--- title: "ChatTTS vs vits" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/2noise-chattts-vs-jaywalnut310-vits" tools: ["2noise-chattts", "jaywalnut310-vits"] --- # ChatTTS vs vits *GraphCanon updated Aug 16, 2026* ## Verdict Pick ChatTTS if chatTTS is a Python-based repository for generating speech tailored to everyday conversations in both Chinese and English; pick vits if vITS stands out in high-quality end-to-end text-to-speech applications due to its integration of variational inference with normalizing flows and adversarial training methods. [ChatTTS](https://2noise.com) reports 40k GitHub stars, 4.3k forks, and 60 open issues, last pushed Apr 10, 2026. [vits](https://jaywalnut310.github.io/vits-demo/index.html) has 7.9k stars, 1.4k forks, and 165 open issues, last pushed Dec 6, 2023. Figures are from public GitHub metadata via [ChatTTS's repository](https://github.com/2noise/ChatTTS) and [vits's repository](https://github.com/jaywalnut310/vits). | | [ChatTTS](/tools/2noise-chattts.md) | [vits](/tools/jaywalnut310-vits.md) | | --- | --- | --- | | Tagline | A generative speech model for daily dialogue | VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech | | Stars | 39,768 | 7,889 | | Forks | 4,257 | 1,384 | | Open issues | 60 | 165 | | Language | Python | Python | | Adopt for | ChatTTS is a Python-based repository for generating speech tailored to everyday conversations in both Chinese and English. | VITS stands out in high-quality end-to-end text-to-speech applications due to its integration of variational inference with normalizing flows and adversarial training methods. | | Persona | - | - | | Runtime | - | - | | License | Licensed under AGPL-3.0, which allows for free use, distribution, and modification as long as these rights are maintained in derivative works. | MIT | | Categories | AI Agents, Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ChatTTS](/tools/2noise-chattts.md) | [vits](/tools/jaywalnut310-vits.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 127d | 966d | | Open issues (now) | 60 | 165 | | Stars delta | +140 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/2noise-chattts/trust.md) | [trust report](/tools/jaywalnut310-vits/trust.md) | ## Shared compatibility - **Python**: [ChatTTS](/tools/2noise-chattts.md) - Python runtime; [vits](/tools/jaywalnut310-vits.md) - Python runtime ## Decision facts: ChatTTS - **Hosting:** unknown - **Requirements:** Python-based implementation means that familiarity with Python is necessary for effective use. - **Adopt for:** ChatTTS is a Python-based repository for generating speech tailored to everyday conversations in both Chinese and English. - **License detail:** Licensed under AGPL-3.0, which allows for free use, distribution, and modification as long as these rights are maintained in derivative works. ## Decision facts: vits - **Requirements:** Min 8 GB RAM; Python >= 3.6 is required. Ensure dependencies like espeak are installed.; The model requires specific datasets: LJ Speech for single-speaker and VCTK for multi-speaker scenarios, with necessary preprocessing. - **Adopt for:** VITS stands out in high-quality end-to-end text-to-speech applications due to its integration of variational inference with normalizing flows and adversarial training methods. ## Choose when ### Choose ChatTTS if… - License: ChatTTS is AGPL-3.0, vits is MIT. - Requirements: Python-based implementation means that familiarity with Python is necessary for effective use.. - Tags unique to ChatTTS: agent, chatgpt, chattts, chinese language. - Also covers AI Agents. - Use ChatTTS when you require text-to-speech functionality for daily dialogue applications, especially those involving Chinese or English languages. ### Choose vits if… - License: vits is MIT, ChatTTS is AGPL-3.0. - Requirements: Min 8 GB RAM; Python >= 3.6 is required. Ensure dependencies like espeak are installed.; The model requires specific datasets: LJ Speech for single-speaker and VCTK for multi-speaker scenarios, with necessary preprocessing.. - Tags unique to vits: deep-learning, pytorch, speech-synthesis, text-to-speech. - Use VITS when you need a single-stage TTS model that generates high-quality, natural-sounding audio similar to ground-truth quality. ## When NOT to use ChatTTS - Avoid using ChatTTS in environments where the speech generation model is expected to handle complex technical jargon or specialized vocabulary, as it's optimized for daily conversation. - Do not use ChatTTS if your project requires the integration of more than two languages simultaneously due to its current support for only Chinese and English. ## When NOT to use vits - Avoid VITS if your project requires low hardware resources. The model's high-quality output comes at the cost of higher computational demands. - Do not opt for VITS if your application strictly needs real-time performance, as it prioritizes sample quality over speed through complex inference processes. ## Common questions ### What is the difference between ChatTTS and vits? ChatTTS: A generative speech model for daily dialogue. vits: VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. See the comparison table for live GitHub stats and shared categories. ### When should I choose ChatTTS over vits? Choose ChatTTS over vits when License: ChatTTS is AGPL-3.0, vits is MIT; Requirements: Python-based implementation means that familiarity with Python is necessary for effective use.; Tags unique to ChatTTS: agent, chatgpt, chattts, chinese language; Also covers AI Agents; Use ChatTTS when you require text-to-speech functionality for daily dialogue applications, especially those involving Chinese or English languages. ### When should I choose vits over ChatTTS? Choose vits over ChatTTS when License: vits is MIT, ChatTTS is AGPL-3.0; Requirements: Min 8 GB RAM; Python >= 3.6 is required. Ensure dependencies like espeak are installed.; The model requires specific datasets: LJ Speech for single-speaker and VCTK for multi-speaker scenarios, with necessary preprocessing.; Tags unique to vits: deep-learning, pytorch, speech-synthesis, text-to-speech; Use VITS when you need a single-stage TTS model that generates high-quality, natural-sounding audio similar to ground-truth quality. ### When should I avoid ChatTTS? Avoid using ChatTTS in environments where the speech generation model is expected to handle complex technical jargon or specialized vocabulary, as it's optimized for daily conversation. Do not use ChatTTS if your project requires the integration of more than two languages simultaneously due to its current support for only Chinese and English. ### When should I avoid vits? Avoid VITS if your project requires low hardware resources. The model's high-quality output comes at the cost of higher computational demands. Do not opt for VITS if your application strictly needs real-time performance, as it prioritizes sample quality over speed through complex inference processes. ### Is ChatTTS or vits more popular on GitHub? ChatTTS has more GitHub stars (39,768 vs 7,889). Stars measure visibility, not whether either tool fits your constraints. ### Are ChatTTS and vits open source? Yes - both are open-source projects on GitHub (ChatTTS: AGPL-3.0, vits: MIT). ### Where can I find alternatives to ChatTTS or vits? GraphCanon lists graph-backed alternatives at [ChatTTS alternatives](/tools/2noise-chattts/alternatives) and [vits alternatives](/tools/jaywalnut310-vits/alternatives) ([ChatTTS markdown twin](/tools/2noise-chattts/alternatives.md), [vits markdown twin](/tools/jaywalnut310-vits/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/2noise-chattts-vs-jaywalnut310-vits.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ChatTTS or vits? ChatTTS: Slowing. vits: 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 ChatTTS and vits? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ChatTTS trust report](/tools/2noise-chattts/trust); [vits trust report](/tools/jaywalnut310-vits/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=2noise-chattts`](/api/graphcanon/graph?tool=2noise-chattts) - 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/_