--- title: "silero-models vs TensorFlowASR" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/snakers4-silero-models-vs-tensorspeech-tensorflowasr" tools: ["snakers4-silero-models", "tensorspeech-tensorflowasr"] --- # silero-models vs TensorFlowASR *GraphCanon updated Jul 31, 2026* ## Verdict Pick silero-models if provides simple access to pre-trained text-to-speech models for various languages via PyTorch Hub, pip installation, and manual caching; pick TensorFlowASR if tensorFlowASR is an advanced Automatic Speech Recognition library built on TensorFlow 2 that supports modern architectures such as Conformer and RNN-Transducer. [silero-models](https://github.com/snakers4/silero-models) reports 6.0k GitHub stars, 369 forks, and 12 open issues, last pushed Jun 4, 2026. [TensorFlowASR](https://huylenguyen.com/asr) has 1.0k stars, 239 forks, and 47 open issues, last pushed Jul 30, 2026. Figures are from public GitHub metadata via [silero-models's repository](https://github.com/snakers4/silero-models) and [TensorFlowASR's repository](https://github.com/TensorSpeech/TensorFlowASR). | | [silero-models](/tools/snakers4-silero-models.md) | [TensorFlowASR](/tools/tensorspeech-tensorflowasr.md) | | --- | --- | --- | | Tagline | Silero Models provide simple access to pre-trained text-to-speech models | Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2 | | Stars | 6,030 | 1,010 | | Forks | 369 | 239 | | Open issues | 12 | 47 | | Language | Jupyter Notebook | Python | | Adopt for | Provides simple access to pre-trained text-to-speech models for various languages via PyTorch Hub, pip installation, and manual caching. | TensorFlowASR is an advanced Automatic Speech Recognition library built on TensorFlow 2 that supports modern architectures such as Conformer and RNN-Transducer. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [silero-models](/tools/snakers4-silero-models.md) | [TensorFlowASR](/tools/tensorspeech-tensorflowasr.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 55d | 0d | | Open issues (now) | 12 | 47 | | Owner type | User | Organization | | Full report | [trust report](/tools/snakers4-silero-models/trust.md) | [trust report](/tools/tensorspeech-tensorflowasr/trust.md) | ## Shared compatibility - **Python**: [silero-models](/tools/snakers4-silero-models.md) - Python runtime; [TensorFlowASR](/tools/tensorspeech-tensorflowasr.md) - Python runtime ## Decision facts: silero-models - **Adopt for:** Provides simple access to pre-trained text-to-speech models for various languages via PyTorch Hub, pip installation, and manual caching. ## Decision facts: TensorFlowASR - **Adopt for:** TensorFlowASR is an advanced Automatic Speech Recognition library built on TensorFlow 2 that supports modern architectures such as Conformer and RNN-Transducer. - **License detail:** Apache-2.0 ## Choose when ### Choose silero-models if… - silero-models is primarily Jupyter Notebook; TensorFlowASR is Python. - License: silero-models is Other, TensorFlowASR is Apache-2.0. - Tags unique to silero-models: armenian, azerbaijani, belarus, colab. - Need easy integration of text-to-speech functionalities in Jupyter Notebooks ### Choose TensorFlowASR if… - TensorFlowASR is primarily Python; silero-models is Jupyter Notebook. - License: TensorFlowASR is Apache-2.0, silero-models is Other. - Tags unique to TensorFlowASR: automatic-speech-recognition, conformer, contextnet, ctc. - TensorFlowASR ships Docker support for self-hosted deployment. - When focusing on state-of-the-art performance with models like Conformer or ContextNet, which are among its supported architectures and not necessarily included in all ASR libraries. ## When NOT to use silero-models - Require advanced customization of the speech synthesis process beyond provided options - Looking for a full end-to-end speech recognition (speech-to-text) solution, as this mainly focuses on text-to-speech ## When NOT to use TensorFlowASR - When Python version constraints are an issue, as TensorFlowASR strictly requires python >= 3.12 on Apple Silicon devices, limiting compatibility. - If your project cannot accommodate the extra installation steps involving `ctc_decoders` and `rnnt_loss`, which implies additional complexity in setting up the environment. ## Common questions ### What is the difference between silero-models and TensorFlowASR? silero-models: Silero Models provide simple access to pre-trained text-to-speech models. TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. See the comparison table for live GitHub stats and shared categories. ### When should I choose silero-models over TensorFlowASR? Choose silero-models over TensorFlowASR when silero-models is primarily Jupyter Notebook; TensorFlowASR is Python; License: silero-models is Other, TensorFlowASR is Apache-2.0; Tags unique to silero-models: armenian, azerbaijani, belarus, colab; Need easy integration of text-to-speech functionalities in Jupyter Notebooks. ### When should I choose TensorFlowASR over silero-models? Choose TensorFlowASR over silero-models when TensorFlowASR is primarily Python; silero-models is Jupyter Notebook; License: TensorFlowASR is Apache-2.0, silero-models is Other; Tags unique to TensorFlowASR: automatic-speech-recognition, conformer, contextnet, ctc; TensorFlowASR ships Docker support for self-hosted deployment; When focusing on state-of-the-art performance with models like Conformer or ContextNet, which are among its supported architectures and not necessarily included in all ASR libraries. ### When should I avoid silero-models? Require advanced customization of the speech synthesis process beyond provided options Looking for a full end-to-end speech recognition (speech-to-text) solution, as this mainly focuses on text-to-speech ### When should I avoid TensorFlowASR? When Python version constraints are an issue, as TensorFlowASR strictly requires python >= 3.12 on Apple Silicon devices, limiting compatibility. If your project cannot accommodate the extra installation steps involving `ctc_decoders` and `rnnt_loss`, which implies additional complexity in setting up the environment. ### Is silero-models or TensorFlowASR more popular on GitHub? silero-models has more GitHub stars (6,030 vs 1,010). Stars measure visibility, not whether either tool fits your constraints. ### Are silero-models and TensorFlowASR open source? Yes - both are open-source projects on GitHub (silero-models: Other, TensorFlowASR: Apache-2.0). ### Where can I find alternatives to silero-models or TensorFlowASR? GraphCanon lists graph-backed alternatives at [silero-models alternatives](/tools/snakers4-silero-models/alternatives) and [TensorFlowASR alternatives](/tools/tensorspeech-tensorflowasr/alternatives) ([silero-models markdown twin](/tools/snakers4-silero-models/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/snakers4-silero-models-vs-tensorspeech-tensorflowasr.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, silero-models or TensorFlowASR? silero-models: Steady. TensorFlowASR: 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 silero-models and TensorFlowASR? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [silero-models trust report](/tools/snakers4-silero-models/trust); [TensorFlowASR trust report](/tools/tensorspeech-tensorflowasr/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=snakers4-silero-models`](/api/graphcanon/graph?tool=snakers4-silero-models) - 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/_