--- title: "TTS vs WaveRNN" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/coqui-ai-tts-vs-fatchord-wavernn" tools: ["coqui-ai-tts", "fatchord-wavernn"] --- # TTS vs WaveRNN *GraphCanon updated Jul 11, 2026* ## Verdict Pick TTS when license: TTS is MPL-2.0, WaveRNN is MIT; pick WaveRNN when license: WaveRNN is MIT, TTS is MPL-2.0. [TTS](http://coqui.ai) reports 46k GitHub stars, 6.2k forks, and 4 open issues, last pushed Aug 16, 2024. [WaveRNN](https://fatchord.github.io/model_outputs/) has 2.2k stars, 687 forks, and 108 open issues, last pushed Jul 2, 2022. Figures are from public GitHub metadata via [TTS's repository](https://github.com/coqui-ai/TTS) and [WaveRNN's repository](https://github.com/fatchord/WaveRNN). | | [TTS](/tools/coqui-ai-tts.md) | [WaveRNN](/tools/fatchord-wavernn.md) | | --- | --- | --- | | Tagline | 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production | WaveRNN Vocoder + TTS | | Stars | 45,737 | 2,187 | | Forks | 6,152 | 687 | | Open issues | 4 | 108 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MPL-2.0 | MIT | | Categories | Model Training, Speech & Audio, Inference & Serving | Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TTS](/tools/coqui-ai-tts.md) | [WaveRNN](/tools/fatchord-wavernn.md) | | --- | --- | --- | | Days since push | 693d | 1469d | | Open issues (now) | 4 | 108 | | Owner type | Organization | User | | Security scan | 137 low (137 low) | No criticals | | Full report | [trust report](/tools/coqui-ai-tts/trust.md) | [trust report](/tools/fatchord-wavernn/trust.md) | ## Shared compatibility - **Python**: [TTS](/tools/coqui-ai-tts.md) - Python runtime; [WaveRNN](/tools/fatchord-wavernn.md) - Python runtime ## Choose when ### Choose TTS if… - License: TTS is MPL-2.0, WaveRNN is MIT. - Tags unique to TTS: deep-learning, glow-tts, hifigan, speaker-encoder. - Also covers Inference & Serving. - TTS ships Docker support for self-hosted deployment. ### Choose WaveRNN if… - License: WaveRNN is MIT, TTS is MPL-2.0. - Tags unique to WaveRNN: tacotron, text-to-speech, tts, speech-synthesis. ## When NOT to use TTS - Last GitHub push was 694 days ago (dormant maintenance, Aug 16, 2024). Validate activity before betting a new project on TTS. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## When NOT to use WaveRNN - Last GitHub push was 1470 days ago (dormant maintenance, Jul 2, 2022). Validate activity before betting a new project on WaveRNN. - 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 TTS and WaveRNN? TTS: 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production. WaveRNN: WaveRNN Vocoder + TTS. See the comparison table for live GitHub stats and shared categories. ### When should I choose TTS over WaveRNN? Choose TTS over WaveRNN when License: TTS is MPL-2.0, WaveRNN is MIT; Tags unique to TTS: deep-learning, glow-tts, hifigan, speaker-encoder; Also covers Inference & Serving; TTS ships Docker support for self-hosted deployment. ### When should I choose WaveRNN over TTS? Choose WaveRNN over TTS when License: WaveRNN is MIT, TTS is MPL-2.0; Tags unique to WaveRNN: tacotron, text-to-speech, tts, speech-synthesis. ### When should I avoid TTS? Last GitHub push was 694 days ago (dormant maintenance, Aug 16, 2024). Validate activity before betting a new project on TTS. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### When should I avoid WaveRNN? Last GitHub push was 1470 days ago (dormant maintenance, Jul 2, 2022). Validate activity before betting a new project on WaveRNN. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is TTS or WaveRNN more popular on GitHub? TTS has more GitHub stars (45,737 vs 2,187). Stars measure visibility, not whether either tool fits your constraints. ### Are TTS and WaveRNN open source? Yes - both are open-source projects on GitHub (TTS: MPL-2.0, WaveRNN: MIT). ### Where can I find alternatives to TTS or WaveRNN? GraphCanon lists graph-backed alternatives at [TTS alternatives](/tools/coqui-ai-tts/alternatives) and [WaveRNN alternatives](/tools/fatchord-wavernn/alternatives) ([TTS markdown twin](/tools/coqui-ai-tts/alternatives.md), [WaveRNN markdown twin](/tools/fatchord-wavernn/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/coqui-ai-tts-vs-fatchord-wavernn.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, TTS or WaveRNN? TTS: Dormant. WaveRNN: 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 TTS and WaveRNN? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TTS trust report](/tools/coqui-ai-tts/trust); [WaveRNN trust report](/tools/fatchord-wavernn/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=coqui-ai-tts`](/api/graphcanon/graph?tool=coqui-ai-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/_