jaywalnut310/vits
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
This project helps content creators, educators, or anyone needing realistic spoken audio from text. It takes written text as input and generates natural-sounding speech, complete with varied pitches and rhythms. You can use it to create voiceovers for videos, audio articles, or interactive voice responses.
7,837 stars. No commits in the last 6 months.
Use this if you need to transform written text into high-quality, natural-sounding audio that rivals human speech and can express different speaking styles.
Not ideal if you need a quick, off-the-shelf solution for simple text-to-speech without customizing the voice or inflection, as it requires some technical setup.
Stars
7,837
Forks
1,386
Language
Python
License
MIT
Category
Last pushed
Dec 06, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/voice-ai/jaywalnut310/vits"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
yeyupiaoling/MASR
Pytorch实现的流式与非流式的自动语音识别框架，同时兼容在线和离线识别，目前支持Conformer、Squeezeformer、DeepSpeech2模型，支持多种数据增强方法。
shivammehta25/Matcha-TTS
[ICASSP 2024] 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
coqui-ai/TTS
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
DigitalPhonetics/IMS-Toucan
Controllable and fast Text-to-Speech for over 7000 languages!
gabrielmittag/NISQA
NISQA - Non-Intrusive Speech Quality and TTS Naturalness Assessment