forked from atomicoo/tacotron2-mandarin
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prepare_dataset.py
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prepare_dataset.py
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"""Datasets preprocessor for TWL ASR Speech."""
__author__ = 'Joee'
import os
import sys
import time
import argparse
import unicodedata
import librosa
from tqdm import tqdm
from hparams import hparams
def run_prepare(args, hparams):
if args.dataset == 'BIAOBEI':
dataset_name = 'biaobei_48000'
dataset_path = os.path.join(os.getcwd(), args.dataset)
if os.path.isdir(dataset_path):
print("BIAOBEI dataset folder already exists")
sys.exit(0)
os.mkdir(dataset_path)
dataset_path = os.path.join(dataset_path, dataset_name)
os.mkdir(dataset_path)
sample_rate = 48000 # original sample rate
total_duration = 0
raw_dataset_path = os.path.join(os.getcwd(), 'BZNSYP')
text_file_path = os.path.join(raw_dataset_path, 'ProsodyLabeling', '000001-010000.txt')
try:
text_file = open(text_file_path, 'r', encoding='utf8')
except FileNotFoundError:
print('text file no exist')
sys.exit(0)
def normalize_text(text):
"""normalize text format"""
text = ''.join(char for char in unicodedata.normalize('NFD', text)
if unicodedata.category(char) != 'Mn')
return text.strip()
def normalize_wave(wave):
"""normalize wave format"""
wave = librosa.resample(wave, sample_rate, hparams.sample_rate)
return wave
for index, each in tqdm(enumerate(text_file.readlines())):
if index % 2 == 0:
basename = each.strip().split()[0]
else:
text = normalize_text(each)
text_file = os.path.join(dataset_path, '{}.trn'.format(basename))
with open(text_file, 'w', encoding='utf8') as f:
f.write(text)
wave_file_path = os.path.join(raw_dataset_path, 'Wave', '{}.wav'.format(basename))
if not os.path.exists(wave_file_path):
# print('wave file no exist')
continue
try:
wave, sr = librosa.load(wave_file_path, sr=None)
except EOFError:
# print('wave format error')
continue
if not sr == sample_rate:
# print('sample rate no match')
continue
# wave = normalize_wave(wave)
duration = librosa.get_duration(wave)
total_duration += duration
wave_file = os.path.join(dataset_path, '{}.wav'.format(basename))
librosa.output.write_wav(wave_file, wave, hparams.sample_rate)
text_file.close()
print("total audio duration: %ss" % (time.strftime('%H:%M:%S', time.gmtime(total_duration))))
elif args.dataset == 'THCHS-30':
dataset_name = 'thchs30_16000'
dataset_path = os.path.join(os.getcwd(), args.dataset)
if os.path.isdir(dataset_path):
print("{} dataset folder already exists".format(args.dataset))
sys.exit(0)
os.mkdir(dataset_path)
dataset_path = os.path.join(dataset_path, dataset_name)
os.mkdir(dataset_path)
sample_rate = 16000 # original sample rate
total_duration = 0
def normalize_text(text):
"""normalize text format"""
text = ''.join(char for char in unicodedata.normalize('NFD', text)
if unicodedata.category(char) != 'Mn')
return text.strip()
def normalize_wave(wave):
"""normalize wave format"""
wave = librosa.resample(wave, sample_rate, hparams.sample_rate)
return wave
raw_dataset_path = os.path.join(os.getcwd(), 'data_thchs30', 'data')
for root, dirs, files in os.walk(raw_dataset_path):
for file in tqdm(files):
if not file.endswith('.wav.trn'):
continue
parts = os.path.join(root, file).split('\\')
basename = parts[-1][:-8]
text_file = os.path.join(raw_dataset_path, '{}.wav.trn'.format(basename))
if not os.path.exists(text_file):
# print('text file {}.wav.trn no exist'.format(basename))
continue
with open(text_file, 'r', encoding='utf8') as f:
lines = f.readlines()
text = lines[1].rstrip('\n')
text = normalize_text(text)
text_file = os.path.join(dataset_path, '{}.trn'.format(basename))
with open(text_file, 'w', encoding='utf8') as f:
f.write(text)
wave_file = os.path.join(raw_dataset_path, '{}.wav'.format(basename))
if not os.path.exists(wave_file):
# print('wave file {}.wav no exist'.format(basename))
continue
try:
wave, sr = librosa.load(wave_file, sr=None)
except EOFError:
# print('wave file {}.wav format error'.format(basename))
continue
if not sr == sample_rate:
# print('sample rate of wave file {}.wav no match'.format(basename))
continue
duration = librosa.get_duration(wave)
total_duration += duration
# wave = normalize_wave(wave)
wave_file = os.path.join(dataset_path, '{}.wav'.format(basename))
librosa.output.write_wav(wave_file, wave, hparams.sample_rate)
print("total audio duration: %ss" % (time.strftime('%H:%M:%S', time.gmtime(total_duration))))
elif args.dataset == 'AISHELL-2':
dataset_name = 'aishell2_16000'
dataset_path = os.path.join(os.getcwd(), args.dataset)
if os.path.isdir(dataset_path):
print("{} dataset folder already exists".format(args.dataset))
sys.exit(0)
os.mkdir(dataset_path)
dataset_path = os.path.join(dataset_path, dataset_name)
os.mkdir(dataset_path)
sample_rate = 16000 # original sample rate
total_duration = 0
raw_dataset_path = os.path.join(os.getcwd(), 'aishell2', 'dataAishell2')
wave_dir_path = os.path.join(raw_dataset_path, 'wav')
text_file_path = os.path.join(raw_dataset_path, 'transcript', 'aishell2_transcript.txt')
try:
text_file = open(text_file_path, 'r', encoding='utf8')
except FileNotFoundError:
print('text file no exist')
sys.exit(0)
def normalize_text(text):
"""normalize text format"""
text = ''.join(char for char in unicodedata.normalize('NFD', text)
if unicodedata.category(char) != 'Mn')
return text.strip()
def normalize_wave(wave):
"""normalize wave format"""
wave = librosa.resample(wave, sample_rate, hparams.sample_rate)
return wave
# for index, each in tqdm(enumerate(text_file.readlines())):
#
def main():
print('preparing dataset..')
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--dataset", choices=['AISHELL-2', 'THCHS-30', 'BIAOBEI'], default='BIAOBEI', help='dataset name')
args = parser.parse_args()
run_prepare(args, hparams)
if __name__ == '__main__':
main()