A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis
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Updated
Nov 6, 2018 - Python
A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis
Multispeaker & Emotional TTS based on Tacotron 2 and Waveglow
Multi-Speaker Pytorch FastSpeech2: Fast and High-Quality End-to-End Text to Speech ✊
Real-Time High-Fidelity Speech Synthesis without GPU
PyTorch implementation of NVIDIA WaveGlow with constant memory cost.
A wavenet and waveglow based singing synthesizing system.
(Multi Speaker) Text-To-Speech (TTS) project
Tensorflow 2.0 implementation of the Nvidia Waveglow model
WaveGLow -- A Flow-based Generative Network for Speech Synthesis . PyTorch Code modified to run on TPUs .
Command-line interface and Python library for synthesizing English texts into speech.
Web app, command-line interface and Python library for synthesizing Chinese texts into speech.
Waveglow Tacotron2 text to speech
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
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.
This is a bot that will accept voice messages and reply back in voice messages.
Synthese vocale avec conditionnement sur tres petit jeu de données. Utilisation des modeles Tacotron2 et WaveGlow de Nvidia avec Pytorch.
Text-to-Speech models based on the NVIDIA's examples.
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