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
├──datasets // Note the datasets folder should be download from the above link
├──egs // Note the egs folder should be download from the above link
├──utils // Note the utils folder should be download from the above link
├── audio.py // audio utils. Note this script should be download from a third party
├── compute-meanvar-stats.py // Compute mean-variance normalization stats. Note this script should be download 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 download from the above link
├── mksubset.py // Make subset of dataset. Note this script should be download from the above link
├── preprocess.py // Preprocess dataset. Note this script should be download from the above link
├── preprocess_normalize.py // Perform meanvar normalization to preprocessed features. Note this script should be download from the above link
├── README.md // descriptions about WaveNet
├── train.py // training scripts
├── 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
├──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
├── 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
├── README.md // Descriptions about WaveNet
├── 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
│ ├──__init__.py
│ ├──dataset.py // generate dataloader and data processing entry
│ ├──callback.py // callbacks to monitor the training
│ ├──lr_generator.py // learning rate generator
│ └──loss.py // loss function definition
│ ├──dataset.py // Generate dataloader and data processing entry
│ ├──callback.py // Callbacks to monitor the training
│ ├──lr_generator.py // Learning rate generator
│ └──loss.py // Loss function definition
└── wavenet_vocoder
├──__init__.py
├──conv.py // extended 1D convolution
├──mixture.py // loss function for training and sample function for testing
├──modules.py // modules for Wavenet construction
├──upsample.py // upsample layer definition
├──util.py // utils. Note this script should be download from the above link
├──conv.py // Extended 1D convolution
├──mixture.py // Loss function for training and sample function for testing
├──modules.py // Modules for Wavenet construction
├──upsample.py // Upsample layer definition
├──util.py // Utils. Note this script should be downloaded from the above link
├──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
huangbo77
4 years ago