Init

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+.lh
+.history
+.vscode
+.idea
+.trunk
+lightning_logs
+
+# Ignore generated sounds
+results/*
+
+# Mock datasets
+datasets_cache/**/*
+
+notebooks/**/*.wav
+notebooks/**/*.mp3
+notebooks/**/*.pth
+
+# Mock audio
+mocks/wav2vec_aligner/audio
+
+# Ignore flagged from gradio
+flagged

config/best_speakers_list.py

@@ -0,0 +1,968 @@
+# chunk_1
+speaker_ids1 = [
+    32, # US, nan 	M 	train-clean-100, min intonation, calm, timbre
+    130, # BR, 	Peter of Buckinghamshire England 	M 	train-other-500, timbre, min intonation, calm, slow
+    164, # US, 	Ed Good 	M 	train-other-500, timbre, intonation, warm and calm, good voice
+    176, # US, 	Micah Sheppard 	M 	train-clean-360, intonation, calm
+    68, # US,	Alex Buie 	M 	train-clean-100,  min intonation, energized
+    15, # US, 	Chip 	M 	train-other-500, intonation, interesting voice
+    145, # US, 	Mr. Baby Man 	M 	train-clean-360, timbre, intonation, interesting voice
+    196, # US, 	Mike Kauffmann 	M 	train-clean-360,  min intonation, average calm
+    160, # BR,	deadwhitemales 	M 	train-clean-100, intonation, energized, interesting voice
+    108, # US, 	Kevin McAsh 	M 	train-clean-360, min intonation, energized
+    99, # US, 	Stewart Wills 	M 	train-clean-100, min intonation, avg energy
+    50, # US, 	Dan Threetrees 	M 	train-clean-360, min intonation, avg energy
+    76, # US, 	ML Cohen 	M 	train-other-500, intonation, energy, timbre
+    142, # US, 	Michael Sirois 	M 	train-other-500, intonation, energy
+    95, # US, 	Vinny Bove 	M 	train-clean-360, intonation, energy, timbre
+    169, # US, 	Richard Grove 	M 	train-other-500, intonation, energy
+    147, # US, 	Eileen George 	F 	train-clean-360, intonation, calm
+    92, # BR 	vlooi 	F 	train-other-500 teenager voice, intonation, energized
+    117, # US, 	Caitlin Kelly 	F 	train-clean-360, intonation, energized, interesting voice
+    89, # US, 	Paula Berinstein 	F 	train-clean-360, min intonation, energized
+    182, # US 	Katy Preston 	F 	train-other-500, intonation, energized
+    105, # US 	Marian Brown 	F 	train-clean-360, min intonation, energized, timbre, quality GOOD
+    11, # BR 	Linton 	F 	train-other-500, min intonation, calm
+    18, # US 	Sherry Crowther 	F 	train-clean-100, intonation, energized, timbre
+    38, # US 	Kurt Copeland 	M 	train-clean-360, intonation, energized
+    52, # US, 	Cori Samuel 	F 	train-other-500, intonation, energized, timbre
+    21, # US, 	Kelly Bescherer 	F 	train-other-500, intonation, energized, timbre
+    48, # US, 	Rosalind Wills 	F 	train-clean-100, intonation, timbre, poor quality, but interesting voice
+    63, # US, 	Linda Wilcox 	F 	train-other-500, very poor quality, but VERY interesting intonation
+    45, # US, 	Catharine Eastman 	F 	train-clean-100, very quality, interesting intonation
+    193, # US, 	Nomenphile 	F 	train-other-500, avg quality, intonation
+    207, # US 	Sage Tyrtle 	F 	train-other-500, avg quality, interesting intonation and timbre
+    73, # US 	Claire Goget 	F 	train-clean-100, avg quality, intonation
+    42, # US 	Jennifer Crispin 	F 	train-clean-360, quality, calm
+    113, # US 	Alice Elizabeth Still 	F 	train-other-500, avg quality, intonation
+    49, # US 	Kristen McQuillin 	F 	train-clean-100, avg quality, interesting intonation and timbre
+    185, # US	Kim Braun 	F 	train-clean-360, avg quality, avg intonation, but interesting timbre
+    165, # US 	Elisabeth Shields 	F 	train-clean-100, avg quality, interesting intonation, timbre
+    36, # US 	chriss the girl 	F 	train-other-500, interesting intonation, timbre
+    96, # US 	Kymm Zuckert 	F 	train-other-500, avg quality, interesting intonation, timbre of middle age wooman
+    102, # US 	Maureen S. O'Brien 	F 	train-clean-100, avg quality, interesting intonation
+    64, # US 	Christiane Levesque 	F 	train-clean-360, young voice, calm
+    30, # BR, 	Ophelia Darcy 	F 	train-other-500, avg quality, intonation
+    0, # US 	Kristin LeMoine 	F 	train-clean-360, avg quality, intonation, fast-speaker
+    192, # US, 	Nocturna 	F 	train-clean-100, avg quality, intonation
+    146, # US 	Jim Cadwell 	M 	train-other-500, avg quality, intonation
+    80, # US	Fox in the Stars 	F 	train-clean-100, avg quality, intonation
+    162, # BR	Mike Gardom 	M 	train-other-500, avg quality, intonation, timbre
+    66, # US 	Maddie 	F 	train-clean-360, intonation, timbre
+    124, # US 	Steve Karafit 	M 	train-clean-100, poor quality, intonation, timbre, COOL
+    123, # US 	Sean McGaughey 	M 	train-clean-360, poor quality, intonation, timbre
+    55, # US, 	Patricia Oakley 	F 	train-clean-360, intonation, timbre
+    91, # BR 	Chris Goringe 	M 	train-other-500, intonation, timbre
+    171, # US	Paul Harvey 	M 	train-clean-360, avg quality, intonation, timbre
+    61, # US	John Greenman 	M 	train-other-500, intonation, timbre
+    122,  # US  carnright 	M 	train-clean-100, avg intonation, timbre
+    82, # BR 	Andy Minter 	M 	train-other-500, intonation, GOOD timbre,
+    118, # US 	Brenda Dayne 	F 	train-clean-360, timbre, calm
+    106, # US	Mark F. Smith 	M 	train-clean-360, GOOD timbre, interesting intonation
+    187, # US	Lenny Glionna Jr. 	M 	train-other-500, intonation, timbre
+    158, # US 	Randy Phillips 	M 	train-clean-100, avg intonation, timbre
+    83, # US	Graham Williams 	M 	train-other-500, intonation, timbre
+    181, # BR 	Jon Ingram 	M 	train-other-500, intonation, timbre
+    8, # US	Denny Sayers 	M 	train-clean-100, timbre
+    205, # US	Robert Garrison 	M 	train-clean-360, timbre, interesting intonation
+    172, # US 	Harvey Chinn 	M 	train-other-500, great timbre, intonation
+]
+# chunk_2
+speaker_ids2 = [
+    305, # US 	Michael Crowl 	M 	train-clean-360, timbre, intonation
+    417, # US	Kiki Baessell 	F 	train-clean-360, timbre, intonation
+    241, # US	Jean O'Sullivan 	F 	train-clean-360, timbre, intonation
+    412, # US	Nichole Karl 	F 	train-other-500, timbre
+    294, # US	Diana Kiesners 	F 	train-clean-360, timbre, intonation
+    414, # US 	Christabel 	F 	train-clean-100, great and calm timbre
+    251, # US	Aaron Benedict 	M 	train-other-500, timbre, intonation
+    218, # US	Joy Chan 	F 	train-other-500, timbre, intonation, quality
+    259, # BR	miette 	F 	train-other-500, timbre, intonatio
+    275, # US	Zale Schafer (Rose May Chamberlin Memorial Foundat 	F 	train-clean-360, timbre, intonation
+    368, # US	Susie G. 	F 	train-other-500, teenage voice, timbre, intonation
+    327, # BR	mjd-s 	F 	train-other-500, timbre, intonation
+    366, # US 	pattymarie 	F 	train-clean-360, calm, timbre
+    399, # US	entada 	F 	train-clean-360, timbre, intonation
+    273, # BR 	Andrew Lebrun 	M 	train-other-500, timbre, intonation
+    352, # US 	Mur Lafferty 	F 	train-clean-360, timbre, enery, young voice
+    283, # US	Bethany Simpson 	F 	train-clean-360, timbre, intonation
+    248, # BR 	fieldsofgold 	M 	train-other-500, timbre, intonation
+    372, # US	Jim Mullins 	M 	train-clean-360, timbre, intonation, quality
+    250, # US	Quentin 	M 	train-clean-360, unique timbre, intonation, quality
+    403, # US	Igor Teaforay 	F 	train-clean-360, timbre, avg intonation
+    375, # US	iscatel 	M 	train-clean-360, unique timbre, avg intonation
+    355, # US 	Lana Taylor 	F 	train-clean-100, unique timbre, intonation
+    220, # US	Tina Tilney 	F 	train-clean-360, unique timbre, high intonation
+    392, # US 	Brooks Jensen 	M 	train-clean-360, timbre, avg speed, avg intonation
+    255, # US 	Caroline Mercier 	F 	train-clean-360, timbre, avg speed, intonation
+    292, # US 	Tamara R. Schwartz 	F 	train-clean-100, timbre, avg speed, intonation
+    258, # US 	Russ Maxwell 	M 	train-clean-360, timbre, intonation, unique voice
+    233, # US	mikenkat 	M 	train-clean-360, timbre, intonation
+    394, # US 	swroot 	F 	train-clean-360, timbre, intonation, unique voice, good quality
+    382, # US	Kelli Robinson 	F 	train-clean-360, timbre, intonation, avg quality
+    322, # US	Mitchell Dwyer 	M 	train-clean-360, timbre, intonation, avg quality, unique voice
+    285, # US	koijmonop 	M 	train-clean-360, timbre, intonation, avg quality, unique voice
+    290, # US	J. M. Smallheer 	F 	train-clean-360, timbre, intonation, avg quality
+    383, # US	Mike Roop 	M 	train-other-500, timbre, intonation, quality, unique voice
+    263, # US 	Eric S. Piotrowski 	M 	train-clean-360, timbre, intonation, avg quality
+    373, # BR	Brooks Seveer 	M 	train-clean-360, timbre, intonation, avg quality, unique voice
+    340, # US	Nick Gallant 	M 	train-clean-100, unique timbre
+    331, # BR 	Luke Venediger 	M 	train-other-500, timbre, intonation
+    234, # BR	Menno 	M 	train-other-500, timbre, intonation, unique voice
+    398, # BR 	Sam Fold 	M 	train-other-500, timbre, intonation
+    254, # BR	Rebecca Dittman 	M 	train-other-500, timbre, intonation
+    264, # US	KentF 	M 	train-clean-360, timbre, intonation
+    214, # US 	Scott Splavec 	M 	train-clean-100, timbre, intonation
+    359, # BR	Greg Bryant 	M 	train-clean-100, timbre, intonation
+    253, # US	Frank 	M 	train-other-500, timbre, intonation
+    249, # US	Bill Stackpole 	M 	train-clean-360, timbre, intonation
+    236, # US	Matthew Shepherd 	M 	train-clean-360, timbre, intonation, unique voice
+    390, # US	JimmyLogan 	M 	train-clean-360, avg intonation, timbre
+    216, # US	Dave Ranson 	M 	train-clean-100, unique voice, timbre, intonation
+    215, # US	Mark Bradford 	M 	train-clean-360, timbre, intonation
+    212, # US	Glen Hallstrom 	M 	train-other-500, timbre, intonation
+    314, # US	Carl Vonnoh, III 	M 	train-clean-360, unique voice, timbre, intonation
+    347, # US	Anadaxis_Canejia 	M 	train-other-500, unique voice, timbre, intonation, good quality
+    330, # US	Aaron Andradne 	M 	train-clean-360, timbre, intonation
+    393, # US	Tim Lundeen 	M 	train-clean-360, timbre, intonation
+    40,  # BR 	Justin Brett 	M 	train-other-500, unique voice, timbre, intonation, good quality
+    386, # US 	Michael Kirkpatrick 	M 	train-clean-360, unique intonation, timbre
+    315, # US	Jean Crevier 	M 	train-other-500, unique intonation, timbre
+    349, # US	brenthumphries 	M 	train-other-500, unique timbre, min intonation
+    242, # US	J. Hall 	M 	train-other-500, unique timbre, intonation
+    308, # US	Eric Connover 	M 	train-other-500, timbre, intonation
+    313, # BR	Tim Bulkeley 	M 	train-other-500, timbre, intonation
+    334, # US	mawrtea 	M 	train-clean-360, unique voice, timbre, intonation
+]
+# chunk_3
+speaker_ids3 = [
+    469, # US 	Christian Pecaut 	M 	train-clean-360, clear voice, min intonation
+    601, # BR	Jonathan Horniblow 	M 	train-other-500, clear voice, min intonation
+    482, # US	Estragon 	M 	train-clean-360, clear voice, min intonation
+    580, # US	Kyle M. 	M 	train-clean-360, clear voice, min intonation, timbre
+    463, # US	Chris Hughes 	M 	train-other-500, clear voice, min intonation, timbre, energized
+    472, # BR	Tim Makarios 	M 	train-other-500, min intonation
+    536, # US	Robert Flach 	M 	train-other-500, intonation, timbre, unique voice
+    569, # US 	Arouet 	M 	train-clean-360, avg intonation, timbre
+    523, # US	Michael Loftus 	M 	train-clean-360, avg intonation, timbre
+    454, # US	Tim Gregory 	M 	train-clean-100, avg intonation
+    425, # US	RedToby 	M 	train-clean-360, avg intonation, timbre, unique voice
+    574, # US	Daniel Shorten 	M 	train-clean-100, avg intonation, timbre, clear voice
+    465, # US 	Leonie Rose 	F 	train-clean-100, avg intonation, timbre
+    481, # US 	Scott Sherris 	M 	train-clean-360, intonation, timbre
+    531, # US 	Fr. Richard Zeile of Detroit 	M 	train-other-500, intonation, timbre, unique voice
+    628, # US	Bryan Ness 	M 	train-clean-100, intonation, timbre, unique voice
+    427, # US 	John Lieder 	M 	train-clean-360, intonation, timbre
+    527, # US	Jason Isbell 	M or F 	train-clean-360, intonation, timbre
+    468, # US	Jenilee 	F 	train-other-500, energized, young voice, intonation
+    441, # US	roolynninms 	F 	train-clean-100, energized, intonation
+    430, # BR 	Millbeach 	F 	train-other-500, intonation, timbre
+    498, # US	Chris Gladis 	M 	train-clean-100, intonation, timbre
+    435, # US 	Debra Lynn 	F 	train-other-500, intonation, timbre
+    499, # US 	Tammy Sanders 	F 	train-clean-100, intonation, timbre, unique voice
+    429, # US 	Giles Baker 	M 	train-other-500, timbre
+    495, # US	Janet Friday 	F 	train-clean-360, INTONATION, timbre
+    456, # US	Catherine Fitz 	F 	train-clean-360, timbre
+    617, # US 	PJ 	F 	train-other-500, timbre
+    445, # US	Jennette Selig 	F 	train-clean-360, intonation, timbre
+    552, # US	Mim Ritty 	F 	train-clean-100, intonation, timbre
+    561, # US	Lorelle Anderson 	F 	train-clean-100, intonation, timbre, quality
+    447, # US	Lee Ann Howlett 	F 	train-clean-360, intonation, timbre, quality
+    604, # BR	Anne-Marie 	F 	train-other-500, intonation, timbre
+    450, # US	Heather Duncan 	F 	train-clean-360, intonation, unique voice
+    502, # US	Lee Elliott 	F 	train-other-500, intonation
+    517, # US	Madame Tusk 	F 	train-other-500, intonation
+    605, # BR	Classicsfan 	F 	train-other-500, intonation, unique voice, timbre
+    426, # US	Megan Stemm-Wade 	F 	train-clean-100, clear voice, min intonation
+    544, # US	Miranda Stinson 	F 	train-clean-360, unique voice
+    568, # US	Michael Yourshaw 	M 	train-other-500, clear voice
+    588, # US	Kalynda 	F 	train-clean-360, clear voice, timbre
+    477, # US	Patti Brugman 	F 	train-other-500, clear voice
+    607, # BR 	Steph 	F 	train-other-500, clear voice
+    479, # US 	Wina Hathaway 	F 	train-other-500, intonation, timbre
+    618, # US	Linnea 	F 	train-other-500, timbre
+    549, # US	AmyAG 	F 	train-other-500, teenager voice
+    596, # US	Jo 	F 	train-other-500, timbre, min intonation
+]
+# chunk 4
+speaker_ids4 = [
+    770, # US	Pete Williams, Pittsburgh, PA 	M 	train-clean-360, clear, calm voice
+    730, # US	Nick Gisburne 	M 	train-other-500, clear, calm voice
+    680, # US	Kevin Kivikko 	M 	train-clean-360, min intonation, calm voice, timbre
+    791, # US	David Kleparek 	M 	train-clean-100, clear, calm voice
+    800, # US	Jack Farrell 	M 	train-clean-360, clear, calm voice, good low tone
+    839, # BR	Ben Dutton 	M 	train-other-500, clear, calm voice, min intonation
+    661, # US	James Gladwin 	M 	train-other-500, calm voice, min intonation
+    648, # US	Muhammad Mussnoon 	M 	train-other-500, timbre
+    798, # US	Wyatt 	M 	train-other-50, calm voice, timbre, avg intonation
+    699, # US	Michael Macedonia 	M 	train-clean-360, calm voice, timbre
+    757, # US	Roger Melin 	M 	train-clean-360, timbre, avg intonation, unique voice
+    782, # US	Alec Daitsman 	M 	train-clean-360, timbre, avg intonation
+    816, # US	Michael Bradford 	M 	train-clean-360, timbre, avg intonation, teenager voice
+    846, # US	Gayland Darnell 	M 	train-clean-360, senior voice, timbre, avg intonation
+    831, # US	David Federman 	M 	train-other-500, timbre, avg intonation
+    817, # US	texttalker 	M 	train-clean-360, timbre, avg intonation
+    819, # US	n8evv 	M 	train-clean-360, timbre, intonation
+    672, # BR	Stuart Bell 	M 	train-other-500, timbre, intonation, clear voice
+    837, # US	johnb 	M 	train-other-500, timbre, intonation
+    775, # US	Ralph Volpi 	M 	train-clean-360, senior voice, timbre, intonation
+    685, # US	Nikki Sullivan 	F 	train-clean-100, clear voice, avg intonation, timbre
+    666, # US	Kelly Dougherty 	M 	train-clean-360, clear voice, avg intonation, timbre
+    809, # US	Anna-Maria Viola 	F 	train-clean-360, timbre, avg intonation
+    640, # US	David A. Stokely 	M 	train-other-500, emotional, timbre, intonation, unique voice
+    818, # US	Matt Warzel 	F 	train-clean-360, timbre, intonation
+    711, # US	Julie Bynum 	F 	train-clean-360, senior voice, timbre, min intonation
+    766, # US	Clive Catterall 	M 	train-other-500, avg intonation, timbre
+    649, # US	Scarlett! 	F 	train-clean-360, intonation, timbre
+    732, # US	Tysto 	M/F	train-clean-360, intonation, timbre
+    769, # US, Alan Brown 	M 	train-other-500, avg intonation, timbre
+    677, # US	Allyson Hester 	F 	train-other-500, intonation, timbre, unique voice
+    833, # US	Alana Jordan 	F 	train-clean-360, intonation, timbre
+    676, # US	Jennifer 	F 	train-clean-100, intonation, timbre
+    710, # US	Sheila Morton 	F 	train-clean-100, intonation, timbre, unique voice
+    645, # US	Micah 	F 	train-other-500, intonation, timbre, unique voice, teenager voice
+    771, # US	Isosceles 	F 	train-clean-360, intonation, timbre
+    636, # US	Matthew Howell 	F 	train-other-500, timbre, avg intonation
+    822, # US	kristiface 	F 	train-clean-360, timbre, intonation, unique voice
+    830, # US	musici123 	F 	train-other-500, timbre, intonation, unique voice
+    657, # US	Shannon 	F 	train-other-500, timbre, intonation
+    797, # US	Chris Jones 	F 	train-other-500, timbre, intonation, calm voice
+    835, # BR	Rachel Lintern 	F 	train-other-500, timbre, intonation
+    761, # US	Susan Umpleby 	F 	train-clean-100, timbre, intonation, unique voice
+    653, # US	cucciasv 	F 	train-other-500, timbre, avg intonation
+    751, # US	Ralph Snelson 	M 	train-other-500, timbre, avg intonation, unique voice
+    848, # BR	senshisteph 	F 	train-other-500, timbre, intonation
+    808, # US	M. J. Boyle 	F 	train-other-500, timbre, intonation, senior voice
+    840, # US	B. Grebe 	F 	train-clean-360, timbre, intonation, unique voice
+    742, # US	Katherine Holt 	F 	train-other-500, timbre, intonation
+    834, # US	Serin 	F 	train-other-500, unique voice
+    668, # US	Jan Baxter 	F 	train-clean-360, intoantion, timbre, calm voice
+    752, # US	Cat Schirf 	F 	train-other-500, intoantion, timbre
+    641, # US	Eliza Horne 	F 	train-other-500, intoantion, timbre
+    644, # US	Cynthia Zocca 	F 	train-clean-360, intoantion, timbre
+    781, # US	Megan Kunkel 	F 	train-other-500, intoantion, timbre, teenager voice
+    727, # US	Jodi Krangle 	F 	train-clean-360, timbre
+    719, # US	Charlene V. Smith 	F 	train-other-500, intoantion, timbre
+    804, # BR	FirstKnight 	F 	train-other-500, unique voice, intoantion, timbre
+    675, # US	inkwelldragon 	F 	train-clean-360, intoantion, timbre
+    697, # US	Jennie Hughes 	F 	train-other-500, timbre
+    731, # IND	Priya, India 	F 	train-other-500, avg intoantion, timbre
+    741, # US 	Nick Marsh 	M 	train-other-500, intoantion, timbre, old man voice, unique voice
+]
+# chunk 5
+speaker_ids5 = [
+    922,  # ricell
+    1000,  # artos
+    1007,  # Mike Conrad
+    858,  # Scott Merrill
+    943,  # Matthew C. Heckel
+    984,  # woggy298
+    936,  # BUAES
+    935,  # Topaz
+    977,  # Logan McCamon
+    946,  # Cantor
+    1030,  # Ancient mariner
+    1046,  # Preston McConkie
+    1022,  # peac
+    908,  # Quentin Manuel
+    924,  # Andrew Coleman
+    964,  # Utek
+    950,  # davechase
+    1020,  # nihilist00
+    1043,  # B. G. Oxford
+    881,  # mpetranech
+    852,  # Steven Proctor
+    995,  # Parrot
+    1045,  # joi
+    1048,  # tornadogrrrl
+    900,  # peaceuntoyou
+    932,  # Raerity
+    1005,  # Beatrice
+    851,  # Jennifer Lott
+    897,  # Jan Dawn Doronila
+    1041,  # mjbrichant
+    863,  # K Hindall
+    937,  # Sarah Gutierrez
+    1049,  # Diana Solomon
+    1001,  # TriciaG
+    934,  # Darla
+    947,  # Larissa Little
+    944,  # Sarafina Suransky
+    870,  # Barbara Bulkeley
+    923,  # Jane Greensmith
+    1047,  # Hannah Dowell
+    967,  # Stephanie Land
+    929,  # Petra
+    963,  # MichelleHarris
+    891,  # anoldfashiongirl
+    890,  # PopularOutcast
+    992,  # Fran
+]
+# chunk_6
+speaker_ids6 = [
+    1143, #	Keith Henige 	M 	train-other-500
+    1159, #	Matt Wills 	M 	train-clean-360
+    1127, #	C.J. Casey 	M 	train-other-500
+    1235, #	Richard Kilmer 	M 	train-other-500
+    1092, #	BenW 	M 	train-other-500
+    1270, # Brendan Tannam 	M 	train-other-500
+    1214, #	David Baldwin 	M 	train-clean-360
+    1255, #	Daniel Paashaus 	M 	train-other-500
+    1152, #	Brian Keith Barnes 	M 	train-clean-360
+    1158, #	StarrDog 	M 	train-other-500
+    1256, #	Graeme Dunlop 	M 	train-other-500
+    1215, #	Kevin Maxson 	M 	train-clean-360
+    1274, #	Jud Niven 	M 	train-clean-360
+    1168, #	Epistomolus 	M 	train-clean-100
+    1089, #	Bill Ruhsam 	M 	train-clean-360
+    1142, #	Jonathan Burchard 	M 	train-other-500
+    1090, #	Termin Dyan 	M 	train-other-500
+    1109, #	Martin Geeson 	M 	train-other-500
+    1230, #	Troy Bond 	M 	train-other-500
+    1150, #	TexasSteve 	M 	train-other-500
+    1191, # Denise Lacey 	F 	train-other-500
+    1259, # 	Megan Argo 	F 	train-other-500
+    1238, #	madmouth 	F 	train-other-500
+    1135, #	Linda Andrus 	F 	train-clean-360
+    1247, #	Sarah LuAnn 	F 	train-clean-100
+    1115, #	Rachel Gatwood 	F 	train-clean-360
+    1065, #	Bob Sherman 	M 	train-other-500
+    1204, #	Dale A. Bade 	F 	train-clean-360
+    1174, #	Frances Marcinkiewicz 	F 	train-
+    1257, #	Availle 	F 	train-other-500
+    1239, #	Rachell Lovett 	F 	train-clean-360
+    1273, #	gmiteva 	F 	train-other-500
+    1242, #	Richard Ellwood 	M 	train-clean-360
+    1093, #	Katie Riley 	F 	train-clean-360
+    1063, # 	SuD 	F 	train-other-500
+    1098, #	Kerry Hiles 	F 	train-other-500
+    1254, #	Rosie 	F 	train-clean-100
+    1157, # 	Bev J Stevens 	F 	train-clean-360
+    1184, #	Joseph Couves 	F 	train-other-500
+    1253, #	Caroline Shapiro 	F 	train-other-500
+    1183, #	Evelyn Clarke 	F 	train-other-500
+    1082, #	Symmie 	F 	train-clean-360
+    1128, #	Linda Ferguson 	F 	train-other-500
+    1108, #	Paul McCartan 	M 	train-other-500
+    1202, #	Joy Easton 	F 	train-clean-360
+    1226, #	serenitylee 	F 	train-clean-360
+    1105, #	Bridget Gaige 	F 	train-clean-360
+    1229, #	RoseA 	F 	train-clean-360
+    1181, #	J. Rebecca Franklin 	F 	train-clean-360
+    1231, #	Abigail Bartels 	F 	train-other-500
+    1182, #	tabithat 	F 	train-other-500
+    1217, #	JimOCR 	M 	train-other-500
+    1171, #	Roberta Carlisle 	F 	train-other-500
+    1268, #	A. Janelle Risa 	F 	train-clean-100
+    1243, #	Rachel P. 	F 	train-clean-360
+    1071, #	js392 	F 	train-other-500
+]
+# chunk_7
+speaker_ids7 = [
+    1428,  # Gary Dzierlenga
+    1315,  # John Dennison
+    1376,  # mevans
+    1330,  # William Peck
+    1400,  # scrawl
+    1314,  # Michael Wolf
+    1425,  # Jonah Cummings
+    1438,  # Tom Barron
+    1281,  # garbageman99
+    1414,  # Preston Scrape
+    1375,  # Frank Adams
+    1410,  # Zachary Johnson
+    1365,  # Eric Leach
+    1302,  # davidb
+    1354,  # Kristen Zaza
+    1346,  # Jeanie
+    1320,  # Anita Fleming
+    1370,  # Savanna Herrold
+    1290,  # Veronica Jenkins
+    1437,  # Charles RUHE
+    1297,  # May Low
+    1440,  # P Moscato
+    1433,  # browneyedgirl32382
+    1366,  # cher0520
+    1285,  # Ric F
+    1399,  # Jeanne Luft
+    1402,  # Angel5
+    1303,  # kiwafruit
+    1301,  # Barbara Clements
+    1453,  # Anna-Lisa Ott
+    1374,  # pachayes
+    1373,  # Maria Therese
+]
+# chunk 8
+speaker_ids8 = [
+    1649,  # Phineas Redux
+    1691,  # sparks0314
+    1672,  # Mike Wajda
+    1539,  # Nathan Jordan
+    1610,  # jgoffena
+    1512,  # Matt Soar
+    1526,  # Mike Harris
+    1647,  # Patrick Reinhart
+    1636,  # jessecoy
+    1676,  # Gargoyle
+    1595,  # Matthew Reece
+    1609,  # Jacob Paul Starr
+    1671,  # bobbybrill
+    1555,  # Andrew Nelson
+    1657,  # alwpoe
+    1592,  # jerryB
+    1505,  # Rom Maczka
+    1565,  # bryan.peterson
+    1644,  # Christopher Maust
+    1695,  # Tina Nuzzi
+    1702,  # Sirmelja
+    1697,  # Lonelle Yoder
+    1596,  # Joyce Couch
+    1660,  # Jerry Romero
+    1524,  # Elizabeth Barr
+    1643,  # Linette Geisel
+    1543,  # Lauren McCullough
+    1613,  # Elsa Youngsteadt
+    1662,  # GabrielleC
+    1587,  # Claudia Wilson
+    1641,  # Kirsten Wever
+    1614,  # Jennifer Dionne
+    1603,  # Christine Rodriguez
+    1546,  # Carrie Heyes
+    1579,  # Linda Velwest
+    1638,  # Laura Victoria
+    1651,  # Debbie Pieterse
+    1554,  # Natalie Sullivan
+    1656,  # Sharon Omi
+    1607,  # Lynda Sizemore
+    1670,  # dmbrought
+    1659,  # kelleywyskiel
+]
+# chunk_9
+speaker_ids9 = [
+    1785, # BR	TRUEBRIT 	M 	train-other-500, timbre, min intonation
+    1776, # US	dan_h 	M 	train-other-500, avg intoantion, timbre
+    1791, # US	David Cummings 	M 	train-clean-360, avg intoantion, timbre
+    1707, # US 	David Olson 	M 	train-other-500, unique voice, intoantion, timbre
+    1801, # US	John O 	M 	train-other-500, unique voice, min intoantion, timbre
+    1903, # US	stephenreader 	M 	train-other-500, velvet unique voice
+    1856, # BR	Craig Gulliver 	M 	train-other-500, avg intoantion, timbre
+    1906, # US	Tony Russell 	M 	train-clean-360, dictor voice, avg intoantion, timbre
+    1806, # US	Marc Pizzuti 	M 	train-other-500, avg intoantion
+    1828, # US	David Wales 	M 	train-clean-100, avg intoantion, timbre
+    1849, # US	Fred DeBerardinis 	M 	train-clean-100, avg intoantion, timbre, unique voice
+    1712, # BR	Steve Belleguelle 	M 	train-other-500, avg intoantion, timbre
+    1853, # US	Ron Altman 	M 	train-other-500, intoantion, unique voice, timbre
+    1805, # US	Vince Dee 	M 	train-clean-100, unique voice, timbre
+    1838, # US	Morey Kunin 	M 	train-clean-360, young voice, intoantion, timbre
+    1862, # US Alexandre Laplante 	M 	train-clean-360, young voice, intoantion, timbre
+    1915, # US	T.E. McHenry 	M 	train-other-500, unique voice, dictor voice, intoantion, timbre
+    1772, # US	haggisreflux 	M 	train-clean-360, intoantion, timbre
+    1831, # BR	Nigel Boydell 	M 	train-other-500, unique voice, intoantion, timbre
+    1706, # BR	Deborah Knight 	F 	train-clean-100, unique voice, intoantion, timbre
+    1908, # US	fshort 	F 	train-other-500
+    1783, # US	Sarah Crampton 	F 	train-clean-360
+    1781, # US	humanode 	M 	train-clean-360, unique voice
+    1779, # US	Wendy Almeida 	F 	train-clean-360
+    1839, # US	James E. Carson 	M 	train-clean-360
+    1724, # US	Juliana M. 	F 	train-clean-360
+    1728, # US	Vinnie Tesla 	M 	train-clean-360
+    1907, # US	Snapdragon 	F 	train-other-500
+    1881, # US	Julienne 	F 	train-other-500
+    1802, # US	selway 	F 	train-other-500
+    1826, # US	John Hoerr 	M 	train-clean-100
+    1725, # BR	Ruth Kidson 	F 	train-other-500
+    1764, # US	ReadWriteLib 	F 	train-clean-360
+    1794, # US	Michelle Remington 	F 	train-clean-360
+    1880, # US	Christine Nendza 	F 	train-clean-360
+    1848, # US	Monica Knuppe 	F 	train-clean-360
+    1736, # US	Spike Holcomb 	F 	train-other-500
+    1841, # US	Elena 	F 	train-clean-360
+    1836, # US	Kendall Ashyby 	F 	train-other-500
+    1741, # US	anjieliu 	F 	train-other-500
+    1803, # US	Susan Hanfield 	F 	train-clean-360
+    1761, # US	EliMarieHK 	F 	train-other-500
+    1745, # US	Janet 	F 	train-clean-360
+    1713, # US	dobsonfly 	F 	train-clean-100
+    1716, # US	EyeBones 	F 	train-clean-360
+    1814, # US	polkadotish 	F 	train-other-500
+    1709, # US	CrowGirl 	F 	train-other-500
+    1763, # US	Gen Jones 	F 	train-clean-360
+    1808, # US 	Rebecca King 	F 	train-clean-360
+    1811, # US	Michelle Day 	F 	train-clean-360
+    1857, # US	Amanda Friday 	F 	train-clean-360
+    1893, # US	KirksVoice 	M 	train-other-500
+    1820, # US	Feyaza 	F 	train-other-500
+    1771, # US Chelsea S. 	F 	train-other-500
+    1718, # US	Caroline Driggs 	F 	train-other-500
+    1752, # US Shana Cohen 	F 	train-clean-360
+    1869, # US	NastassiaS 	F 	train-other-500
+    1863, # US	Tika Sabu 	F 	train-other-500
+    1723, # US 	Rachel Bossier 	F 	train-other-500
+    1798, # US	C. L. W. Rollins 	F 	train-other-500
+    1755, # US Yvonne Smith 	F 	train-clean-360
+    1738, # US	Lois C. Johnson 	F 	train-clean-360
+    1887, # US 	Jenna Lanman 	F 	train-clean-360
+]
+# chunk 10/11
+speaker_ids10_11 = [
+    1956,  # Thomas Meaney
+    2049,  # AdrianBisson
+    1978,  # John Trevithick
+    2001,  # Wesseling
+    2114,  # Larry Beasley
+    2032,  # doonaboon
+    2087,  # James Bendall
+    2011,  # pekein
+    2056,  # acloward
+    2007,  # Art Leung
+    2084,  # Eberle Thomas
+    2115,  # Pete Milan
+    1987,  # Andrew White
+    1959,  # DVoice
+    1954,  # Szindbad
+    2036,  # T.K. Kirven
+    1947,  # Barbara Edelman
+    2045,  # Linda Ciano
+    1979,  # roeg11
+    2075,  # Joy S Grape
+    2091,  # Caroline Hemmerly Kunkle
+    2023,  # Vickie Ranz
+    2014,  # Eden Rea-Hedrick
+    1965,  # redhed3095
+    1989,  # Joannemmp
+    2040,  # MJ Franck
+    1996,  # Mary in Arkansas
+    1957,  # Sarika Pawar
+    2100,  # Katherine
+    2069,  # Asta1234
+    2096,  # Tara Dow
+    2095,  # Diana Dolan
+    1995,  # Jill Janovetz
+    2017,  # CaprishaPage
+    2010,  # Peggy
+    1998,  # voicebynatalie
+    1952,  # Katalina Watt
+    2094,  # Meg Cowan
+    2065,  # Muriel
+    2312,  # Jon Kerfoot
+    2217,  # Jesse Crisp-Sears
+    2197,  # Mike Nelson
+    2282,  # Robert Snoza
+    2192,  # Sammy Bean
+    2268,  # Greg Giordano
+    2278,  # Jake Woldstad
+    2241,  # Steven Reynolds
+    2239,  # amaskill
+    2225,  # nomorejeffs
+    2283,  # Tim Cote
+    2230,  # Sam Naishtat
+    2151,  # MaxSitting
+    2141,  # KateC
+    2314,  # Cheri Jordan
+    2127,  # Ron Lockhart
+    2147,  # Shawn Bayern
+    2251,  # Wiley Combs
+    2195,  # Lynne Thompson
+    2272,  # JamesMcAndrew
+    2156,  # C F de Rosset
+    2292,  # Arnold
+    2143,  # Suebee
+    2333,  # Anita Slusser
+    2233,  # Alexis Castro
+    2305,  # Brooke Cunningham
+    2247,  # Lois Browne
+    2171,  # Carolyne
+    2172,  # Demosthenes
+    2291,  # lewildesen
+    2194,  # Iridescat
+    2331,  # Madam Fickle
+    2317,  # helengraves
+    2234,  # Coreena
+    2209,  # Samantha J Gubitz
+    2152,  # Kristel Tretter
+    2267,  # Frances Brown
+    2275,  # NatalieOram
+    2298,  # Sheila Wood
+    2138,  # Jeannie Tirado
+    2220,  # Loveday
+]
+# chunk_12
+speaker_ids12 = [
+    2403, # 	Ian Quinlan 	M 	train-clean-360
+    2436, # IND	josembi 	M 	train-other-500
+    2387, # 	Brett G. Hirsch 	M 	train-other-500
+    2444, # 	dsilber01 	M 	train-clean-360
+    2419, # Gary Dana 	M 	train-clean-100
+    2453, # 	Krzysztof Rowinski 	M 	train-clean-360
+    2451, #	DeanOBuchanan 	M 	train-clean-100
+    2473, #	Eric Metzler 	M 	train-clean-360
+    2415, #	Patrick Eaton 	M 	train-other-500
+    2379, #	pjhoury 	M 	train-other-500
+    2377, #	Jon Kissack 	M 	train-clean-100
+    2355, # 	yeknod 	M 	train-other-500
+    2452, #	Walt Allan 	M 	train-other-500
+    2401, #	Matt Parker 	M 	train-clean-360
+    2359, #	Doug Reed 	M 	train-other-500
+    2425, #	noblesavage 	M 	train-clean-100
+    2390, #	sdaeley17 	M 	train-clean-360
+    2461, #	ScottReyonoldsVoice 	M 	train-clean-360
+    2371, #	Alexander Hatton 	M 	train-clean-360
+    2479, #	Daisy Flaim 	F 	train-clean-100
+    2483, #	Tammy Porter 	F 	train-clean-360
+    2372, #	Lynne Ray 	F 	train-clean-360
+    2422, #	Jude Somers 	F 	train-clean-360
+    2357, #	William Gavula 	M 	train-other-500
+    2439, #	KHand 	F 	train-clean-360
+    2441, # 	Alison Stewart 	F 	train-clean-360
+    2413, # 	Joanne Rochon 	F 	train-clean-360
+    2383, #	Emma Joyce 	F 	train-other-500
+    2378, #	Jackie Drown 	F 	train-clean-360
+    2352, #	Jaimie Noy 	F 	train-clean-100
+    2397, # 	Rebecca Braunert-Plunkett 	F 	train-other-500
+    2394, #	TinaNygard2 	F 	train-clean-100
+    2447, #	Deena Rhoads 	F 	train-clean-360
+    2358, # 	Betty Perry 	F 	train-clean-360
+    2471, #	MariaS 	F 	train-other-500
+    2468, #	Erin Schellhase 	F 	train-clean-360
+    2370, # 	gloriousjob 	M 	train-clean-360
+    2341, # 	Haili 	F 	train-other-500
+    2469, #	Kevin Owens 	M 	train-clean-100
+    2448, #	Emily Maynard 	F 	train-clean-360
+    2351, # Nick Bulka 	M 	train-other-500
+]
+
+speaker_ids = speaker_ids1 + speaker_ids2 + speaker_ids3 + speaker_ids4 + speaker_ids5 + speaker_ids6 + speaker_ids7 + speaker_ids8 + speaker_ids9 + speaker_ids10_11 + speaker_ids12
+
+# Selected for the fine-tuning
+selected_speakers = [
+    574, #	Daniel Shorten 	M 	train-clean-100
+    242, #	J. Hall 	M 	train-other-500
+    536, #	Robert Flach 	M 	train-other-500
+    82,  #	Andy Minter 	M 	train-other-500
+    672, #	Stuart Bell 	M 	train-other-500
+    315, #	Jean Crevier 	M 	train-other-500
+    628, #	Bryan Ness 	M 	train-clean-100
+    61,  #	John Greenman 	M 	train-other-500
+    649, #	Scarlett! 	F 	train-clean-360
+    105, #	Marian Brown 	F 	train-clean-360
+    399, #	entada 	F 	train-clean-360
+    89,  # 	Paula Berinstein 	F 	train-clean-360
+    502, #	Lee Elliott 	F 	train-other-500
+    102, #	Maureen S. O'Brien 	F 	train-clean-100
+    544, #	Miranda Stinson 	F 	train-clean-360
+    653, #	cucciasv 	F 	train-other-500
+    465, #	Leonie Rose 	F 	train-clean-100
+    96,  #	Kymm Zuckert 	F 	train-other-500
+    447, #	Lee Ann Howlett 	F 	train-clean-360
+    165, # 	Elisabeth Shields 	F 	train-clean-100
+    430, # 	Millbeach 	F 	train-other-500
+    214, #	Scott Splavec 	M 	train-clean-100
+    666, #	Kelly Dougherty 	M 	train-clean-360
+    481, #	Scott Sherris 	M 	train-clean-360
+    463, #	Chris Hughes 	M 	train-other-500
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+]

config/latest_selection.txt

@@ -0,0 +1,331 @@
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+233	mikenkat	No	Yes	No	TRUE	FALSE	FALSE
+390	JimmyLogan	Yes	No	No	TRUE	FALSE	FALSE
+393	Tim Lundeen	No	No	No	FALSE	FALSE	FALSE
+425	RedToby	Yes	No	Yes	TRUE	FALSE	TRUE
+398	Sam Fold	No	No	No	FALSE	FALSE	FALSE
+372	Jim Mullins	Yes	No	No	TRUE	FALSE	FALSE
+99	Stewart Wills	No	No	No	FALSE	FALSE	FALSE
+340	Nick Gallant	No	No	No	FALSE	FALSE	FALSE
+40	JemmaBlythe	No	No	No	FALSE	FALSE	FALSE
+118	Brenda Dayne	Yes	Yes	Yes	TRUE	TRUE	TRUE
+640	David A. Stokely	Yes	No	No	TRUE	FALSE	FALSE
+50	Dan Threetrees	Yes	No	Yes	TRUE	FALSE	TRUE
+373	Brooks Seveer	Yes	Yes	No	TRUE	FALSE	TRUE
+124	Steve Karafit	No	No	No	FALSE	FALSE	FALSE
+314	Carl Vonnoh, III	Yes	Yes	Yes	TRUE	TRUE	TRUE
+531	Fr. Richard Zeile of Detroit	Yes	No	No	TRUE	FALSE	FALSE
+383	Mike Roop	Yes	No	No	TRUE	FALSE	FALSE
+710	Sheila Morton	Yes	No	Yes	TRUE	FALSE	TRUE
+450	Heather Duncan	Yes	No	Yes	TRUE	FALSE	TRUE
+645	Micah	Yes	Yes	No	TRUE	FALSE	TRUE
+517	Madame Tusk	Yes	Yes	No	TRUE	FALSE	TRUE
+479	Wina Hathaway	Yes	No	No	TRUE	FALSE	FALSE
+30	Ophelia Darcy	No	No	No	FALSE	FALSE	FALSE
+220	Tina Tilney	No	No	No	FALSE	FALSE	FALSE
+63	Linda Wilcox	Yes	Yes	No	TRUE	FALSE	TRUE
+283	Bethany Simpson	Yes	No	No	TRUE	FALSE	FALSE
+644	Cynthia Zocca	Yes	Yes	No	TRUE	FALSE	TRUE
+677	Allyson Hester	No	No	No	FALSE	FALSE	FALSE
+21	Kelly Bescherer	Yes	No	No	TRUE	FALSE	FALSE
+552	Mim Ritty	No	No	No	FALSE	FALSE	FALSE
+80	Fox in the Stars	Yes	No	Yes	TRUE	FALSE	TRUE
+394	swroot	Yes	Yes	No	TRUE	FALSE	TRUE
+426	Megan Stemm-Wade	Yes	No	No	TRUE	FALSE	FALSE
+91	Chris Goringe	Yes	No	Yes	TRUE	FALSE	TRUE
+108	Kevin McAsh	Yes	No	Yes	TRUE	FALSE	TRUE
+130	Peter of Buckinghamshire England	Yes	No	No	TRUE	FALSE	FALSE
+661	James Gladwin	Yes	Yes	Yes	TRUE	TRUE	TRUE
+216	Dave Ranson	No	No	No	FALSE	FALSE	FALSE
+164	Ed Good	Yes	Yes	Yes	TRUE	TRUE	TRUE
+308	Eric Connover	Yes	Yes	Yes	TRUE	TRUE	TRUE
+569	Arouet	Yes	No	No	TRUE	FALSE	FALSE
+313	Tim Bulkeley	No	No	No	FALSE	FALSE	FALSE
+212	Glen Hallstrom	No	No	No	FALSE	FALSE	FALSE
+15	Chip	Yes	No	No	TRUE	FALSE	FALSE
+469	Christian Pecaut	Yes	Yes	No	TRUE	FALSE	TRUE
+294	Diana Kiesners	No	No	No	FALSE	FALSE	FALSE
+192	Nocturna	Yes	Yes	No	TRUE	FALSE	TRUE
+73	Claire Goget	Yes	No	No	TRUE	FALSE	FALSE
+417	Kiki Baessell	Yes	No	Yes	TRUE	FALSE	TRUE
+636	Matthew Howell	Yes	No	No	TRUE	FALSE	FALSE
+36	chriss the girl	Yes	No	No	TRUE	FALSE	FALSE
+668	Jan Baxter	Yes	Yes	Yes	TRUE	TRUE	TRUE
+403	Igor Teaforay	No	No	No	FALSE	FALSE	FALSE
+618	Linnea	No	No	No	FALSE	FALSE	FALSE
+596	Jo	Yes	No	Yes	TRUE	FALSE	TRUE
+499	Tammy Sanders	No	No	No	FALSE	FALSE	FALSE
+207	Sage Tyrtle	No	No	No	FALSE	FALSE	FALSE
+1346	Jeanie	No	No	No	FALSE	FALSE	FALSE
+1109	Martin Geeson	Yes	No	Yes	TRUE	FALSE	TRUE
+770	Pete Williams, Pittsburgh, PA	Yes	No	Yes	TRUE	FALSE	TRUE
+1247	Sarah LuAnn	Yes	Yes	No	TRUE	FALSE	TRUE
+1526	Mike Harris	No	No	No	FALSE	FALSE	FALSE
+908	Quentin Manuel	Yes	No	Yes	TRUE	FALSE	TRUE
+1183	Evelyn Clarke	Yes	No	No	TRUE	FALSE	FALSE
+1438	Tom Barron	Yes	No	No	TRUE	FALSE	FALSE
+1022	peac	No	No	No	FALSE	FALSE	FALSE
+1603	Christine Rodriguez	No	No	No	FALSE	FALSE	FALSE
+1425	Jonah Cummings	No	No	No	FALSE	FALSE	FALSE
+731	Priya, India	Yes	No	No	TRUE	FALSE	FALSE
+782	Alec Daitsman	Yes	Yes	No	TRUE	FALSE	TRUE
+1090	Termin Dyan	Yes	No	No	TRUE	FALSE	FALSE
+995	Parrot	Yes	Yes	No	TRUE	FALSE	TRUE
+923	Jane Greensmith	Yes	No	Yes	TRUE	FALSE	TRUE
+766	Clive Catterall	No	No	No	FALSE	FALSE	FALSE
+822	kristiface	Yes	Yes	Yes	TRUE	TRUE	TRUE
+897	Jan Dawn Doronila	Yes	No	No	TRUE	FALSE	FALSE
+1579	Linda Velwest	No	No	No	FALSE	FALSE	FALSE
+964	Utek	No	No	No	FALSE	FALSE	FALSE
+1414	Preston Scrape	Yes	No	Yes	TRUE	FALSE	TRUE
+834	Serin	No	No	No	FALSE	FALSE	FALSE
+1302	davidb	Yes	No	Yes	TRUE	FALSE	TRUE
+1135	Linda Andrus	Yes	No	Yes	TRUE	FALSE	TRUE
+1440	P Moscato	Yes	Yes	No	TRUE	FALSE	TRUE
+870	Barbara Bulkeley	No	Yes	No	TRUE	FALSE	FALSE
+1256	Graeme Dunlop	Yes	No	No	TRUE	FALSE	FALSE
+1255	Daniel Paashaus	No	No	No	FALSE	FALSE	FALSE
+1157	Bev J Stevens	No	No	No	FALSE	FALSE	FALSE
+934	Darla	No	No	Yes	TRUE	FALSE	FALSE
+1281	garbageman99	Yes	No	Yes	TRUE	FALSE	TRUE
+819	n8evv	Yes	No	No	TRUE	FALSE	FALSE
+1041	mjbrichant	Yes	No	Yes	TRUE	FALSE	TRUE
+863	K Hindall	No	No	No	FALSE	FALSE	FALSE
+1303	kiwafruit	No	No	No	FALSE	FALSE	FALSE
+1115	Rachel Gatwood	No	No	No	FALSE	FALSE	FALSE
+1539	Nathan Jordan	No	No	No	FALSE	FALSE	FALSE
+1428	Gary Dzierlenga	No	No	No	FALSE	FALSE	FALSE
+1049	Diana Solomon	No	No	No	FALSE	FALSE	FALSE
+1546	Carrie Heyes	No	No	No	FALSE	FALSE	FALSE
+1089	Bill Ruhsam	No	No	No	FALSE	FALSE	FALSE
+1142	Jonathan Burchard	Yes	No	Yes	TRUE	FALSE	TRUE
+1375	Frank Adams	Yes	No	No	TRUE	FALSE	FALSE
+881	mpetranech	Yes	No	Yes	TRUE	FALSE	TRUE
+798	Wyatt	No	No	No	FALSE	FALSE	FALSE
+1647	Patrick Reinhart	No	No	No	FALSE	FALSE	FALSE
+1587	Claudia Wilson	Yes	No	No	TRUE	FALSE	FALSE
+830	musici123	Yes	No	No	TRUE	FALSE	FALSE
+1592	jerryB	No	No	No	FALSE	FALSE	FALSE
+839	Ben Dutton	No	No	No	FALSE	FALSE	FALSE
+835	Rachel Lintern	Yes	No	Yes	TRUE	FALSE	TRUE
+1273	gmiteva	Yes	No	No	TRUE	FALSE	FALSE
+932	Raerity	Yes	Yes	No	TRUE	FALSE	TRUE
+1108	Paul McCartan	No	No	No	FALSE	FALSE	FALSE
+732	Tysto	Yes	Yes	No	TRUE	FALSE	TRUE
+781	Megan Kunkel	No	No	No	FALSE	FALSE	FALSE
+1555	Andrew Nelson	No	No	No	FALSE	FALSE	FALSE
+1437	Charles RUHE	No	No	No	FALSE	FALSE	FALSE
+1402	Angel5	Yes	No	Yes	TRUE	FALSE	TRUE
+963	MichelleHarris	No	No	No	FALSE	FALSE	FALSE
+1181	J. Rebecca Franklin	No	No	No	FALSE	FALSE	FALSE
+818	Matt Warzel	No	No	No	FALSE	FALSE	FALSE
+1285	Ric F	Yes	No	No	TRUE	FALSE	FALSE
+797	Chris Jones	Yes	No	No	TRUE	FALSE	FALSE
+1505	Rom Maczka	Yes	No	No	TRUE	FALSE	FALSE
+1214	David Baldwin	No	No	No	FALSE	FALSE	FALSE
+1636	jessecoy	No	No	No	FALSE	FALSE	FALSE
+929	Petra	Yes	Yes	No	TRUE	FALSE	TRUE
+1171	Roberta Carlisle	No	No	No	FALSE	FALSE	FALSE
+817	texttalker	Yes	Yes	No	TRUE	FALSE	TRUE
+1433	browneyedgirl32382	Yes	Yes	Yes	TRUE	TRUE	TRUE
+1158	StarrDog	Yes	No	No	TRUE	FALSE	FALSE
+1000	artos	No	No	No	FALSE	FALSE	FALSE
+848	senshisteph	Yes	No	No	TRUE	FALSE	FALSE
+1596	Joyce Couch	Yes	Yes	No	TRUE	FALSE	TRUE
+757	Roger Melin	Yes	No	No	TRUE	FALSE	FALSE
+1168	Epistomolus	Yes	No	No	TRUE	FALSE	FALSE
+741	Nick Marsh	Yes	No	No	TRUE	FALSE	FALSE
+1649	Phineas Redux	No	No	No	FALSE	FALSE	FALSE
+851	Jennifer Lott	Yes	Yes	Yes	TRUE	TRUE	TRUE
+808	M. J. Boyle	No	No	No	FALSE	FALSE	FALSE
+1595	Matthew Reece	No	No	No	FALSE	FALSE	FALSE
+1370	Savanna Herrold	Yes	Yes	Yes	TRUE	TRUE	TRUE
+1565	bryan.peterson	No	Yes	No	TRUE	FALSE	FALSE
+944	Sarafina Suransky	Yes	No	No	TRUE	FALSE	FALSE
+1268	A. Janelle Risa	Yes	No	No	TRUE	FALSE	FALSE
+771	Isosceles	No	No	No	FALSE	FALSE	FALSE
+752	Cat Schirf	No	No	No	FALSE	FALSE	FALSE
+800	Jack Farrell	No	No	No	FALSE	FALSE	FALSE
+1005	Beatrice	Yes	No	No	TRUE	FALSE	FALSE
+1229	RoseA	No	No	No	FALSE	FALSE	FALSE
+943	Matthew C. Heckel	No	No	No	FALSE	FALSE	FALSE
+891	anoldfashiongirl	Yes	No	No	TRUE	FALSE	FALSE
+1226	serenitylee	No	No	No	FALSE	FALSE	FALSE
+1253	Caroline Shapiro	Yes	No	No	TRUE	FALSE	FALSE
+1204	Dale A. Bade	Yes	No	Yes	TRUE	FALSE	TRUE
+1230	Troy Bond	Yes	Yes	No	TRUE	FALSE	TRUE
+791	David Kleparek	Yes	Yes	No	TRUE	FALSE	TRUE
+1184	Joseph Couves	No	No	No	FALSE	FALSE	FALSE
+1001	TriciaG	No	No	No	FALSE	FALSE	FALSE
+804	FirstKnight	Yes	Yes	No	TRUE	FALSE	TRUE
+1641	Kirsten Wever	No	No	No	FALSE	FALSE	FALSE
+1259	Megan Argo	No	No	No	FALSE	FALSE	FALSE
+1231	Abigail Bartels	No	No	No	FALSE	FALSE	FALSE
+1410	Zachary Johnson	Yes	No	No	TRUE	FALSE	FALSE
+1030	Ancient mariner	Yes	No	No	TRUE	FALSE	FALSE
+1093	Katie Riley	No	No	No	FALSE	FALSE	FALSE
+1254	Rosie	No	No	No	FALSE	FALSE	FALSE
+1365	Eric Leach	Yes	No	No	TRUE	FALSE	FALSE
+831	David Federman	No	No	No	FALSE	FALSE	FALSE
+1989	Joannemmp	No	No	No	FALSE	FALSE	FALSE
+1707	David Olson	No	No	No	FALSE	FALSE	FALSE
+1849	Fred DeBerardinis	Yes	No	No	TRUE	FALSE	FALSE
+1808	Rebecca King	Yes	No	Yes	TRUE	FALSE	TRUE
+2292	Arnold	No	No	No	FALSE	FALSE	FALSE
+2415	Patrick Eaton	No	No	No	FALSE	FALSE	FALSE
+1656	Sharon Omi	Yes	Yes	No	TRUE	FALSE	TRUE
+1676	Gargoyle	No	Yes	No	TRUE	FALSE	FALSE
+1881	Julienne	No	No	Yes	TRUE	FALSE	FALSE
+2036	T.K. Kirven	No	No	No	FALSE	FALSE	FALSE
+1761	EliMarieHK	Yes	No	No	TRUE	FALSE	FALSE
+2115	Pete Milan	Yes	Yes	Yes	TRUE	TRUE	TRUE
+1803	Susan Hanfield	Yes	No	No	TRUE	FALSE	FALSE
+1798	C. L. W. Rollins	Yes	No	No	TRUE	FALSE	FALSE
+1723	Rachel Bossier	Yes	No	No	TRUE	FALSE	FALSE
+2341	Haili	Yes	No	Yes	TRUE	FALSE	TRUE
+2468	Erin Schellhase	Yes	Yes	No	TRUE	FALSE	TRUE
+1725	Ruth Kidson	Yes	No	No	TRUE	FALSE	FALSE
+2010	Peggy	Yes	No	No	TRUE	FALSE	FALSE
+1853	Ron Altman	Yes	No	No	TRUE	FALSE	FALSE
+2359	Doug Reed	No	No	No	FALSE	FALSE	FALSE
+2422	Jude Somers	No	No	No	FALSE	FALSE	FALSE
+2234	Coreena	No	No	No	FALSE	FALSE	FALSE
+2156	C F de Rosset	No	No	Yes	TRUE	FALSE	FALSE
+2483	Tammy Porter	No	No	No	FALSE	FALSE	FALSE
+1781	humanode	No	No	No	FALSE	FALSE	FALSE
+2275	NatalieOram	No	No	No	FALSE	FALSE	FALSE
+2390	sdaeley17	No	No	No	FALSE	FALSE	FALSE
+2314	Cheri Jordan	No	No	No	FALSE	FALSE	FALSE
+2413	Joanne Rochon	No	No	No	FALSE	FALSE	FALSE
+1697	Lonelle Yoder	No	No	No	FALSE	FALSE	FALSE
+1718	Caroline Driggs	Yes	Yes	No	TRUE	FALSE	TRUE
+2387	Brett G. Hirsch	No	No	No	FALSE	FALSE	FALSE
+2331	Madam Fickle	No	No	No	FALSE	FALSE	FALSE
+1783	Sarah Crampton	Yes	Yes	Yes	TRUE	TRUE	TRUE
+2397	Rebecca Braunert-Plunkett	No	No	No	FALSE	FALSE	FALSE
+2357	William Gavula	No	No	No	FALSE	FALSE	FALSE
+1670	dmbrought	No	No	No	FALSE	FALSE	FALSE
+1987	Andrew White	No	No	No	FALSE	FALSE	FALSE
+1755	Yvonne Smith	Yes	Yes	No	TRUE	FALSE	TRUE
+2192	Sammy Bean	Yes	Yes	No	TRUE	FALSE	TRUE
+1716	EyeBones	Yes	No	No	TRUE	FALSE	FALSE
+1828	David Wales	No	No	No	FALSE	FALSE	FALSE
+2251	Wiley Combs	No	No	No	FALSE	FALSE	FALSE
+2065	Muriel	No	No	No	FALSE	FALSE	FALSE
+2017	CaprishaPage	Yes	No	No	TRUE	FALSE	FALSE
+1947	Barbara Edelman	No	No	Yes	TRUE	FALSE	FALSE
+1738	Lois C. Johnson	No	No	No	FALSE	FALSE	FALSE
+1791	David Cummings	No	Yes	No	TRUE	FALSE	FALSE
+2045	Linda Ciano	No	No	No	FALSE	FALSE	FALSE
+2452	Walt Allan	No	No	Yes	TRUE	FALSE	FALSE
+2040	MJ Franck	No	No	No	FALSE	FALSE	FALSE
+1831	Nigel Boydell	Yes	No	No	TRUE	FALSE	FALSE
+2371	Alexander Hatton	Yes	Yes	No	TRUE	FALSE	TRUE
+1954	Szindbad	No	No	No	FALSE	FALSE	FALSE
+1836	Kendall Ashyby	Yes	Yes	No	TRUE	FALSE	TRUE
+2436	josembi	No	No	No	FALSE	FALSE	FALSE
+2383	Emma Joyce	No	No	No	FALSE	FALSE	FALSE
+2278	Jake Woldstad	No	No	No	FALSE	FALSE	FALSE
+1741	anjieliu	Yes	Yes	No	TRUE	FALSE	TRUE
+1857	Amanda Friday	Yes	No	No	TRUE	FALSE	FALSE
+2370	gloriousjob	No	No	No	FALSE	FALSE	FALSE
+1907	Snapdragon	No	No	No	FALSE	FALSE	FALSE
+2225	nomorejeffs	No	Yes	No	TRUE	FALSE	FALSE
+2439	KHand	Yes	Yes	No	TRUE	FALSE	TRUE
+2239	amaskill	No	Yes	No	TRUE	FALSE	FALSE
+2007	Art Leung	No	No	No	FALSE	FALSE	FALSE
+2283	Tim Cote	No	No	No	FALSE	FALSE	FALSE
+1712	Steve Belleguelle	Yes	Yes	Yes	TRUE	TRUE	TRUE
+2094	Meg Cowan	Yes	No	No	TRUE	FALSE	FALSE
+1772	haggisreflux	No	No	No	FALSE	FALSE	FALSE
+2317	helengraves	Yes	No	No	TRUE	FALSE	FALSE
+2241	Steven Reynolds	No	No	No	FALSE	FALSE	FALSE
+2011	pekein	Yes	No	No	TRUE	FALSE	FALSE
+1826	John Hoerr	Yes	No	No	TRUE	FALSE	FALSE
+1695	Tina Nuzzi	Yes	No	No	TRUE	FALSE	FALSE
+2451	DeanOBuchanan	Yes	No	No	TRUE	FALSE	FALSE
+1771	Chelsea S.	Yes	No	No	TRUE	FALSE	FALSE
+2441	Alison Stewart	No	No	No	FALSE	FALSE	FALSE
+1745	Janet	No	No	No	FALSE	FALSE	FALSE
+2358	Betty Perry	No	No	No	FALSE	FALSE	FALSE
+2197	Mike Nelson	Yes	Yes	Yes	TRUE	TRUE	TRUE
+2014	Eden Rea-Hedrick	Yes	No	No	TRUE	FALSE	FALSE
+1672	Mike Wajda	No	No	No	FALSE	FALSE	FALSE
+2394	TinaNygard2	No	Yes	No	TRUE	FALSE	FALSE
+1657	alwpoe	No	No	No	FALSE	FALSE	FALSE
+1728	Vinnie Tesla	Yes	Yes	No	TRUE	FALSE	TRUE
+1805	Vince Dee	Yes	No	Yes	TRUE	FALSE	TRUE
+2143	Suebee	Yes	Yes	No	TRUE	FALSE	TRUE
+2084	Eberle Thomas	Yes	No	Yes	TRUE	FALSE	TRUE
+2479	Daisy Flaim	No	No	No	FALSE	FALSE	FALSE
+2152	Kristel Tretter	No	No	No	FALSE	FALSE	FALSE
+2268	Greg Giordano	No	No	No	FALSE	FALSE	FALSE
+1839	James E. Carson	No	No	No	FALSE	FALSE	FALSE
+2056	acloward	Yes	Yes	No	TRUE	FALSE	TRUE
+1814	polkadotish	No	No	No	FALSE	FALSE	FALSE
+2127	Ron Lockhart	No	No	No	FALSE	FALSE	FALSE
+2114	Larry Beasley	No	Yes	Yes	TRUE	FALSE	TRUE
+2469	Kevin Owens	Yes	No	No	TRUE	FALSE	FALSE
+2447	Deena Rhoads	Yes	Yes	No	TRUE	FALSE	TRUE
+1724	Juliana M.	Yes	No	No	TRUE	FALSE	FALSE
+1869	NastassiaS	Yes	Yes	Yes	TRUE	TRUE	TRUE
+2209	Samantha J Gubitz	Yes	Yes	Yes	TRUE	TRUE	TRUE
+2171	Carolyne	No	Yes	No	TRUE	FALSE	FALSE
+2403	Ian Quinlan	Yes	No	No	TRUE	FALSE	FALSE
+2032	doonaboon	Yes	No	No	TRUE	FALSE	FALSE
+x	Joy S Grape	No	No	No	FALSE	FALSE	FALSE

config/phone2idx.json

@@ -0,0 +1,65 @@
+{
+    "<mask>": 1,
+    "<pad>": 0,
+    "<unk>": 2,
+    "[COMMA]": 17,
+    "[EXCLAMATION MARK]": 50,
+    "[FULL STOP]": 30,
+    "[QUESTION MARK]": 54,
+    "[SEMICOLON]": 56,
+    "[SILENCE]": 3,
+    "a\u026a": 26,
+    "a\u028a": 46,
+    "b": 21,
+    "d": 19,
+    "d\u0292": 42,
+    "e\u026a": 4,
+    "f": 27,
+    "h": 38,
+    "i": 8,
+    "j": 31,
+    "k": 23,
+    "l": 22,
+    "m": 10,
+    "n": 15,
+    "o\u028a": 32,
+    "p": 11,
+    "s": 28,
+    "t": 13,
+    "t\u0283": 43,
+    "u": 24,
+    "v": 5,
+    "w": 29,
+    "z": 20,
+    "\u00e6": 18,
+    "\u00f0": 34,
+    "\u014b": 36,
+    "\u0251": 39,
+    "\u0254": 12,
+    "\u0254\u026a": 16,
+    "\u025b": 6,
+    "\u025c\u02de": 37,
+    "\u0261": 33,
+    "\u026a": 9,
+    "\u0279": 7,
+    "\u0283": 35,
+    "\u028a": 49,
+    "\u028c": 14,
+    "\u0292": 25,
+    "\u02cca\u026a": 52,
+    "\u02cca\u028a": 60,
+    "\u02cce\u026a": 58,
+    "\u02cci": 44,
+    "\u02cco\u028a": 45,
+    "\u02ccu": 51,
+    "\u02cc\u00e6": 59,
+    "\u02cc\u0251": 61,
+    "\u02cc\u0254": 57,
+    "\u02cc\u0254\u026a": 62,
+    "\u02cc\u025b": 53,
+    "\u02cc\u025c\u02de": 47,
+    "\u02cc\u026a": 40,
+    "\u02cc\u028a": 48,
+    "\u02cc\u028c": 55,
+    "\u03b8": 41
+}

config/speaker2idx.json

@@ -0,0 +1,2442 @@
+{
+    "100": 1017,
+    "1006": 1635,
+    "101": 718,
+    "1012": 706,
+    "1018": 688,
+    "102": 2191,
+    "1025": 588,
+    "1027": 591,
+    "1028": 291,
+    "103": 15,
+    "1034": 182,
+    "104": 1979,
+    "1040": 195,
+    "1046": 459,
+    "1049": 2264,
+    "1050": 819,
+    "1051": 2174,
+    "1052": 374,
+    "1053": 878,
+    "1054": 864,
+    "1060": 1001,
+    "1061": 576,
+    "1065": 1420,
+    "1066": 366,
+    "1069": 210,
+    "107": 1333,
+    "1079": 381,
+    "1081": 190,
+    "1084": 1514,
+    "1085": 1288,
+    "1088": 107,
+    "1089": 2383,
+    "1092": 1555,
+    "1093": 480,
+    "1094": 1588,
+    "1096": 1339,
+    "1097": 1311,
+    "1098": 92,
+    "110": 1506,
+    "1100": 1080,
+    "1107": 1355,
+    "111": 1659,
+    "1110": 1540,
+    "1112": 489,
+    "1116": 54,
+    "112": 560,
+    "1121": 755,
+    "1124": 2160,
+    "1132": 1837,
+    "114": 1044,
+    "115": 1088,
+    "1152": 1417,
+    "1154": 1936,
+    "116": 2354,
+    "1160": 252,
+    "1161": 1405,
+    "1165": 694,
+    "1166": 2026,
+    "1168": 1516,
+    "1171": 1940,
+    "1179": 1547,
+    "118": 235,
+    "1182": 621,
+    "1183": 188,
+    "1184": 2107,
+    "1187": 1648,
+    "1188": 2404,
+    "119": 435,
+    "1195": 774,
+    "1200": 1382,
+    "121": 2384,
+    "1212": 1018,
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+    "1221": 2401,
+    "1222": 520,
+    "1224": 545,
+    "1225": 2065,
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+    "123": 1319,
+    "1230": 1682,
+    "1235": 212,
+    "1239": 1670,
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+    "125": 97,
+    "1250": 2037,
+    "1252": 1488,
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+    "1259": 286,
+    "126": 712,
+    "1260": 1808,
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+    "1263": 3,
+    "1264": 1035,
+    "1265": 660,
+    "1266": 1235,
+    "127": 1182,
+    "1271": 812,
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+    "128": 1343,
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+    "1283": 658,
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+    "1291": 2002,
+    "1296": 699,
+    "1298": 1560,
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+    "1316": 1113,
+    "1320": 2390,
+    "1322": 890,
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+    "133": 2129,
+    "1331": 1811,
+    "1334": 49,
+    "1335": 427,
+    "1336": 818,
+    "1337": 865,
+    "1341": 2082,
+    "1342": 1232,
+    "1343": 811,
+    "1347": 2070,
+    "1348": 849,
+    "1349": 501,
+    "1353": 1361,
+    "1355": 242,
+    "1363": 147,
+    "1365": 722,
+    "1367": 1415,
+    "1370": 1384,
+    "1373": 2018,
+    "1374": 2009,
+    "1379": 728,
+    "1382": 1066,
+    "1383": 1032,
+    "1384": 1300,
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config/speaker_id_mapping_libri.json

@@ -0,0 +1 @@
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config/speakers.json

@@ -0,0 +1 @@
+{"3": {"id": 3, "name": "Kara Shallenberg", "gender": "F"}, "8": {"id": 8, "name": "Denny Sayers", "gender": "M"}, "9": {"id": 9, "name": "Sean McKinley", "gender": "M"}, "14": {"id": 14, "name": "Betsie Bush", "gender": "F"}, "18": {"id": 18, "name": "Sherry Crowther", "gender": "F"}, "19": {"id": 19, "name": "Vicki Barbour", "gender": "F"}, "32": {"id": 32, "name": "nan", "gender": "M"}, "41": {"id": 41, "name": "Hugh McGuire", "gender": "M"}, "45": {"id": 45, "name": "Catharine Eastman", "gender": "F"}, "48": {"id": 48, "name": "Rosalind Wills", "gender": "F"}, "49": {"id": 49, "name": "Kristen McQuillin", "gender": "F"}, "59": {"id": 59, "name": "Karen Savage", "gender": "F"}, "68": {"id": 68, "name": "Alex Buie", "gender": "M"}, "73": {"id": 73, "name": "Claire Goget", "gender": "F"}, "80": {"id": 80, "name": "Fox in the Stars", "gender": "F"}, "88": {"id": 88, "name": "Andrew Miller", "gender": "M"}, "99": {"id": 99, "name": "Stewart Wills", "gender": "M"}, "100": {"id": 100, "name": "Heather Barnett", "gender": "F"}, "102": {"id": 102, "name": "Maureen S. O'Brien", "gender": "F"}, "103": {"id": 103, "name": "Joplin James", "gender": "M"}, "112": {"id": 112, "name": "shanda_w", "gender": "F"}, "119": {"id": 119, "name": "Deb Bacon-Ziegler", "gender": "F"}, "122": {"id": 122, "name": "carnright", "gender": "M"}, "124": {"id": 124, "name": "Steve Karafit", "gender": "M"}, "132": {"id": 132, "name": "Becky Miller", "gender": "F"}, "134": {"id": 134, "name": "Mary Reagan", "gender": "F"}, "138": {"id": 138, "name": "Alan Davis Drake (1945-2010)", "gender": "M"}, "152": {"id": 152, "name": "Barbara Wedge", "gender": "F"}, "155": {"id": 155, "name": "Caroline Morse", "gender": "F"}, "156": {"id": 156, "name": "Chris Peterson", "gender": "F"}, "158": {"id": 158, "name": "Randy Phillips", "gender": "M"}, "160": {"id": 160, "name": "deadwhitemales", "gender": "M"}, "165": {"id": 165, "name": "Elisabeth Shields", "gender": "F"}, "167": {"id": 167, "name": "Elizabeth Palmer", "gender": "F"}, "170": {"id": 170, "name": "Aaron Teiser", "gender": "M"}, "186": {"id": 186, "name": "kumarei", "gender": "M"}, "192": {"id": 192, "name": "Nocturna", "gender": "F"}, "194": {"id": 194, "name": "Eric Dennison", "gender": "M"}, "197": {"id": 197, "name": "Brian Roberg", "gender": "M"}, "201": {"id": 201, "name": "Norah Piehl", "gender": "F"}, "206": {"id": 206, "name": "Sandra in Wales, United Kingdom", "gender": "F"}, "208": {"id": 208, "name": "Dave Foss", "gender": "M"}, "209": {"id": 209, "name": "Steve Hartzog", "gender": "M"}, "214": {"id": 214, "name": "Scott Splavec", "gender": "M"}, "216": {"id": 216, "name": "Dave Ranson", "gender": "M"}, "226": {"id": 226, "name": "Neal Foley", "gender": "M"}, "261": {"id": 261, "name": "Joy Scaglione", "gender": "F"}, "274": {"id": 274, "name": "toriasuncle", "gender": "M"}, "284": {"id": 284, "name": "Anne", "gender": "F"}, "292": {"id": 292, "name": "Tamara R. 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config/speakers.tsv

@@ -0,0 +1,2485 @@
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config/vocab_phonemes.txt

@@ -0,0 +1,63 @@
+[MASK]
+[PAD]
+[UNK]
+[COMMA]
+[EXCLAMATION MARK]
+[FULL STOP]
+[QUESTION MARK]
+[SEMICOLON]
+[SILENCE]
+aɪ
+aʊ
+b
+d
+dʒ
+eɪ
+f
+h
+i
+j
+k
+l
+m
+n
+oʊ
+p
+s
+t
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+w
+z
+æ
+ð
+ŋ
+ɑ
+ɔ
+ɔɪ
+ɛ
+ɜ˞
+ɡ
+ɪ
+ɹ
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+ʊ
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+ʒ
+ˌaɪ
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+ˌɑ
+ˌɔ
+ˌɔɪ
+ˌɛ
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+θ

demo/config.py

@@ -0,0 +1,109 @@
+speakers_delightful_22050 = {
+    "Dan Threetrees": 50,
+    "Cori Samuel": 52,
+    "Linda Wilcox": 63,
+    "Christiane Levesque": 64,
+    "Fox in the Stars": 80,
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+    "Ed Good": 164,
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+    "fieldsofgold": 248,
+    "Eric Connover": 308,
+    "Carl Vonnoh, III": 314,
+    "Brooks Seveer": 373,
+    "swroot": 394,
+    "Christabel": 414,
+    "Kiki Baessell": 417,
+    "RedToby": 425,
+    "Heather Duncan": 450,
+    "Christian Pecaut": 469,
+    "Scott Sherris": 481,
+    "Madame Tusk": 517,
+    "Miranda Stinson": 544,
+    "Daniel Shorten": 574,
+    "Jo": 596,
+    "Anne-Marie": 604,
+    "PJ": 617,
+    "Cynthia Zocca": 644,
+    "Micah": 645,
+    "Scarlett!": 649,
+    "cucciasv": 653,
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+    "Jan Baxter": 668,
+    "Nikki Sullivan": 685,
+    "Sheila Morton": 710,
+    "Tysto": 732,
+    "Pete Williams, Pittsburgh, PA": 770,
+    "Alec Daitsman": 782,
+    "David Kleparek": 791,
+    "FirstKnight": 804,
+    "texttalker": 817,
+    "kristiface": 822,
+    "Rachel Lintern": 835,
+    "Jennifer Lott": 851,
+    "mpetranech": 881,
+    "Quentin Manuel": 908,
+    "Jane Greensmith": 923,
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+    "Raerity": 932,
+    "Parrot": 995,
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+    "Angel5": 1402,
+    "Preston Scrape": 1414,
+    "browneyedgirl32382": 1433,
+    "P Moscato": 1440,
+    "Joyce Couch": 1596,
+    "Sharon Omi": 1656,
+    "Steve Belleguelle": 1712,
+    "Caroline Driggs": 1718,
+    "Vinnie Tesla": 1728,
+    "anjieliu": 1741,
+    "Yvonne Smith": 1755,
+    "Sarah Crampton": 1783,
+    "Vince Dee": 1805,
+    "Rebecca King": 1808,
+    "Kendall Ashyby": 1836,
+    "NastassiaS": 1869,
+    "acloward": 2056,
+    "Eberle Thomas": 2084,
+    "Larry Beasley": 2114,
+    "Pete Milan": 2115,
+    "Suebee": 2143,
+    "Sammy Bean": 2192,
+    "Mike Nelson": 2197,
+    "Samantha J Gubitz": 2209,
+    "Haili": 2341,
+    "Alexander Hatton": 2371,
+    "KHand": 2439,
+    "Deena Rhoads": 2447,
+    "Erin Schellhase": 2468,
+}
+
+speakers_hifi_tts = {
+    "Cori Samuel": 92,
+    "Phil Benson": 6097,
+    "John Van Stan": 9017,
+    "Mike Pelton": 6670,
+    "Tony Oliva": 6671,
+    "Maria Kasper": 8051,
+    "Helen Taylor": 9136,
+    "Sylviamb": 11614,
+    "Celine Major": 11697,
+    "LikeManyWaters": 12787,
+}

en_us_cmudict_ipa_forward.pt

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mocks/metadata.txt

@@ -0,0 +1,3 @@
+0001|Alice
+0002|Bob
+0003|Charlie

mocks/speakers.json

@@ -0,0 +1,5 @@
+{
+    "Alice": 0,
+    "Bob": 1,
+    "Charlie": 2
+}

mocks/test_compute_yin.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9a4db041f1f2f053d1477775dc177d27989d5186c0849cc2ab24e337c2c4115
+size 5632

models/__init__.py

@@ -0,0 +1 @@
+from .delightful_hifi import DelightfulHiFi

models/config/__init__.py

@@ -0,0 +1,3 @@
+from .configs import *
+from .langs import *
+from .symbols import *

models/config/configs.py

@@ -0,0 +1,438 @@
+from dataclasses import dataclass, field
+from typing import List, Literal, Tuple, Union
+
+PreprocessLangType = Literal["english_only", "multilingual"]
+
+
+@dataclass
+class STFTConfig:
+    filter_length: int
+    hop_length: int
+    win_length: int
+    n_mel_channels: int
+    mel_fmin: int
+    mel_fmax: int
+
+
+# Base class used with the Univnet vocoder
+@dataclass
+class PreprocessingConfig:
+    language: PreprocessLangType
+    stft: STFTConfig
+    sampling_rate: int = 22050
+    min_seconds: float = 0.5
+    max_seconds: float = 6.0
+    use_audio_normalization: bool = True
+    workers: int = 8
+
+
+@dataclass
+class PreprocessingConfigUnivNet(PreprocessingConfig):
+    stft: STFTConfig = field(
+        default_factory=lambda: STFTConfig(
+            filter_length=1024,
+            hop_length=256,
+            win_length=1024,
+            n_mel_channels=100,  # univnet
+            mel_fmin=20,
+            mel_fmax=11025,
+        ),
+    )
+
+
+@dataclass
+class PreprocessingConfigHifiGAN(PreprocessingConfig):
+    stft: STFTConfig = field(
+        default_factory=lambda: STFTConfig(
+            filter_length=1024,
+            hop_length=256,
+            win_length=1024,
+            n_mel_channels=80,  # For univnet 100
+            mel_fmin=20,
+            mel_fmax=11025,
+        ),
+    )
+
+    def __post_init__(self):
+        r"""It modifies the 'stft' attribute based on the 'sampling_rate' attribute.
+        If 'sampling_rate' is 44100, 'stft' is set with specific values for this rate.
+        If 'sampling_rate' is not 22050 or 44100, a ValueError is raised.
+
+        Raises:
+            ValueError: If 'sampling_rate' is not 22050 or 44100.
+        """
+        if self.sampling_rate == 44100:
+            self.stft = STFTConfig(
+                filter_length=2048,
+                hop_length=512,  # NOTE: 441 ?? https://github.com/jik876/hifi-gan/issues/116#issuecomment-1436999858
+                win_length=2048,
+                n_mel_channels=80,  # Based on https://github.com/jik876/hifi-gan/issues/116
+                mel_fmin=20,
+                mel_fmax=11025,
+            )
+        if self.sampling_rate not in [22050, 44100]:
+            raise ValueError("Sampling rate must be 22050 or 44100")
+
+
+@dataclass
+class AcousticTrainingOptimizerConfig:
+    learning_rate: float
+    weight_decay: float
+    lr_decay: float
+    betas: Tuple[float, float] = (0.9, 0.98)
+    eps: float = 0.000000001
+    grad_clip_thresh: float = 1.0
+    warm_up_step: float = 4000
+    anneal_steps: List[int] = field(default_factory=list)
+    anneal_rate: float = 0.3
+
+
+@dataclass
+class AcousticFinetuningConfig:
+    batch_size = 5
+    grad_acc_step = 3
+    train_steps = 30000
+    log_step = 100
+    synth_step = 250
+    val_step = 4000
+    save_step = 250
+    freeze_bert_until = 0
+    mcd_gen_max_samples = 400
+    only_train_speaker_until = 5000
+    optimizer_config: AcousticTrainingOptimizerConfig = field(
+        default_factory=lambda: AcousticTrainingOptimizerConfig(
+            learning_rate=0.0002,
+            weight_decay=0.001,
+            lr_decay=0.99999,
+        ),
+    )
+
+
+@dataclass
+class AcousticPretrainingConfig:
+    batch_size = 5
+    grad_acc_step = 5
+    train_steps = 500000
+    log_step = 20
+    synth_step = 250
+    val_step = 4000
+    save_step = 1000
+    freeze_bert_until = 4000
+    mcd_gen_max_samples = 400
+    only_train_speaker_until = 0
+    optimizer_config: AcousticTrainingOptimizerConfig = field(
+        default_factory=lambda: AcousticTrainingOptimizerConfig(
+            learning_rate=0.0002,
+            weight_decay=0.01,
+            lr_decay=1.0,
+        ),
+    )
+
+
+AcousticTrainingConfig = Union[AcousticFinetuningConfig, AcousticPretrainingConfig]
+
+
+@dataclass
+class ConformerConfig:
+    n_layers: int
+    n_heads: int
+    n_hidden: int
+    p_dropout: float
+    kernel_size_conv_mod: int
+    kernel_size_depthwise: int
+    with_ff: bool
+
+
+@dataclass
+class ReferenceEncoderConfig:
+    bottleneck_size_p: int
+    bottleneck_size_u: int
+    ref_enc_filters: List[int]
+    ref_enc_size: int
+    ref_enc_strides: List[int]
+    ref_enc_pad: List[int]
+    ref_enc_gru_size: int
+    ref_attention_dropout: float
+    token_num: int
+    predictor_kernel_size: int
+
+
+@dataclass
+class VarianceAdaptorConfig:
+    n_hidden: int
+    kernel_size: int
+    emb_kernel_size: int
+    p_dropout: float
+    n_bins: int
+
+
+@dataclass
+class AcousticLossConfig:
+    ssim_loss_alpha: float
+    mel_loss_alpha: float
+    aligner_loss_alpha: float
+    pitch_loss_alpha: float
+    energy_loss_alpha: float
+    u_prosody_loss_alpha: float
+    p_prosody_loss_alpha: float
+    dur_loss_alpha: float
+    binary_align_loss_alpha: float
+    binary_loss_warmup_epochs: int
+
+
+@dataclass
+class AcousticENModelConfig:
+    speaker_embed_dim: int = 1024
+    lang_embed_dim: int = 1
+    encoder: ConformerConfig = field(
+        default_factory=lambda: ConformerConfig(
+            n_layers=6,
+            n_heads=8,
+            n_hidden=512,
+            p_dropout=0.1,
+            kernel_size_conv_mod=7,
+            kernel_size_depthwise=7,
+            with_ff=True,
+        ),
+    )
+    decoder: ConformerConfig = field(
+        default_factory=lambda: ConformerConfig(
+            n_layers=6,
+            n_heads=8,
+            n_hidden=512,
+            p_dropout=0.1,
+            kernel_size_conv_mod=11,
+            kernel_size_depthwise=11,
+            with_ff=True,
+        ),
+    )
+    reference_encoder: ReferenceEncoderConfig = field(
+        default_factory=lambda: ReferenceEncoderConfig(
+            bottleneck_size_p=4,
+            bottleneck_size_u=256,
+            ref_enc_filters=[32, 32, 64, 64, 128, 128],
+            ref_enc_size=3,
+            ref_enc_strides=[1, 2, 1, 2, 1],
+            ref_enc_pad=[1, 1],
+            ref_enc_gru_size=32,
+            ref_attention_dropout=0.2,
+            token_num=32,
+            predictor_kernel_size=5,
+        ),
+    )
+    variance_adaptor: VarianceAdaptorConfig = field(
+        default_factory=lambda: VarianceAdaptorConfig(
+            n_hidden=512,
+            kernel_size=5,
+            emb_kernel_size=3,
+            p_dropout=0.5,
+            n_bins=256,
+        ),
+    )
+    loss: AcousticLossConfig = field(
+        default_factory=lambda: AcousticLossConfig(
+            ssim_loss_alpha=1.0,
+            mel_loss_alpha=1.0,
+            aligner_loss_alpha=1.0,
+            pitch_loss_alpha=1.0,
+            energy_loss_alpha=1.0,
+            u_prosody_loss_alpha=0.25,
+            p_prosody_loss_alpha=0.25,
+            dur_loss_alpha=1.0,
+            binary_align_loss_alpha=0.1,
+            binary_loss_warmup_epochs=10,
+        ),
+    )
+
+
+@dataclass
+class AcousticMultilingualModelConfig:
+    speaker_embed_dim: int = 1024
+    lang_embed_dim: int = 256
+    encoder: ConformerConfig = field(
+        default_factory=lambda: ConformerConfig(
+            n_layers=6,
+            n_heads=8,
+            n_hidden=512,
+            p_dropout=0.1,
+            kernel_size_conv_mod=7,
+            kernel_size_depthwise=7,
+            with_ff=True,
+        ),
+    )
+    decoder: ConformerConfig = field(
+        default_factory=lambda: ConformerConfig(
+            n_layers=6,
+            n_heads=8,
+            n_hidden=512,
+            p_dropout=0.1,
+            kernel_size_conv_mod=11,
+            kernel_size_depthwise=11,
+            with_ff=True,
+        ),
+    )
+    reference_encoder: ReferenceEncoderConfig = field(
+        default_factory=lambda: ReferenceEncoderConfig(
+            bottleneck_size_p=4,
+            bottleneck_size_u=256,
+            ref_enc_filters=[32, 32, 64, 64, 128, 128],
+            ref_enc_size=3,
+            ref_enc_strides=[1, 2, 1, 2, 1],
+            ref_enc_pad=[1, 1],
+            ref_enc_gru_size=32,
+            ref_attention_dropout=0.2,
+            token_num=32,
+            predictor_kernel_size=5,
+        ),
+    )
+    variance_adaptor: VarianceAdaptorConfig = field(
+        default_factory=lambda: VarianceAdaptorConfig(
+            n_hidden=512,
+            kernel_size=5,
+            emb_kernel_size=3,
+            p_dropout=0.5,
+            n_bins=256,
+        ),
+    )
+    loss: AcousticLossConfig = field(
+        default_factory=lambda: AcousticLossConfig(
+            ssim_loss_alpha=1.0,
+            mel_loss_alpha=1.0,
+            aligner_loss_alpha=1.0,
+            pitch_loss_alpha=1.0,
+            energy_loss_alpha=1.0,
+            u_prosody_loss_alpha=0.25,
+            p_prosody_loss_alpha=0.25,
+            dur_loss_alpha=1.0,
+            binary_align_loss_alpha=0.1,
+            binary_loss_warmup_epochs=10,
+        ),
+    )
+
+
+AcousticModelConfigType = Union[AcousticENModelConfig, AcousticMultilingualModelConfig]
+
+
+@dataclass
+class VocoderBasicConfig:
+    segment_size: int = 16384
+    learning_rate: float = 0.0001
+    adam_b1: float = 0.5
+    adam_b2: float = 0.9
+    lr_decay: float = 0.995
+    synth_interval: int = 250
+    checkpoint_interval: int = 250
+    stft_lamb: float = 2.5
+
+
+@dataclass
+class VocoderPretrainingConfig(VocoderBasicConfig):
+    batch_size: int = 14
+    grad_accum_steps: int = 1
+    train_steps: int = 1000000
+    stdout_interval: int = 25
+    validation_interval: int = 2000
+
+
+@dataclass
+class VocoderFinetuningConfig(VocoderBasicConfig):
+    batch_size: int = 5
+    grad_accum_steps: int = 3
+    train_steps: int = 10000
+    stdout_interval: int = 100
+    validation_interval: int = 4000
+
+
+VoicoderTrainingConfig = Union[VocoderPretrainingConfig, VocoderFinetuningConfig]
+
+
+@dataclass
+class VocoderGeneratorConfig:
+    noise_dim: int
+    channel_size: int
+    dilations: List[int]
+    strides: List[int]
+    lReLU_slope: float
+    kpnet_conv_size: int
+
+
+@dataclass
+class VocoderMPDConfig:
+    periods: List[int]
+    kernel_size: int
+    stride: int
+    use_spectral_norm: bool
+    lReLU_slope: float
+
+
+@dataclass
+class VocoderMRDConfig:
+    resolutions: List[Tuple[int, int, int]]
+    use_spectral_norm: bool
+    lReLU_slope: float
+
+
+@dataclass
+class VocoderModelConfig:
+    gen: VocoderGeneratorConfig = field(
+        default_factory=lambda: VocoderGeneratorConfig(
+            noise_dim=64,
+            channel_size=32,
+            dilations=[1, 3, 9, 27],
+            strides=[8, 8, 4],
+            lReLU_slope=0.2,
+            kpnet_conv_size=3,
+        ),
+    )
+    mpd: VocoderMPDConfig = field(
+        default_factory=lambda: VocoderMPDConfig(
+            periods=[2, 3, 5, 7, 11],
+            kernel_size=5,
+            stride=3,
+            use_spectral_norm=False,
+            lReLU_slope=0.2,
+        ),
+    )
+    mrd: VocoderMRDConfig = field(
+        default_factory=lambda: VocoderMRDConfig(
+            resolutions=[(1024, 120, 600), (2048, 240, 1200), (512, 50, 240)],
+            use_spectral_norm=False,
+            lReLU_slope=0.2,
+        ),
+    )
+
+
+#####################
+# HI-FI GAN CONFIGS #
+#####################
+
+
+@dataclass
+class HifiGanPretrainingConfig(VocoderBasicConfig):
+    segment_size: int = 16384
+    learning_rate: float = 0.0002
+    adam_b1: float = 0.8
+    adam_b2: float = 0.99
+    lr_decay: float = 0.9995
+    lReLU_slope: float = 0.1
+    l1_factor: int = 45
+    sampling_rate_acoustic: int = 22050
+    sampling_rate_vocoder: int = 44100
+
+
+@dataclass
+class HifiGanConfig:
+    resblock: str = "1"
+    upsample_rates: List[int] = field(
+        default_factory=lambda: [8, 8, 4, 2],
+    )
+    upsample_kernel_sizes: List[int] = field(
+        default_factory=lambda: [16, 16, 4, 4],
+    )
+    upsample_initial_channel: int = 512
+    resblock_kernel_sizes: List[int] = field(
+        default_factory=lambda: [3, 7, 11],
+    )
+    resblock_dilation_sizes: List[List[int]] = field(
+        default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+    )

models/config/experimental_configs.py

@@ -0,0 +1,100 @@
+from dataclasses import dataclass
+
+
+# TODO: DEPRECATED!
+@dataclass
+class PostNetConfig:
+    p_dropout: float
+    postnet_embedding_dim: int
+    postnet_kernel_size: int
+    postnet_n_convolutions: int
+
+postnet_expetimental = PostNetConfig(
+    p_dropout=0.1,
+    postnet_embedding_dim=512,
+    postnet_kernel_size=5,
+    postnet_n_convolutions=3,
+)
+
+# TODO: DEPRECATED!
+@dataclass
+class DiffusionConfig:
+    # model parameters
+    model: str
+    n_mel_channels: int
+    multi_speaker: bool
+    # denoiser parameters
+    residual_channels: int
+    residual_layers: int
+    denoiser_dropout: float
+    noise_schedule_naive: str
+    timesteps: int
+    shallow_timesteps: int
+    min_beta: float
+    max_beta: float
+    s: float
+    pe_scale: int
+    keep_bins: int
+    # trainsformer params
+    encoder_hidden: int
+    decoder_hidden: int
+    speaker_embed_dim: int
+    # loss params
+    noise_loss: str
+
+
+diff_en = DiffusionConfig(
+    # model parameters
+    model="shallow",
+    n_mel_channels=100,
+    multi_speaker=True,
+    # denoiser parameters
+    # residual_channels=256,
+    # residual_channels=384,
+    residual_channels=100,
+    residual_layers=20,
+    denoiser_dropout=0.2,
+    noise_schedule_naive="vpsde",
+    timesteps=10,
+    shallow_timesteps=1,
+    min_beta=0.1,
+    max_beta=40,
+    s=0.008,
+    keep_bins=80,
+    pe_scale=1000,
+    # trainsformer params
+    # encoder_hidden=100,
+    encoder_hidden=512,
+    decoder_hidden=512,
+    # Speaker_emb + lang_emb
+    speaker_embed_dim=1025,
+    # loss params
+    noise_loss="l1",
+)
+
+diff_multi = DiffusionConfig(
+    # model parameters
+    model="shallow",
+    n_mel_channels=100,
+    multi_speaker=True,
+    # denoiser parameters
+    # residual_channels=256,
+    residual_channels=100,
+    residual_layers=20,
+    denoiser_dropout=0.2,
+    noise_schedule_naive="vpsde",
+    timesteps=10,
+    shallow_timesteps=1,
+    min_beta=0.1,
+    max_beta=40,
+    s=0.008,
+    pe_scale=1000,
+    keep_bins=80,
+    # trainsformer params
+    encoder_hidden=512,
+    decoder_hidden=512,
+    # Speaker_emb + lang_emb
+    speaker_embed_dim=1280,
+    # loss params
+    noise_loss="l1",
+)

models/config/langs.py

@@ -0,0 +1,65 @@
+from dataclasses import dataclass
+from typing import Dict
+
+from models.config import PreprocessLangType
+
+# TODO: now we only support english, but we need to support other languages!
+SUPPORTED_LANGUAGES = [
+    "bg",
+    "cs",
+    "de",
+    "en",
+    "es",
+    "fr",
+    "ha",
+    "hr",
+    "ko",
+    "pl",
+    "pt",
+    "ru",
+    "sv",
+    "sw",
+    "th",
+    "tr",
+    "uk",
+    "vi",
+    "zh",
+]
+
+# Mappings from symbol to numeric ID and vice versa:
+lang2id = {s: i for i, s in enumerate(SUPPORTED_LANGUAGES)}
+id2lang = dict(enumerate(SUPPORTED_LANGUAGES))
+
+@dataclass
+class LangItem:
+    r"""A class for storing language information."""
+
+    phonemizer: str
+    phonemizer_espeak: str
+    nemo: str
+    processing_lang_type: PreprocessLangType
+
+langs_map: Dict[str, LangItem] = {
+    "en": LangItem(
+        phonemizer="en_us",
+        phonemizer_espeak="en-us",
+        nemo="en",
+        processing_lang_type="english_only",
+    ),
+}
+
+def get_lang_map(lang: str) -> LangItem:
+    r"""Returns a LangItem object for the given language.
+
+    Args:
+        lang (str): The language to get the LangItem for.
+
+    Raises:
+        ValueError: If the language is not supported.
+
+    Returns:
+        LangItem: The LangItem object for the given language.
+    """
+    if lang not in langs_map:
+        raise ValueError(f"Language {lang} is not supported!")
+    return langs_map[lang]

models/config/speakers.py

@@ -0,0 +1,451 @@
+import json
+from typing import Dict, List
+
+# Load the ID mapping
+with open("training/datasets/speaker_id_mapping_libri.json") as f:
+    id_mapping = json.load(f)
+
+# Create a reverse mapping
+reverse_mapping: Dict[int, int] = {int(v): int(k) for k, v in id_mapping.items()}
+
+# Selected for the fine-tuning
+# train-960 subset of LibriTTS
+selected_speakers = [
+    574,  # Daniel Shorten 	M 	train-clean-100
+    242,  # J. Hall 	M 	train-other-500
+    536,  # Robert Flach 	M 	train-other-500
+    82,  # Andy Minter 	M 	train-other-500
+    672,  # Stuart Bell 	M 	train-other-500
+    315,  # Jean Crevier 	M 	train-other-500
+    628,  # Bryan Ness 	M 	train-clean-100
+    61,  # John Greenman 	M 	train-other-500
+    649,  # Scarlett! 	F 	train-clean-360
+    105,  # Marian Brown 	F 	train-clean-360
+    399,  # entada 	F 	train-clean-360
+    89,  # 	Paula Berinstein 	F 	train-clean-360
+    502,  # Lee Elliott 	F 	train-other-500
+    102,  # Maureen S. O'Brien 	F 	train-clean-100
+    544,  # Miranda Stinson 	F 	train-clean-360
+    653,  # cucciasv 	F 	train-other-500
+    465,  # Leonie Rose 	F 	train-clean-100
+    96,  # Kymm Zuckert 	F 	train-other-500
+    447,  # Lee Ann Howlett 	F 	train-clean-360
+    165,  # 	Elisabeth Shields 	F 	train-clean-100
+    430,  # 	Millbeach 	F 	train-other-500
+    214,  # Scott Splavec 	M 	train-clean-100
+    666,  # Kelly Dougherty 	M 	train-clean-360
+    481,  # Scott Sherris 	M 	train-clean-360
+    463,  # Chris Hughes 	M 	train-other-500
+    273,  # Andrew Lebrun 	M 	train-other-500
+    172,  # Harvey Chinn 	M 	train-other-500
+    83,  # Graham Williams 	M 	train-other-500
+    523,  # Michael Loftus 	M 	train-clean-360
+    38,  # Kurt Copeland 	M 	train-clean-360
+    248,  # fieldsofgold 	M 	train-other-500
+    234,  # Menno 	M 	train-other-500
+    145,  # Mr. Baby Man 	M 	train-clean-360
+    250,  # Quentin 	M 	train-clean-360
+    498,  # Chris Gladis 	M 	train-clean-100
+    123,  # Sean McGaughey 	M 	train-clean-360
+    171,  # Paul Harvey 	M 	train-clean-360
+    49,  # 	Kristen McQuillin 	F 	train-clean-100
+    588,  # 	Kalynda 	F 	train-clean-360
+    117,  # 	Caitlin Kelly 	F 	train-clean-360
+    657,  # Shannon 	F 	train-other-500
+    275,  # Zale Schafer (Rose May Chamberlin Memorial Foundat 	F 	train-clean-360
+    604,  # Anne-Marie 	F 	train-other-500
+    64,  # Christiane Levesque 	F 	train-clean-360
+    685,  # Nikki Sullivan 	F 	train-clean-100
+    355,  # Lana Taylor 	F 	train-clean-100
+    185,  # Kim Braun 	F 	train-clean-360
+    52,  # Cori Samuel 	F 	train-other-500
+    218,  # Joy Chan 	F 	train-other-500
+    549,  # AmyAG 	F 	train-other-500
+    617,  # PJ 	F 	train-other-500
+    414,  # Christabel 	F 	train-clean-100
+    382,  # Kelli Robinson 	F 	train-clean-360
+    76,  # 	ML Cohen 	M 	train-other-500
+    176,  # Micah Sheppard 	M 	train-clean-360
+    233,  # mikenkat 	M 	train-clean-360
+    390,  # JimmyLogan 	M 	train-clean-360
+    393,  # 	Tim Lundeen 	M 	train-clean-360
+    425,  # RedToby 	M 	train-clean-360
+    398,  # Sam Fold 	M 	train-other-500
+    372,  # Jim Mullins 	M 	train-clean-360
+    99,  # Stewart Wills 	M 	train-clean-100
+    340,  # 	Nick Gallant 	M 	train-clean-100
+    40,  # JemmaBlythe 	F 	train-other-500
+    118,  # 	Brenda Dayne 	F 	train-clean-360
+    640,  # David A. Stokely 	M 	train-other-500
+    50,  # Dan Threetrees 	M 	train-clean-360
+    373,  # Brooks Seveer 	M 	train-clean-360
+    124,  # Steve Karafit 	M 	train-clean-100
+    314,  # Carl Vonnoh, III 	M 	train-clean-360
+    531,  # Fr. Richard Zeile of Detroit 	M 	train-other-500
+    383,  # Mike Roop 	M 	train-other-500
+    710,  # Sheila Morton 	F 	train-clean-100
+    450,  # Heather Duncan 	F 	train-clean-360
+    645,  # Micah 	M 	train-other-500
+    517,  # Madame Tusk 	F 	train-other-500
+    479,  # Wina Hathaway 	F 	train-other-500
+    30,  # Ophelia Darcy 	F 	train-other-500
+    220,  # Tina Tilney 	F 	train-clean-360
+    63,  # Linda Wilcox 	F 	train-other-500
+    283,  # Bethany Simpson 	F 	train-clean-360
+    644,  # Cynthia Zocca 	F 	train-clean-360
+    677,  # Allyson Hester 	F 	train-other-500
+    21,  # Kelly Bescherer 	F 	train-other-500
+    552,  # Mim Ritty 	F 	train-clean-100
+    80,  # 	Fox in the Stars 	F 	train-clean-100
+    394,  # swroot 	F 	train-clean-360
+    426,  # Megan Stemm-Wade 	F 	train-clean-100
+    91,  # Chris Goringe 	M 	train-other-500
+    108,  # Kevin McAsh 	M 	train-clean-360
+    130,  # 	Peter of Buckinghamshire England 	M 	train-other-500
+    661,  # James Gladwin 	M 	train-other-500
+    216,  # Dave Ranson 	M 	train-clean-100
+    164,  # Ed Good 	M 	train-other-500
+    308,  # Eric Connover 	M 	train-other-500
+    569,  # Arouet 	M 	train-clean-360
+    313,  # Tim Bulkeley 	M 	train-other-500
+    212,  # Glen Hallstrom 	M 	train-other-500
+    15,  # 	Chip 	M 	train-other-500
+    469,  # Christian Pecaut 	M 	train-clean-360
+    294,  # 	Diana Kiesners 	F 	train-clean-360
+    192,  # Nocturna 	F 	train-clean-100
+    73,  # Claire Goget 	F 	train-clean-100
+    417,  # Kiki Baessell 	F 	train-clean-360
+    636,  # Matthew Howell 	F 	train-other-500
+    36,  # chriss the girl 	F 	train-other-500
+    668,  # Jan Baxter 	F 	train-clean-360
+    403,  # Igor Teaforay 	F 	train-clean-360
+    618,  # Linnea 	F 	train-other-500
+    596,  # Jo 	F 	train-other-500
+    499,  # Tammy Sanders 	F 	train-clean-100
+    207,  # Sage Tyrtle 	F 	train-other-500
+    1346,  # Jeanie 	F 	train-other-500
+    1109,  # Martin Geeson 	M 	train-other-500
+    770,  # Pete Williams, Pittsburgh, PA 	M 	train-clean-360
+    1247,  # Sarah LuAnn 	F 	train-clean-100
+    1526,  # 	Mike Harris 	M 	train-other-500
+    908,  # Quentin Manuel 	M 	train-clean-360
+    1183,  # 	Evelyn Clarke 	F 	train-other-500
+    1438,  # Tom Barron 	M 	train-other-500
+    1022,  # 	peac 	M 	train-clean-100
+    1603,  # 	Christine Rodriguez 	F 	train-clean-360
+    1425,  # 	Jonah Cummings 	M 	train-clean-360
+    731,  # 	Priya, India 	F 	train-other-500
+    782,  # Alec Daitsman 	M 	train-clean-360
+    1090,  # Termin Dyan 	M 	train-other-500
+    995,  # Parrot 	M 	train-other-500
+    923,  # Jane Greensmith 	F 	train-clean-360
+    766,  # Clive Catterall 	M 	train-other-500
+    822,  # kristiface 	F 	train-clean-360
+    897,  # Jan Dawn Doronila 	F 	train-clean-360
+    1579,  # Linda Velwest 	F 	train-clean-360
+    964,  # Utek 	M 	train-clean-360
+    1414,  # 	Preston Scrape 	M 	train-other-500
+    834,  # Serin 	F 	train-other-500
+    1302,  # davidb 	M 	train-clean-360
+    1135,  # Linda Andrus 	F 	train-clean-360
+    1440,  # 	P Moscato 	F 	train-clean-360
+    870,  # Barbara Bulkeley 	F 	train-clean-360
+    1256,  # Graeme Dunlop 	M 	train-other-500
+    1255,  # Daniel Paashaus 	M 	train-other-500
+    1157,  # Bev J Stevens 	F 	train-clean-360
+    934,  # Darla 	F 	train-other-500
+    1281,  # garbageman99 	M 	train-clean-360
+    819,  # n8evv 	M 	train-clean-360
+    1041,  # mjbrichant 	F 	train-other-500
+    863,  # K Hindall 	F 	train-clean-360
+    1303,  # kiwafruit 	F 	train-clean-100
+    1115,  # Rachel Gatwood 	F 	train-clean-360
+    1539,  # Nathan Jordan 	M 	train-other-500
+    1428,  # Gary Dzierlenga 	M 	train-other-500
+    1049,  # Diana Solomon 	F 	train-other-500
+    1546,  # Carrie Heyes 	F 	train-other-500
+    1089,  # Bill Ruhsam 	M 	train-clean-360
+    1142,  # Jonathan Burchard 	M 	train-other-500
+    1375,  # Frank Adams 	M 	train-clean-360
+    881,  # mpetranech 	M 	train-other-500
+    798,  # Wyatt 	M 	train-other-500
+    1647,  # 	Patrick Reinhart 	M 	train-clean-360
+    1587,  # Claudia Wilson 	F 	train-clean-360
+    830,  # musici123 	F 	train-other-500
+    1592,  # jerryB 	M 	train-other-500
+    839,  # Ben Dutton 	M 	train-other-500
+    835,  # Rachel Lintern 	F 	train-other-500
+    1273,  # gmiteva 	F 	train-other-500
+    932,  # Raerity 	F 	train-other-500
+    1108,  # Paul McCartan 	M 	train-other-500
+    732,  # Tysto 	M 	train-clean-360
+    781,  # Megan Kunkel 	F 	train-other-500
+    1555,  # Andrew Nelson 	M 	train-clean-360
+    1437,  # Charles RUHE 	M 	train-clean-360
+    1402,  # Angel5 	F 	train-other-500
+    963,  # MichelleHarris 	F 	train-clean-360
+    1181,  # J. Rebecca Franklin 	F 	train-clean-360
+    818,  # Matt Warzel 	F 	train-clean-360
+    1285,  # Ric F 	M 	train-clean-100
+    797,  # Chris Jones 	F 	train-other-500
+    1505,  # Rom Maczka 	M 	train-clean-360
+    1214,  # David Baldwin 	M 	train-clean-360
+    1636,  # jessecoy 	M 	train-other-500
+    929,  # Petra 	F 	train-other-500
+    1171,  # 	Roberta Carlisle 	F 	train-other-500
+    817,  # texttalker 	M 	train-clean-360
+    1433,  # browneyedgirl32382 	F 	train-clean-360
+    1158,  # StarrDog 	M 	train-other-500
+    1000,  # artos 	M 	train-other-500
+    848,  # senshisteph 	F 	train-other-500
+    1596,  # Joyce Couch 	F 	train-other-500
+    757,  # Roger Melin 	M 	train-clean-360
+    1168,  # Epistomolus 	M 	train-clean-100
+    741,  # Nick Marsh 	M 	train-other-500
+    1649,  # Phineas Redux 	M 	train-other-500
+    851,  # Jennifer Lott 	F 	train-clean-360
+    808,  # M. J. Boyle 	F 	train-other-500
+    1595,  # Matthew Reece 	M 	train-clean-360
+    1370,  # Savanna Herrold 	F 	train-other-500
+    1565,  # bryan.peterson 	M 	train-other-500
+    944,  # Sarafina Suransky 	F 	train-other-500
+    1268,  # A. Janelle Risa 	F 	train-clean-100
+    771,  # Isosceles 	F 	train-clean-360
+    752,  # Cat Schirf 	F 	train-other-500
+    800,  # Jack Farrell 	M 	train-clean-360
+    1005,  # Beatrice 	F 	train-other-500
+    1229,  # RoseA 	F 	train-clean-360
+    943,  # Matthew C. Heckel 	M 	train-clean-360
+    891,  # anoldfashiongirl 	F 	train-other-500
+    1226,  # serenitylee 	F 	train-clean-360
+    1253,  # Caroline Shapiro 	F 	train-other-500
+    1204,  # Dale A. Bade 	F 	train-clean-360
+    1230,  # Troy Bond 	M 	train-other-500
+    791,  # David Kleparek 	M 	train-clean-100
+    1184,  # Joseph Couves 	F 	train-other-500
+    1001,  # TriciaG 	F 	train-clean-360
+    804,  # FirstKnight 	F 	train-other-500
+    1641,  # Kirsten Wever 	F 	train-clean-100
+    1259,  # 	Megan Argo 	F 	train-other-500
+    1231,  # Abigail Bartels 	F 	train-other-500
+    1410,  # 	Zachary Johnson 	M 	train-other-500
+    1030,  # Ancient mariner 	M 	train-other-500
+    1093,  # Katie Riley 	F 	train-clean-360
+    1254,  # Rosie 	F 	train-clean-100
+    1365,  # Eric Leach 	M 	train-clean-360
+    831,  # David Federman 	M 	train-other-500
+    1989,  # 	Joannemmp 	F 	train-clean-100
+    1707,  # David Olson 	M 	train-other-500
+    1849,  # Fred DeBerardinis 	M 	train-clean-100
+    1808,  # Rebecca King 	F 	train-clean-360
+    2292,  # Arnold 	M 	train-clean-100
+    2415,  # Patrick Eaton 	M 	train-other-500
+    1656,  # Sharon Omi 	F 	train-clean-100
+    1676,  # Gargoyle 	M 	train-clean-360
+    1881,  # Julienne 	F 	train-other-500
+    2036,  # T.K. Kirven 	F 	train-other-500
+    1761,  # EliMarieHK 	F 	train-other-500
+    2115,  # Pete Milan 	M 	train-other-500
+    1803,  # Susan Hanfield 	F 	train-clean-360
+    1798,  # C. L. W. Rollins 	F 	train-other-500
+    1723,  # Rachel Bossier 	F 	train-other-500
+    2341,  # Haili 	F 	train-other-500
+    2468,  # Erin Schellhase 	F 	train-clean-360
+    1725,  # Ruth Kidson 	F 	train-other-500
+    2010,  # Peggy 	F 	train-other-500
+    1853,  # Ron Altman 	M 	train-other-500
+    2359,  # Doug Reed 	M 	train-other-500
+    2422,  # Jude Somers 	F 	train-clean-360
+    2234,  # Coreena 	F 	train-other-500
+    2156,  # 	C F de Rosset 	F 	train-other-500
+    2483,  # Tammy Porter 	F 	train-clean-360
+    1781,  # humanode 	M 	train-clean-360
+    2275,  # NatalieOram 	F 	train-other-500
+    2390,  # sdaeley17 	M 	train-clean-360
+    2314,  # Cheri Jordan 	F 	train-clean-360
+    2413,  # Joanne Rochon 	F 	train-clean-360
+    1697,  # 	Lonelle Yoder 	F 	train-other-500
+    1718,  # 	Caroline Driggs 	F 	train-other-500
+    2387,  # Brett G. Hirsch 	M 	train-other-500
+    2331,  # Madam Fickle 	F 	train-clean-100
+    1783,  # Sarah Crampton 	F 	train-clean-360
+    2397,  # Rebecca Braunert-Plunkett 	F 	train-other-500
+    2357,  # William Gavula 	M 	train-other-500
+    1670,  # dmbrought 	M 	train-other-500
+    1987,  # Andrew White 	M 	train-clean-360
+    1755,  # 	Yvonne Smith 	F 	train-clean-360
+    2192,  # Sammy Bean 	M 	train-other-500
+    1716,  # EyeBones 	F 	train-clean-360
+    1828,  # David Wales 	M 	train-clean-100
+    2251,  # Wiley Combs 	M 	train-clean-360
+    2065,  # Muriel 	F 	train-clean-360
+    2017,  # CaprishaPage 	F 	train-other-500
+    1947,  # Barbara Edelman 	F 	train-other-500
+    1738,  # Lois C. Johnson 	F 	train-clean-360
+    1791,  # David Cummings 	M 	train-clean-360
+    2045,  # Linda Ciano 	F 	train-clean-360
+    2452,  # 	Walt Allan 	M 	train-other-500
+    2040,  # MJ Franck 	F 	train-other-500
+    1831,  # Nigel Boydell 	M 	train-other-500
+    2371,  # Alexander Hatton 	M 	train-clean-360
+    1954,  # Szindbad 	M 	train-other-500
+    1836,  # Kendall Ashyby 	F 	train-other-500
+    2436,  # 	josembi 	M 	train-other-500
+    2383,  # 	Emma Joyce 	F 	train-other-500
+    2278,  # Jake Woldstad 	M 	train-clean-360
+    1741,  # 	anjieliu 	F 	train-other-500
+    1857,  # Amanda Friday 	F 	train-clean-360
+    2370,  # 	gloriousjob 	M 	train-clean-360
+    1907,  # 	Snapdragon 	F 	train-other-500
+    2225,  # 	nomorejeffs 	M 	train-clean-360
+    2439,  # KHand 	F 	train-clean-360
+    2239,  # amaskill 	M 	train-other-500
+    2007,  # Art Leung 	F 	train-clean-360
+    2283,  # Tim Cote 	M 	train-clean-360
+    1712,  # 	Steve Belleguelle 	M 	train-other-500
+    2094,  # Meg Cowan 	F 	train-clean-360
+    1772,  # 	haggisreflux 	M 	train-clean-360
+    2317,  # 	helengraves 	F 	train-clean-360
+    2241,  # 	Steven Reynolds 	M 	train-clean-360
+    2011,  # 	pekein 	M 	train-clean-360
+    1826,  # 	John Hoerr 	M 	train-clean-100
+    1695,  # Tina Nuzzi 	F 	train-clean-360
+    2451,  # DeanOBuchanan 	M 	train-clean-100
+    1771,  # Chelsea S. 	F 	train-other-500
+    2441,  # Alison Stewart 	F 	train-clean-360
+    1745,  # Janet 	F 	train-clean-360
+    2358,  # 	Betty Perry 	F 	train-clean-360
+    2197,  # Mike Nelson 	M 	train-other-500
+    2014,  # 	Eden Rea-Hedrick 	F 	train-other-500
+    1672,  # 	Mike Wajda 	M 	train-clean-360
+    2394,  # TinaNygard2 	F 	train-clean-100
+    1657,  # alwpoe 	M 	train-clean-360
+    1728,  # Vinnie Tesla 	M 	train-clean-360
+    1805,  # 	Vince Dee 	M 	train-clean-100
+    2143,  # 	Suebee 	F 	train-clean-360
+    2084,  # Eberle Thomas 	M 	train-other-500
+    2479,  # Daisy Flaim 	F 	train-clean-100
+    2152,  # Kristel Tretter 	F 	train-clean-360
+    2268,  # Greg Giordano 	M 	train-clean-360
+    1839,  # James E. Carson 	M 	train-clean-360
+    2056,  # 	acloward 	M 	train-clean-360
+    1814,  # polkadotish 	F 	train-other-500
+    2127,  # Ron Lockhart 	M 	train-clean-100
+    2114,  # Larry Beasley 	M 	train-clean-360
+    2469,  # 	Kevin Owens 	M 	train-clean-100
+    2447,  # Deena Rhoads 	F 	train-clean-360
+    1724,  # Juliana M. 	F 	train-clean-360
+    1869,  # 	NastassiaS 	F 	train-other-500
+    2209,  # Samantha J Gubitz 	F 	train-clean-360
+    2171,  # 	Carolyne 	F 	train-other-500
+    2403,  # Ian Quinlan 	M 	train-clean-360
+    2032,  # 	doonaboon 	M 	train-other-500
+    2075,  # Joy S Grape 	F 	train-clean-360
+]
+
+# Convert the model speaker IDs back to the dataset speaker IDs
+# dataset_speaker_ids: List[int] = [
+#     reverse_mapping.get(int(speaker_id)) for speaker_id in selected_speakers
+# ]  # type: ignore
+
+# Save the selected speaker IDs
+latest_selection: List[int] = [
+    574,
+    649,
+    102,
+    544,
+    653,
+    666,
+    481,
+    248,
+    123,
+    171,
+    604,
+    64,
+    685,
+    52,
+    218,
+    617,
+    414,
+    425,
+    118,
+    50,
+    373,
+    314,
+    710,
+    450,
+    645,
+    517,
+    63,
+    644,
+    80,
+    394,
+    91,
+    108,
+    661,
+    164,
+    308,
+    469,
+    192,
+    417,
+    668,
+    596,
+    1109,
+    770,
+    1247,
+    908,
+    782,
+    995,
+    923,
+    822,
+    1414,
+    1302,
+    1135,
+    1440,
+    1281,
+    1041,
+    1142,
+    881,
+    835,
+    932,
+    732,
+    1402,
+    929,
+    817,
+    1433,
+    1596,
+    851,
+    1370,
+    1204,
+    1230,
+    791,
+    804,
+    1808,
+    1656,
+    2115,
+    2341,
+    2468,
+    1718,
+    1783,
+    1755,
+    2192,
+    2371,
+    1836,
+    1741,
+    2439,
+    1712,
+    2197,
+    1728,
+    1805,
+    2143,
+    2084,
+    2056,
+    2114,
+    2447,
+    1869,
+    2209,
+]
+
+dataset_speaker_ids: List[int] = [
+    reverse_mapping.get(int(speaker_id)) for speaker_id in latest_selection
+]  # type: ignore

models/config/stats.json

@@ -0,0 +1,3 @@
+{
+    "pitch": [85.0, 255.0]
+}

models/config/symbols.py

@@ -0,0 +1,98 @@
+# NOTE: for the backward comp.
+# Prepare the phonemes list and dictionary for the embedding
+phoneme_basic_symbols = [
+    # IPA symbols
+    "a",
+    "b",
+    "d",
+    "e",
+    "f",
+    "g",
+    "h",
+    "i",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "p",
+    "r",
+    "s",
+    "t",
+    "u",
+    "v",
+    "w",
+    "x",
+    "y",
+    "z",
+    "æ",
+    "ç",
+    "ð",
+    "ø",
+    "ŋ",
+    "œ",
+    "ɐ",
+    "ɑ",
+    "ɔ",
+    "ə",
+    "ɛ",
+    "ɝ",
+    "ɹ",
+    "ɡ",
+    "ɪ",
+    "ʁ",
+    "ʃ",
+    "ʊ",
+    "ʌ",
+    "ʏ",
+    "ʒ",
+    "ʔ",
+    "ˈ",
+    "ˌ",
+    "ː",
+    "̃",
+    "̍",
+    "̥",
+    "̩",
+    "̯",
+    "͡",
+    "θ",
+    # Punctuation
+    "!",
+    "?",
+    ",",
+    ".",
+    "-",
+    ":",
+    ";",
+    '"',
+    "'",
+    "(",
+    ")",
+    " ",
+]
+
+# TODO: add support for other languages
+# _letters_accented = "µßàáâäåæçèéêëìíîïñòóôöùúûüąćęłńœśşźżƒ"
+# _letters_cyrilic = "абвгдежзийклмнопрстуфхцчшщъыьэюяёєіїґӧ"
+# _pad = "$"
+
+# This is the list of symbols from StyledTTS2
+_punctuation = ';:,.!?¡¿—…"«»“”'
+_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
+_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
+
+# Combine all symbols
+symbols = list(_punctuation) + list(_letters) + list(_letters_ipa)
+
+# Add only unique symbols
+phones = phoneme_basic_symbols + [
+    symbol for symbol in symbols if symbol not in phoneme_basic_symbols
+]
+
+# TODO: Need to understand how to replace this
+# len(phones) == 184, leave it as is at this point
+symbols = [str(el) for el in range(256)]
+symbol2id = {s: i for i, s in enumerate(symbols)}
+id2symbol = {i: s for i, s in enumerate(symbols)}

models/delightful_hifi.py

@@ -0,0 +1,59 @@
+from lightning.pytorch.core import LightningModule
+from torch import Tensor
+
+from models.config import PreprocessingConfigHifiGAN as PreprocessingConfig
+from models.tts.delightful_tts.delightful_tts import DelightfulTTS
+from models.vocoder.hifigan import HifiGan
+
+
+class DelightfulHiFi(LightningModule):
+    def __init__(
+        self,
+        delightful_checkpoint_path: str,
+        hifi_checkpoint_path: str,
+        lang: str = "en",
+        sampling_rate: int = 44100,
+    ):
+        super().__init__()
+
+        self.sampling_rate = sampling_rate
+
+        self.preprocess_config = PreprocessingConfig(
+            "multilingual",
+            sampling_rate=sampling_rate,
+        )
+
+        self.delightful_tts = DelightfulTTS.load_from_checkpoint(
+            delightful_checkpoint_path,
+            # kwargs to be used in the model
+            lang=lang,
+            sampling_rate=sampling_rate,
+            preprocess_config=self.preprocess_config,
+        )
+        self.delightful_tts.freeze()
+
+        self.hifi_gan = HifiGan.load_from_checkpoint(
+            hifi_checkpoint_path,
+        )
+        self.hifi_gan.freeze()
+
+    def forward(
+        self,
+        text: str,
+        speaker_idx: Tensor,
+    ) -> Tensor:
+        r"""Performs a forward pass through the AcousticModel.
+        This code must be run only with the loaded weights from the checkpoint!
+
+        Args:
+            text (str): The input text.
+            speaker_idx (Tensor): The index of the speaker.
+
+        Returns:
+            Tensor: The generated waveform with hifi-gan.
+        """
+        mel_pred = self.delightful_tts.forward(text, speaker_idx)
+
+        wav = self.hifi_gan.generator.forward(mel_pred)
+
+        return wav

models/delightful_univnet.py

@@ -0,0 +1,60 @@
+from lightning.pytorch.core import LightningModule
+from torch import Tensor
+
+from models.config import AcousticENModelConfig
+from models.config import PreprocessingConfigUnivNet as PreprocessingConfig
+from models.tts.delightful_tts.delightful_tts import DelightfulTTS
+from models.vocoder.univnet import UnivNet
+
+
+class DelightfulUnivnet(LightningModule):
+    def __init__(
+        self,
+        delightful_checkpoint_path: str,
+        lang: str = "en",
+        sampling_rate: int = 22050,
+    ):
+        super().__init__()
+
+        self.sampling_rate = sampling_rate
+
+        self.preprocess_config = PreprocessingConfig(
+            "english_only",
+            sampling_rate=sampling_rate,
+        )
+
+        self.delightful_tts = DelightfulTTS.load_from_checkpoint(
+            delightful_checkpoint_path,
+            strict=False,
+            # kwargs to be used in the model
+            preprocess_config=self.preprocess_config,
+            model_config=AcousticENModelConfig(),
+            lang=lang,
+            sampling_rate=sampling_rate,
+        )
+        self.delightful_tts.freeze()
+
+        # Don't need to use separated checkpoint, prev checkpoint used
+        self.univnet = UnivNet()
+        self.univnet.freeze()
+
+    def forward(
+        self,
+        text: str,
+        speaker_idx: Tensor,
+    ) -> Tensor:
+        r"""Performs a forward pass through the AcousticModel.
+        This code must be run only with the loaded weights from the checkpoint!
+
+        Args:
+            text (str): The input text.
+            speaker_idx (Tensor): The index of the speaker.
+
+        Returns:
+            Tensor: The generated waveform with hifi-gan.
+        """
+        mel_pred = self.delightful_tts.forward(text, speaker_idx)
+
+        wav = self.univnet.forward(mel_pred)
+
+        return wav

models/generators/delightful_univnet.py

@@ -0,0 +1,547 @@
+from typing import List
+
+from lightning.pytorch.core import LightningModule
+import torch
+from torch.optim import AdamW, Optimizer, swa_utils
+from torch.optim.lr_scheduler import ExponentialLR
+from torch.utils.data import DataLoader
+
+from models.config import (
+    AcousticENModelConfig,
+    AcousticFinetuningConfig,
+    AcousticPretrainingConfig,
+    AcousticTrainingConfig,
+    VocoderFinetuningConfig,
+    VocoderModelConfig,
+    VocoderPretrainingConfig,
+    VoicoderTrainingConfig,
+    get_lang_map,
+    lang2id,
+)
+from models.config import (
+    PreprocessingConfigUnivNet as PreprocessingConfig,
+)
+from models.helpers.dataloaders import train_dataloader
+from models.helpers.tools import get_mask_from_lengths
+
+# Models
+from models.tts.delightful_tts.acoustic_model import AcousticModel
+from models.vocoder.univnet.discriminator import Discriminator
+from models.vocoder.univnet.generator import Generator
+from training.loss import FastSpeech2LossGen, UnivnetLoss
+from training.preprocess.normalize_text import NormalizeText
+
+# Updated version of the tokenizer
+from training.preprocess.tokenizer_ipa_espeak import TokenizerIpaEspeak as TokenizerIPA
+
+
+class DelightfulUnivnet(LightningModule):
+    r"""DEPRECATED: This idea is basically wrong. The model should synthesis pretty well mel spectrograms and then use them to generate the waveform based on the good quality mel-spec.
+
+    Trainer for the acoustic model.
+
+    Args:
+        fine_tuning (bool, optional): Whether to use fine-tuning mode or not. Defaults to False.
+        lang (str): Language of the dataset.
+        n_speakers (int): Number of speakers in the dataset.generation during training.
+        batch_size (int): The batch size.
+        acc_grad_steps (int): The number of gradient accumulation steps.
+        swa_steps (int): The number of steps for the SWA update.
+    """
+
+    def __init__(
+        self,
+        fine_tuning: bool = True,
+        lang: str = "en",
+        n_speakers: int = 5392,
+        batch_size: int = 12,
+        acc_grad_steps: int = 5,
+        swa_steps: int = 1000,
+    ):
+        super().__init__()
+
+        # Switch to manual optimization
+        self.automatic_optimization = False
+        self.acc_grad_steps = acc_grad_steps
+        self.swa_steps = swa_steps
+
+        self.lang = lang
+        self.fine_tuning = fine_tuning
+        self.batch_size = batch_size
+
+        lang_map = get_lang_map(lang)
+        normilize_text_lang = lang_map.nemo
+
+        self.tokenizer = TokenizerIPA(lang)
+        self.normilize_text = NormalizeText(normilize_text_lang)
+
+        # Acoustic model
+        self.train_config_acoustic: AcousticTrainingConfig
+
+        if self.fine_tuning:
+            self.train_config_acoustic = AcousticFinetuningConfig()
+        else:
+            self.train_config_acoustic = AcousticPretrainingConfig()
+
+        self.preprocess_config = PreprocessingConfig("english_only")
+        self.model_config_acoustic = AcousticENModelConfig()
+
+        # TODO: fix the arguments!
+        self.acoustic_model = AcousticModel(
+            preprocess_config=self.preprocess_config,
+            model_config=self.model_config_acoustic,
+            # NOTE: this parameter may be hyperparameter that you can define based on the demands
+            n_speakers=n_speakers,
+        )
+
+        # Initialize SWA
+        self.swa_averaged_acoustic = swa_utils.AveragedModel(self.acoustic_model)
+
+        # NOTE: in case of training from 0 bin_warmup should be True!
+        self.loss_acoustic = FastSpeech2LossGen(bin_warmup=False)
+
+        # Vocoder models
+        self.model_config_vocoder = VocoderModelConfig()
+
+        self.train_config: VoicoderTrainingConfig = (
+            VocoderFinetuningConfig() if fine_tuning else VocoderPretrainingConfig()
+        )
+
+        self.univnet = Generator(
+            model_config=self.model_config_vocoder,
+            preprocess_config=self.preprocess_config,
+        )
+        self.swa_averaged_univnet = swa_utils.AveragedModel(self.univnet)
+
+        self.discriminator = Discriminator(model_config=self.model_config_vocoder)
+        self.swa_averaged_discriminator = swa_utils.AveragedModel(self.discriminator)
+
+        self.loss_univnet = UnivnetLoss()
+
+    def forward(
+        self, text: str, speaker_idx: torch.Tensor, lang: str = "en"
+    ) -> torch.Tensor:
+        r"""Performs a forward pass through the AcousticModel.
+        This code must be run only with the loaded weights from the checkpoint!
+
+        Args:
+            text (str): The input text.
+            speaker_idx (torch.Tensor): The index of the speaker.
+            lang (str): The language.
+
+        Returns:
+            torch.Tensor: The output of the AcousticModel.
+        """
+        normalized_text = self.normilize_text(text)
+        _, phones = self.tokenizer(normalized_text)
+
+        # Convert to tensor
+        x = torch.tensor(
+            phones,
+            dtype=torch.int,
+            device=speaker_idx.device,
+        ).unsqueeze(0)
+
+        speakers = speaker_idx.repeat(x.shape[1]).unsqueeze(0)
+
+        langs = (
+            torch.tensor(
+                [lang2id[lang]],
+                dtype=torch.int,
+                device=speaker_idx.device,
+            )
+            .repeat(x.shape[1])
+            .unsqueeze(0)
+        )
+
+        y_pred = self.acoustic_model.forward(
+            x=x,
+            speakers=speakers,
+            langs=langs,
+        )
+
+        mel_lens = torch.tensor(
+            [y_pred.shape[2]],
+            dtype=torch.int32,
+            device=y_pred.device,
+        )
+
+        wav = self.univnet.infer(y_pred, mel_lens)
+
+        return wav
+
+    # TODO: don't forget about torch.no_grad() !
+    # default used by the Trainer
+    # trainer = Trainer(inference_mode=True)
+    # Use `torch.no_grad` instead
+    # trainer = Trainer(inference_mode=False)
+    def training_step(self, batch: List, batch_idx: int):
+        r"""Performs a training step for the model.
+
+        Args:
+        batch (List): The batch of data for training. The batch should contain:
+            - ids: List of indexes.
+            - raw_texts: Raw text inputs.
+            - speakers: Speaker identities.
+            - texts: Text inputs.
+            - src_lens: Lengths of the source sequences.
+            - mels: Mel spectrogram targets.
+            - pitches: Pitch targets.
+            - pitches_stat: Statistics of the pitches.
+            - mel_lens: Lengths of the mel spectrograms.
+            - langs: Language identities.
+            - attn_priors: Prior attention weights.
+            - wavs: Waveform targets.
+            - energies: Energy targets.
+        batch_idx (int): Index of the batch.
+
+        Returns:
+            - 'loss': The total loss for the training step.
+        """
+        (
+            _,
+            _,
+            speakers,
+            texts,
+            src_lens,
+            mels,
+            pitches,
+            _,
+            mel_lens,
+            langs,
+            attn_priors,
+            audio,
+            energies,
+        ) = batch
+
+        #####################################
+        ##    Acoustic model train step    ##
+        #####################################
+
+        outputs = self.acoustic_model.forward_train(
+            x=texts,
+            speakers=speakers,
+            src_lens=src_lens,
+            mels=mels,
+            mel_lens=mel_lens,
+            pitches=pitches,
+            langs=langs,
+            attn_priors=attn_priors,
+            energies=energies,
+        )
+
+        y_pred = outputs["y_pred"]
+        log_duration_prediction = outputs["log_duration_prediction"]
+        p_prosody_ref = outputs["p_prosody_ref"]
+        p_prosody_pred = outputs["p_prosody_pred"]
+        pitch_prediction = outputs["pitch_prediction"]
+        energy_pred = outputs["energy_pred"]
+        energy_target = outputs["energy_target"]
+
+        src_mask = get_mask_from_lengths(src_lens)
+        mel_mask = get_mask_from_lengths(mel_lens)
+
+        (
+            acc_total_loss,
+            acc_mel_loss,
+            acc_ssim_loss,
+            acc_duration_loss,
+            acc_u_prosody_loss,
+            acc_p_prosody_loss,
+            acc_pitch_loss,
+            acc_ctc_loss,
+            acc_bin_loss,
+            acc_energy_loss,
+        ) = self.loss_acoustic.forward(
+            src_masks=src_mask,
+            mel_masks=mel_mask,
+            mel_targets=mels,
+            mel_predictions=y_pred,
+            log_duration_predictions=log_duration_prediction,
+            u_prosody_ref=outputs["u_prosody_ref"],
+            u_prosody_pred=outputs["u_prosody_pred"],
+            p_prosody_ref=p_prosody_ref,
+            p_prosody_pred=p_prosody_pred,
+            pitch_predictions=pitch_prediction,
+            p_targets=outputs["pitch_target"],
+            durations=outputs["attn_hard_dur"],
+            attn_logprob=outputs["attn_logprob"],
+            attn_soft=outputs["attn_soft"],
+            attn_hard=outputs["attn_hard"],
+            src_lens=src_lens,
+            mel_lens=mel_lens,
+            energy_pred=energy_pred,
+            energy_target=energy_target,
+            step=self.trainer.global_step,
+        )
+
+        self.log(
+            "acc_total_loss", acc_total_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "acc_mel_loss", acc_mel_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "acc_ssim_loss", acc_ssim_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "acc_duration_loss",
+            acc_duration_loss,
+            sync_dist=True,
+            batch_size=self.batch_size,
+        )
+        self.log(
+            "acc_u_prosody_loss",
+            acc_u_prosody_loss,
+            sync_dist=True,
+            batch_size=self.batch_size,
+        )
+        self.log(
+            "acc_p_prosody_loss",
+            acc_p_prosody_loss,
+            sync_dist=True,
+            batch_size=self.batch_size,
+        )
+        self.log(
+            "acc_pitch_loss", acc_pitch_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "acc_ctc_loss", acc_ctc_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "acc_bin_loss", acc_bin_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "acc_energy_loss",
+            acc_energy_loss,
+            sync_dist=True,
+            batch_size=self.batch_size,
+        )
+
+        #####################################
+        ##    Univnet model train step     ##
+        #####################################
+        fake_audio = self.univnet.forward(y_pred)
+
+        res_fake, period_fake = self.discriminator(fake_audio.detach())
+        res_real, period_real = self.discriminator(audio)
+
+        (
+            voc_total_loss_gen,
+            voc_total_loss_disc,
+            voc_stft_loss,
+            voc_score_loss,
+            voc_esr_loss,
+            voc_snr_loss,
+        ) = self.loss_univnet.forward(
+            audio,
+            fake_audio,
+            res_fake,
+            period_fake,
+            res_real,
+            period_real,
+        )
+
+        self.log(
+            "voc_total_loss_gen",
+            voc_total_loss_gen,
+            sync_dist=True,
+            batch_size=self.batch_size,
+        )
+        self.log(
+            "voc_total_loss_disc",
+            voc_total_loss_disc,
+            sync_dist=True,
+            batch_size=self.batch_size,
+        )
+        self.log(
+            "voc_stft_loss", voc_stft_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "voc_score_loss", voc_score_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "voc_esr_loss", voc_esr_loss, sync_dist=True, batch_size=self.batch_size
+        )
+        self.log(
+            "voc_snr_loss", voc_snr_loss, sync_dist=True, batch_size=self.batch_size
+        )
+
+        # Manual optimizer
+        # Access your optimizers
+        optimizers = self.optimizers()
+        schedulers = self.lr_schedulers()
+
+        ####################################
+        # Acoustic model manual optimizer ##
+        ####################################
+        opt_acoustic: Optimizer = optimizers[0]  # type: ignore
+        sch_acoustic: ExponentialLR = schedulers[0]  # type: ignore
+
+        opt_univnet: Optimizer = optimizers[0]  # type: ignore
+        sch_univnet: ExponentialLR = schedulers[0]  # type: ignore
+
+        opt_discriminator: Optimizer = optimizers[1]  # type: ignore
+        sch_discriminator: ExponentialLR = schedulers[1]  # type: ignore
+
+        # Backward pass for the acoustic model
+        # NOTE: the loss is divided by the accumulated gradient steps
+        self.manual_backward(acc_total_loss / self.acc_grad_steps, retain_graph=True)
+
+        # Perform manual optimization univnet
+        self.manual_backward(
+            voc_total_loss_gen / self.acc_grad_steps, retain_graph=True
+        )
+        self.manual_backward(
+            voc_total_loss_disc / self.acc_grad_steps, retain_graph=True
+        )
+
+        # accumulate gradients of N batches
+        if (batch_idx + 1) % self.acc_grad_steps == 0:
+            # Acoustic model optimizer step
+            # clip gradients
+            self.clip_gradients(
+                opt_acoustic, gradient_clip_val=0.5, gradient_clip_algorithm="norm"
+            )
+
+            # optimizer step
+            opt_acoustic.step()
+            # Scheduler step
+            sch_acoustic.step()
+            # zero the gradients
+            opt_acoustic.zero_grad()
+
+            # Univnet model optimizer step
+            # clip gradients
+            self.clip_gradients(
+                opt_univnet, gradient_clip_val=0.5, gradient_clip_algorithm="norm"
+            )
+            self.clip_gradients(
+                opt_discriminator, gradient_clip_val=0.5, gradient_clip_algorithm="norm"
+            )
+
+            # optimizer step
+            opt_univnet.step()
+            opt_discriminator.step()
+
+            # Scheduler step
+            sch_univnet.step()
+            sch_discriminator.step()
+
+            # zero the gradients
+            opt_univnet.zero_grad()
+            opt_discriminator.zero_grad()
+
+        # Update SWA model every swa_steps
+        if self.trainer.global_step % self.swa_steps == 0:
+            self.swa_averaged_acoustic.update_parameters(self.acoustic_model)
+            self.swa_averaged_univnet.update_parameters(self.univnet)
+            self.swa_averaged_discriminator.update_parameters(self.discriminator)
+
+    def on_train_epoch_end(self):
+        r"""Updates the averaged model after each optimizer step with SWA."""
+        self.swa_averaged_acoustic.update_parameters(self.acoustic_model)
+        self.swa_averaged_univnet.update_parameters(self.univnet)
+        self.swa_averaged_discriminator.update_parameters(self.discriminator)
+
+    def configure_optimizers(self):
+        r"""Configures the optimizer used for training.
+
+        Returns
+            tuple: A tuple containing three dictionaries. Each dictionary contains the optimizer and learning rate scheduler for one of the models.
+        """
+        ####################################
+        # Acoustic model optimizer config ##
+        ####################################
+        # Compute the gamma and initial learning rate based on the current step
+        lr_decay = self.train_config_acoustic.optimizer_config.lr_decay
+        default_lr = self.train_config_acoustic.optimizer_config.learning_rate
+
+        init_lr = (
+            default_lr
+            if self.trainer.global_step == 0
+            else default_lr * (lr_decay**self.trainer.global_step)
+        )
+
+        optimizer_acoustic = AdamW(
+            self.acoustic_model.parameters(),
+            lr=init_lr,
+            betas=self.train_config_acoustic.optimizer_config.betas,
+            eps=self.train_config_acoustic.optimizer_config.eps,
+            weight_decay=self.train_config_acoustic.optimizer_config.weight_decay,
+        )
+
+        scheduler_acoustic = ExponentialLR(optimizer_acoustic, gamma=lr_decay)
+
+        ####################################
+        # Univnet model optimizer config ##
+        ####################################
+        optim_univnet = AdamW(
+            self.univnet.parameters(),
+            self.train_config.learning_rate,
+            betas=(self.train_config.adam_b1, self.train_config.adam_b2),
+        )
+        scheduler_univnet = ExponentialLR(
+            optim_univnet,
+            gamma=self.train_config.lr_decay,
+            last_epoch=-1,
+        )
+
+        ####################################
+        # Discriminator optimizer config ##
+        ####################################
+        optim_discriminator = AdamW(
+            self.discriminator.parameters(),
+            self.train_config.learning_rate,
+            betas=(self.train_config.adam_b1, self.train_config.adam_b2),
+        )
+        scheduler_discriminator = ExponentialLR(
+            optim_discriminator,
+            gamma=self.train_config.lr_decay,
+            last_epoch=-1,
+        )
+
+        return (
+            {"optimizer": optimizer_acoustic, "lr_scheduler": scheduler_acoustic},
+            {"optimizer": optim_univnet, "lr_scheduler": scheduler_univnet},
+            {"optimizer": optim_discriminator, "lr_scheduler": scheduler_discriminator},
+        )
+
+    def on_train_end(self):
+        # Update SWA models after training
+        swa_utils.update_bn(self.train_dataloader(), self.swa_averaged_acoustic)
+        swa_utils.update_bn(self.train_dataloader(), self.swa_averaged_univnet)
+        swa_utils.update_bn(self.train_dataloader(), self.swa_averaged_discriminator)
+
+    def train_dataloader(
+        self,
+        num_workers: int = 5,
+        root: str = "datasets_cache/LIBRITTS",
+        cache: bool = True,
+        cache_dir: str = "datasets_cache",
+        mem_cache: bool = False,
+        url: str = "train-960",
+    ) -> DataLoader:
+        r"""Returns the training dataloader, that is using the LibriTTS dataset.
+
+        Args:
+            num_workers (int): The number of workers.
+            root (str): The root directory of the dataset.
+            cache (bool): Whether to cache the preprocessed data.
+            cache_dir (str): The directory for the cache.
+            mem_cache (bool): Whether to use memory cache.
+            url (str): The URL of the dataset.
+
+        Returns:
+            Tupple[DataLoader, DataLoader]: The training and validation dataloaders.
+        """
+        return train_dataloader(
+            batch_size=self.batch_size,
+            num_workers=num_workers,
+            root=root,
+            cache=cache,
+            cache_dir=cache_dir,
+            mem_cache=mem_cache,
+            url=url,
+            lang=self.lang,
+        )

models/generators/tests/test_delightful_univnet.py

@@ -0,0 +1,30 @@
+# import os
+# import unittest
+
+# from lightning.pytorch import Trainer
+
+# from models.generators.delightful_univnet import DelightfulUnivnet
+
+# checkpoint = "checkpoints/logs_new_training_libri-360_energy_epoch=263-step=45639.ckpt"
+
+# # NOTE: this is needed to avoid CUDA_LAUNCH_BLOCKING error
+# os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
+
+# DEPRECATED
+# class TestDelightfulUnivnet(unittest.TestCase):
+#     def test_train_steps(self):
+#         default_root_dir = "checkpoints/acoustic"
+
+#         trainer = Trainer(
+#             default_root_dir=default_root_dir,
+#             limit_train_batches=1,
+#             max_epochs=1,
+#             accelerator="cpu",
+#         )
+
+#         module = DelightfulUnivnet(batch_size=1, acc_grad_steps=1, swa_steps=1)
+
+#         train_dataloader = module.train_dataloader(2, cache=False, mem_cache=False)
+
+#         result = trainer.fit(model=module, train_dataloaders=train_dataloader)
+#         self.assertIsNone(result)

models/helpers/__init__.py

@@ -0,0 +1,2 @@
+from .acoustic import *
+from .tools import *

models/helpers/acoustic.py

@@ -0,0 +1,79 @@
+import math
+
+import torch
+
+
+def positional_encoding(
+    d_model: int, length: int,
+) -> torch.Tensor:
+    r"""Function to calculate positional encoding for transformer model.
+
+    Args:
+        d_model (int): Dimension of the model (often corresponds to embedding size).
+        length (int): Length of sequences.
+
+    Returns:
+        torch.Tensor: Tensor having positional encodings.
+    """
+    # Initialize placeholder for positional encoding
+    pe = torch.zeros(length, d_model)
+
+    # Generate position indices and reshape to have shape (length, 1)
+    position = torch.arange(0, length, dtype=torch.float).unsqueeze(1)
+
+    # Calculate term for division
+    div_term = torch.exp(
+        torch.arange(0, d_model, 2).float()
+        * -(math.log(10000.0) / d_model),
+    )
+
+    # Assign sin of position * div_term to even indices in the encoding matrix
+    pe[:, 0::2] = torch.sin(position * div_term)
+
+    # Assign cos of position * div_term to odd indices in the encoding matrix
+    pe[:, 1::2] = torch.cos(position * div_term)
+
+    # Add an extra dimension to match expected output shape
+    return pe.unsqueeze(0)
+
+
+def pitch_phoneme_averaging(
+        durations: torch.Tensor,
+        pitches: torch.Tensor,
+        max_phoneme_len: int) -> torch.Tensor:
+    r"""Function to compute the average pitch values over the duration of each phoneme.
+
+    Args:
+        durations (torch.Tensor): Duration of each phoneme for each sample in a batch.
+                                  Shape: (batch_size, n_phones)
+        pitches (torch.Tensor): Per-frame pitch values for each sample in a batch.
+                                Shape: (batch_size, n_mel_timesteps)
+        max_phoneme_len (int): Maximum length of the phoneme sequence in a batch.
+
+    Returns:
+        pitches_averaged (torch.Tensor): Tensor containing the averaged pitch values
+                                         for each phoneme. Shape: (batch_size, max_phoneme_len)
+    """
+    # Initialize placeholder for averaged pitch values, filling with zeros
+    pitches_averaged = torch.zeros(
+        (pitches.shape[0], max_phoneme_len), device=pitches.device,
+    )
+    # Loop over each sample in the batch
+    for batch_idx in range(durations.shape[0]):
+        # Set the starting index of pitch sequence
+        start_idx = 0
+        # Loop over each phoneme duration
+        for i, duration in enumerate(durations[batch_idx]):
+            # Convert duration to integer
+            duration = duration.int().item()
+            # If the duration is not zero
+            if duration != 0:
+                # Calculate the mean pitch value for the duration of the current phoneme
+                mean = torch.mean(pitches[batch_idx, start_idx : start_idx + duration])
+                # Store the averaged pitch value
+                pitches_averaged[batch_idx][i] = mean
+                # Update the starting index for the next phoneme
+                start_idx += duration
+
+    # Return tensor with the averaged pitch values
+    return pitches_averaged

models/helpers/dataloaders.py

@@ -0,0 +1,141 @@
+from typing import List, Optional, Tuple
+
+from sklearn.model_selection import train_test_split
+from torch.utils.data import DataLoader, SequentialSampler
+
+from training.datasets import LibriTTSDatasetAcoustic
+
+
+def train_dataloader(
+    batch_size: int = 6,
+    num_workers: int = 5,
+    root: str = "datasets_cache/LIBRITTS",
+    cache: bool = True,
+    cache_dir: str = "datasets_cache",
+    mem_cache: bool = False,
+    url: str = "train-clean-360",
+    lang: str = "en",
+    selected_speaker_ids: Optional[List[int]] = None,
+) -> DataLoader:
+    r"""Returns the training dataloader, that is using the LibriTTS dataset.
+
+    Args:
+        batch_size (int): The batch size.
+        num_workers (int): The number of workers.
+        root (str): The root directory of the dataset.
+        cache (bool): Whether to cache the preprocessed data.
+        cache_dir (str): The directory for the cache.
+        mem_cache (bool): Whether to use memory cache.
+        url (str): The URL of the dataset.
+        lang (str): The language of the dataset.
+        selected_speaker_ids (Optional[List[int]]): A list of selected speakers.
+
+    Returns:
+        DataLoader: The training and validation dataloaders.
+    """
+    dataset = LibriTTSDatasetAcoustic(
+        root=root,
+        lang=lang,
+        cache=cache,
+        cache_dir=cache_dir,
+        mem_cache=mem_cache,
+        url=url,
+        selected_speaker_ids=selected_speaker_ids,
+    )
+
+    train_loader = DataLoader(
+        dataset,
+        # 4x80Gb max 10 sec audio
+        # batch_size=20, # self.train_config.batch_size,
+        # 4*80Gb max ~20.4 sec audio
+        batch_size=batch_size,
+        # TODO: find the optimal num_workers
+        num_workers=num_workers,
+        persistent_workers=True,
+        pin_memory=True,
+        shuffle=False,
+        collate_fn=dataset.collate_fn,
+    )
+
+    return train_loader
+
+
+def train_val_dataloader(
+    batch_size: int = 6,
+    num_workers: int = 5,
+    root: str = "datasets_cache/LIBRITTS",
+    cache: bool = True,
+    cache_dir: str = "datasets_cache",
+    mem_cache: bool = False,
+    url: str = "train-clean-360",
+    lang: str = "en",
+    validation_split: float = 0.02,  # Percentage of data to use for validation
+) -> Tuple[DataLoader, DataLoader]:
+    r"""Returns the training dataloader, that is using the LibriTTS dataset.
+
+    Args:
+        batch_size (int): The batch size.
+        num_workers (int): The number of workers.
+        root (str): The root directory of the dataset.
+        cache (bool): Whether to cache the preprocessed data.
+        cache_dir (str): The directory for the cache.
+        mem_cache (bool): Whether to use memory cache.
+        url (str): The URL of the dataset.
+        lang (str): The language of the dataset.
+        validation_split (float): The percentage of data to use for validation.
+
+    Returns:
+        Tupple[DataLoader, DataLoader]: The training and validation dataloaders.
+    """
+    dataset = LibriTTSDatasetAcoustic(
+        root=root,
+        lang=lang,
+        cache=cache,
+        cache_dir=cache_dir,
+        mem_cache=mem_cache,
+        url=url,
+    )
+
+    # Split dataset into train and validation
+    train_indices, val_indices = train_test_split(
+        list(range(len(dataset))),
+        test_size=validation_split,
+        random_state=42,
+    )
+
+    # Create Samplers
+    train_sampler = SequentialSampler(train_indices)
+    val_sampler = SequentialSampler(val_indices)
+
+    # dataset = LibriTTSMMDatasetAcoustic("checkpoints/libri_preprocessed_data.pt")
+    train_loader = DataLoader(
+        dataset,
+        # 4x80Gb max 10 sec audio
+        # batch_size=20, # self.train_config.batch_size,
+        # 4*80Gb max ~20.4 sec audio
+        batch_size=batch_size,
+        # TODO: find the optimal num_workers
+        num_workers=num_workers,
+        sampler=train_sampler,
+        persistent_workers=True,
+        pin_memory=True,
+        shuffle=False,
+        collate_fn=dataset.collate_fn,
+    )
+
+    val_loader = DataLoader(
+        dataset,
+        # 4x80Gb max 10 sec audio
+        # batch_size=20, # self.train_config.batch_size,
+        # 4*80Gb max ~20.4 sec audio
+        batch_size=batch_size,
+        # TODO: find the optimal num_workers
+        num_workers=num_workers,
+        sampler=val_sampler,
+        persistent_workers=True,
+        pin_memory=True,
+        shuffle=False,
+        collate_fn=dataset.collate_fn,
+    )
+
+    return train_loader, val_loader

models/helpers/initializer.py

@@ -0,0 +1,335 @@
+from dataclasses import dataclass
+from typing import Tuple
+
+import torch
+
+from models.config import (
+    SUPPORTED_LANGUAGES,
+    AcousticENModelConfig,
+    AcousticModelConfigType,
+    AcousticPretrainingConfig,
+)
+from models.config import (
+    PreprocessingConfigUnivNet as PreprocessingConfig,
+)
+from models.helpers import positional_encoding, tools
+from models.tts.delightful_tts.acoustic_model import AcousticModel
+from models.tts.delightful_tts.attention.conformer import Conformer
+
+
+@dataclass
+class ConformerConfig:
+    dim: int
+    n_layers: int
+    n_heads: int
+    embedding_dim: int
+    p_dropout: float
+    kernel_size_conv_mod: int
+    with_ff: bool
+
+
+def get_test_configs(
+    srink_factor: int = 4,
+) -> Tuple[PreprocessingConfig, AcousticENModelConfig, AcousticPretrainingConfig]:
+    r"""Returns a tuple of configuration objects for testing purposes.
+
+    Args:
+        srink_factor (int, optional): The shrink factor to apply to the model configuration. Defaults to 4.
+
+    Returns:
+        Tuple[PreprocessingConfig, AcousticENModelConfig, AcousticPretrainingConfig]: A tuple of configuration objects for testing purposes.
+
+    This function returns a tuple of configuration objects for testing purposes. The configuration objects are as follows:
+    - `PreprocessingConfig`: A configuration object for preprocessing.
+    - `AcousticENModelConfig`: A configuration object for the acoustic model.
+    - `AcousticPretrainingConfig`: A configuration object for acoustic pretraining.
+
+    The `srink_factor` parameter is used to shrink the dimensions of the model configuration to prevent out of memory issues during testing.
+    """
+    preprocess_config = PreprocessingConfig("english_only")
+    model_config = AcousticENModelConfig()
+
+    model_config.speaker_embed_dim = model_config.speaker_embed_dim // srink_factor
+    model_config.encoder.n_hidden = model_config.encoder.n_hidden // srink_factor
+    model_config.decoder.n_hidden = model_config.decoder.n_hidden // srink_factor
+    model_config.variance_adaptor.n_hidden = (
+        model_config.variance_adaptor.n_hidden // srink_factor
+    )
+
+    acoustic_pretraining_config = AcousticPretrainingConfig()
+
+    return (preprocess_config, model_config, acoustic_pretraining_config)
+
+
+# Function to initialize a Conformer with a given AcousticModelConfigType configuration
+def init_conformer(
+    model_config: AcousticModelConfigType,
+) -> Tuple[Conformer, ConformerConfig]:
+    r"""Function to initialize a `Conformer` with a given `AcousticModelConfigType` configuration.
+
+    Args:
+        model_config (AcousticModelConfigType): The object that holds the configuration details.
+
+    Returns:
+        Conformer: Initialized Conformer object.
+
+    The function sets the details of the `Conformer` object based on the `model_config` parameter.
+    The `Conformer` configuration is set as follows:
+    - dim: The number of hidden units, taken from the encoder part of the `model_config.encoder.n_hidden`.
+    - n_layers: The number of layers, taken from the encoder part of the `model_config.encoder.n_layers`.
+    - n_heads: The number of attention heads, taken from the encoder part of the `model_config.encoder.n_heads`.
+    - embedding_dim: The sum of dimensions of speaker embeddings and language embeddings.
+      The speaker_embed_dim and lang_embed_dim are a part of the `model_config.speaker_embed_dim`.
+    - p_dropout: Dropout rate taken from the encoder part of the `model_config.encoder.p_dropout`.
+      It adds a regularization parameter to prevent overfitting.
+    - kernel_size_conv_mod: The kernel size for the convolution module taken from the encoder part of the `model_config.encoder.kernel_size_conv_mod`.
+    - with_ff: A Boolean value denoting if feedforward operation is involved, taken from the encoder part of the `model_config.encoder.with_ff`.
+
+    """
+    conformer_config = ConformerConfig(
+        dim=model_config.encoder.n_hidden,
+        n_layers=model_config.encoder.n_layers,
+        n_heads=model_config.encoder.n_heads,
+        embedding_dim=model_config.speaker_embed_dim
+        + model_config.lang_embed_dim,  # speaker_embed_dim + lang_embed_dim = 385
+        p_dropout=model_config.encoder.p_dropout,
+        kernel_size_conv_mod=model_config.encoder.kernel_size_conv_mod,
+        with_ff=model_config.encoder.with_ff,
+    )
+
+    model = Conformer(**vars(conformer_config))
+
+    return model, conformer_config
+
+
+@dataclass
+class AcousticModelConfig:
+    preprocess_config: PreprocessingConfig
+    model_config: AcousticENModelConfig
+    n_speakers: int
+
+
+def init_acoustic_model(
+    preprocess_config: PreprocessingConfig,
+    model_config: AcousticENModelConfig,
+    n_speakers: int = 10,
+) -> Tuple[AcousticModel, AcousticModelConfig]:
+    r"""Function to initialize an `AcousticModel` with given preprocessing and model configurations.
+
+    Args:
+        preprocess_config (PreprocessingConfig): Configuration object for pre-processing.
+        model_config (AcousticENModelConfig): Configuration object for English Acoustic model.
+        n_speakers (int, optional): Number of speakers. Defaults to 10.
+
+    Returns:
+        AcousticModel: Initialized Acoustic Model.
+
+    The function creates an `AcousticModelConfig` instance which is then used to initialize the `AcousticModel`.
+    The `AcousticModelConfig` is configured as follows:
+    - preprocess_config: Pre-processing configuration.
+    - model_config: English Acoustic model configuration.
+    - fine_tuning: Boolean flag set to True indicating the model is for fine-tuning.
+    - n_speakers: Number of speakers.
+
+    """
+    # Create an AcousticModelConfig instance
+    acoustic_model_config = AcousticModelConfig(
+        preprocess_config=preprocess_config,
+        model_config=model_config,
+        n_speakers=n_speakers,
+    )
+
+    model = AcousticModel(**vars(acoustic_model_config))
+
+    return model, acoustic_model_config
+
+
+@dataclass
+class ForwardTrainParams:
+    x: torch.Tensor
+    speakers: torch.Tensor
+    src_lens: torch.Tensor
+    mels: torch.Tensor
+    mel_lens: torch.Tensor
+    enc_len: torch.Tensor
+    pitches: torch.Tensor
+    pitches_range: Tuple[float, float]
+    energies: torch.Tensor
+    langs: torch.Tensor
+    attn_priors: torch.Tensor
+    use_ground_truth: bool = True
+
+
+def init_forward_trains_params(
+    model_config: AcousticENModelConfig,
+    acoustic_pretraining_config: AcousticPretrainingConfig,
+    preprocess_config: PreprocessingConfig,
+    n_speakers: int = 10,
+) -> ForwardTrainParams:
+    r"""Function to initialize the parameters for forward propagation during training.
+
+    Args:
+        model_config (AcousticENModelConfig): Configuration object for English Acoustic model.
+        acoustic_pretraining_config (AcousticPretrainingConfig): Configuration object for acoustic pretraining.
+        preprocess_config (PreprocessingConfig): Configuration object for pre-processing.
+        n_speakers (int, optional): Number of speakers. Defaults to 10.
+
+    Returns:
+        ForwardTrainParams: Initialized parameters for forward propagation during training.
+
+    The function initializes the ForwardTrainParams object with the following parameters:
+    - x: Tensor containing the input sequences. Shape: [speaker_embed_dim, batch_size]
+    - speakers: Tensor containing the speaker indices. Shape: [speaker_embed_dim, batch_size]
+    - src_lens: Tensor containing the lengths of source sequences. Shape: [batch_size]
+    - mels: Tensor containing the mel spectrogram. Shape: [batch_size, speaker_embed_dim, encoder.n_hidden]
+    - mel_lens: Tensor containing the lengths of mel sequences. Shape: [batch_size]
+    - pitches: Tensor containing the pitch values. Shape: [batch_size, speaker_embed_dim, encoder.n_hidden]
+    - energies: Tensor containing the energy values. Shape: [batch_size, speaker_embed_dim, encoder.n_hidden]
+    - langs: Tensor containing the language indices. Shape: [speaker_embed_dim, batch_size]
+    - attn_priors: Tensor containing the attention priors. Shape: [batch_size, speaker_embed_dim, speaker_embed_dim]
+    - use_ground_truth: Boolean flag indicating if ground truth values should be used or not.
+
+    All the Tensors are initialized with random values.
+    """
+    return ForwardTrainParams(
+        # x: Tensor containing the input sequences. Shape: [speaker_embed_dim, batch_size]
+        x=torch.randint(
+            1,
+            255,
+            (
+                model_config.speaker_embed_dim,
+                acoustic_pretraining_config.batch_size,
+            ),
+        ),
+        pitches_range=(0.0, 1.0),
+        # speakers: Tensor containing the speaker indices. Shape: [speaker_embed_dim, batch_size]
+        speakers=torch.randint(
+            1,
+            n_speakers - 1,
+            (
+                model_config.speaker_embed_dim,
+                acoustic_pretraining_config.batch_size,
+            ),
+        ),
+        # src_lens: Tensor containing the lengths of source sequences. Shape: [speaker_embed_dim]
+        src_lens=torch.cat(
+            [
+                # torch.tensor([self.model_config.speaker_embed_dim]),
+                torch.randint(
+                    1,
+                    acoustic_pretraining_config.batch_size + 1,
+                    (model_config.speaker_embed_dim,),
+                ),
+            ],
+            dim=0,
+        ),
+        # mels: Tensor containing the mel spectrogram. Shape: [batch_size, stft.n_mel_channels, encoder.n_hidden]
+        mels=torch.randn(
+            model_config.speaker_embed_dim,
+            preprocess_config.stft.n_mel_channels,
+            model_config.encoder.n_hidden,
+        ),
+        # enc_len: Tensor containing the lengths of mel sequences. Shape: [speaker_embed_dim]
+        enc_len=torch.cat(
+            [
+                torch.randint(
+                    1,
+                    model_config.speaker_embed_dim,
+                    (model_config.speaker_embed_dim - 1,),
+                ),
+                torch.tensor([model_config.speaker_embed_dim]),
+            ],
+            dim=0,
+        ),
+        # mel_lens: Tensor containing the lengths of mel sequences. Shape: [batch_size]
+        mel_lens=torch.cat(
+            [
+                torch.randint(
+                    1,
+                    model_config.speaker_embed_dim,
+                    (model_config.speaker_embed_dim - 1,),
+                ),
+                torch.tensor([model_config.speaker_embed_dim]),
+            ],
+            dim=0,
+        ),
+        # pitches: Tensor containing the pitch values. Shape: [batch_size, speaker_embed_dim, encoder.n_hidden]
+        pitches=torch.randn(
+            # acoustic_pretraining_config.batch_size,
+            model_config.speaker_embed_dim,
+            # model_config.speaker_embed_dim,
+            model_config.encoder.n_hidden,
+        ),
+        # energies: Tensor containing the energy values. Shape: [batch_size, speaker_embed_dim, encoder.n_hidden]
+        energies=torch.randn(
+            model_config.speaker_embed_dim,
+            1,
+            model_config.encoder.n_hidden,
+        ),
+        # langs: Tensor containing the language indices. Shape: [speaker_embed_dim, batch_size]
+        langs=torch.randint(
+            1,
+            len(SUPPORTED_LANGUAGES) - 1,
+            (
+                model_config.speaker_embed_dim,
+                acoustic_pretraining_config.batch_size,
+            ),
+        ),
+        # attn_priors: Tensor containing the attention priors. Shape: [batch_size, speaker_embed_dim, speaker_embed_dim]
+        attn_priors=torch.randn(
+            model_config.speaker_embed_dim,
+            model_config.speaker_embed_dim,
+            acoustic_pretraining_config.batch_size,
+        ),
+        use_ground_truth=True,
+    )
+
+
+def init_mask_input_embeddings_encoding_attn_mask(
+    acoustic_model: AcousticModel,
+    forward_train_params: ForwardTrainParams,
+    model_config: AcousticENModelConfig,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    r"""Function to initialize masks for padding positions, input sequences, embeddings, positional encoding and attention masks.
+
+    Args:
+        acoustic_model (AcousticModel): Initialized Acoustic Model.
+        forward_train_params (ForwardTrainParams): Parameters for the forward training process.
+        model_config (AcousticENModelConfig): Configuration object for English Acoustic model.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: A tuple containing the following elements:
+            - src_mask: Tensor containing the masks for padding positions in the source sequences. Shape: [1, batch_size]
+            - x: Tensor containing the input sequences. Shape: [speaker_embed_dim, batch_size, speaker_embed_dim]
+            - embeddings: Tensor containing the embeddings. Shape: [speaker_embed_dim, batch_size, speaker_embed_dim + lang_embed_dim]
+            - encoding: Tensor containing the positional encoding. Shape: [lang_embed_dim, max(forward_train_params.mel_lens), model_config.encoder.n_hidden]
+            - attn_maskЖ Tensor containing the attention masks. Shape: [1, 1, 1, batch_size]
+
+    The function starts by generating masks for padding positions in the source and mel sequences.
+    Then, it uses the acoustic model to get the input sequences and embeddings.
+    Finally, it computes the positional encoding.
+
+    """
+    # Generate masks for padding positions in the source sequences and mel sequences
+    # src_mask: Tensor containing the masks for padding positions in the source sequences. Shape: [1, batch_size]
+    src_mask = tools.get_mask_from_lengths(forward_train_params.src_lens)
+
+    # x: Tensor containing the input sequences. Shape: [speaker_embed_dim, batch_size, speaker_embed_dim]
+    # embeddings: Tensor containing the embeddings. Shape: [speaker_embed_dim, batch_size, speaker_embed_dim + lang_embed_dim]
+    x, embeddings = acoustic_model.get_embeddings(
+        token_idx=forward_train_params.x,
+        speaker_idx=forward_train_params.speakers,
+        src_mask=src_mask,
+        lang_idx=forward_train_params.langs,
+    )
+
+    # encoding: Tensor containing the positional encoding
+    # Shape: [lang_embed_dim, max(forward_train_params.mel_lens), encoder.n_hidden]
+    encoding = positional_encoding(
+        model_config.encoder.n_hidden,
+        max(x.shape[1], int(forward_train_params.mel_lens.max().item())),
+    )
+
+    attn_mask = src_mask.view((src_mask.shape[0], 1, 1, src_mask.shape[1]))
+
+    return src_mask, x, embeddings, encoding, attn_mask

models/helpers/tests/test_dataloaders.py

@@ -0,0 +1,38 @@
+import unittest
+from unittest.mock import patch
+
+from torch.utils.data import DataLoader
+
+from models.helpers.dataloaders import train_dataloader, train_val_dataloader
+
+
+class TestDataLoader(unittest.TestCase):
+    def test_train_dataloader(self):
+        train_loader = train_dataloader(
+            batch_size=2,
+            num_workers=2,
+            cache=False,
+            mem_cache=False,
+        )
+
+        # Assertions
+        self.assertIsInstance(train_loader, DataLoader)
+
+        for batch in train_loader:
+            self.assertEqual(len(batch), 13)
+            break
+
+    def test_train_val_dataloader(self):
+        train_loader, val_loader = train_val_dataloader(
+            batch_size=2,
+            num_workers=2,
+            cache=False,
+            mem_cache=False,
+        )
+
+        # Assertions
+        self.assertIsInstance(train_loader, DataLoader)
+        self.assertIsInstance(val_loader, DataLoader)
+
+if __name__ == "__main__":
+    unittest.main()

models/helpers/tests/test_pitch_phoneme_averaging.py

@@ -0,0 +1,36 @@
+import unittest
+
+import torch
+
+from models.helpers import (
+    pitch_phoneme_averaging,
+)
+
+
+class TestPitchPhonemeAveraging(unittest.TestCase):
+    def test_pitch_phoneme_averaging(self):
+        # Initialize inputs
+        durations = torch.tensor([[5, 1, 3, 0], [2, 4, 0, 0]], dtype=torch.float32)
+        num_phonemes = durations.shape[-1]
+        max_length = int(torch.sum(durations, dim=1).int().max().item())
+        pitches = torch.rand(2, max_length)
+
+        max_phoneme_len = num_phonemes
+
+        # Call the pitch_phoneme_averaging method
+        result = pitch_phoneme_averaging(durations, pitches, max_phoneme_len)
+
+        # Assert output type
+        self.assertIsInstance(result, torch.Tensor)
+
+        # Assert output shape
+        expected_shape = durations.shape
+        self.assertEqual(result.shape, expected_shape)
+
+        # Assert all pitch values are within [0,1] since input pitch values were from a uniform distribution over [0,1]
+        self.assertTrue(torch.all((result >= 0) & (result <= 1)))
+
+
+# Run tests
+if __name__ == "__main__":
+    unittest.main()

models/helpers/tests/test_position_encoding.py

@@ -0,0 +1,30 @@
+# Required Libraries
+import unittest
+
+import torch
+
+from models.helpers import positional_encoding
+
+
+class TestPositionalEncoding(unittest.TestCase):
+    def test_positional_encoding(self):
+        # Test with d_model=128, length=10
+        d_model = 128
+        length = 10
+        result = positional_encoding(d_model, length)
+
+        # Assert that output is a torch.Tensor
+        self.assertIsInstance(result, torch.Tensor)
+
+        # Assert the output tensor shape is correct
+        # The extra dimension from unsqueeze is considered
+        expected_shape = (1, length, d_model)
+        self.assertEqual(result.shape, expected_shape)
+
+        # Assert that values lie in the range [-1, 1]
+        self.assertTrue(torch.all((result >= -1) & (result <= 1)))
+
+
+# Run tests
+if __name__ == "__main__":
+    unittest.main()

models/helpers/tests/tests_tools/test_calc_same_padding.py

@@ -0,0 +1,34 @@
+import unittest
+
+from models.helpers.tools import calc_same_padding
+
+
+class TestCalcSamePadding(unittest.TestCase):
+    def test_odd_kernel_size(self):
+        """Test that the padding returns correct tuple for odd-sized kernel."""
+        kernel_size = 3  # an odd-sized kernel
+        pad = calc_same_padding(kernel_size)
+        self.assertEqual(pad, (1, 1))  # we expect padding of size 1 on both sides
+
+    def test_even_kernel_size(self):
+        """Test that the padding returns correct tuple for even-sized kernel."""
+        kernel_size = 4  # an even-sized kernel
+        pad = calc_same_padding(kernel_size)
+        self.assertEqual(
+            pad, (2, 1),
+        )  # we expect padding of size 2 on one side, and size 1 on the other side
+
+    def test_one_kernel_size(self):
+        """Test that the padding returns correct tuple for kernel of size 1."""
+        kernel_size = 1  # kernel of size 1
+        pad = calc_same_padding(kernel_size)
+        self.assertEqual(pad, (0, 0))  # we expect no padding on both sides
+
+    def test_zero_kernel_size(self):
+        """Test that the padding raises ValueError for invalid kernel size of zero."""
+        with self.assertRaises(ValueError):
+            calc_same_padding(0)  # a kernel of size 0 is not valid
+
+
+if __name__ == "__main__":
+    unittest.main()

models/helpers/tests/tests_tools/test_get_mask_from_lengths.py

@@ -0,0 +1,37 @@
+import unittest
+
+import torch
+
+from models.helpers.tools import get_mask_from_lengths
+
+
+class TestGetMaskFromLengths(unittest.TestCase):
+    def setUp(self):
+        # Test cases: [2, 3, 1, 4]
+        self.input_lengths = torch.tensor([2, 3, 1, 4])
+
+    def test_get_mask_from_lengths(self):
+        expected_output = torch.tensor(
+            [
+                [False, False, True, True],
+                [False, False, False, True],
+                [False, True, True, True],
+                [False, False, False, False],
+            ],
+        )
+
+        # Call the function with the test cases
+        output = get_mask_from_lengths(self.input_lengths)
+
+        # Check if the output is a tensor
+        self.assertIsInstance(output, torch.Tensor)
+
+        # Check if the output shape is as expected
+        self.assertEqual(output.shape, expected_output.shape)
+
+        # Check if the output values are as expected
+        self.assertTrue(torch.all(output.eq(expected_output)))
+
+
+if __name__ == "__main__":
+    unittest.main()

models/helpers/tests/tests_tools/test_initialize_embeddings.py

@@ -0,0 +1,45 @@
+import unittest
+
+import numpy as np
+import torch
+
+from models.helpers.tools import initialize_embeddings
+
+
+class TestInitializeEmbeddings(unittest.TestCase):
+    def test_initialize_embeddings(self):
+        # Test with correct input shape
+        shape = (5, 10)
+        result = initialize_embeddings(shape)
+        # Assert output is torch.Tensor
+        self.assertIsInstance(result, torch.Tensor)
+        # Assert output shape
+        self.assertEqual(result.shape, shape)
+        # Assert type of elements
+        self.assertEqual(result.dtype, torch.float32)
+
+        # Assert standard deviation is close to expected (within some tolerance)
+        expected_stddev = np.sqrt(2 / shape[1])
+        tolerance = 0.1
+        self.assertLessEqual(abs(result.std().item() - expected_stddev), tolerance)
+
+        # Test with incorrect number of dimensions in shape
+        incorrect_shape = (5, 10, 15)
+        with self.assertRaises(AssertionError) as context:
+            initialize_embeddings(incorrect_shape)
+        self.assertEqual(
+            str(context.exception), "Can only initialize 2-D embedding matrices ...",
+        )
+
+        # Test with zero dimensions in shape
+        zero_dim_shape = ()
+        with self.assertRaises(AssertionError) as context:
+            initialize_embeddings(zero_dim_shape)
+        self.assertEqual(
+            str(context.exception), "Can only initialize 2-D embedding matrices ...",
+        )
+
+
+# Run tests
+if __name__ == "__main__":
+    unittest.main()

models/helpers/tests/tests_tools/test_pad.py

@@ -0,0 +1,36 @@
+import unittest
+
+import torch
+
+from models.helpers.tools import pad
+
+
+class TestPad(unittest.TestCase):
+    def test_1D_tensors_pad(self):
+        # 1D tensor inputs
+        tensors = [torch.ones(n) for n in range(1, 11)]
+        # Ten 1D tensors of length 1 to 10
+        max_len = max(t.numel() for t in tensors)
+
+        # Call the function
+        result = pad(tensors, max_len)
+
+        # Check the output shape is as expected
+        self.assertTrue(all(t.numel() == max_len for t in result))
+
+    def test_2D_tensors_pad(self):
+        # 2D tensor inputs
+        tensors = [torch.ones(n, 5) for n in range(1, 11)]
+        max_len = max(t.size(0) for t in tensors)
+
+        # Call the function
+        result = pad(tensors, max_len)
+
+        # Check the output shape is as expected
+        self.assertTrue(all(t.size(0) == max_len for t in result))
+        # Make sure second dimension wasn't changed
+        self.assertTrue(all(t.size(1) == 5 for t in result))
+
+
+if __name__ == "__main__":
+    unittest.main()

models/helpers/tests/tests_tools/test_stride_lens_downsampling.py

@@ -0,0 +1,52 @@
+import unittest
+
+import torch
+
+from models.helpers import (
+    stride_lens_downsampling,
+)
+
+
+class TestStrideLens(unittest.TestCase):
+    def test_stride_lens(self):
+        # Define test case inputs
+        input_lengths = torch.tensor([5, 7, 10, 12])
+        stride = 2
+
+        # Correct output for this would be ceil([5, 7, 10, 12] / 2) => [3, 4, 5, 6]
+        expected_output = torch.tensor([3, 4, 5, 6])
+
+        # Call the function with the test cases
+        output = stride_lens_downsampling(input_lengths, stride)
+
+        # Check if the output is a tensor
+        self.assertIsInstance(output, torch.Tensor)
+
+        # Check if the output shape is as expected
+        self.assertEqual(output.shape, expected_output.shape)
+
+        # Check if the output values are as expected
+        self.assertTrue(torch.all(output.eq(expected_output)))
+
+    def test_stride_lens_default_stride(self):
+        # Define test case inputs. Here, we do not provide the stride.
+        input_lengths = torch.tensor([10, 20, 4, 11])
+
+        # Correct output for this would be ceil([10, 20, 4, 11] / 2) => [5, 10, 2, 6]
+        expected_output = torch.tensor([5, 10, 2, 6])
+
+        # Call the function with the test cases
+        output = stride_lens_downsampling(input_lengths)
+
+        # Check if the output is a tensor
+        self.assertIsInstance(output, torch.Tensor)
+
+        # Check if the output shape is as expected
+        self.assertEqual(output.shape, expected_output.shape)
+
+        # Check if the output values are as expected
+        self.assertTrue(torch.all(output.eq(expected_output)))
+
+
+if __name__ == "__main__":
+    unittest.main()