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PortaSpeech

HuggingFace🤗 Demo

Quick Start

Install Dependencies

Install dependencies following readme.md

Set Config Path and Experiment Name

PortaSpeech (normal)

export CONFIG_NAME=egs/datasets/audio/lj/ps_flow_nips2021.yaml  
export MY_EXP_NAME=ps_normal_exp

PortaSpeech (small)

export CONFIG_NAME=egs/datasets/audio/lj/ps_flow_small_nips2021.yaml
export MY_EXP_NAME=ps_small_exp

Preprocess and binary dataset

Prepare dataset following prepare_data.md

Prepare Vocoder

Prepare vocoder following prepare_vocoder.md

Training

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --reset

You can check the training and validation curves open Tensorboard via:

tensorboard --logdir checkpoints/$MY_EXP_NAME

Inference (Testing)

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $PS_CONFIG --exp_name $MY_EXP_NAME --infer

Pretrained Model

PortaSpeech (normal)

Download checkpoints from https://github.com/NATSpeech/NATSpeech/releases/download/pretrained_models/ps_normal_exp.zip and unzip it to checkpoints/ps_normal_exp. Then you can directly run inference command:

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --exp_name ps_normal_exp --infer

PortaSpeech (small)

Download checkpoints from https://github.com/NATSpeech/NATSpeech/releases/download/pretrained_models/ps_small_exp.zip and unzip it to checkpoints/ps_small_exp. Then you can directly run inference command:

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --exp_name ps_small_exp --infer

Citation

If you find this useful for your research, please use the following.

@article{ren2021portaspeech,
  title={PortaSpeech: Portable and High-Quality Generative Text-to-Speech},
  author={Ren, Yi and Liu, Jinglin and Zhao, Zhou},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}