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Figure: End-to-end SingFox pipeline illustrating data collection, preprocessing, singfake generation, and ground-truth modeling using multiple vocoders and diffusion-based frameworks.
#SingFox All the trained pytorch models are openly available on : https://www.kaggle.com/datasets/artharking/pytorch-models-singfox/data mp3_to_wav.py --> Converts MP3 audio files to WAV format for consistent processing. resampling.py --> Resamples all WAV files to a target sample rate (e.g., 16 kHz) for uniformity. peak_zormalization.py --> Applies peak normalization to adjust audio levels so the loudest peak reaches a target dBFS. rms_normalization.py --> Applies RMS normalization to ensure consistent perceived loudness across files. check_normatization.py --> Verifies whether files are properly normalized (checks peak and RMS levels). merge_audio_files.py --> Merges all WAV files into a single long WAV file, with optional silence between clips. divide_audio_file.py --> Splits each WAV file into fixed-length segments (e.g., 2 seconds) and saves them with consistent naming. distribute_dataset_chunks.py – Splits audio files by language and type into fixed-size chunks across multiple organized directories for singfake generation. dataset_from_dirs_code.ipynb - If the structure is like 2 folders with n (n=number of directories) subdirectories, and each subdirectory has language folder which contains audios, then the code will make a dataset we want in base form. T6_formation.py - Forms T6 from T5 dataset Track_T7_former.py - forms T7 from various datasets ht_figure.py => It plots the figure of line 1 (this is singing dataset), and line 2 (this is singfox dataset)