Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech"

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Deep Learning GradTTS
Overview

GradTTS

Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv)

About this repo

This is an unofficial implementation of GradTTS. We created this project based on GlowTTS (https://github.com/jaywalnut310/glow-tts). We replace the GlowDecoder with DiffusionDecoder which follows the settings of the original paper. In addition, we also replace torch.distributed with horovod for convenience and we don't use fp16 now.

Training and inference

Please go to egs/ folder, and see run.sh and inference_waveglow_vocoder.py for example use. Before training, please download and extract the LJ Speech dataset, then rename or create a link to the dataset folder: ln -s /path/to/LJSpeech-1.1/wavs DUMMY. And build Monotonic Alignment Search Code (Cython): cd monotonic_align; python setup.py build_ext --inplace. Before inference, you should download waveglow checkpoint from download_link and put it into the waveglow folder.

Reference Materials

Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech

GlowTTS

Score-Based Generative Modeling through Stochastic Differential Equations

score_sde_pytorch

denoising-diffusion-pytorch

Authors

Heyang Xue(https://github.com/WelkinYang) and Qicong Xie(https://github.com/QicongXie)

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Comments
  • Grad TTS in multispeaker setting

    Grad TTS in multispeaker setting

    I observed that in model.py "gin_channels" is provided in DiffusionGenerator.

    I would like to know if Grad-TTS supports multispeaker TTS training ?

    Can you also provide pretrained model trained with LJS dataset ?

    I had some difficulties on installing Horovod on GPU cluters on server side, so I changed the train.py from Horovod to torch.distributed.

    Thank you for repo.

    opened by ajinkyakulkarni14 1
  • Wave sampels generated

    Wave sampels generated

    Thank you for your greate work and share. Could you please release some wave sampels? or Could you simplly evaluate the quality of the synthsized wavs you got, is them as good as the original paper claimed ?

    opened by Liujingxiu23 4
Owner
HeyangXue1997
Speech synthesis/Singing voice synthesis/machine learning @ aslp, nwpu, Xi'an, ShannXi, China
HeyangXue1997
GitHub
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