--- title: "whisper.cpp vs TensorFlowASR" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ggml-org-whisper-cpp-vs-tensorspeech-tensorflowasr" tools: ["ggml-org-whisper-cpp", "tensorspeech-tensorflowasr"] --- # whisper.cpp vs TensorFlowASR *GraphCanon updated Jul 12, 2026* ## Verdict Pick whisper.cpp when whisper.cpp is primarily C++; TensorFlowASR is Python; pick TensorFlowASR when tensorFlowASR is primarily Python; whisper.cpp is C++. [whisper.cpp](https://github.com/ggml-org/whisper.cpp) reports 52k GitHub stars, 5.9k forks, and 1.2k open issues, last pushed Jul 11, 2026. [TensorFlowASR](https://huylenguyen.com/asr) has 1.0k stars, 239 forks, and 47 open issues, last pushed Jun 11, 2025. Figures are from public GitHub metadata via [whisper.cpp's repository](https://github.com/ggml-org/whisper.cpp) and [TensorFlowASR's repository](https://github.com/TensorSpeech/TensorFlowASR). | | [whisper.cpp](/tools/ggml-org-whisper-cpp.md) | [TensorFlowASR](/tools/tensorspeech-tensorflowasr.md) | | --- | --- | --- | | Tagline | Port of OpenAI's Whisper model in C/C++ for speech-to-text inference | :zap: TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords | | Stars | 51,715 | 1,009 | | Forks | 5,898 | 239 | | Open issues | 1,216 | 47 | | Language | C++ | Python | | Adopt for | A port of OpenAI's Whisper model to C++, optimized with `ggml`, for lightweight speech-to-text transcription. | - | | Persona | - | - | | Runtime | - | - | | License | MIT License - permissive license that allows users to use the software in any way, including closed-source applications. | Apache-2.0 | | Categories | Speech & Audio, Inference & Serving | Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [whisper.cpp](/tools/ggml-org-whisper-cpp.md) | [TensorFlowASR](/tools/tensorspeech-tensorflowasr.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 394d | | Open issues (now) | 1.2k | 47 | | Security scan | No lockfile | 15 low (15 low) | | Full report | [trust report](/tools/ggml-org-whisper-cpp/trust.md) | [trust report](/tools/tensorspeech-tensorflowasr/trust.md) | ## Decision facts: whisper.cpp - **Requirements:** Requires C++ setup and knowledge; Needs audio files converted into a compatible format supported by `ggml`. - **Adopt for:** A port of OpenAI's Whisper model to C++, optimized with `ggml`, for lightweight speech-to-text transcription. - **License detail:** MIT License - permissive license that allows users to use the software in any way, including closed-source applications. ## Choose when ### Choose whisper.cpp if… - whisper.cpp is primarily C++; TensorFlowASR is Python. - License: whisper.cpp is MIT, TensorFlowASR is Apache-2.0. - Requirements: Requires C++ setup and knowledge; Needs audio files converted into a compatible format supported by `ggml`.. - Tags unique to whisper.cpp: speech-to-text, openai, transformer, speech-recognition. - Also covers Inference & Serving. - You need a lightweight solution that does not require Python or PyTorch ### Choose TensorFlowASR if… - TensorFlowASR is primarily Python; whisper.cpp is C++. - License: TensorFlowASR is Apache-2.0, whisper.cpp is MIT. - Tags unique to TensorFlowASR: jasper, automatic-speech-recognition, end2end, ctc. - Also covers Model Training. - TensorFlowASR ships Docker support for self-hosted deployment. ## When NOT to use whisper.cpp - When you prefer to work with higher-level languages like Python which might offer more ease-of-use and extensive libraries - If your project requires real-time speech transcription and has limited computational resources as `ggml` optimization might still require significant CPU/GPU power for high-performance ## When NOT to use TensorFlowASR - Last GitHub push was 395 days ago (dormant maintenance, Jun 11, 2025). Validate activity before betting a new project on TensorFlowASR. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between whisper.cpp and TensorFlowASR? whisper.cpp: Port of OpenAI's Whisper model in C/C++ for speech-to-text inference. TensorFlowASR: :zap: TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords. See the comparison table for live GitHub stats and shared categories. ### When should I choose whisper.cpp over TensorFlowASR? Choose whisper.cpp over TensorFlowASR when whisper.cpp is primarily C++; TensorFlowASR is Python; License: whisper.cpp is MIT, TensorFlowASR is Apache-2.0; Requirements: Requires C++ setup and knowledge; Needs audio files converted into a compatible format supported by `ggml`.; Tags unique to whisper.cpp: speech-to-text, openai, transformer, speech-recognition; Also covers Inference & Serving; You need a lightweight solution that does not require Python or PyTorch. ### When should I choose TensorFlowASR over whisper.cpp? Choose TensorFlowASR over whisper.cpp when TensorFlowASR is primarily Python; whisper.cpp is C++; License: TensorFlowASR is Apache-2.0, whisper.cpp is MIT; Tags unique to TensorFlowASR: jasper, automatic-speech-recognition, end2end, ctc; Also covers Model Training; TensorFlowASR ships Docker support for self-hosted deployment. ### When should I avoid whisper.cpp? When you prefer to work with higher-level languages like Python which might offer more ease-of-use and extensive libraries If your project requires real-time speech transcription and has limited computational resources as `ggml` optimization might still require significant CPU/GPU power for high-performance ### When should I avoid TensorFlowASR? Last GitHub push was 395 days ago (dormant maintenance, Jun 11, 2025). Validate activity before betting a new project on TensorFlowASR. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is whisper.cpp or TensorFlowASR more popular on GitHub? whisper.cpp has more GitHub stars (51,715 vs 1,009). Stars measure visibility, not whether either tool fits your constraints. ### Are whisper.cpp and TensorFlowASR open source? Yes - both are open-source projects on GitHub (whisper.cpp: MIT, TensorFlowASR: Apache-2.0). ### Where can I find alternatives to whisper.cpp or TensorFlowASR? GraphCanon lists graph-backed alternatives at [whisper.cpp alternatives](/tools/ggml-org-whisper-cpp/alternatives) and [TensorFlowASR alternatives](/tools/tensorspeech-tensorflowasr/alternatives) ([whisper.cpp markdown twin](/tools/ggml-org-whisper-cpp/alternatives.md), [TensorFlowASR markdown twin](/tools/tensorspeech-tensorflowasr/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/ggml-org-whisper-cpp-vs-tensorspeech-tensorflowasr.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, whisper.cpp or TensorFlowASR? whisper.cpp: Very active. TensorFlowASR: Dormant. 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 whisper.cpp and TensorFlowASR? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [whisper.cpp trust report](/tools/ggml-org-whisper-cpp/trust); [TensorFlowASR trust report](/tools/tensorspeech-tensorflowasr/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ggml-org-whisper-cpp`](/api/graphcanon/graph?tool=ggml-org-whisper-cpp) - 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/_