k2-fsa/sherpa-onnx
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
Usage guide
sherpa-onnx is an open-source project around aarch64, android, arm32 with 13,237 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in C++, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating sherpa-onnx for C++ AI workflows.
- Comparing a GitHub project with 13,237 stars and current repository activity.
Pros
- sherpa-onnx has visible GitHub traction with 13,237 stars. Topics: aarch64, android, arm32.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
sherpa-onnx should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
sherpa-onnx architecture preview
sherpa-onnx's main path starts at the entry surface, runs through sherpa-onnx core runtime, combines LLM / model client, Runtime context, GitHub, and returns User-facing result.
Entry
CLI / terminal entry
sherpa-onnx is primarily entered through a developer command or terminal workflow.
git clone https://github.com/k2-fsa/sherpa-onnx.git
Runtime
sherpa-onnx core runtime
The core coordinates project logic, configuration, and AI-related execution in C++.
C++
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
Install tutorial
Before you install
- Local build tools for compiling the project
- A clean working directory for the first test run
Check the runtime environment
sherpa-onnx may require a local build toolchain. Check the compiler, package manager, and system dependencies first.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/k2-fsa/sherpa-onnx.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
Speech-to-text, text-to-speech, speaker diarization, speech enhancemen
This is one of the documented reasons to evaluate sherpa-onnx before choosing a stack.
Focus area: aarch64
This is one of the documented reasons to evaluate sherpa-onnx before choosing a stack.
Speech project comparison
Compare sherpa-onnx with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official sherpa-onnx setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Build flags and hardware acceleration options can materially change runtime performance.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented sherpa-onnx example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is sherpa-onnx?
sherpa-onnx is an open-source speech project. 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
How do I install sherpa-onnx?
Start with the official README. The first detected setup step is: git clone https://github.com/k2-fsa/sherpa-onnx.git.
Is sherpa-onnx beginner-friendly?
If you already know the C++ ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can sherpa-onnx be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does sherpa-onnx need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt sherpa-onnx?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.