SenseVoice Small
Fast multilingual speech recognition from FunAudioLLM — non-autoregressive SenseVoice, tuned for Chinese, Cantonese, English, Japanese and Korean
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let's line up this week's release plan,
the beta build can ship on Friday,
then loop in design and QA,
Why it fits
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.
What you can do with it
- 🌏 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 —
.oasrpacks run with no Python at inference, engineered for peak performance on CPU & GPU
Other models
Developer details CLI commands, file hashes, and per-quant downloads — for scripting and verification.
Model facts
Usage
These are CLI / local-server examples. The desktop app runs this model without typing a command — use Install above.
$ openasr pull sensevoice-small:fp16 ↓ sensevoice-small.oasr 448.4 MB ✓ verified sha256 $ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/sensevoice-small/fp16/sensevoice-small-fp16.oasr ✓ local transcript · 0 bytes sent
$ openasr serve --backend native --model-pack ~/.openasr/models/sensevoice-small/fp16/sensevoice-small-fp16.oasr --addr 127.0.0.1:8080 ▶ http://127.0.0.1:8080 · model=sensevoice-small · 0 bytes will leave this host
from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8080/v1", api_key="local") audio = open("meeting.wav", "rb") text = client.audio.transcriptions.create(model="sensevoice-small", file=audio)