--- title: "vits vs Confucius4-TTS" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jaywalnut310-vits-vs-netease-youdao-confucius4-tts" tools: ["jaywalnut310-vits", "netease-youdao-confucius4-tts"] --- # vits vs Confucius4-TTS *GraphCanon updated Aug 24, 2026* ## Verdict 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; pick Confucius4-TTS if powerful cross-lingual TTS for multilingual audio projects with no need for model fine-tuning. [vits](https://jaywalnut310.github.io/vits-demo/index.html) reports 7.9k GitHub stars, 1.4k forks, and 165 open issues, last pushed Dec 6, 2023. [Confucius4-TTS](https://github.com/netease-youdao/Confucius4-TTS) has 773 stars, 76 forks, and 11 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [vits's repository](https://github.com/jaywalnut310/vits) and [Confucius4-TTS's repository](https://github.com/netease-youdao/Confucius4-TTS). | | [vits](/tools/jaywalnut310-vits.md) | [Confucius4-TTS](/tools/netease-youdao-confucius4-tts.md) | | --- | --- | --- | | Tagline | VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech | Multilingual and Cross-Lingual Zero-Shot TTS Engine | | Stars | 7,889 | 773 | | Forks | 1,384 | 76 | | Open issues | 165 | 11 | | Language | Python | Python | | 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. | Powerful cross-lingual TTS for multilingual audio projects with no need for model fine-tuning. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vits](/tools/jaywalnut310-vits.md) | [Confucius4-TTS](/tools/netease-youdao-confucius4-tts.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 966d | 5d | | Open issues (now) | 165 | 11 | | Stars delta | Unknown | +60 (30d) | | Open issues delta | Unknown | +1 (30d) | | Full report | [trust report](/tools/jaywalnut310-vits/trust.md) | [trust report](/tools/netease-youdao-confucius4-tts/trust.md) | ## Shared compatibility - **Python**: [vits](/tools/jaywalnut310-vits.md) - Python runtime; [Confucius4-TTS](/tools/netease-youdao-confucius4-tts.md) - Python runtime ## 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. ## Decision facts: Confucius4-TTS - **Adopt for:** Powerful cross-lingual TTS for multilingual audio projects with no need for model fine-tuning. ## Choose when ### Choose vits if… - License: vits is MIT, Confucius4-TTS is Other. - 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: tts. - Use VITS when you need a single-stage TTS model that generates high-quality, natural-sounding audio similar to ground-truth quality. ### Choose Confucius4-TTS if… - License: Confucius4-TTS is Other, vits is MIT. - Tags unique to Confucius4-TTS: audio, cross-lingual, fine-tuning, multi-lingual. - Project requires support for multiple languages without the necessity of model fine-tuning. ## 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. ## When NOT to use Confucius4-TTS - When project strictly demands language-specific TTS refinement and customization. - For scenarios needing real-time low-latency TTS performance as Confucius4-TTS focuses on cross-lingual capabilities rather than speed optimizations. ## Common questions ### What is the difference between vits and Confucius4-TTS? vits: VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. Confucius4-TTS: Multilingual and Cross-Lingual Zero-Shot TTS Engine. See the comparison table for live GitHub stats and shared categories. ### When should I choose vits over Confucius4-TTS? Choose vits over Confucius4-TTS when License: vits is MIT, Confucius4-TTS is Other; 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: tts; 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 choose Confucius4-TTS over vits? Choose Confucius4-TTS over vits when License: Confucius4-TTS is Other, vits is MIT; Tags unique to Confucius4-TTS: audio, cross-lingual, fine-tuning, multi-lingual; Project requires support for multiple languages without the necessity of model fine-tuning. ### 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. ### When should I avoid Confucius4-TTS? When project strictly demands language-specific TTS refinement and customization. For scenarios needing real-time low-latency TTS performance as Confucius4-TTS focuses on cross-lingual capabilities rather than speed optimizations. ### Is vits or Confucius4-TTS more popular on GitHub? vits has more GitHub stars (7,889 vs 773). Stars measure visibility, not whether either tool fits your constraints. ### Are vits and Confucius4-TTS open source? Yes - both are open-source projects on GitHub (vits: MIT, Confucius4-TTS: Other). ### Where can I find alternatives to vits or Confucius4-TTS? GraphCanon lists graph-backed alternatives at [vits alternatives](/tools/jaywalnut310-vits/alternatives) and [Confucius4-TTS alternatives](/tools/netease-youdao-confucius4-tts/alternatives) ([vits markdown twin](/tools/jaywalnut310-vits/alternatives.md), [Confucius4-TTS markdown twin](/tools/netease-youdao-confucius4-tts/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/jaywalnut310-vits-vs-netease-youdao-confucius4-tts.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, vits or Confucius4-TTS? vits: Dormant. Confucius4-TTS: 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 vits and Confucius4-TTS? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [vits trust report](/tools/jaywalnut310-vits/trust); [Confucius4-TTS trust report](/tools/netease-youdao-confucius4-tts/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jaywalnut310-vits`](/api/graphcanon/graph?tool=jaywalnut310-vits) - 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/_