"""VCTK Dataset download and preprocessing.
"""
"""
Copyright 2017 Parag K. Mital. See also NOTICE.md.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import os
from scipy.io import wavfile
from cadl.utils import download_and_extract_tar
from glob import glob
import subprocess
import numpy as np
[docs]def get_dataset(saveto='vctk', convert_to_16khz=False):
"""Download the VCTK dataset and convert to wav files.
More info:
http://homepages.inf.ed.ac.uk/jyamagis/
page3/page58/page58.html
This interface downloads the VCTK dataset and attempts to
convert the flac to wave files using ffmpeg. If you do not have ffmpeg
installed, this function will not be able to convert the files to waves.
Parameters
----------
saveto : str
Directory to save the resulting dataset ['vctk']
convert_to_16khz : bool, optional
Description
Returns
-------
TYPE
Description
"""
if not os.path.exists(saveto):
download_and_extract_tar(
'http://homepages.inf.ed.ac.uk/jyamagis/' +
'release/VCTK-Corpus.tar.gz',
saveto)
wavs = glob('{}/**/*.16khz.wav'.format(saveto), recursive=True)
if convert_to_16khz and len(wavs) == 0:
wavs = glob('{}/**/*.wav'.format(saveto), recursive=True)
for f in wavs:
subprocess.check_call(
['ffmpeg', '-i', f, '-f', 'wav', '-ar', '16000', '-y', '%s.16khz.wav' % f])
wavs = glob('{}/**/*.16khz.wav'.format(saveto), recursive=True)
dataset = []
for wav_i in wavs:
chapter_i, utter_i = wav_i.split('/')[-2:]
dataset.append({
'name': wav_i,
'chapter': chapter_i,
'utterance': utter_i.split('-')[-1].strip('.wav')})
return dataset
[docs]def batch_generator(dataset, batch_size=32, max_sequence_length=6144,
maxval=32768.0, threshold=0.2, normalize=True):
"""Summary
Parameters
----------
dataset : TYPE
Description
batch_size : int, optional
Description
max_sequence_length : int, optional
Description
maxval : float, optional
Description
threshold : float, optional
Description
normalize : bool, optional
Description
Yields
------
TYPE
Description
"""
n_batches = len(dataset) // batch_size
for batch_i in range(n_batches):
cropped_wavs = []
while len(cropped_wavs) < batch_size:
idx_i = np.random.choice(np.arange(len(dataset)))
fname_i = dataset[idx_i]['name']
wav_i = wavfile.read(fname_i)[1]
if len(wav_i) > max_sequence_length:
sample = np.random.choice(range(len(wav_i) - max_sequence_length))
cropped_wav = wav_i[sample:sample + max_sequence_length]
if np.max(np.abs(cropped_wav) / maxval) > threshold:
if normalize:
cropped_wav = cropped_wav / maxval
cropped_wavs.append(cropped_wav)
yield np.array(cropped_wavs, np.float32)