k2-fsa/sherpa-onnx
Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, RK NPU, Axera NPU, Ascend NPU, x86_64 servers, websocket server/client, support 12 programming languages observed · 2026-08-28
Health v2 · maintenance only
95/100
- Activity 99
- Release rhythm 87
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 6.0
- age_days: 1462
- days_rel: 8
- days_push: 8
- n_releases_24m: 91
Adoption not part of the score
14411 stars · 1652 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
sherpa-onnx is an offline speech processing toolkit built on next-gen Kaldi and onnxruntime, supporting speech-to-text, text-to-speech, speaker diarization/verification, VAD, keyword spotting, speech enhancement, and source separation without Internet access. It runs on a wide range of platforms including Android, iOS, embedded boards, and NPUs, with bindings for 12 programming languages.
Use cases
- run offline speech recognition on android
- convert text to speech locally without internet
- transcribe audio on raspberry pi
- add voice activity detection to an app
- do speaker diarization on device
- embed asr into an ios app
- run speech recognition on embedded arm boards
- build a local voice assistant
When to choose
- you need fully offline, on-device speech recognition or TTS
- you target mobile, embedded, or NPU hardware like Android, iOS, Raspberry Pi, or RISC-V
- you need bindings across many languages (C++, Python, Java, Swift, Go, etc.)
- you want streaming and non-streaming ASR with VAD and keyword spotting in one toolkit
When to avoid
- you need cloud-hosted, managed speech APIs with vendor-managed models
- you want training or fine-tuning of speech models rather than inference
- you need non-speech audio tasks like music generation
- you prefer PyTorch-native pipelines instead of ONNX export
Facets
library · maturity active
speech-recognition tts audio-processing sdk websocket cli speech-processing embedded-systems cross-platform machine-learning windows cpp python wasm embedded cross-platform onnx onnxruntime asr vad speaker-diarization keyword-spotting offline kaldi npu raspberry-pi risc-v speech-enhancement source-separation harmonyos natural-language-processing android ios macos linux docker
2 sources
- readme: https://github.com/k2-fsa/sherpa-onnx · fetched 2026-08-28 · 0e3ff0ce36f5
- homepage: https://k2-fsa.github.io/sherpa/onnx/index.html · fetched 2026-08-29 · 368e5002a04e
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| k2-fsa/sherpa-onnx | main | 95 |
For agents
markdown · JSON · MCP: product_card(name="k2-fsa/sherpa-onnx")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem