Comparison
dc_tts vs Matcha-TTS
Verdict
Pick dc_tts if dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies; pick Matcha-TTS if matcha-TTS employs non-autoregressive probabilistic techniques with deep learning for fast TTS generation.
Markdown twin · dc_tts alternatives · Matcha-TTS alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | dc_tts | Matcha-TTS |
|---|---|---|
| Maintenance | Dormant (1254d since push) As of Sep 20, 2026 · github_public_v1 | Active (19d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- dc_tts
- A TensorFlow Implementation of DC-TTS
- Matcha-TTS
- Matcha-TTS is a fast TTS architecture with conditional flow matching
Stars
- dc_tts
- 1.2k
- Matcha-TTS
- 1.4k
Forks
- dc_tts
- 360
- Matcha-TTS
- 216
Open issues
- dc_tts
- 68
- Matcha-TTS
- 34
Language
- dc_tts
- Python
- Matcha-TTS
- Jupyter Notebook
Adopt for
- dc_tts
- dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies.
- Matcha-TTS
- Matcha-TTS employs non-autoregressive probabilistic techniques with deep learning for fast TTS generation.
Persona
- dc_tts
- -
- Matcha-TTS
- -
Runtime
- dc_tts
- -
- Matcha-TTS
- -
License
- dc_tts
- Apache-2.0
- Matcha-TTS
- MIT
Last pushed
- dc_tts
- Apr 14, 2023
- Matcha-TTS
- Aug 31, 2026
Categories
- dc_tts
- Speech & Audio
- Matcha-TTS
- Speech & Audio
Trust and health
Maintenance
- dc_tts
- Dormant (18%)
- Matcha-TTS
- Active (82%)
Days since push
- dc_tts
- 1254d
- Matcha-TTS
- 19d
Open issues (now)
- dc_tts
- 68
- Matcha-TTS
- 34
Stars delta
- dc_tts
- +3 (30d)
- Matcha-TTS
- +19 (30d)
Open issues delta
- dc_tts
- 0 (30d)
- Matcha-TTS
- -1 (30d)
OSV dependency advisories
- dc_tts
- No lockfile (source not queried)
- Matcha-TTS
- Published findings
Full report
- dc_tts
- Trust report
- Matcha-TTS
- Trust report
Choose dc_tts if…
- dc_tts is primarily Python; Matcha-TTS is Jupyter Notebook.
- License: dc_tts is Apache-2.0, Matcha-TTS 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, tts.
- 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 Apr 14, 2023 (dormant maintenance). Validate activity before betting a new project on dc_tts.
Choose Matcha-TTS if…
- Matcha-TTS is primarily Jupyter Notebook; dc_tts is Python.
- License: Matcha-TTS is MIT, dc_tts is Apache-2.0.
- Tags unique to Matcha-TTS: deep-learning, diffusion-models, flow-matching, non-autoregressive.
- When you require rapid deployment of text-to-speech engines without autoregressive dependencies
When NOT to use Matcha-TTS
- If your project requires real-time adaptability to user input that necessitates autoregressive methods
- In scenarios where licensing flexibility is not a priority, favoring proprietary systems with dedicated support
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Kyubyong/dc_tts) · observed Sep 20, 2026
- GitHub forks (Kyubyong/dc_tts) · observed Sep 20, 2026
- Last push (Kyubyong/dc_tts) · observed Apr 14, 2023
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (shivammehta25/Matcha-TTS) · observed Sep 20, 2026
- GitHub forks (shivammehta25/Matcha-TTS) · observed Sep 20, 2026
- Last push (shivammehta25/Matcha-TTS) · observed Aug 31, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: dc_tts 1.2k · Matcha-TTS 1.4k (synced Sep 20, 2026).
Common questions
- What is the difference between dc_tts and Matcha-TTS?
- dc_tts: A TensorFlow Implementation of DC-TTS. Matcha-TTS: Matcha-TTS is a fast TTS architecture with conditional flow matching. See the comparison table for live GitHub stats and shared categories.
- When should I choose dc_tts over Matcha-TTS?
- Choose dc_tts over Matcha-TTS when dc_tts is primarily Python; Matcha-TTS is Jupyter Notebook; License: dc_tts is Apache-2.0, Matcha-TTS 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, tts; dc_tts is a text-to-speech model based on TensorFlow and requires specific version dependencies. - When should I choose Matcha-TTS over dc_tts?
- Choose Matcha-TTS over dc_tts when Matcha-TTS is primarily Jupyter Notebook; dc_tts is Python; License: Matcha-TTS is MIT, dc_tts is Apache-2.0; Tags unique to Matcha-TTS: deep-learning, diffusion-models, flow-matching, non-autoregressive; When you require rapid deployment of text-to-speech engines without autoregressive dependencies.
- When should I avoid dc_tts?
- Last GitHub push was Apr 14, 2023 (dormant maintenance). Validate activity before betting a new project on dc_tts.
- When should I avoid Matcha-TTS?
- If your project requires real-time adaptability to user input that necessitates autoregressive methods In scenarios where licensing flexibility is not a priority, favoring proprietary systems with dedicated support
- Is dc_tts or Matcha-TTS more popular on GitHub?
- Matcha-TTS has more GitHub stars (1,358 vs 1,159). Stars measure visibility, not whether either tool fits your constraints.
- Are dc_tts and Matcha-TTS open source?
- Yes - both are open-source projects on GitHub (dc_tts: Apache-2.0, Matcha-TTS: MIT).
- Where can I find alternatives to dc_tts or Matcha-TTS?
- GraphCanon lists graph-backed alternatives at dc_tts alternatives and Matcha-TTS alternatives (dc_tts markdown twin, Matcha-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, dc_tts or Matcha-TTS?
- dc_tts: Dormant. Matcha-TTS: 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 dc_tts and Matcha-TTS?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dc_tts trust report; Matcha-TTS trust report.