.0×

Recommended: q4_K_M (smallest) or q5_K_M (best).

Get it running (no Python, no build)

These are GGUF weights for the FunASR llama.cpp runtime — 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 (tag runtime-llamacpp-v*)
Deployment guide & qualified benchmarks → funasr.com/deploy/llama-cpp

bash download-funasr-model.sh nano ./gguf
llama-funasr-cli --enc ./gguf/funasr-encoder-f16.gguf -m ./gguf/qwen3-0.6b-q8_0.gguf --vad ./gguf/fsmn-vad.gguf -a audio.wav

Files

file	size	notes
`funasr-encoder-f16.gguf`	470 MB	audio encoder + adaptor (f16)
`qwen3-0.6b-q8_0.gguf`	805 MB	LLM decoder, recommended (Q8_0)
`qwen3-0.6b-q4km.gguf`	484 MB	LLM decoder, smaller (Q4_K_M)

Usage (needs both the encoder and the LLM gguf)

llama-funasr-cli --enc funasr-encoder-f16.gguf -m qwen3-0.6b-q8_0.gguf -a audio.wav --vad fsmn-vad.gguf

On CPU: 8.30 % CER on the 184-clip Mandarin benchmark (vs whisper.cpp 22–31 %).