---
id: sherpa-onnx
version: "1.13.5"
license: Apache licensed, as found in the LICENSE file
license_treatment: permissive
maintenance: active
---

# sherpa-onnx

License: permissive · Maintenance: active · Downloads: 634.7K/mo

## What it is and what it does

Sherpa-onnx is a Python wrapper around ONNX Runtime for running speech and audio AI models locally without cloud dependencies. It supports a wide range of tasks—speech-to-text (streaming and offline), text-to-speech, speaker identification and diarization, voice activity detection, keyword spotting, audio tagging, speech enhancement, and source separation—across diverse platforms including Linux, macOS, Windows, Android, iOS, and specialized hardware like Raspberry Pi and NVIDIA Jetson boards.

The package is built on ONNX Runtime and requires sherpa-onnx-core as its runtime dependency. It targets developers who need on-device audio AI without external API calls, with support for multiple programming languages and NPU accelerators (Rockchip, Qualcomm, Ascend). Installation uses prebuilt wheels for Python 3.7+ on common architectures, though model files must be obtained separately.

Use it for:

- Build offline speech-to-text applications for edge devices or privacy-sensitive environments
- Add real-time voice commands and keyword spotting to embedded systems or IoT devices
- Implement speaker identification or diarization for audio analysis and meeting transcription
- Deploy text-to-speech synthesis on mobile or server applications without cloud API costs
- Process audio locally on Raspberry Pi, Jetson, or other ARM-based platforms
- Integrate voice activity detection or speech enhancement into audio pipelines

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Sherpa-onnx runs speech recognition, text-to-speech, speaker identification, voice activity detection, keyword spotting, and other audio processing tasks locally using ONNX models across multiple platforms and architectures.

Yes. Sherpa-onnx is actively maintained, permissively licensed, has no known vulnerabilities, and offers broad platform and task coverage for local audio AI. Install it if you need on-device speech or audio processing without external dependencies. The medium install friction is offset by prebuilt wheels and strong community adoption.

## Install

pip install sherpa-onnx
uv add sherpa-onnx
poetry add sherpa-onnx

## Installing sherpa-onnx

Before you install: Medium install friction due to compiled wheels for multiple Python versions and architectures; however, prebuilt wheels are available for common platforms (Linux x86_64, macOS, Windows, ARM variants). Active maintenance with recent releases (3 days since last update) and 14181 repository stars suggest reliable ongoing support.

License in practice: Apache licensed under permissive terms, allowing commercial and private use with minimal restrictions; suitable for most production and proprietary projects.

Quickstart:

pip install sherpa-onnx

import sherpa_onnx
# Requires a pre-trained ONNX model file and audio input
recognizer = sherpa_onnx.OfflineRecognizer.from_pretrained(...)
result = recognizer.recognize(audio_data)

Requires a pre-trained ONNX model file (not included in the package); audio input must be in a supported format.

Verify before relying:

- Whether pre-trained models are bundled or must be downloaded separately
- Performance characteristics (latency, memory usage) on different hardware
- Supported audio formats and sample rates for each task
- Whether GPU acceleration is available or only CPU inference

## Package facts

- License: Apache licensed, as found in the LICENSE file (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 634.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

local speech recognition ASR, text to speech TTS offline, speaker diarization identification, voice activity detection VAD, keyword spotting audio, speech enhancement local, audio tagging classification, source separation audio, speech-recognition, audio-processing, edge-ai

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