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# -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
# FileName [ preprocess_alignment.py ]
# Synopsis [ preprocess phone alignment for the LibriSpeech dataset ]
# Author [ Andy T. Liu (Andi611) ]
# Copyright [ Copyleft(c), Speech Lab, NTU, Taiwan ]
# Reference [ https://github.com/BogiHsu/Phone-Recognizer/blob/815cf9375045c053fa57d17fad0fa14fdc3c7bee/loader.py#L28 ]
"""*********************************************************************************************"""
###############
# IMPORTATION #
###############
import os
import pickle
import argparse
import numpy as np
import pandas as pd
from tqdm import tqdm
from pathlib import Path
from utility.audio import sample_rate, _stft_parameters
#############################
# PREPROCESS CONFIGURATIONS #
#############################
def get_preprocess_args():
parser = argparse.ArgumentParser(description='preprocess arguments for LibriSpeech dataset.')
parser.add_argument('--data_path', default='./data/libri_alignment', type=str, help='Path to raw LibriSpeech alignment')
parser.add_argument('--output_path', default='./data/libri_phone', type=str, help='Path to store output', required=False)
args = parser.parse_args()
return args
####################
# PHONE PREPROCESS #
####################
def phone_preprocess(data_path, output_path, sets, unaligned):
print('Data sets :')
for idx, s in enumerate(sets):
print('\t', idx, ':', s)
todo_sets = input('Please enter the index for preprocessing sets (seperate w/ space): ')
sets = [sets[int(s)] for s in todo_sets.split(' ')]
# compute phone2idx
idx = 0
phone2idx = {}
for s in sets:
print('')
print('Computing', s, 'data...')
for path in tqdm(list(Path(os.path.join(data_path, s)).rglob("*.txt"))):
check_name = path.as_posix().split('/')[-1].split('.')[0]
if check_name not in unaligned and check_name != 'unaligned': # ignore the unaligned files and `unaligned.txt` itself
for line in open(path).readlines():
phone = line.strip('\n').split(' ')[-1]
if phone not in phone2idx:
phone2idx[phone] = idx
idx += 1
print('Phone set:')
print(phone2idx)
print(len(phone2idx), 'distinct phones found in', sets)
with open(os.path.join(output_path, 'phone2idx.pkl'), "wb") as fp:
pickle.dump(phone2idx, fp)
for s in sets:
print('')
print('Preprocessing', s, 'data...')
todo = list(Path(os.path.join(data_path, s)).rglob("*.txt"))
print(len(todo),'audio files found in', s)
if not os.path.exists(os.path.join(output_path, s)):
os.makedirs(os.path.join(output_path, s))
print('Preprocessing phone alignments...', flush=True)
for path in tqdm(todo):
check_name = path.as_posix().split('/')[-1].split('.')[0]
if check_name not in unaligned and check_name != 'unaligned': # ignore the unaligned files and `unaligned.txt` itself
x = []
file = open(path).readlines()
for line in file:
line = line.strip('\n').split(' ')
x += time_to_frame(start_time=float(line[0]), end_time=float(line[1]), phone=phone2idx[line[2]])
x = np.asarray(x)
path_to_save = str(path).replace(data_path.split('/')[-1], output_path.split('/')[-1]).replace('txt', 'pkl')
with open(path_to_save, "wb") as fp:
pickle.dump(x, fp)
print('Phone preprocessing complete!')
#################
# TIME TO FRAME #
#################
def time_to_frame(start_time, end_time, phone):
phones = []
start_time = int(start_time * sample_rate)
end_time = int(end_time * sample_rate)
_, hop_length, win_length = _stft_parameters(sample_rate=sample_rate)
h_window = win_length * 0.5 # select the middle of a window
start_time = (start_time - h_window) if start_time >= h_window else 0
end_time = (end_time - h_window) if end_time >= h_window else 0
times = (end_time // hop_length) - (start_time // hop_length) \
+ (1 if start_time % hop_length == 0 else 0) - (1 if end_time % hop_length == 0 else 0)
phones += [phone] * int(times)
return phones
########
# MAIN #
########
def main():
# get arguments
args = get_preprocess_args()
# mkdir
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
# dump unaligned text
try:
file = open(os.path.join(args.data_path, 'train-clean-360/unaligned.txt')).readlines()
unaligned = [str(line).split('\t')[0].split(' ')[0] for line in file]
print('Unaligned list: ', unaligned)
unaligned_pkl = ['train-clean-360/' + u + '.npy' for u in unaligned]
with open(os.path.join(args.output_path, 'unaligned.pkl'), "wb") as fp:
pickle.dump(unaligned_pkl, fp)
except:
raise ValueError('Did not find unaligned.txt!')
# Process data
sets = ['train-clean-360', 'test-clean'] # only two sets available for now
# sets = ['train-clean-100','train-clean-360','train-other-500','dev-clean','dev-other','test-clean','test-other']
phone_preprocess(args.data_path, args.output_path, sets, unaligned)
if __name__ == '__main__':
main() |