Project Structure

Let’s take a look at the overall structure of the project. You may familiar with this project structure (recipes/<dataset>/<model>) if you have used ESPNet and SpeechBrain before. AudioZEN is inspired by them, but it is more simpler.

AudioZEN includes a core package (/audiozen) and a series of training recipes (/recipes). The core package is named audiozen, which provides common audio signal processing tools and deep learning trainers. As we have installed audiozen in editable mode, we can call audiozen package everywhere in the code. In addition, we can modify the source code of audiozen package directly. Any changes to the original package would reflect directly in your environment. For example, we can call audiozen package in recipes folder to train models on specific datasets and call audiozen package in tools folder to preprocess data. The recipes in the recipes folder are used to research the audio/speech signal processing. The recipe concept was introduced by Kaldi first, providing a convenient and reproducible way to organize and save the deep learning training pipelines.

The directory structure is as follows:

├── audiozen/
│   ├── acoustics/
│   ├── dataset/
│   ├── model/
│   │   ├── module/
│   └── trainer/
├── docs/
├── notebooks/
├── recipes/
│   └── intel_ndns/
│       ├── sdnn_delays/
│       │   ├── baseline.toml
│       │   ├── model.py
│       │   └── trainer.py
│       ├── dataloader.py
│       ├── loss.py
│       └── run.py
└── tools/

  • audiozen/: The core of the project. After installing audiozen in the editable mode, we can call audiozen package everywhere in the project.

    • acoustics/: Contain the code for audio signal processing.

    • dataset/: Contain the data loading and processing code.

    • model/: Contain the code for model definition and training.

    • trainer/: Contain the code for training and evaluation.

    • …

  • docs/: Contains the project’s documentation. We use Sphinx Documentation Generator to build the documentation.

  • recipes/: Contains the recipes for specific experiments. It follows a <dataset_name>/<model_name> structure.

  • tools/: Contains the code for additional tools, such as data preprocessing, model conversion, etc.

In the recipes folder, we name the subdirectory after the dataset. create a subdirectory for the dataset named after the model. For example, recipes/intel_ndns/ saves the models trained on the Intel Neuromorphic DNS Challenge dataset. It contains commonly-used data loading classes, training, and inference scripts.

  • run.py: The entry of the entire project, which can be used to train and evaluate all models in the intel_ndns directory.

  • dataloader.py: The data loading and processing code for the Intel Neuromorphic DNS Challenge dataset.

  • loss.py: The loss function commonly used in the Intel Neuromorphic DNS Challenge dataset.

sdnn_delays/ contains the SDNN baseline model for this dataset, including the model structure and trainer:

  • <exp_id>.toml: The training configuration file for this model.

  • trainer.py: The trainer for this model, which contains the operations and operations to be executed in each training, validation and test round.

  • model.py: The structure of the current model.

  • run.py (optional): The entry of the current model, which can be used to train the current model. If this file is not present, the run.py file in the recipes/intel_ndns directory will be used.