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# DEEPFAKE AUDIO - encoder_train.py (Neural Identity Orchestration)
# ==================================================================================================
#
# 📝 DESCRIPTION
# This script manages the training lifecycle of the Speaker Encoder. It optimizes a
# d-vector based neural network to minimize the GE2E (Generalized End-to-End) loss.
# The goal is to maximize the similarity between embeddings of the same speaker
# while minimizing similarity between different speakers, enabling high-fidelity
# zero-shot voice cloning.
#
# 👤 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 utils.argutils import print_args
from encoder.train import train
from pathlib import Path
import argparse
if __name__ == "__main__":
# --- INTERFACE COMMANDS ---
parser = argparse.ArgumentParser(
description="Encoder Training Hub: Optimizing identity embeddings from preprocessed data.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
# --- SESSION DEFINITION ---
parser.add_argument("run_id", type=str,
help="Identifier for this training experiment. Models and logs will be organized under this ID.")
parser.add_argument("clean_data_root", type=Path,
help="Root path to the mel-spectrograms generated by encoder_preprocess.py.")
# --- STORAGE & TELEMETRY ---
parser.add_argument("-m", "--models_dir", type=Path, default="saved_models",
help="Parent directory for serialized weights, backups, and diagnostic plots.")
parser.add_argument("-v", "--vis_every", type=int, default=10,
help="Iteration frequency for updating training curves and loss metrics.")
parser.add_argument("-u", "--umap_every", type=int, default=100,
help="Frequency of UMAP projections to visualize speaker cluster separation.")
parser.add_argument("-s", "--save_every", type=int, default=500,
help="Step interval for materializing model weights (.pt) on disk.")
parser.add_argument("-b", "--backup_every", type=int, default=7500,
help="Interval for creating immutable rolling backups of the model state.")
parser.add_argument("-f", "--force_restart", action="store_true",
help="Bypass existing checkpoints and initialize weights from distribution (restart from scratch).")
# --- VISUALIZATION SERVER ---
parser.add_argument("--visdom_server", type=str, default="http://localhost",
help="Remote address of the Visdom dashboard server.")
parser.add_argument("--no_visdom", action="store_true",
help="Inhibit rich visual telemetry (not recommended for production monitoring).")
args = parser.parse_args()
# --- EXECUTION ---
print_args(args, parser)
print("🤝🏻 Scholarly Partnership: Amey Thakur & Mega Satish")
print("🚀 Initiating Neural Training Pipeline - Monitoring d-vector clusters...")
# Delegate to the internal training engine.
train(**vars(args))
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