7 Repositories
Python melgan Libraries
Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch
Parallel WaveGAN implementation with Pytorch This repository provides UNOFFICIAL pytorch implementations of the following models: Parallel WaveGAN Mel
MelGAN test on audio decoding
Official repository for the paper MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis The original work URL: https://github.com
Source code for "Taming Visually Guided Sound Generation" (Oral at the BMVC 2021)
Taming Visually Guided Sound Generation • [Project Page] • [ArXiv] • [Poster] • • Listen for the samples on our project page. Overview We propose to t
Include MelGAN, HifiGAN and Multiband-HifiGAN, maybe NHV in the future.
Fast (GAN Based Neural) Vocoder Chinese README Todo Submit demo Support NHV Discription Include MelGAN, HifiGAN and Multiband-HifiGAN, maybe include N
A fast Text-to-Speech (TTS) model. Work well for English, Mandarin/Chinese, Japanese, Korean, Russian and Tibetan (so far). 快速语音合成模型,适用于英语、普通话/中文、日语、韩语、俄语和藏语(当前已测试)。
简体中文 | English 并行语音合成 [TOC] 新进展 2021/04/20 合并 wavegan 分支到 main 主分支,删除 wavegan 分支! 2021/04/13 创建 encoder 分支用于开发语音风格迁移模块! 2021/04/13 softdtw 分支 支持使用 Sof
TTS is a library for advanced Text-to-Speech generation.
TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. TTS comes with pretrained models, tools for measuring dataset quality and already used in 20+ languages for products and research projects.
TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, Korean, Chinese, German and Easy to adapt for other languages)
🤪 TensorFlowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron-2, Melgan, Multiband-Melgan, FastSpeech, FastSpeech2 based-on TensorFlow 2. With Tensorflow 2, we can speed-up training/inference progress, optimizer further by using fake-quantize aware and pruning, make TTS models can be run faster than real-time and be able to deploy on mobile devices or embedded systems.