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| # ================================================================================================== | |
| # DEEPFAKE AUDIO - encoder_preprocess.py (Acoustic Feature Extraction) | |
| # ================================================================================================== | |
| # | |
| # 📝 DESCRIPTION | |
| # This script serves as the primary data ingestion layer for the Speaker Encoder. It | |
| # processes raw waveforms from massive speech datasets (LibriSpeech, VoxCeleb) and extracts | |
| # high-dimensional Mel-Spectrogram features. These features are essentially "voice prints" | |
| # that allow the encoder to learn speaker-independent embedding representations. | |
| # | |
| # 👤 AUTHORS | |
| # - Amey Thakur (https://github.com/Amey-Thakur) | |
| # - Mega Satish (https://github.com/msatmod) | |
| # | |
| # 🤝🏻 CREDITS | |
| # Original Real-Time Voice Cloning methodology by CorentinJ | |
| # Repository: https://github.com/CorentinJ/Real-Time-Voice-Cloning | |
| # | |
| # 🔗 PROJECT LINKS | |
| # Repository: https://github.com/Amey-Thakur/DEEPFAKE-AUDIO | |
| # Video Demo: https://youtu.be/i3wnBcbHDbs | |
| # Research: https://github.com/Amey-Thakur/DEEPFAKE-AUDIO/blob/main/DEEPFAKE-AUDIO.ipynb | |
| # | |
| # 📜 LICENSE | |
| # Released under the MIT License | |
| # Release Date: 2021-02-06 | |
| # ================================================================================================== | |
| from encoder.preprocess import preprocess_librispeech, preprocess_voxceleb1, preprocess_voxceleb2 | |
| from utils.argutils import print_args | |
| from pathlib import Path | |
| import argparse | |
| if __name__ == "__main__": | |
| # --- INTERFACE CONFIGURATION --- | |
| class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter): | |
| """Custom formatter to preserve formatting in the help description.""" | |
| pass | |
| parser = argparse.ArgumentParser( | |
| description="Acoustic Pre-processor: Normalizes raw datasets into mel-spectrogram arrays.\n" | |
| "Required datasets: LibriSpeech, VoxCeleb1, or VoxCeleb2.", | |
| formatter_class=MyFormatter | |
| ) | |
| # --- PATH DEFINITIONS --- | |
| parser.add_argument("datasets_root", type=Path, | |
| help="Root directory where raw datasets are extracted.") | |
| parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, | |
| help="Destination for processed neural features. Defaults to <datasets_root>/SV2TTS/encoder/") | |
| # --- PROCESSING PARAMETERS --- | |
| parser.add_argument("-d", "--datasets", type=str, | |
| default="librispeech_other,voxceleb1,voxceleb2", | |
| help="Comma-separated identifiers of datasets to include in the pipeline.") | |
| parser.add_argument("-s", "--skip_existing", action="store_true", | |
| help="Optimize by skipping files that have already been materialized on disk.") | |
| parser.add_argument("--no_trim", action="store_true", | |
| help="Inhibit silent period removal (Voice Activity Detection). Not recommended for high-fidelity training.") | |
| args = parser.parse_args() | |
| # --- HARDWARE & VAD VALIDATION --- | |
| # We verify the presence of 'webrtcvad' as it is critical for ensuring non-silent training samples. | |
| if not hasattr(args, "no_trim") or not args.no_trim: | |
| try: | |
| import webrtcvad | |
| except: | |
| print("⚠️ Scholarly Warning: 'webrtcvad' not detected. This is required for speech silence removal.") | |
| raise ModuleNotFoundError("Please install 'webrtcvad' or use --no_trim for a degraded run.") | |
| # --- ARCHITECTURAL ORCHESTRATION --- | |
| args.datasets = args.datasets.split(",") | |
| if not hasattr(args, "out_dir"): | |
| args.out_dir = args.datasets_root.joinpath("SV2TTS", "encoder") | |
| assert args.datasets_root.exists(), "Fatal: datasets_root not found. 🤝🏻 Ensure pathing." | |
| args.out_dir.mkdir(exist_ok=True, parents=True) | |
| # --- EXECUTION --- | |
| print_args(args, parser) | |
| print("🤝🏻 Scholarly Partnership: Amey Thakur & Mega Satish") | |
| preprocess_func = { | |
| "librispeech_other": preprocess_librispeech, | |
| "voxceleb1": preprocess_voxceleb1, | |
| "voxceleb2": preprocess_voxceleb2, | |
| } | |
| args_dict = vars(args) | |
| datasets_to_process = args_dict.pop("datasets") | |
| for dataset in datasets_to_process: | |
| print("\n🚀 Initiating Neural Feature Extraction for: %s" % dataset) | |
| preprocess_func[dataset](**args_dict) | |