Comparison
vits vs dc_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 dc_tts if dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies.
Markdown twin · vits alternatives · dc_tts alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | vits | dc_tts |
|---|---|---|
| Maintenance | Dormant (966d since push) As of 3w · github_public_v1 | Dormant (1203d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · 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
- dc_tts
- A TensorFlow Implementation of DC-TTS
Stars
- vits
- 7.9k
- dc_tts
- 1.2k
Forks
- vits
- 1.4k
- dc_tts
- 360
Open issues
- vits
- 165
- dc_tts
- 68
Language
- vits
- Python
- dc_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.
- dc_tts
- dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies.
Persona
- vits
- -
- dc_tts
- -
Runtime
- vits
- -
- dc_tts
- -
License
- vits
- MIT
- dc_tts
- Apache-2.0
Last pushed
- vits
- Dec 6, 2023
- dc_tts
- Apr 14, 2023
Categories
- vits
- Speech & Audio
- dc_tts
- Speech & Audio
Trust and health
Days since push
- vits
- 966d
- dc_tts
- 1203d
Open issues (now)
- vits
- 165
- dc_tts
- 68
OSV dependency advisories
- vits
- Published findings
- dc_tts
- No lockfile (source not queried)
Full report
- vits
- Trust report
- dc_tts
- Trust report
Choose vits if…
- License: vits is MIT, dc_tts is Apache-2.0.
- 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.
Choose dc_tts if…
- License: dc_tts is Apache-2.0, vits is MIT.
- Requirements: Depends on TensorFlow >=1.3 and has compatibility issues with updated APIs for `tf.contrib.layers.layer_norm`.; Requires Python packages like NumPy, librosa, tqdm, matplotlib, and scipy..
- Tags unique to dc_tts: speech, speech-to-text, tensorflow.
- dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies.
When NOT to use dc_tts
- Last GitHub push was 1228 days ago (dormant maintenance, Apr 14, 2023). Validate activity before betting a new project on dc_tts.
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 (Kyubyong/dc_tts) · observed Jul 30, 2026
- GitHub forks (Kyubyong/dc_tts) · observed Jul 30, 2026
- Last push (Kyubyong/dc_tts) · observed Apr 14, 2023
- License file (Apache-2.0) · observed Jul 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: vits 7.9k · dc_tts 1.2k (synced Jul 29, 2026).
Common questions
- What is the difference between vits and dc_tts?
- vits: VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. dc_tts: A TensorFlow Implementation of DC-TTS. See the comparison table for live GitHub stats and shared categories.
- When should I choose vits over dc_tts?
- Choose vits over dc_tts when License: vits is MIT, dc_tts is Apache-2.0; 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 choose dc_tts over vits?
- Choose dc_tts over vits when License: dc_tts is Apache-2.0, vits is MIT; Requirements: Depends on TensorFlow >=1.3 and has compatibility issues with updated APIs for
tf.contrib.layers.layer_norm.; Requires Python packages like NumPy, librosa, tqdm, matplotlib, and scipy.; Tags unique to dc_tts: speech, speech-to-text, tensorflow; dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies. - 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 dc_tts?
- Last GitHub push was 1228 days ago (dormant maintenance, Apr 14, 2023). Validate activity before betting a new project on dc_tts.
- Is vits or dc_tts more popular on GitHub?
- vits has more GitHub stars (7,889 vs 1,156). Stars measure visibility, not whether either tool fits your constraints.
- Are vits and dc_tts open source?
- Yes - both are open-source projects on GitHub (vits: MIT, dc_tts: Apache-2.0).
- Where can I find alternatives to vits or dc_tts?
- GraphCanon lists graph-backed alternatives at vits alternatives and dc_tts alternatives (vits markdown twin, dc_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 dc_tts?
- vits: Dormant. dc_tts: 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 vits and dc_tts?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: vits trust report; dc_tts trust report.