OpenASR/sensevoice-small

SenseVoice Small · OpenASR

Fast multilingual speech recognition from FunAudioLLM — non-autoregressive SenseVoice, tuned for Chinese, Cantonese, English, Japanese and Korean

Native speech-to-text in the OpenASR runtime — engineered for peak performance on CPU & GPU, no Python at inference time.

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✨ Highlights

• 🌏 Multilingual, zh-first — high-precision Mandarin, Cantonese, English, Japanese and Korean with automatic language detection
• ⚡ Non-autoregressive speed — an end-to-end architecture the upstream clocks at about 70 ms for 10 seconds of audio, 15 times faster than Whisper-Large
• 🀄 Chinese benchmark strength — trained on over 400,000 hours of speech; the upstream reports better Chinese and Cantonese accuracy than Whisper on AISHELL and WenetSpeech
• 🪶 Compact and local — a small checkpoint that transcribes fully offline, from a 130 MB q4_k build up to full-fidelity fp16
• 🦀 Native in OpenASR — .oasr packs run with no Python at inference, engineered for peak performance on CPU & GPU

🚀 Quickstart

# 1. Install the OpenASR CLI  ·  https://openasr.org
# 2. Pull a build (pick a quant — see the table below)
openasr pull sensevoice-small:fp16

# 3. Transcribe
openasr transcribe audio.wav --model sensevoice-small

All builds for this model:

openasr pull sensevoice-small:fp16
openasr pull sensevoice-small:q8
openasr pull sensevoice-small:q4

📦 Available builds

Quant	File (.oasr)	Size	RAM peak	RTF · M1 CPU	RTF · M1 GPU	JFK ΔWER vs fp16
fp16	sensevoice-small-fp16.oasr	470 MB	745 MB	0.18×	0.04×	0.0%
q8_0	sensevoice-small-q8_0.oasr	252 MB	514 MB	0.18×	0.04×	0.0%
q4_k	sensevoice-small-q4_k.oasr	136 MB	395 MB	0.23×	0.05×	0.0%

_{RTF = real-time factor on the fixed 11s JFK clip (lower is faster); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. fp16 is the recommended default — near-reference quality at a fraction of the footprint.}

🧠 About SenseVoice Small

SenseVoice Small is the compact member of SenseVoice, the speech understanding model family open-sourced by FunAudioLLM (Alibaba). Trained on more than 400,000 hours of speech, it delivers high-precision transcription with automatic language detection for Mandarin Chinese, Cantonese, English, Japanese and Korean, and the upstream card reports Chinese and Cantonese accuracy ahead of Whisper on open benchmarks such as AISHELL and WenetSpeech. Its non-autoregressive end-to-end architecture makes inference exceptionally fast — the upstream team clocks about 70 ms for 10 seconds of audio, 15x faster than Whisper-Large. The upstream model also carries speech emotion recognition and audio event detection; the OpenASR packs currently surface plain transcription only (emotion/event tags are not yet exposed). This OpenASR repo repackages the original weights as .oasr packs that run natively in the OpenASR runtime — no Python at inference time. The fp16 build is the recommended default for maximum fidelity; q8_0 halves the footprint at near-reference quality and q4_k suits tight-memory devices.

⚙️ How these packs were made

Converted from FunAudioLLM/SenseVoiceSmall with the OpenASR importer:

openasr model-pack import sensevoice <src> <out>.oasr \
  --package-id sensevoice-small --quantization {fp16,q8-0,q4-k}

The .oasr container is GGUF-backed; packs use zero-copy mmap weight binding and graph buffer reuse to keep peak memory low.

⚖️ License

These packs inherit the upstream model's license: FunASR Model License v1.1 (source). OpenASR packaging retains the upstream copyright and NOTICE; the only modifications are format conversion and quantization.

🙏 Acknowledgements

This pack is a redistribution of SenseVoice Small, created and open-sourced by the FunAudioLLM team at Alibaba (FunAudioLLM/SenseVoiceSmall), built on the FunASR open-source speech toolkit from Alibaba's ModelScope community. All credit for the original architecture, training, and weights belongs to the FunAudioLLM and FunASR teams; the license is inherited from and identical to the upstream model — the FunASR Model License v1.1, which permits commercial use and requires this attribution. Thank you to FunAudioLLM, the FunASR team, and Alibaba for releasing their work openly. OpenASR only performs format conversion, quantization, runtime verification, and local-inference adaptation.

🔗 Links

• 🦀 OpenASR — https://github.com/QuintinShaw/openasr
• 🌐 Website — https://openasr.org
• 🤗 Upstream model — FunAudioLLM/SenseVoiceSmall