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DiffSpeech

HuggingFace🤗 Demo

Quick Start

Install Dependencies

Install dependencies following readme.md

Set Config Path and Experiment Name

export CONFIG_NAME=egs/datasets/audio/lj/ds.yaml
export MY_EXP_NAME=ds_exp

Preprocess and binary dataset

Prepare dataset following prepare_data.md

Prepare Vocoder

Prepare vocoder following prepare_vocoder.md

Training

First, you need a pre-trained FastSpeech2 checkpoint chckpoints/fs2_exp/model_ckpt_steps_160000.ckpt. To train a FastSpeech 2 model, run:

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config egs/datasets/audio/lj/fs2_orig.yaml --exp_name fs2_exp --reset

Then, run:

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 $CONFIG_NAME --exp_name $MY_EXP_NAME --infer

Pre-trained Model

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

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

Citation

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

@article{liu2021diffsinger,
  title={Diffsinger: Singing voice synthesis via shallow diffusion mechanism},
  author={Liu, Jinglin and Li, Chengxi and Ren, Yi and Chen, Feiyang and Liu, Peng and Zhao, Zhou},
  journal={arXiv preprint arXiv:2105.02446},
  volume={2},
  year={2021}
 }