---

license: other

license_name: funasr-model-license-v1.1

license_link: https://huggingface.co/FunAudioLLM/SenseVoiceSmall/blob/main/LICENSE

language:

• zh
• yue
• en
• ja
• ko

pipeline_tag: automatic-speech-recognition

tags:

• audio
• speech-recognition
• transcription
• ggml
• gguf
• funasr
• sensevoice
• sanm
• multilingual
• language-identification
• emotion-recognition
• audio-event-detection

library_name: ggml

base_model: FunAudioLLM/SenseVoiceSmall

---

SenseVoiceSmall — GGUF (ggml-quantised)

GGUF / ggml conversion of FunAudioLLM/SenseVoiceSmall for use with the sensevoice backend in CrispStrobe/CrispASR.

SenseVoiceSmall is Alibaba's multi-task encoder-only ASR: one forward pass through a 70-block SANM encoder emits the full transcript plus the spoken language ID, emotion, and audio-event tags through a single CTC head. Non-autoregressive design → 15× faster than Whisper-Large (70 ms for 10 s of audio in upstream's measurements).

• 70-block SenseVoiceEncoderSmall (1 entry block @ 560→512 + 49 main blocks + 20 tp blocks, all 512-dim, 4 heads, FSMN k=11 depthwise convolution branch — the same encoder body Fun-ASR-Nano-2512 ships, just here paired with a CTC head instead of an LLM decoder)
• 4 query embeddings (language / event / emotion / textnorm) prepended to the LFR fbank features so the encoder can emit rich annotations at those positions
• CTC head (ctc.ctc_lo, 25055 SentencePiece pieces)
• 50+ languages with native LID (no whisper-tiny pre-step needed)
• Three quants shipped (May 2026): F16 (448 MB), Q8_0 (240 MB), Q4_K (129 MB — recommended default). All three produce byte-identical transcripts on English (JFK) and Japanese (JSUT) clips end-to-end on M1 Metal. 72 tensors stay F16 in the Q4_K/Q8_0 quants because their leading dim isn't quant-block-aligned: 70× attn.fsmn.w (kernel=11 depthwise convolution) and 2× attn.qkv.w (560-dim input from the SANM context concat); the other ~280 weight matrices quantize cleanly.

What you get in the output

By default, stdout shows the clean transcript:

And so my fellow Americans ask not what your country can do for you, ask what you can do for your country.

With -oj the JSON output exposes the four rich-annotation tags as

explicit fields:

{
  "text":        "And so my fellow Americans...",
  "language":    "en",
  "audio_event": "Speech",
  "emotion":     "ANGRY",
  "itn_flag":    "withitn"
}

The legacy sensevoice_transcribe() C ABI still returns the original

prefixed string for callers that want it that way:

<|en|><|HAPPY|><|Speech|><|withitn|>And so my fellow Americans...
<|zh|><|NEUTRAL|><|Speech|><|withitn|>开饭时间早上9点至下午5点。

New callers should use sensevoice_transcribe_structured() which

returns the same six fields as a struct sensevoice_result.

Tag value sets:

• Languages: zh / en / yue / ja / ko / nospeech
• Emotions: HAPPY / SAD / ANGRY / NEUTRAL / EMO_UNKNOWN
• Audio events: Speech / Music / Applause / Laughter / Cry / BGM (and more — the upstream set is open-ended)
• Text norm: withitn (Arabic digits, punctuation) or woitn (raw)

Files

| File | Size | Notes |

| --- | ---: | --- |

| sensevoice-small-q4_k.gguf | 129 MB | Recommended default. 2× faster on M1 vs F16; byte-identical transcript on tested clips. Auto-download target for --backend sensevoice -m auto. |

| sensevoice-small-q8_0.gguf | 240 MB | Larger but slightly closer to F16 numerically on borderline emotion-tag argmax cases. |

| sensevoice-small-f16.gguf | 448 MB | F16 reference weights. Use when you want bit-stability against the upstream PyTorch reference for diff testing. |

Quick Start

git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release
cmake --build build --target crispasr-cli

./build/bin/crispasr \
    --backend sensevoice \
    -m /path/to/sensevoice-small-q4_k.gguf \
    -f samples/jfk.wav -l en

# Or auto-download (resolves to Q4_K by default):
./build/bin/crispasr --backend sensevoice -m auto -f samples/jfk.wav -l en

Verification

crispasr-diff sensevoice is 76/76 PASS, byte-identical generated_text,

on Alibaba's own example zh.mp3; 75/76 PASS on samples/jfk.wav with

the single difference being the emotion-tag argmax flipping between

<|ANGRY|> and <|EMO_UNKNOWN|> (F16/op-order pushes that one slot

across a near-tied boundary; the transcript itself is byte-identical

in both runs). On Apple M1 Metal the runtime hits 15-22× realtime.

Licence + attribution

Upstream FunAudioLLM/SenseVoiceSmall:

• Code (the funasr Python package): Apache-2.0.
• Model weights: FunASR Model License v1.1 (Alibaba) — commercial use OK with attribution. Confirmed on the upstream-tracking discussion in CrispStrobe/CrispASR#99.

These GGUF files are a quantised / repackaged distribution of the upstream weights and inherit the FunASR Model License v1.1. Please attribute Alibaba / FunAudioLLM in downstream products.

> If you use this model, please also cite the upstream FunASR work.

> See the upstream model card for the canonical citation.

Provenance and EU AI Act Art. 53 note

• Upstream model: FunAudioLLM/SenseVoiceSmall — published by FunAudioLLM.
• Upstream licence: other. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.