Datasets:

cdminix
/

libritts-r-aligned

	"""LibriTTS dataset with forced alignments."""

	import os
	from pathlib import Path
	import hashlib
	import pickle

	import datasets
	import pandas as pd
	import numpy as np
	from alignments.datasets.librispeech import LibrittsRDataset
	from tqdm.contrib.concurrent import process_map
	from tqdm.auto import tqdm
	from multiprocessing import cpu_count
	from phones.convert import Converter
	import torchaudio
	import torchaudio.transforms as AT
	from functools import lru_cache

	logger = datasets.logging.get_logger(__name__)

	_PHONESET = "arpabet"

	_VERBOSE = os.environ.get("LIBRITTS_VERBOSE", True)
	_MAX_WORKERS = os.environ.get("LIBRITTS_MAX_WORKERS", cpu_count())
	_PATH = os.environ.get("LIBRITTS_PATH", os.environ.get("HF_DATASETS_CACHE", None))
	if _PATH is not None and not os.path.exists(_PATH):
	os.makedirs(_PATH)

	_VERSION = "1.1.0"

	_CITATION = """\
	@article{koizumi2023libritts,
	title={LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus},
	author={Koizumi, Yuma and Zen, Heiga and Karita, Shigeki and Ding, Yifan and Yatabe, Kohei and Morioka, Nobuyuki and Bacchiani, Michiel and Zhang, Yu and Han, Wei and Bapna, Ankur},
	journal={arXiv preprint arXiv:2305.18802},
	year={2023}
	}

	@article{zen2019libritts,
	title={LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech},
	author={Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui},
	journal={Interspeech},
	year={2019}
	}
	@article{https://doi.org/10.48550/arxiv.2211.16049,
	author = {Minixhofer, Christoph and Klejch, Ondřej and Bell, Peter},
	title = {Evaluating and reducing the distance between synthetic and real speech distributions},
	year = {2022}
	}
	"""

	_DESCRIPTION = """\
	Dataset used for loading TTS spectrograms and waveform audio with alignments and a number of configurable "measures", which are extracted from the raw audio.
	"""

	_URL = "http://www.openslr.org/resources/141/"
	_URLS = {
	"dev-clean": _URL + "dev_clean.tar.gz",
	"dev-other": _URL + "dev_other.tar.gz",
	"test-clean": _URL + "test_clean.tar.gz",
	"test-other": _URL + "test_other.tar.gz",
	"train-clean-100": _URL + "train_clean_100.tar.gz",
	"train-clean-360": _URL + "train_clean_360.tar.gz",
	"train-other-500": _URL + "train_other_500.tar.gz",
	}

	@lru_cache(maxsize=1000)
	def get_speaker_prompts(speaker, hash_ds):
	ds = hash_ds.df
	speaker_prompts = ds[ds["speaker"] == speaker]
	speaker_prompts = tuple(speaker_prompts["audio"])
	return speaker_prompts

	class LibriTTSAlignConfig(datasets.BuilderConfig):
	"""BuilderConfig for LibriTTSAlign."""

	def __init__(self, sampling_rate=22050, hop_length=256, win_length=1024, **kwargs):
	"""BuilderConfig for LibriTTSAlign.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(LibriTTSAlignConfig, self).__init__(**kwargs)

	self.sampling_rate = sampling_rate
	self.hop_length = hop_length
	self.win_length = win_length

	if _PATH is None:
	raise ValueError("Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory.")
	elif _PATH == os.environ.get("HF_DATASETS_CACHE", None):
	logger.warning("Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory. Using HF_DATASETS_CACHE as a fallback.")

	class LibriTTSAlign(datasets.GeneratorBasedBuilder):
	"""LibriTTSAlign dataset."""

	BUILDER_CONFIGS = [
	LibriTTSAlignConfig(
	name="libritts",
	version=datasets.Version(_VERSION, ""),
	),
	]

	def _info(self):
	features = {
	"id": datasets.Value("string"),
	"speaker": datasets.Value("string"),
	"text": datasets.Value("string"),
	"start": datasets.Value("float32"),
	"end": datasets.Value("float32"),
	# phone features
	"phones": datasets.Sequence(datasets.Value("string")),
	"phone_durations": datasets.Sequence(datasets.Value("int32")),
	# audio feature
	"audio": datasets.Value("string"),
	"audio_speaker_prompt": datasets.Sequence(datasets.Value("string")),
	}

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(features),
	supervised_keys=None,
	homepage="https://github.com/MiniXC/MeasureCollator",
	citation=_CITATION,
	task_templates=None,
	)

	def _split_generators(self, dl_manager):
	ds_dict = {}
	for name, url in _URLS.items():
	ds_dict[name] = self._create_alignments_ds(name, url)
	splits = [
	datasets.SplitGenerator(
	name=key.replace("-", "."),
	gen_kwargs={"ds": self._create_data(value)}
	)
	for key, value in ds_dict.items()
	]
	# dataframe with all data
	data_train = self._create_data([ds_dict["train-clean-100"], ds_dict["train-clean-360"], ds_dict["train-other-500"]])
	data_dev = self._create_data([ds_dict["dev-clean"], ds_dict["dev-other"]])
	data_test = self._create_data([ds_dict["test-clean"], ds_dict["test-other"]])
	splits += [
	datasets.SplitGenerator(
	name="train.all",
	gen_kwargs={
	"ds": data_train,
	}
	),
	datasets.SplitGenerator(
	name="dev.all",
	gen_kwargs={
	"ds": data_dev,
	}
	),
	datasets.SplitGenerator(
	name="test.all",
	gen_kwargs={
	"ds": data_test,
	}
	),
	]
	data_all = pd.concat([data_train, data_dev, data_test])
	# create a new split which takes one sample from each speaker in data_all and puts it into the dev split
	# we then remove these samples from data_all
	speakers = data_all["speaker"].unique()
	# seed for reproducibility
	np.random.seed(42)
	self.data_all = data_all
	del data_all
	data_dev_all = [
	x for x in
	process_map(
	self._create_dev_split,
	speakers,
	chunksize=1000,
	max_workers=_MAX_WORKERS,
	desc="creating dev split",
	tqdm_class=tqdm,
	)
	if x is not None
	]
	data_dev_all = pd.concat(data_dev_all)
	data_all = self.data_all
	data_all = data_all[data_all["speaker"].isin(data_dev_all["speaker"].unique())]
	data_all = data_all[~data_all["basename"].isin(data_dev_all["basename"].unique())]
	del self.data_all
	self.speaker2idxs = {}
	self.speaker2idxs["all"] = {speaker: idx for idx, speaker in enumerate(sorted(list(data_dev_all["speaker"].unique())))}
	self.speaker2idxs["train"] = {speaker: idx for idx, speaker in enumerate(sorted(list(data_train["speaker"].unique())))}
	self.speaker2idxs["dev"] = {speaker: idx for idx, speaker in enumerate(sorted(list(data_dev["speaker"].unique())))}
	self.speaker2idxs["test"] = {speaker: idx for idx, speaker in enumerate(sorted(list(data_test["speaker"].unique())))}
	splits += [
	datasets.SplitGenerator(
	name="train",
	gen_kwargs={
	"ds": data_all,
	}
	),
	datasets.SplitGenerator(
	name="dev",
	gen_kwargs={
	"ds": data_dev_all,
	}
	),
	]
	self.alignments_ds = None
	self.data = None
	return splits

	def _create_dev_split(self, speaker):
	data_speaker = self.data_all[self.data_all["speaker"] == speaker]
	if len(data_speaker) < 10:
	print(f"Speaker {speaker} has only {len(data_speaker)} samples, skipping")
	return None
	else:
	data_speaker = data_speaker.sample(2)
	return data_speaker

	def _create_alignments_ds(self, name, url):
	self.empty_textgrids = 0
	ds_hash = hashlib.md5(os.path.join(_PATH, f"{name}-alignments").encode()).hexdigest()
	pkl_path = os.path.join(_PATH, f"{ds_hash}.pkl")
	if os.path.exists(pkl_path):
	ds = pickle.load(open(pkl_path, "rb"))
	else:
	tgt_dir = os.path.join(_PATH, f"{name}-alignments")
	src_dir = os.path.join(_PATH, f"{name}-data")
	if os.path.exists(tgt_dir):
	src_dir = None
	url = None
	if os.path.exists(src_dir):
	url = None
	ds = LibrittsRDataset(
	target_directory=tgt_dir,
	source_directory=src_dir,
	source_url=url,
	verbose=_VERBOSE,
	tmp_directory=os.path.join(_PATH, f"{name}-tmp"),
	chunk_size=1000,
	)
	pickle.dump(ds, open(pkl_path, "wb"))
	return ds, ds_hash

	def _create_data(self, data):
	entries = []
	self.phone_cache = {}
	self.phone_converter = Converter()
	if not isinstance(data, list):
	data = [data]
	hashes = [ds_hash for ds, ds_hash in data]
	ds = [ds for ds, ds_hash in data]
	self.ds = ds
	del data
	for i, ds in enumerate(ds):
	if os.path.exists(os.path.join(_PATH, f"{hashes[i]}-entries.pkl")):
	add_entries = pickle.load(open(os.path.join(_PATH, f"{hashes[i]}-entries.pkl"), "rb"))
	else:
	add_entries = [
	entry
	for entry in process_map(
	self._create_entry,
	zip([i] * len(ds), np.arange(len(ds))),
	chunksize=10_000,
	max_workers=_MAX_WORKERS,
	desc=f"processing dataset {hashes[i]}",
	tqdm_class=tqdm,
	)
	if entry is not None
	]
	pickle.dump(add_entries, open(os.path.join(_PATH, f"{hashes[i]}-entries.pkl"), "wb"))
	entries += add_entries
	if self.empty_textgrids > 0:
	logger.warning(f"Found {self.empty_textgrids} empty textgrids")
	del self.ds, self.phone_cache, self.phone_converter
	df = pd.DataFrame(
	entries,
	columns=[
	"phones",
	"duration",
	"start",
	"end",
	"audio",
	"speaker",
	"text",
	"basename",
	],
	)
	return df

	def _create_entry(self, dsi_idx):
	dsi, idx = dsi_idx
	item = self.ds[dsi][idx]
	start, end = item["phones"][0][0], item["phones"][-1][1]

	phones = []
	durations = []

	for i, p in enumerate(item["phones"]):
	s, e, phone = p
	phone.replace("ˌ", "")
	r_phone = phone.replace("0", "").replace("1", "")
	if len(r_phone) > 0:
	phone = r_phone
	if "[" not in phone:
	o_phone = phone
	if o_phone not in self.phone_cache:
	phone = self.phone_converter(
	phone, _PHONESET, lang=None
	)[0]
	self.phone_cache[o_phone] = phone
	phone = self.phone_cache[o_phone]
	phones.append(phone)
	durations.append(
	int(
	np.round(e * self.config.sampling_rate / self.config.hop_length)
	- np.round(s * self.config.sampling_rate / self.config.hop_length)
	)
	)

	if start >= end:
	self.empty_textgrids += 1
	return None

	return (
	phones,
	durations,
	start,
	end,
	item["wav"],
	str(item["speaker"]).split("/")[-1],
	item["transcript"],
	Path(item["wav"]).name,
	)

	def _generate_examples(self, ds):
	j = 0
	hash_col = "audio"
	hash_ds = HashableDataFrame(ds, hash_col)
	for i, row in ds.iterrows():
	# 10kB is the minimum size of a wav file for our purposes
	if Path(row["audio"]).stat().st_size >= 10_000:
	if len(row["phones"]) < 384:
	speaker_prompts = get_speaker_prompts(row["speaker"], hash_ds)
	result = {
	"id": row["basename"],
	"speaker": row["speaker"],
	"text": row["text"],
	"start": row["start"],
	"end": row["end"],
	"phones": row["phones"],
	"phone_durations": row["duration"],
	"audio": str(row["audio"]),
	"audio_speaker_prompt": speaker_prompts,
	}
	yield j, result
	j += 1

	class HashableDataFrame():
	def __init__(self, df, hash_col):
	self.df = df
	self.hash_col = hash_col
	self.hash = hashlib.md5(self.df[self.hash_col].values).hexdigest()
	# to integer
	self.hash = int(self.hash, 16)

	def __hash__(self):
	return self.hash

	def __eq__(self, other):
	return self.hash == other.hash