PortaSpeech / docs /diffspeech.md
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# Run DiffSpeech
## Quick Start
### Install Dependencies
Install dependencies following [readme.md](../readme.md)
### Set Config Path and Experiment Name
```bash
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_data.md)
### Prepare Vocoder
Prepare vocoder following [prepare_vocoder.md](./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:
```bash
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config egs/datasets/audio/lj/fs2_orig.yaml --exp_name fs2_exp --reset
```
Then, run:
```bash
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --reset
```
You can check the training and validation curves open Tensorboard via:
```bash
tensorboard --logdir checkpoints/$MY_EXP_NAME
```
## Inference (Testing)
```bash
CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --infer
```
## Citation
If you find this useful for your research, please use the following.
```bib
@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}
}
```