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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} | |
| } | |
| ``` | |