--- title: "dc_tts vs TensorFlowASR" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kyubyong-dc-tts-vs-tensorspeech-tensorflowasr" tools: ["kyubyong-dc-tts", "tensorspeech-tensorflowasr"] --- # dc_tts vs TensorFlowASR *GraphCanon updated Jul 31, 2026* ## Verdict Pick dc_tts if dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies; 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. [dc_tts](https://github.com/Kyubyong/dc_tts) reports 1.2k GitHub stars, 360 forks, and 68 open issues, last pushed Apr 14, 2023. [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 [dc_tts's repository](https://github.com/Kyubyong/dc_tts) and [TensorFlowASR's repository](https://github.com/TensorSpeech/TensorFlowASR). | | [dc_tts](/tools/kyubyong-dc-tts.md) | [TensorFlowASR](/tools/tensorspeech-tensorflowasr.md) | | --- | --- | --- | | Tagline | A TensorFlow Implementation of DC-TTS | Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2 | | Stars | 1,156 | 1,010 | | Forks | 360 | 239 | | Open issues | 68 | 47 | | Language | Python | Python | | Adopt for | dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies. | 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 | Apache-2.0 | Apache-2.0 | | Categories | Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dc_tts](/tools/kyubyong-dc-tts.md) | [TensorFlowASR](/tools/tensorspeech-tensorflowasr.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1203d | 0d | | Open issues (now) | 68 | 47 | | Owner type | User | Organization | | Full report | [trust report](/tools/kyubyong-dc-tts/trust.md) | [trust report](/tools/tensorspeech-tensorflowasr/trust.md) | ## Decision facts: dc_tts - **Requirements:** Depends on TensorFlow >=1.3 and has compatibility issues with updated APIs for `tf.contrib.layers.layer_norm`.; Requires Python packages like NumPy, librosa, tqdm, matplotlib, and scipy. - **Adopt for:** dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies. ## 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 dc_tts if… - Requirements: Depends on TensorFlow >=1.3 and has compatibility issues with updated APIs for `tf.contrib.layers.layer_norm`.; Requires Python packages like NumPy, librosa, tqdm, matplotlib, and scipy.. - Tags unique to dc_tts: speech, speech-to-text, tensorflow, tts. - dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies. ### Choose TensorFlowASR if… - 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 dc_tts - Last GitHub push was 1229 days ago (dormant maintenance, Apr 14, 2023). Validate activity before betting a new project on dc_tts. ## 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 dc_tts and TensorFlowASR? dc_tts: A TensorFlow Implementation of DC-TTS. 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 dc_tts over TensorFlowASR? Choose dc_tts over TensorFlowASR when Requirements: Depends on TensorFlow >=1.3 and has compatibility issues with updated APIs for `tf.contrib.layers.layer_norm`.; Requires Python packages like NumPy, librosa, tqdm, matplotlib, and scipy.; Tags unique to dc_tts: speech, speech-to-text, tensorflow, tts; dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies. ### When should I choose TensorFlowASR over dc_tts? Choose TensorFlowASR over dc_tts when 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 dc_tts? Last GitHub push was 1229 days ago (dormant maintenance, Apr 14, 2023). Validate activity before betting a new project on dc_tts. ### 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 dc_tts or TensorFlowASR more popular on GitHub? dc_tts has more GitHub stars (1,156 vs 1,010). Stars measure visibility, not whether either tool fits your constraints. ### Are dc_tts and TensorFlowASR open source? Yes - both are open-source projects on GitHub (dc_tts: Apache-2.0, TensorFlowASR: Apache-2.0). ### Where can I find alternatives to dc_tts or TensorFlowASR? GraphCanon lists graph-backed alternatives at [dc_tts alternatives](/tools/kyubyong-dc-tts/alternatives) and [TensorFlowASR alternatives](/tools/tensorspeech-tensorflowasr/alternatives) ([dc_tts markdown twin](/tools/kyubyong-dc-tts/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/kyubyong-dc-tts-vs-tensorspeech-tensorflowasr.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dc_tts or TensorFlowASR? dc_tts: Dormant. 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 dc_tts and TensorFlowASR? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dc_tts trust report](/tools/kyubyong-dc-tts/trust); [TensorFlowASR trust report](/tools/tensorspeech-tensorflowasr/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kyubyong-dc-tts`](/api/graphcanon/graph?tool=kyubyong-dc-tts) - 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/_