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# DEEPFAKE AUDIO - encoder/data_objects/random_cycler.py (Constrained Stochastic Iteration)
# ==================================================================================================
#
# 📝 DESCRIPTION
# This utility provides a 'RandomCycler' class designed for uniform yet stochastic
# sampling of dataset items. It ensures that every item in a collection is
# seen with a guaranteed frequency, avoiding potential biases or 'starvation'
# during neural optimization steps while maintaining sufficient randomness.
#
# 👤 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 random
class RandomCycler:
"""
Uniform Stochastic Sampler:
Maintains a sequence where each item is guaranteed to appear within a controlled
interval, ensuring balanced categorical exposure during training.
"""
def __init__(self, source):
if len(source) == 0:
raise Exception("Fatal: Cannot initialize RandomCycler with an empty collection.")
self.all_items = list(source)
self.next_items = []
def sample(self, count: int):
"""
Retrieves a 'count' number of items, replenishing and shuffling the internal
pool as needed to maintain stochastisity without repetition within a cycle.
"""
shuffle = lambda l: random.sample(l, len(l))
out = []
while count > 0:
# High-Volume Requests
if count >= len(self.all_items):
out.extend(shuffle(list(self.all_items)))
count -= len(self.all_items)
continue
# Partial Pool Refresh
n = min(count, len(self.next_items))
out.extend(self.next_items[:n])
count -= n
self.next_items = self.next_items[n:]
if len(self.next_items) == 0:
self.next_items = shuffle(list(self.all_items))
return out
def __next__(self):
"""Standard Python iterator hook for single-sample acquisition."""
return self.sample(1)[0]
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