data/README.md

Data Module

This directory contains all the necessary components for data loading, processing, and augmentation.

Files

dataset.py

This file defines the RawStems dataset class, which is the core of the data pipeline. It dynamically creates training examples by mixing a target stem with other stems based on a specified Signal-to-Noise Ratio (SNR).

RawStems

A PyTorch Dataset that loads and processes raw audio stems for music source restoration tasks.

__init__ Arguments:

• target_stem (str): The name of the target stem folder (e.g., "Voc" or "Gtr_EG").
• root_directory (Union[str, Path]): The root directory containing subfolders for each song.
• file_list (Optional[Union[str, Path]]): Path to a .txt file where each line is a path to a song folder, relative to root_directory.
• sr (int): The target sample rate to load audio at. Default: 44100.
• clip_duration (float): The duration of the audio clips to be extracted, in seconds. Default: 3.0.
• snr_range (Tuple[float, float]): A tuple representing the min and max SNR (in dB) for mixing the target stem with the noise (other stems). Default: (0.0, 10.0).
• apply_augmentation (bool): Whether to apply on-the-fly augmentations to the audio. Default: True.

augment.py

This file implements the audio augmentation pipelines using the pedalboard library.

StemAugmentation

Applies a chain of augmentations suitable for the target audio source before it's mixed. This simulates variations in recording quality and effects.

• Effects include: Random EQ, Resampling, Compression, Distortion, and Reverb.

MixtureAugmentation

Applies a chain of augmentations to the final mixture audio. This simulates artifacts that could occur on a fully mixed track.

• Effects include: Limiting, Resampling, and MP3 a.k.a Codec compression.