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):
"""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") 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, wav_file, speaker_name])
return items
def mozilla(root_path, meta_file):
"""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") 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, wav_file, speaker_name])
return items
def mozilla_de(root_path, meta_file):
"""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, wav_file, speaker_name])
return items
def mailabs(root_path, meta_files=None):
"""Normalizes M-AI-Labs meta data files to TTS format"""
speaker_regex = re.compile("by_book/(male|female)/(?P<speaker_name>[^/]+)/")
if meta_files is None:
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:
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")
print(" | > {}".format(csv_file))
with open(txt_file, "r") as ttf:
for line in ttf:
cols = line.split("|")
if meta_files is None:
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, wav_file, speaker_name])
else:
raise RuntimeError("> File %s does not exist!" % (wav_file))
return items
def ljspeech(root_path, meta_file):
"""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[1]
items.append([text, wav_file, speaker_name])
return items
def sam_accenture(root_path, meta_file):
"""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, wav_file, speaker_name])
return items
def ruslan(root_path, meta_file):
"""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 = "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, "RUSLAN", cols[0] + ".wav")
text = cols[1]
items.append([text, wav_file, speaker_name])
return items
def css10(root_path, meta_file):
"""Normalizes the CSS10 dataset file to TTS format"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "ljspeech"
with open(txt_file, "r") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = os.path.join(root_path, cols[0])
text = cols[1]
items.append([text, wav_file, speaker_name])
return items
def nancy(root_path, meta_file):
"""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") 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, wav_file, speaker_name])
return items
def common_voice(root_path, meta_file):
"""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") as ttf:
for line in ttf:
if line.startswith("client_id"):
continue
cols = line.split("\t")
text = cols[2]
speaker_name = cols[0]
wav_file = os.path.join(root_path, "clips", cols[1].replace(".mp3", ".wav"))
items.append([text, wav_file, "MCV_" + speaker_name])
return items
def libri_tts(root_path, meta_files=None):
"""https://ai.google/tools/datasets/libri-tts/"""
items = []
if meta_files is None:
meta_files = glob(f"{root_path}/**/*trans.tsv", recursive=True)
for meta_file in meta_files:
_meta_file = os.path.basename(meta_file).split(".")[0]
speaker_name = _meta_file.split("_")[0]
chapter_id = _meta_file.split("_")[1]
_root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
with open(meta_file, "r") 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, wav_file, "LTTS_" + speaker_name])
for item in items:
assert os.path.exists(item[1]), f" [!] wav files don't exist - {item[1]}"
return items
def custom_turkish(root_path, meta_file):
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, wav_file, 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):
"""BRSpeech 3.0 beta"""
txt_file = os.path.join(root_path, meta_file)
items = []
with open(txt_file, "r") 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_name = cols[3]
items.append([text, wav_file, speaker_name])
return items
def vctk(root_path, meta_files=None, wavs_path="wav48"):
"""homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
test_speakers = meta_files
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]
if isinstance(test_speakers, list): # if is list ignore this speakers ids
if speaker_id in test_speakers:
continue
with open(meta_file) 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, wav_file, "VCTK_" + speaker_id])
return items
def vctk_slim(root_path, meta_files=None, wavs_path="wav48"):
"""homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
items = []
txt_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
for text_file in txt_files:
_, speaker_id, txt_file = os.path.relpath(text_file, root_path).split(os.sep)
file_id = txt_file.split(".")[0]
if isinstance(meta_files, list): # if is list ignore this speakers ids
if speaker_id in meta_files:
continue
wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + ".wav")
items.append([None, wav_file, "VCTK_" + speaker_id])
return items
# ======================================== VOX CELEB ===========================================
def voxceleb2(root_path, meta_file=None):
"""
:param meta_file Used only for consistency with load_meta_data api
"""
return _voxcel_x(root_path, meta_file, voxcel_idx="2")
def voxceleb1(root_path, meta_file=None):
"""
:param meta_file Used only for consistency with load_meta_data 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") 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") 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") as f:
return [x.strip().split("|") for x in f.readlines()]
def baker(root_path: str, meta_file: str) -> List[List[str]]:
"""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") 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, wav_path, speaker_name])
return items
def kokoro(root_path, meta_file):
"""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") 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, wav_file, speaker_name])
return items