lucascs@proton.me Lucas C.S. Build the NVIDIA CUDA execution provider (requires the CUDA Toolkit; the nvcc host compiler is resolved from the installed toolkit, not hardcoded) Build the example CLI tools that record and play audio. Adds no system dependency: upstream vendors and statically links portaudio Build and install the Python bindings (pybind11) into site-packages Build text-to-speech support, which pulls the vendored espeak-ng and piper-phonemize sources Build the WebSocket server and client examples sherpa-onnx is the speech toolkit of the k2-fsa project: speech-to-text, text-to-speech, speaker diarization, voice activity detection, source separation and keyword spotting, all running on ONNX Runtime with no PyTorch dependency. This is a source build linked against the system sci-libs/onnxruntime rather than a bundled copy, which is what SHERPA_ONNX_USE_PRE_INSTALLED_ONNXRUNTIME_IF_AVAILABLE selects. Upstream's CMake build resolves roughly a dozen small dependencies through FetchContent at configure time — eigen, asio, cargs, json, kaldi-decoder, kaldi-native-fbank, kaldifst, openfst, kissfft, simple-sentencepiece, hclust-cpp, and optionally espeak-ng, piper-phonemize, portaudio, websocketpp and pybind11. This ebuild declares every one of them in SRC_URI, pinned by tag or full commit SHA, and stages them into the source directory so CMake resolves them locally. The build therefore needs no network access and carries no RESTRICT="network-sandbox". Model files are not bundled; each task loads its own from upstream. See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/ k2-fsa/sherpa-onnx https://github.com/k2-fsa/sherpa-onnx/issues