Comparison
vits vs Confucius4-TTS
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.
Markdown twin · vits alternatives · Confucius4-TTS alternatives
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Trust & integrity
| Signal | vits | Confucius4-TTS |
|---|---|---|
| Maintenance | Dormant (966d since push) As of 3w · github_public_v1 | Very active (5d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- vits
- 7.9k
- Confucius4-TTS
- 773
Forks
- vits
- 1.4k
- Confucius4-TTS
- 76
Open issues
- vits
- 165
- Confucius4-TTS
- 11
Language
- vits
- Python
- Confucius4-TTS
- Python
Adopt for
- vits
- 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.
- Confucius4-TTS
- Powerful cross-lingual TTS for multilingual audio projects with no need for model fine-tuning.
Persona
- vits
- -
- Confucius4-TTS
- -
Runtime
- vits
- -
- Confucius4-TTS
- -
License
- vits
- MIT
- Confucius4-TTS
- Other
Last pushed
- vits
- Dec 6, 2023
- Confucius4-TTS
- Aug 18, 2026
Categories
- vits
- Speech & Audio
- Confucius4-TTS
- Speech & Audio
Trust and health
Maintenance
- vits
- Dormant (18%)
- Confucius4-TTS
- Very active (96%)
Days since push
- vits
- 966d
- Confucius4-TTS
- 5d
Open issues (now)
- vits
- 165
- Confucius4-TTS
- 11
Stars delta
- vits
- Unknown
- Confucius4-TTS
- +60 (30d)
Open issues delta
- vits
- Unknown
- Confucius4-TTS
- +1 (30d)
OSV dependency advisories
- vits
- Published findings
- Confucius4-TTS
- No lockfile (source not queried)
Full report
- vits
- Trust report
- Confucius4-TTS
- Trust report
Shared compatibility
- Python · vits: Python runtime · Confucius4-TTS: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (jaywalnut310/vits) · observed Jul 29, 2026
- GitHub forks (jaywalnut310/vits) · observed Jul 29, 2026
- Last push (jaywalnut310/vits) · observed Dec 6, 2023
- License file (MIT) · observed Jul 29, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (netease-youdao/Confucius4-TTS) · observed Aug 24, 2026
- GitHub forks (netease-youdao/Confucius4-TTS) · observed Aug 24, 2026
- Last push (netease-youdao/Confucius4-TTS) · observed Aug 18, 2026
- License file (Other) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: vits 7.9k · Confucius4-TTS 773 (synced Jul 29, 2026).
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 and Confucius4-TTS alternatives (vits markdown twin, Confucius4-TTS markdown twin), 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 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; Confucius4-TTS trust report.