voconly-org/sensevoice-small-gguf overview
SenseVoice Small GGUF A multilingual ASR model from Alibaba's Tongyi Lab, converted to GGUF format for broad compatibility. Model Overview SenseVoice Small fea…
Runs locally from ~164.5 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | voconly-org/sensevoice-small-gguf |
|---|---|
| Author | voconly-org |
| Pipeline | automatic-speech-recognition |
| License | apache-2.0 |
| Base model | iic/SenseVoiceSmall |
| Last modified | 2026-09-07T07:27:53.000Z |
Model README
---
license: apache-2.0
language: [zh, en, yue, ja, ko, ar, de, fr, es, pt, it, ru, th, vi, hi, tr, id, ms, nl, sv, da, fi, pl, cs, fil, fa, el, ro, hu, mk]
tags:
- asr
- automatic-speech-recognition
- sensevoice
- gguf
- multilingual
- speech-to-text
- alibaba
base_model:
- iic/SenseVoiceSmall
---
SenseVoice-Small GGUF
A multilingual ASR model from Alibaba's Tongyi Lab, converted to GGUF format for broad compatibility.
Model Overview
SenseVoice-Small features:
- Parameters: Small variant
- Language Support: Multiple languages including Chinese, English, Cantonese, and more
- Quantization Format: GGUF (llama.cpp compatible)
- Architecture: FunASR-based speech recognition
- Features: High-accuracy recognition, real-time transcription, noise robustness
- Use Cases: Speech-to-text, subtitle generation, meeting transcription, multilingual transcription
Available Quantized Versions
| Filename | Quantization Type | File Size | Description |
|----------|-------------------|-----------|-------------|
| sensevoice-small-f16.gguf | F16 | ~449 MB | Full precision, highest accuracy |
| sensevoice-small-q5_k_m.gguf | Q5_K_M | ~164 MB | Balanced accuracy and speed |
| sensevoice-small-q8_0.gguf | Q8_0 | ~241 MB | High-accuracy quantization |
Usage
llama.cpp Command Line
./main -m sensevoice-small-f16.gguf -f audio.wav --output-txt
Python (llama-cpp-python)
from llama_cpp import Llama
# Load model
llm = Llama(
model_path="sensevoice-small-f16.gguf",
n_gpu_layers=-1, # Use GPU acceleration
)
# Perform speech recognition
result = llm.transcribe("audio.wav")
print(result["text"])
Performance Features
- High multilingual recognition accuracy
- Excellent Chinese recognition, supports Mandarin and Cantonese
- Noise resistance capabilities
- Supports real-time streaming transcription
Technical Specifications
- Architecture: Transformer-based encoder-decoder structure
- Sample Rate: 16kHz
- Input Format: WAV/PCM audio
- Output Format: Text
License
Apache 2.0 License
Acknowledgments
- Original Model: iic/SenseVoiceSmall
- Source: Alibaba DAMO Academy ModelScope
Run voconly-org/sensevoice-small-gguf with guIDE
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