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README.md

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+---
+license: apache-2.0
+language:
+- zh
+- en
+library_name: gguf
+tags:
+- automatic-speech-recognition
+- asr
+- sensevoice
+- funasr
+- llama.cpp
+- ggml
+- cpu
+- chinese
+pipeline_tag: automatic-speech-recognition
+---
+
+# SenseVoiceSmall · GGUF (FunASR llama.cpp runtime)
+
+GGUF build of **[SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall)** (SAN-M encoder + CTC) for the zero-Python, CPU/edge **[FunASR llama.cpp runtime](https://github.com/FunAudioLLM/SenseVoice/tree/main/runtime/llama.cpp)** — multilingual ASR with language / emotion / event tags, **~20× real-time on CPU**.
+
+## Files
+| file | size | notes |
+|---|---|---|
+| `sensevoice-small-f16.gguf` | 470 MB | **recommended** (f16 matmul weights) |
+| `sensevoice-small.gguf` | 936 MB | f32 reference |
+
+## Usage
+```bash
+# 1. get the VAD too (for long audio): huggingface-cli download FunAudioLLM/fsmn-vad-GGUF
+llama-funasr-sensevoice -m sensevoice-small-f16.gguf -a audio.wav --vad fsmn-vad.gguf > ids.txt
+```
+On CPU (8 threads) this reaches **8.01 % CER** on the 184-clip Mandarin benchmark — vs whisper.cpp 22–31 %. See the [benchmark](https://github.com/FunAudioLLM/SenseVoice/blob/main/runtime/llama.cpp/BENCHMARKS.md).
+
+## Links
+- 🧩 Runtime & build: **[SenseVoice · runtime/llama.cpp](https://github.com/FunAudioLLM/SenseVoice/tree/main/runtime/llama.cpp)** — ⭐ **Star [SenseVoice](https://github.com/FunAudioLLM/SenseVoice)!**
+- Source model: [FunAudioLLM/SenseVoiceSmall](https://huggingface.co/FunAudioLLM/SenseVoiceSmall)