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
A wavenet and waveglow based singing synthesizing system.
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 .
Real-Time High-Fidelity Speech Synthesis without GPU
Waveglow Tacotron2 text to speech
Text-to-Speech models based on the NVIDIA's examples.
Multispeaker & Emotional TTS based on Tacotron 2 and Waveglow
Another PyTorch implementation of Tacotron2 MMI (with waveglow) which supports n_frames_per_step>1 mode(reduction windows) and diagonal guided attention for robust alignments.
This is a bot that will accept voice messages and reply back in voice messages.
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.
Tacotron 2 - PyTorch implementation with faster-than-realtime inference
Synthese vocale avec conditionnement sur tres petit jeu de données. Utilisation des modeles Tacotron2 et WaveGlow de Nvidia avec Pytorch.
(Multi Speaker) Text-To-Speech (TTS) project
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