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Comparison

ChatTTS vs vits

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.

Markdown twin · ChatTTS alternatives · vits alternatives

GraphCanon updated 1w

ChatTTS logo

ChatTTS

2noise/ChatTTS

★ 40kpushed Apr 10, 2026
vs
vits logo

vits

jaywalnut310/vits

★ 7.9kpushed Dec 6, 2023

Trust & integrity

SignalChatTTSvits
Maintenance
Slowing (127d since push)
As of 1w · github_public_v1
Dormant (966d since push)
As of 3w · github_public_v1
Provenance
Not a fork · Organization account
As of 1w · github_public_v1
Not a fork · Personal account
As of 3w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
Published findings
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
ChatTTS trust reportvits trust reportTrust methodology

Tagline

ChatTTS
A generative speech model for daily dialogue
vits
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

Stars

ChatTTS
40k
vits
7.9k

Forks

ChatTTS
4.3k
vits
1.4k

Open issues

ChatTTS
60
vits
165

Language

ChatTTS
Python
vits
Python

Adopt for

ChatTTS
ChatTTS is a Python-based repository for generating speech tailored to everyday conversations in both Chinese and English.
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.

Persona

ChatTTS
-
vits
-

Runtime

ChatTTS
-
vits
-

License

ChatTTS
Licensed under AGPL-3.0, which allows for free use, distribution, and modification as long as these rights are maintained in derivative works.
vits
MIT

Last pushed

ChatTTS
Apr 10, 2026
vits
Dec 6, 2023

Categories

ChatTTS
AI Agents, Speech & Audio
vits
Speech & Audio

Trust and health

Maintenance

ChatTTS
Slowing (36%)
vits
Dormant (18%)

Days since push

ChatTTS
127d
vits
966d

Open issues (now)

ChatTTS
60
vits
165

Stars delta

ChatTTS
+140 (30d)
vits
Unknown

Open issues delta

ChatTTS
-1 (30d)
vits
Unknown

Owner type

ChatTTS
Organization
vits
User

OSV dependency advisories

ChatTTS
No lockfile (source not queried)
vits
Published findings

Full report

ChatTTS
Trust report
vits
Trust report

Shared compatibility

  • Python · ChatTTS: Python runtime · vits: Python runtime

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.

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.
GitHubDetails

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 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.
GitHubDetails

Explore

ChatTTS trust report →vits trust report →AI Agents category →Speech & Audio category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: ChatTTS 40k · vits 7.9k (synced Aug 16, 2026).

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 and vits alternatives (ChatTTS markdown twin, vits 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, 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; vits trust report.

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