@ -65,15 +65,20 @@ Dataset used: [The LJ Speech Dataset](<https://keithito.com/LJ-Speech-Dataset>)
.
├── audio
└──wavenet
├── scripts
│ ├──run_distribute_train_gpu.sh // launch distributed training with gpu platform(8p)
│ ├──run_eval_cpu.sh // launch evaluation with cpu platform
│ ├──run_eval_gpu.sh // launch evaluation with gpu platform
│ ├──run_standalone_train_cpu.sh // launch standalone training with cpu platform
│ └──run_standalone_train_gpu.sh // launch standalone training with gpu platform(1p)
├──datasets // Note the datasets folder should be downloaded from the above link
├──egs // Note the egs folder should be downloaded from the above link
├──utils // Note the utils folder should be downloaded from the above link
├── audio.py // Audio utils. Note this script should be downloaded from a third party
├── audio.py // Audio utils. Note this script should be downloaded from the above link
├── compute-meanvar-stats.py // Compute mean-variance normalization stats. Note this script should be downloaded from the above link
├── evaluate.py // Evaluation
├── export.py // Convert mindspore model to air model
├── hparams.py // Hyper-parameter configuration. Note this script should be downloaded from the above link
├── lrschedule.py // Learning rate scheduler. Note this script should be downloaded from the above link
├── mksubset.py // Make subset of dataset. Note this script should be downloaded from the above link
├── preprocess.py // Preprocess dataset. Note this script should be downloaded from the above link
├── preprocess_normalize.py // Perform meanvar normalization to preprocessed features. Note this script should be downloaded from the above link
@ -205,13 +210,13 @@ you can run the following command to train the network:
```bash
Standalone training
GPU:
python train.py --data_path=/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/ --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --checkpoint_dir=path_to_save_ckpt
sh ./scripts/run_standalone_train_gpu.sh [CUDA_DEVICE_ID] [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_save_ckpt]
CPU:
python train.py --data_path=/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/ --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --checkpoint_dir=path_to_save_ckpt --platform=CPU
sh ./scripts/run_standalone_train_cpu.sh [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_save_ckpt]
Distributed training (on GPU only )
CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' mpirun --allow-run-as-root -n 8 python train.py ----data_path= /path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/ --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --checkpoint_dir=path_to_save_ckpt --is_distributed=True
Distributed training(8p )
sh ./scripts/run_distribute_train_gpu.sh [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_save_ckpt]
```
## [Evaluation Process ](#contents )
@ -221,11 +226,14 @@ this [link](https://bbs.huaweicloud.com/forum/thread-94852-1-1.html)
```bash
Evaluation
GPU:
python evaluate.py --data_path=/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/eval --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --pretrain_ckpt=path_to_load_ckpt --is_numpy --output_path=path_to_save_audio
GPU (using numpy):
sh ./scripts/run_eval_gpu.sh [CUDA_DEVICE_ID] [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_load_ckpt] is_numpy [path_to_save_audio]
GPU (using mindspore):
sh ./scripts/run_eval_gpu.sh [CUDA_DEVICE_ID] [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_load_ckpt] [path_to_save_audio]
CPU:
python evaluate.py --data_path=/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/eval --preset=/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json --pretrain_ckpt=path_to_load_ckpt --is_numpy --output_path=path_to_save_audio --platform=CPU
sh ./scripts/run_eval_cpu.sh [/path_to_egs/egs/gaussian/dump/lj/logmelspectrogram/norm/] [/path_to_egs/egs/gaussian/conf/gaussian_wavenet.json] [path_to_load_ckpt] [is_numpy] [path_to_save_audio]
```
## [Convert Process ](#contents )