{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"sherpa-onnx-core provides pre-built ONNX runtime binaries for speech recognition, synthesis, speaker identification, audio tagging, and other audio processing tasks across multiple platforms and architectures.","skillfed_tags":["speech-processing","edge-inference","offline-ai"],"use_cases":["Build offline speech-to-text applications on mobile, embedded Linux, or desktop without cloud API calls","Implement real-time voice activity detection and keyword spotting for always-on voice interfaces","Deploy speaker identification or diarization in privacy-sensitive environments where audio cannot leave the device","Add text-to-speech synthesis to applications running on Raspberry Pi, Jetson, or other edge hardware","Integrate speech enhancement or source separation into audio processing pipelines on resource-constrained devices"],"what_it_does":"sherpa-onnx-core is a Python wrapper around pre-compiled ONNX runtime binaries for running speech and audio AI models locally. It bundles the core C++ libraries needed to execute speech recognition (streaming and non-streaming), text-to-speech, speaker diarization, voice activity detection, keyword spotting, audio tagging, and speech enhancement\u2014all without external service dependencies.\n\nThe package targets embedded and edge devices as well as desktop environments, supporting x86, ARM (32 and 64-bit), RISC-V, and specialized NPUs (Rockchip, Qualcomm, Ascend). It has no Python runtime dependencies, only platform-specific binary wheels, making it lightweight for deployment. Users typically pair it with pre-trained ONNX models from the sherpa-onnx ecosystem to perform inference.","worth_installing":"Yes, if you need local, offline speech or audio AI inference. The package is actively maintained, has no Python dependencies, supports a wide range of platforms and architectures, and carries a permissive license. Install friction is moderate due to platform-specific wheels, but pre-built binaries eliminate the need to compile ONNX Runtime yourself. Verify that your target platform is in the supported list before committing."},"id":"sherpa-onnx-core","links":{"html":"https://skillfed.io/packages/sherpa-onnx-core","md":"https://skillfed.io/packages/sherpa-onnx-core.md","pypi":"https://pypi.org/project/sherpa-onnx-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":null,"license_treatment":"permissive","name":"sherpa-onnx-core","python_support":"unspecified","summary":"Core shared libraries for sherpa-onnx"},"popularity":{"monthly_downloads":526448,"position":6176,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.13.5"}