WaveNet is a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones. We support training and evaluation on GPU.
WaveNet is a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones. We support training and evaluation on both GPU and CPU.
[Paper](https://arxiv.org/pdf/1609.03499.pdf): ord A, Dieleman S, Zen H, et al. Wavenet: A generative model for raw audio
[Paper](https://arxiv.org/pdf/1609.03499.pdf): ord A, Dieleman S, Zen H, et al. Wavenet: A generative model for raw audio
├──datasets // Note the datasets folder should be download from the above link
├──datasets // Note the datasets folder should be downloaded from the above link
├──egs // Note the egs folder should be download from the above link
├──egs // Note the egs folder should be downloaded from the above link
├──utils // Note the utils folder should be download 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 download from a third party
├── audio.py // Audio utils. Note this script should be downloaded from a third party
├── compute-meanvar-stats.py // Compute mean-variance normalization stats. Note this script should be download 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
├── evaluate.py // Evaluation
├── export.py // convert mindspore model to air model
├── export.py // Convert mindspore model to air model
├── hparams.py // hyper-parameter configuration. Note this script should be download from the above link
├── hparams.py // Hyper-parameter configuration. Note this script should be downloaded from the above link
├── mksubset.py // Make subset of dataset. Note this script should be download from the above link
├── lrschedule.py // Learning rate scheduler. Note this script should be downloaded from the above link
├── preprocess.py // Preprocess dataset. Note this script should be download from the above link
├── mksubset.py // Make subset of 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 download from the above link
├── preprocess.py // Preprocess dataset. Note this script should be downloaded from the above link
├── README.md // descriptions about WaveNet
├── preprocess_normalize.py // Perform meanvar normalization to preprocessed features. Note this script should be downloaded from the above link
├── train.py // training scripts
├── README.md // Descriptions about WaveNet
├── train_pytorch.py // Note this script should be download from the above link. The initial name of this script is train.py in the project from the link
├── train.py // Training scripts
├── train_pytorch.py // Note this script should be downloaded from the above link. The initial name of this script is train.py in the project from the link
├── src
├── src
│ ├──__init__.py
│ ├──__init__.py
│ ├──dataset.py // generate dataloader and data processing entry
│ ├──dataset.py // Generate dataloader and data processing entry
│ ├──callback.py // callbacks to monitor the training
│ ├──callback.py // Callbacks to monitor the training
│ ├──lr_generator.py // learning rate generator
│ ├──lr_generator.py // Learning rate generator
│ └──loss.py // loss function definition
│ └──loss.py // Loss function definition
└── wavenet_vocoder
└── wavenet_vocoder
├──__init__.py
├──__init__.py
├──conv.py // extended 1D convolution
├──conv.py // Extended 1D convolution
├──mixture.py // loss function for training and sample function for testing
├──mixture.py // Loss function for training and sample function for testing
├──modules.py // modules for Wavenet construction
├──modules.py // Modules for Wavenet construction
├──upsample.py // upsample layer definition
├──upsample.py // Upsample layer definition
├──util.py // utils. Note this script should be download from the above link
├──util.py // Utils. Note this script should be downloaded from the above link
├──wavenet.py // WaveNet networks
├──wavenet.py // WaveNet networks
└──tfcompat // Note this script should be download from the above link
└──tfcompat // Note this script should be downloaded from the above link
mindspore-ci-bot
4 years ago
committed by
Gitee