vits
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
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.
Good fit when
- Use VITS when you need a single-stage TTS model that generates high-quality, natural-sounding audio similar to ground-truth quality.
- VITS is recommended for users requiring expressivity in synthesized speech, as it includes a stochastic duration predictor enabling diverse rhythmic variations.
Avoid when
- 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.
- 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.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1019d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 20, 2026
- Security (OSV)
- 37 low (37 low)
- As of Jul 11, 2026
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Install
pip install vitsSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A parallel end-to-end text-to-speech model that generates high-quality audio through variational inference, normalizing flows, and adversarial training.
Capability facts
- Languages
- python
Source: github.language · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
VITS: Conditional Variational Autoencoder with Adversarial Learning for End to End Text to Speech Jaehyeon Kim, Jungil Kong, and Juhee Son In our recent paper, we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End to End Text to Speech. Several re...
For agents
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