--- title: "SimpleTuner vs sherpa-onnx" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bghira-simpletuner-vs-k2-fsa-sherpa-onnx" tools: ["bghira-simpletuner", "k2-fsa-sherpa-onnx"] --- # SimpleTuner vs sherpa-onnx *GraphCanon updated Jul 11, 2026* ## Verdict Pick SimpleTuner when simpleTuner is primarily Python; sherpa-onnx is C++; pick sherpa-onnx when sherpa-onnx is primarily C++; SimpleTuner is Python. [SimpleTuner](https://github.com/bghira/SimpleTuner) reports 2.9k GitHub stars, 285 forks, and 21 open issues, last pushed Jul 8, 2026. [sherpa-onnx](https://k2-fsa.github.io/sherpa/onnx/index.html) has 13k stars, 1.5k forks, and 600 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [SimpleTuner's repository](https://github.com/bghira/SimpleTuner) and [sherpa-onnx's repository](https://github.com/k2-fsa/sherpa-onnx). | | [SimpleTuner](/tools/bghira-simpletuner.md) | [sherpa-onnx](/tools/k2-fsa-sherpa-onnx.md) | | --- | --- | --- | | Tagline | A general fine-tuning kit geared toward image/video/audio diffusion models. | 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 | | Stars | 2,878 | 13,499 | | Forks | 285 | 1,545 | | Open issues | 21 | 600 | | Language | Python | C++ | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | Apache-2.0 | | Categories | Computer Vision, Speech & Audio | Computer Vision, Inference & Serving, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [SimpleTuner](/tools/bghira-simpletuner.md) | [sherpa-onnx](/tools/k2-fsa-sherpa-onnx.md) | | --- | --- | --- | | Days since push | 2d | 1d | | Open issues (now) | 21 | 600 | | Owner type | User | Organization | | Full report | [trust report](/tools/bghira-simpletuner/trust.md) | [trust report](/tools/k2-fsa-sherpa-onnx/trust.md) | ## Choose when ### Choose SimpleTuner if… - SimpleTuner is primarily Python; sherpa-onnx is C++. - License: SimpleTuner is AGPL-3.0, sherpa-onnx is Apache-2.0. - Tags unique to SimpleTuner: diffusers, diffusion-models, fine-tuning, flux-dev. ### Choose sherpa-onnx if… - sherpa-onnx is primarily C++; SimpleTuner is Python. - License: sherpa-onnx is Apache-2.0, SimpleTuner is AGPL-3.0. - Tags unique to sherpa-onnx: aarch64, android, arm32, asr. - Also covers Inference & Serving. ## When NOT to use sherpa-onnx - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between SimpleTuner and sherpa-onnx? SimpleTuner: A general fine-tuning kit geared toward image/video/audio diffusion models.. 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. See the comparison table for live GitHub stats and shared categories. ### When should I choose SimpleTuner over sherpa-onnx? Choose SimpleTuner over sherpa-onnx when SimpleTuner is primarily Python; sherpa-onnx is C++; License: SimpleTuner is AGPL-3.0, sherpa-onnx is Apache-2.0; Tags unique to SimpleTuner: diffusers, diffusion-models, fine-tuning, flux-dev. ### When should I choose sherpa-onnx over SimpleTuner? Choose sherpa-onnx over SimpleTuner when sherpa-onnx is primarily C++; SimpleTuner is Python; License: sherpa-onnx is Apache-2.0, SimpleTuner is AGPL-3.0; Tags unique to sherpa-onnx: aarch64, android, arm32, asr; Also covers Inference & Serving. ### When should I avoid sherpa-onnx? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is SimpleTuner or sherpa-onnx more popular on GitHub? sherpa-onnx has more GitHub stars (13,499 vs 2,878). Stars measure visibility, not whether either tool fits your constraints. ### Are SimpleTuner and sherpa-onnx open source? Yes - both are open-source projects on GitHub (SimpleTuner: AGPL-3.0, sherpa-onnx: Apache-2.0). ### Where can I find alternatives to SimpleTuner or sherpa-onnx? GraphCanon lists graph-backed alternatives at [SimpleTuner alternatives](/tools/bghira-simpletuner/alternatives) and [sherpa-onnx alternatives](/tools/k2-fsa-sherpa-onnx/alternatives) ([SimpleTuner markdown twin](/tools/bghira-simpletuner/alternatives.md), [sherpa-onnx markdown twin](/tools/k2-fsa-sherpa-onnx/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/bghira-simpletuner-vs-k2-fsa-sherpa-onnx.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, SimpleTuner or sherpa-onnx? SimpleTuner: Very active. sherpa-onnx: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for SimpleTuner and sherpa-onnx? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [SimpleTuner trust report](/tools/bghira-simpletuner/trust); [sherpa-onnx trust report](/tools/k2-fsa-sherpa-onnx/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bghira-simpletuner`](/api/graphcanon/graph?tool=bghira-simpletuner) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_