A Tensorflow Implementation of the FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
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Updated
Aug 12, 2020
A Tensorflow Implementation of the FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
An Android application that allows visually impaired people to hear which bus lines are passing next to them.
An Android application that acts as a speaking assistant for the hearing impaired people.
Convert Image to audio using ViT, GPT and FastSpeech
Created this repo as a part of the project "Speech Technologies in Indian languages". About Indic TTS for Indian Languages: This is a project on developing text-to-speech (TTS) synthesis systems for Indian languages, improving quality of synthesis, as well as small foot print TTS integrated with disability aids and various other applications.
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
Aligning latent space of speaking style with human perception using a re-embedding strategy
This repository contain the code of the main part of my master thesis degree at Politecnico di Torino in Data science & Engineering
Multi-Speaker FastSpeech2 applicable to Korean. Description about train and synthesize in detail.
This is the experimental description of MnTTS2.
Refactored version of https://github.com/ming024/FastSpeech2
Unofficial implementation of ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
The Implementation of FastSpeech2 Based on Pytorch.
Use FastSpeech2 and HiFi-GAN to easily perform end-to-end Korean speech synthesis.
Multi-Speaker Pytorch FastSpeech2: Fast and High-Quality End-to-End Text to Speech ✊
A Non-Autoregressive End-to-End Text-to-Speech (text-to-wav), supporting a family of SOTA unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate E2E-TTS
Desktop application for neural speech synthesis written in C++
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