Medix-AI/coqui-tts
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coqui-tts
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coqui-tts/TTS/tts/datasets/preprocess.py

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import os
import re
import sys
import xml.etree.ElementTree as ET
from glob import glob
from pathlib import Path
from typing import List
from tqdm import tqdm
from TTS.tts.utils.generic_utils import split_dataset
####################
# UTILITIES
####################
def load_meta_data(datasets, eval_split=True):
meta_data_train_all = []
meta_data_eval_all = [] if eval_split else None
for dataset in datasets:
name = dataset['name']
root_path = dataset['path']
meta_file_train = dataset['meta_file_train']
meta_file_val = dataset['meta_file_val']
# setup the right data processor
preprocessor = get_preprocessor_by_name(name)
# load train set
meta_data_train = preprocessor(root_path, meta_file_train)
print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
# load evaluation split if set
if eval_split:
if meta_file_val is None:
meta_data_eval, meta_data_train = split_dataset(meta_data_train)
else:
meta_data_eval = preprocessor(root_path, meta_file_val)
meta_data_eval_all += meta_data_eval
meta_data_train_all += meta_data_train
# load attention masks for duration predictor training
if 'meta_file_attn_mask' in dataset and dataset['meta_file_attn_mask'] is not None:
meta_data = dict(load_attention_mask_meta_data(dataset['meta_file_attn_mask']))
for idx, ins in enumerate(meta_data_train_all):
attn_file = meta_data[ins[1]].strip()
meta_data_train_all[idx].append(attn_file)
if meta_data_eval_all is not None:
for idx, ins in enumerate(meta_data_eval_all):
attn_file = meta_data[ins[1]].strip()
meta_data_eval_all[idx].append(attn_file)
return meta_data_train_all, meta_data_eval_all
def load_attention_mask_meta_data(metafile_path):
"""Load meta data file created by compute_attention_masks.py"""
with open(metafile_path, 'r') as f:
lines = f.readlines()
meta_data = []
for line in lines:
wav_file, attn_file = line.split('|')
meta_data.append([wav_file, attn_file])
return meta_data
def get_preprocessor_by_name(name):
"""Returns the respective preprocessing function."""
thismodule = sys.modules[__name__]
return getattr(thismodule, name.lower())
########################
# 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
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