A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis
-
Updated
Nov 6, 2018 - Python
A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis
A wavenet and waveglow based singing synthesizing system.
WaveGLow -- A Flow-based Generative Network for Speech Synthesis . PyTorch Code modified to run on TPUs .
Real-Time High-Fidelity Speech Synthesis without GPU
W.I.P. talking discord buddy! Uses any finetuned (or not) GPT-2 model along with a custom Tacotron2 + Waveglow model to create a custom discord bot that actively participates in voice chats.
homework for deep generation. Combine FastSpeech2 with different vocoders ⭐REFERENCE (modify origin repos): https://github.com/ming024/FastSpeech2 https://github.com/NVIDIA/waveglow https://github.com/mindslab-ai/univnet https://github.com/jik876/hifi-gan
Multi-Speaker Pytorch FastSpeech2: Fast and High-Quality End-to-End Text to Speech ✊
PyTorch implementation of NVIDIA WaveGlow with constant memory cost.
(Multi Speaker) Text-To-Speech (TTS) project
Web app, command-line interface and Python library for synthesizing Chinese texts into speech.
Command-line interface and Python library for synthesizing English texts into speech.
Add a description, image, and links to the waveglow topic page so that developers can more easily learn about it.
To associate your repository with the waveglow topic, visit your repo's landing page and select "manage topics."