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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] | |