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| # ================================================================================================== | |
| # DEEPFAKE AUDIO - vocoder_train.py (Waveform Reconstruction Neural Training) | |
| # ================================================================================================== | |
| # | |
| # 📝 DESCRIPTION | |
| # This script manages the training of the WaveRNN-based Vocoder. It optimizes a | |
| # recurrent neural network to reconstruct high-fidelity time-domain audio from | |
| # mel-spectrogram sequences. For optimal results, it uses the GTA (Ground-Truth | |
| # Aligned) spectrograms generated during the preprocessing phase, allowing the | |
| # vocoder to compensate for common synthesizer artifacts. | |
| # | |
| # 👤 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 | |
| # ================================================================================================== | |
| import argparse | |
| from pathlib import Path | |
| # --- CORE VOCATION ENGINE --- | |
| from utils.argutils import print_args | |
| from vocoder.train import train | |
| if __name__ == "__main__": | |
| # --- TRAINING PARAMETERS --- | |
| parser = argparse.ArgumentParser( | |
| description="Vocoder Training Hub: Reconstructing waveforms from synthetic spectrograms.", | |
| formatter_class=argparse.ArgumentDefaultsHelpFormatter | |
| ) | |
| # --- SESSION DEFINITION --- | |
| parser.add_argument("run_id", type=str, | |
| help="Identifier for this experimental run. Weights and metrics will be archived under this ID.") | |
| parser.add_argument("datasets_root", type=Path, | |
| help="Root path to the SV2TTS training directory.") | |
| # --- PATH OVERRIDES --- | |
| parser.add_argument("--syn_dir", type=Path, default=argparse.SUPPRESS, | |
| help="Custom path to synthesizer-level data. Defaults to <datasets_root>/SV2TTS/synthesizer/.") | |
| parser.add_argument("--voc_dir", type=Path, default=argparse.SUPPRESS, | |
| help="Custom path to vocoder-level GTA data. Defaults to <datasets_root>/SV2TTS/vocoder/.") | |
| parser.add_argument("-m", "--models_dir", type=Path, default="saved_models", | |
| help="Destination for saved weights, rolling backups, and audio samples.") | |
| # --- TRAINING LOGIC --- | |
| parser.add_argument("-g", "--ground_truth", action="store_true", | |
| help="Bypass GTA spectrograms and train directly on original ground truth data.") | |
| parser.add_argument("-s", "--save_every", type=int, default=1000, | |
| help="Iteration interval for weight persistence on disk.") | |
| parser.add_argument("-b", "--backup_every", type=int, default=25000, | |
| help="Frequency of immutable state backups.") | |
| parser.add_argument("-f", "--force_restart", action="store_true", | |
| help="Initialize weights from scratch, ignoring existing checkpoints.") | |
| args = parser.parse_args() | |
| # --- ARCHITECTURAL RESOLUTION --- | |
| if not hasattr(args, "syn_dir"): | |
| args.syn_dir = args.datasets_root / "SV2TTS" / "synthesizer" | |
| if not hasattr(args, "voc_dir"): | |
| args.voc_dir = args.datasets_root / "SV2TTS" / "vocoder" | |
| # Finalize workspace preparations. | |
| del args.datasets_root | |
| args.models_dir.mkdir(exist_ok=True) | |
| # --- EXECUTION --- | |
| print_args(args, parser) | |
| print("🤝🏻 Scholarly Partnership: Amey Thakur & Mega Satish") | |
| print("🚀 Initiating Vocoder Training Pipeline - Optimizing wave reconstruction...") | |
| # Delegate to the WaveRNN training module. | |
| train(**vars(args)) | |