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# DEEPFAKE AUDIO - encoder/data_objects/utterance.py (Vocal Unit Representation)
# ==================================================================================================
#
# 📝 DESCRIPTION
# This module defines the 'Utterance' class, representing a single spoken phrase
# or acoustic segment. It provides mechanisms for loading preprocessed Mel-scale
# filterbanks from the disk and handles stochastic temporal cropping (random
# partials) to increase data variety during training.
#
# 👤 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 numpy as np
class Utterance:
"""
Acoustic Data Container:
Manages the lifecycle of a single vocal sample, from disk retrieval to
stochastic temporal segmentation.
"""
def __init__(self, frames_fpath, wave_fpath):
self.frames_fpath = frames_fpath
self.wave_fpath = wave_fpath
def get_frames(self):
"""Deserializes the Mel-Spectrogram matrix from the filesystem."""
return np.load(self.frames_fpath)
def random_partial(self, n_frames):
"""
Spatio-Temporal Cropping:
Cuts a random segment of 'n_frames' from the full utterance.
This technique acts as a form of temporal data augmentation.
"""
frames = self.get_frames()
if frames.shape[0] == n_frames:
start = 0
else:
# Stochastic offset selection
start = np.random.randint(0, frames.shape[0] - n_frames)
end = start + n_frames
return frames[start:end], (start, end) |