import os
import re
import xml.etree.ElementTree as ET
from glob import glob
from pathlib import Path
from typing import List
from tqdm import tqdm
########################
# DATASETS
########################
def tweb(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalize TWEB dataset.
https://www.kaggle.com/bryanpark/the-world-english-bible-speech-dataset
"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "tweb"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("\t")
wav_file = os.path.join(root_path, cols[0] + ".wav")
text = cols[1]
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items
def mozilla(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes Mozilla meta data files to TTS format"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "mozilla"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = cols[1].strip()
text = cols[0].strip()
wav_file = os.path.join(root_path, "wavs", wav_file)
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items
def mozilla_de(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes Mozilla meta data files to TTS format"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "mozilla"
with open(txt_file, "r", encoding="ISO 8859-1") as ttf:
for line in ttf:
cols = line.strip().split("|")
wav_file = cols[0].strip()
text = cols[1].strip()
folder_name = f"BATCH_{wav_file.split('_')[0]}_FINAL"
wav_file = os.path.join(root_path, folder_name, wav_file)
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items
def mailabs(root_path, meta_files=None, ignored_speakers=None):
"""Normalizes M-AI-Labs meta data files to TTS format
Args:
root_path (str): root folder of the MAILAB language folder.
meta_files (str): list of meta files to be used in the training. If None, finds all the csv files
recursively. Defaults to None
"""
speaker_regex = re.compile("by_book/(male|female)/(?P<speaker_name>[^/]+)/")
if not meta_files:
csv_files = glob(root_path + "/**/metadata.csv", recursive=True)
else:
csv_files = meta_files
# meta_files = [f.strip() for f in meta_files.split(",")]
items = []
for csv_file in csv_files:
if os.path.isfile(csv_file):
txt_file = csv_file
else:
txt_file = os.path.join(root_path, csv_file)
folder = os.path.dirname(txt_file)
# determine speaker based on folder structure...
speaker_name_match = speaker_regex.search(txt_file)
if speaker_name_match is None:
continue
speaker_name = speaker_name_match.group("speaker_name")
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_name in ignored_speakers:
continue
print(" | > {}".format(csv_file))
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
if not meta_files:
wav_file = os.path.join(folder, "wavs", cols[0] + ".wav")
else:
wav_file = os.path.join(root_path, folder.replace("metadata.csv", ""), "wavs", cols[0] + ".wav")
if os.path.isfile(wav_file):
text = cols[1].strip()
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
else:
# M-AI-Labs have some missing samples, so just print the warning
print("> File %s does not exist!" % (wav_file))
return items
def ljspeech(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes the LJSpeech meta data file to TTS format
https://keithito.com/LJ-Speech-Dataset/"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "ljspeech"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
text = cols[2]
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items
def ljspeech_test(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes the LJSpeech meta data file for TTS testing
https://keithito.com/LJ-Speech-Dataset/"""
txt_file = os.path.join(root_path, meta_file)
items = []
with open(txt_file, "r", encoding="utf-8") as ttf:
speaker_id = 0
for idx, line in enumerate(ttf):
# 2 samples per speaker to avoid eval split issues
if idx % 2 == 0:
speaker_id += 1
cols = line.split("|")
wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
text = cols[2]
items.append({"text": text, "audio_file": wav_file, "speaker_name": f"ljspeech-{speaker_id}"})
return items
def sam_accenture(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes the sam-accenture meta data file to TTS format
https://github.com/Sam-Accenture-Non-Binary-Voice/non-binary-voice-files"""
xml_file = os.path.join(root_path, "voice_over_recordings", meta_file)
xml_root = ET.parse(xml_file).getroot()
items = []
speaker_name = "sam_accenture"
for item in xml_root.findall("./fileid"):
text = item.text
wav_file = os.path.join(root_path, "vo_voice_quality_transformation", item.get("id") + ".wav")
if not os.path.exists(wav_file):
print(f" [!] {wav_file} in metafile does not exist. Skipping...")
continue
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items
def ruslan(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes the RUSLAN meta data file to TTS format
https://ruslan-corpus.github.io/"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "ruslan"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = os.path.join(root_path, "RUSLAN", cols[0] + ".wav")
text = cols[1]
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items
def css10(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes the CSS10 dataset file to TTS format"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "css10"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = os.path.join(root_path, cols[0])
text = cols[1]
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items
def nancy(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes the Nancy meta data file to TTS format"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "nancy"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
utt_id = line.split()[1]
text = line[line.find('"') + 1 : line.rfind('"') - 1]
wav_file = os.path.join(root_path, "wavn", utt_id + ".wav")
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items
def common_voice(root_path, meta_file, ignored_speakers=None):
"""Normalize the common voice meta data file to TTS format."""
txt_file = os.path.join(root_path, meta_file)
items = []
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
if line.startswith("client_id"):
continue
cols = line.split("\t")
text = cols[2]
speaker_name = cols[0]
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_name in ignored_speakers:
continue
wav_file = os.path.join(root_path, "clips", cols[1].replace(".mp3", ".wav"))
items.append({"text": text, "audio_file": wav_file, "speaker_name": "MCV_" + speaker_name})
return items
def libri_tts(root_path, meta_files=None, ignored_speakers=None):
"""https://ai.google/tools/datasets/libri-tts/"""
items = []
if not meta_files:
meta_files = glob(f"{root_path}/**/*trans.tsv", recursive=True)
else:
if isinstance(meta_files, str):
meta_files = [os.path.join(root_path, meta_files)]
for meta_file in meta_files:
_meta_file = os.path.basename(meta_file).split(".")[0]
with open(meta_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("\t")
file_name = cols[0]
speaker_name, chapter_id, *_ = cols[0].split("_")
_root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
wav_file = os.path.join(_root_path, file_name + ".wav")
text = cols[2]
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_name in ignored_speakers:
continue
items.append({"text": text, "audio_file": wav_file, "speaker_name": f"LTTS_{speaker_name}"})
for item in items:
assert os.path.exists(item["audio_file"]), f" [!] wav files don't exist - {item['audio_file']}"
return items
def custom_turkish(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "turkish-female"
skipped_files = []
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = os.path.join(root_path, "wavs", cols[0].strip() + ".wav")
if not os.path.exists(wav_file):
skipped_files.append(wav_file)
continue
text = cols[1].strip()
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
print(f" [!] {len(skipped_files)} files skipped. They don't exist...")
return items
# ToDo: add the dataset link when the dataset is released publicly
def brspeech(root_path, meta_file, ignored_speakers=None):
"""BRSpeech 3.0 beta"""
txt_file = os.path.join(root_path, meta_file)
items = []
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
if line.startswith("wav_filename"):
continue
cols = line.split("|")
wav_file = os.path.join(root_path, cols[0])
text = cols[2]
speaker_id = cols[3]
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_id in ignored_speakers:
continue
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_id})
return items
def vctk(root_path, meta_files=None, wavs_path="wav48_silence_trimmed", mic="mic1", ignored_speakers=None):
"""VCTK dataset v0.92.
URL:
https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip
This dataset has 2 recordings per speaker that are annotated with ```mic1``` and ```mic2```.
It is believed that (😄 ) ```mic1``` files are the same as the previous version of the dataset.
mic1:
Audio recorded using an omni-directional microphone (DPA 4035).
Contains very low frequency noises.
This is the same audio released in previous versions of VCTK:
https://doi.org/10.7488/ds/1994
mic2:
Audio recorded using a small diaphragm condenser microphone with
very wide bandwidth (Sennheiser MKH 800).
Two speakers, p280 and p315 had technical issues of the audio
recordings using MKH 800.
"""
file_ext = "flac"
items = []
meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
for meta_file in meta_files:
_, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
file_id = txt_file.split(".")[0]
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_id in ignored_speakers:
continue
with open(meta_file, "r", encoding="utf-8") as file_text:
text = file_text.readlines()[0]
# p280 has no mic2 recordings
if speaker_id == "p280":
wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + f"_mic1.{file_ext}")
else:
wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + f"_{mic}.{file_ext}")
if os.path.exists(wav_file):
items.append({"text": text, "audio_file": wav_file, "speaker_name": "VCTK_" + speaker_id})
else:
print(f" [!] wav files don't exist - {wav_file}")
return items
def vctk_old(root_path, meta_files=None, wavs_path="wav48", ignored_speakers=None):
"""homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
items = []
meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
for meta_file in meta_files:
_, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
file_id = txt_file.split(".")[0]
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_id in ignored_speakers:
continue
with open(meta_file, "r", encoding="utf-8") as file_text:
text = file_text.readlines()[0]
wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + ".wav")
items.append({"text": text, "audio_file": wav_file, "speaker_name": "VCTK_old_" + speaker_id})
return items
def esd(root_path, meta_files, ignored_speakers=None):
"""Emotional Speech Dataset (ESD): https://github.com/HLTSingapore/Emotional-Speech-Data"""
items = []
if meta_files is None or meta_files == "":
raise ValueError(
"You need to specify the partitions to load. Available partitions: 'train', 'evaluation', and 'test'"
)
if isinstance(meta_files, str):
meta_files = [meta_files]
txt_files = glob(os.path.join(root_path, "**/*.txt"), recursive=True)
for meta_file in txt_files:
speaker_id, _ = os.path.relpath(meta_file, root_path).split(os.sep)
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_id in ignored_speakers:
continue
with open(meta_file, "r", encoding="utf-8") as file_text:
try:
metadata = file_text.readlines()
except Exception as e:
print(f"The file {meta_file} break the import with the following error: ")
raise e
for data in metadata:
# this dataset have problems with csv separator, some files use just space others \t
data = data.replace("\n", "").replace("\t", " ")
if not data:
print(meta_file, data)
continue
splits = data.split(" ")
file_id = splits[0]
emotion_id = splits[-1]
# all except the first and last position is the sentence
text = " ".join(splits[1:-1])
for split in meta_files:
wav_file = os.path.join(root_path, speaker_id, emotion_id, split, file_id + ".wav")
if os.path.exists(wav_file):
items.append({"text": text, "audio_file": wav_file, "speaker_name": "ESD_" + speaker_id})
return items
def open_bible(root_path, meta_files="train", ignore_digits_sentences=True, ignored_speakers=None):
"""ToDo: Refer the paper when available"""
items = []
split_dir = meta_files
meta_files = glob(f"{os.path.join(root_path, split_dir)}/**/*.txt", recursive=True)
for meta_file in meta_files:
_, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
file_id = txt_file.split(".")[0]
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_id in ignored_speakers:
continue
with open(meta_file, "r", encoding="utf-8") as file_text:
text = file_text.readline().replace("\n", "")
# ignore sentences that contains digits
if ignore_digits_sentences and any(map(str.isdigit, text)):
continue
wav_file = os.path.join(root_path, split_dir, speaker_id, file_id + ".flac")
items.append({"text": text, "audio_file": wav_file, "speaker_name": "OB_" + speaker_id})
return items
def mls(root_path, meta_files=None, ignored_speakers=None):
"""http://www.openslr.org/94/"""
items = []
with open(os.path.join(root_path, meta_files), "r", encoding="utf-8") as meta:
for line in meta:
file, text = line.split("\t")
text = text[:-1]
speaker, book, *_ = file.split("_")
wav_file = os.path.join(root_path, os.path.dirname(meta_files), "audio", speaker, book, file + ".wav")
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker in ignored_speakers:
continue
items.append({"text": text, "audio_file": wav_file, "speaker_name": "MLS_" + speaker})
return items
# ======================================== VOX CELEB ===========================================
def voxceleb2(root_path, meta_file=None, **kwargs): # pylint: disable=unused-argument
"""
:param meta_file Used only for consistency with load_tts_samples api
"""
return _voxcel_x(root_path, meta_file, voxcel_idx="2")
def voxceleb1(root_path, meta_file=None, **kwargs): # pylint: disable=unused-argument
"""
:param meta_file Used only for consistency with load_tts_samples api
"""
return _voxcel_x(root_path, meta_file, voxcel_idx="1")
def _voxcel_x(root_path, meta_file, voxcel_idx):
assert voxcel_idx in ["1", "2"]
expected_count = 148_000 if voxcel_idx == "1" else 1_000_000
voxceleb_path = Path(root_path)
cache_to = voxceleb_path / f"metafile_voxceleb{voxcel_idx}.csv"
cache_to.parent.mkdir(exist_ok=True)
# if not exists meta file, crawl recursively for 'wav' files
if meta_file is not None:
with open(str(meta_file), "r", encoding="utf-8") as f:
return [x.strip().split("|") for x in f.readlines()]
elif not cache_to.exists():
cnt = 0
meta_data = []
wav_files = voxceleb_path.rglob("**/*.wav")
for path in tqdm(
wav_files,
desc=f"Building VoxCeleb {voxcel_idx} Meta file ... this needs to be done only once.",
total=expected_count,
):
speaker_id = str(Path(path).parent.parent.stem)
assert speaker_id.startswith("id")
text = None # VoxCel does not provide transciptions, and they are not needed for training the SE
meta_data.append(f"{text}|{path}|voxcel{voxcel_idx}_{speaker_id}\n")
cnt += 1
with open(str(cache_to), "w", encoding="utf-8") as f:
f.write("".join(meta_data))
if cnt < expected_count:
raise ValueError(f"Found too few instances for Voxceleb. Should be around {expected_count}, is: {cnt}")
with open(str(cache_to), "r", encoding="utf-8") as f:
return [x.strip().split("|") for x in f.readlines()]
def emotion(root_path, meta_file, ignored_speakers=None):
"""Generic emotion dataset"""
txt_file = os.path.join(root_path, meta_file)
items = []
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
if line.startswith("file_path"):
continue
cols = line.split(",")
wav_file = os.path.join(root_path, cols[0])
speaker_id = cols[1]
emotion_id = cols[2].replace("\n", "")
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_id in ignored_speakers:
continue
items.append({"audio_file": wav_file, "speaker_name": speaker_id, "emotion_name": emotion_id})
return items
def baker(root_path: str, meta_file: str, **kwargs) -> List[List[str]]: # pylint: disable=unused-argument
"""Normalizes the Baker meta data file to TTS format
Args:
root_path (str): path to the baker dataset
meta_file (str): name of the meta dataset containing names of wav to select and the transcript of the sentence
Returns:
List[List[str]]: List of (text, wav_path, speaker_name) associated with each sentences
"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "baker"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
wav_name, text = line.rstrip("\n").split("|")
wav_path = os.path.join(root_path, "clips_22", wav_name)
items.append({"text": text, "audio_file": wav_path, "speaker_name": speaker_name})
return items
def kokoro(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Japanese single-speaker dataset from https://github.com/kaiidams/Kokoro-Speech-Dataset"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "kokoro"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
text = cols[2].replace(" ", "")
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
return items