Spaces:
Running
Running
File size: 2,838 Bytes
1d8403e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | # ==================================================================================================
# DEEPFAKE AUDIO - encoder/data_objects/speaker.py (Categorical Identity Representation)
# ==================================================================================================
#
# 📝 DESCRIPTION
# This module implements the 'Speaker' abstraction, encapulating all linguistic
# and acoustic metadata for a single individual. It manages the retrieval
# and segmented sampling of utterances, acting as a gateway to the serialized
# Mel-Spectrograms used in neural distillation.
#
# 👤 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 encoder.data_objects.random_cycler import RandomCycler
from encoder.data_objects.utterance import Utterance
from pathlib import Path
class Speaker:
"""
Categorical Data Container:
Aggregates all speech samples associated with a unique institutional speaker ID.
"""
def __init__(self, root: Path):
self.root = root
self.name = root.name
self.utterances = None
self.utterance_cycler = None
def _load_utterances(self):
"""Lazy-loading of utterance metadata from the serialized index (_sources.txt)."""
with self.root.joinpath("_sources.txt").open("r") as sources_file:
sources = [l.split(",") for l in sources_file]
# Identity Mapping: frames_fname -> original_wave_fpath
sources = {frames_fname: wave_fpath for frames_fname, wave_fpath in sources}
self.utterances = [Utterance(self.root.joinpath(f), w) for f, w in sources.items()]
self.utterance_cycler = RandomCycler(self.utterances)
def random_partial(self, count, n_frames):
"""
Samples a batch of <count> unique partial utterances.
Ensures diverse temporal coverage within the speaker's available vocal range.
"""
if self.utterances is None:
self._load_utterances()
# Stochastic selection of utterances
utterances = self.utterance_cycler.sample(count)
# Spatio-temporal cropping: (utterance, frames, crop_range)
a = [(u,) + u.random_partial(n_frames) for u in utterances]
return a
|