AudioDataSet

This repo record and lable audio data by UMA8 and Respeaker mic array

AudioDataSet_ A pipeline for recording audio by UMA8 and Respeaker mic arrey

A clean template to kickstart your deep learning project 🚀⚡🔥
Click on Use this template to initialize new repository.

Suggestions are always welcome!

📌  Introduction

Main Technologies

PyTorch Lightning - a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code.

Hydra - a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line.

Main Ideas

Project Structure

The directory structure of new project looks like this:

├── .devcontainer                   <- Github Actions workflows
│
├── configs                   <- Hydra configs
│   ├── callbacks                <- Callbacks configs
│   ├── data                     <- Data configs
│   ├── debug                    <- Debugging configs
│   ├── experiment               <- Experiment configs
│   ├── extras                   <- Extra utilities configs
│   ├── hparams_search           <- Hyperparameter search configs
│   ├── hydra                    <- Hydra configs
│   ├── local                    <- Local configs
│   ├── logger                   <- Logger configs
│   ├── model                    <- Model configs
│   ├── paths                    <- Project paths configs
│   ├── trainer                  <- Trainer configs
│   │
│   ├── eval.yaml             <- Main config for evaluation
│   └── train.yaml            <- Main config for training
│
├── data                   <- Project data
│
├── logs                   <- Logs generated by hydra and lightning loggers
│
├── notebooks              <- Jupyter notebooks. Naming convention is a number (for ordering),
│                             the creator's initials, and a short `-` delimited description,
│                             e.g. `1.0-jqp-initial-data-exploration.ipynb`.
│
├── scripts                <- Shell scripts
│
├── AudioDataSet                    <- Source code
│   ├── callbacks                <- Callbacks configs
│   ├── configs                  <- configs
│   ├── data                     <- Data scripts
│   ├── datamodule               <- Datmodules
│   ├── models                   <- Model scripts
│   ├── utils                    <- Utility scripts
│   │
│   ├── eval.py                  <- Run evaluation
│   └── train.py                 <- Run training
│
├── tests                  <- Tests of any kind
│
├── .gitignore                <- List of files ignored by git
├── .gitmodules               <- List of submodules by git
├── .project-root             <- File for inferring the position of project root directory
├── pyproject.toml            <- Configuration options for testing and linting
├── requirements.txt          <- File for installing python dependencies
└── README.md

🚀  Quickstart

# clone project
git clone https://github.com/ashleve/lightning-hydra-template
cd lightning-hydra-template

# [OPTIONAL] create conda environment
conda create -n myenv python=3.9
conda activate myenv

# install pytorch according to instructions
# https://pytorch.org/get-started/

# install requirements
pip install -r requirements.txt

Citation

@misc{       ,
      title={AudioLC: }, 
      author={Paria Vali Zadeh},
      year={202.},
      eprint={},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/}, 
}