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import numpy as np
import argparse
import csv
import os
import glob
import datetime
import time
import logging
import h5py
import librosa
from utilities import (create_folder, get_filename, create_logging,
float32_to_int16, pad_or_truncate, read_metadata)
import config
def split_unbalanced_csv_to_partial_csvs(args):
"""Split unbalanced csv to part csvs. Each part csv contains up to 50000 ids.
"""
unbalanced_csv_path = args.unbalanced_csv
unbalanced_partial_csvs_dir = args.unbalanced_partial_csvs_dir
create_folder(unbalanced_partial_csvs_dir)
with open(unbalanced_csv_path, 'r') as f:
lines = f.readlines()
lines = lines[3:] # Remove head info
audios_num_per_file = 50000
files_num = int(np.ceil(len(lines) / float(audios_num_per_file)))
for r in range(files_num):
lines_per_file = lines[r * audios_num_per_file :
(r + 1) * audios_num_per_file]
out_csv_path = os.path.join(unbalanced_partial_csvs_dir,
'unbalanced_train_segments_part{:02d}.csv'.format(r))
with open(out_csv_path, 'w') as f:
f.write('empty\n')
f.write('empty\n')
f.write('empty\n')
for line in lines_per_file:
f.write(line)
print('Write out csv to {}'.format(out_csv_path))
def download_wavs(args):
"""Download videos and extract audio in wav format.
"""
# Paths
csv_path = args.csv_path
audios_dir = args.audios_dir
mini_data = args.mini_data
if mini_data:
logs_dir = '_logs/download_dataset/{}'.format(get_filename(csv_path))
else:
logs_dir = '_logs/download_dataset_minidata/{}'.format(get_filename(csv_path))
create_folder(audios_dir)
create_folder(logs_dir)
create_logging(logs_dir, filemode='w')
logging.info('Download log is saved to {}'.format(logs_dir))
# Read csv
with open(csv_path, 'r') as f:
lines = f.readlines()
lines = lines[3:] # Remove csv head info
if mini_data:
lines = lines[0 : 10] # Download partial data for debug
download_time = time.time()
# Download
for (n, line) in enumerate(lines):
items = line.split(', ')
audio_id = items[0]
start_time = float(items[1])
end_time = float(items[2])
duration = end_time - start_time
logging.info('{} {} start_time: {:.1f}, end_time: {:.1f}'.format(
n, audio_id, start_time, end_time))
# Download full video of whatever format
video_name = os.path.join(audios_dir, '_Y{}.%(ext)s'.format(audio_id))
os.system("youtube-dl --quiet -o '{}' -x https://www.youtube.com/watch?v={}"\
.format(video_name, audio_id))
video_paths = glob.glob(os.path.join(audios_dir, '_Y' + audio_id + '.*'))
# If download successful
if len(video_paths) > 0:
video_path = video_paths[0] # Choose one video
# Add 'Y' to the head because some video ids are started with '-'
# which will cause problem
audio_path = os.path.join(audios_dir, 'Y' + audio_id + '.wav')
# Extract audio in wav format
os.system("ffmpeg -loglevel panic -i {} -ac 1 -ar 32000 -ss {} -t 00:00:{} {} "\
.format(video_path,
str(datetime.timedelta(seconds=start_time)), duration,
audio_path))
# Remove downloaded video
os.system("rm {}".format(video_path))
logging.info("Download and convert to {}".format(audio_path))
logging.info('Download finished! Time spent: {:.3f} s'.format(
time.time() - download_time))
logging.info('Logs can be viewed in {}'.format(logs_dir))
def pack_waveforms_to_hdf5(args):
"""Pack waveform and target of several audio clips to a single hdf5 file.
This can speed up loading and training.
"""
# Arguments & parameters
audios_dir = args.audios_dir
csv_path = args.csv_path
waveforms_hdf5_path = args.waveforms_hdf5_path
mini_data = args.mini_data
clip_samples = config.clip_samples
classes_num = config.classes_num
sample_rate = config.sample_rate
id_to_ix = config.id_to_ix
# Paths
if mini_data:
prefix = 'mini_'
waveforms_hdf5_path += '.mini'
else:
prefix = ''
create_folder(os.path.dirname(waveforms_hdf5_path))
logs_dir = '_logs/pack_waveforms_to_hdf5/{}{}'.format(prefix, get_filename(csv_path))
create_folder(logs_dir)
create_logging(logs_dir, filemode='w')
logging.info('Write logs to {}'.format(logs_dir))
# Read csv file
meta_dict = read_metadata(csv_path, classes_num, id_to_ix)
if mini_data:
mini_num = 10
for key in meta_dict.keys():
meta_dict[key] = meta_dict[key][0 : mini_num]
audios_num = len(meta_dict['audio_name'])
# Pack waveform to hdf5
total_time = time.time()
with h5py.File(waveforms_hdf5_path, 'w') as hf:
hf.create_dataset('audio_name', shape=((audios_num,)), dtype='S20')
hf.create_dataset('waveform', shape=((audios_num, clip_samples)), dtype=np.int16)
hf.create_dataset('target', shape=((audios_num, classes_num)), dtype=np.bool)
hf.attrs.create('sample_rate', data=sample_rate, dtype=np.int32)
# Pack waveform & target of several audio clips to a single hdf5 file
for n in range(audios_num):
audio_path = os.path.join(audios_dir, meta_dict['audio_name'][n])
if os.path.isfile(audio_path):
logging.info('{} {}'.format(n, audio_path))
(audio, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)
audio = pad_or_truncate(audio, clip_samples)
hf['audio_name'][n] = meta_dict['audio_name'][n].encode()
hf['waveform'][n] = float32_to_int16(audio)
hf['target'][n] = meta_dict['target'][n]
else:
logging.info('{} File does not exist! {}'.format(n, audio_path))
logging.info('Write to {}'.format(waveforms_hdf5_path))
logging.info('Pack hdf5 time: {:.3f}'.format(time.time() - total_time))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(dest='mode')
parser_split = subparsers.add_parser('split_unbalanced_csv_to_partial_csvs')
parser_split.add_argument('--unbalanced_csv', type=str, required=True, help='Path of unbalanced_csv file to read.')
parser_split.add_argument('--unbalanced_partial_csvs_dir', type=str, required=True, help='Directory to save out split unbalanced partial csv.')
parser_download_wavs = subparsers.add_parser('download_wavs')
parser_download_wavs.add_argument('--csv_path', type=str, required=True, help='Path of csv file containing audio info to be downloaded.')
parser_download_wavs.add_argument('--audios_dir', type=str, required=True, help='Directory to save out downloaded audio.')
parser_download_wavs.add_argument('--mini_data', action='store_true', default=True, help='Set true to only download 10 audios for debugging.')
parser_pack_wavs = subparsers.add_parser('pack_waveforms_to_hdf5')
parser_pack_wavs.add_argument('--csv_path', type=str, required=True, help='Path of csv file containing audio info to be downloaded.')
parser_pack_wavs.add_argument('--audios_dir', type=str, required=True, help='Directory to save out downloaded audio.')
parser_pack_wavs.add_argument('--waveforms_hdf5_path', type=str, required=True, help='Path to save out packed hdf5.')
parser_pack_wavs.add_argument('--mini_data', action='store_true', default=False, help='Set true to only download 10 audios for debugging.')
args = parser.parse_args()
if args.mode == 'split_unbalanced_csv_to_partial_csvs':
split_unbalanced_csv_to_partial_csvs(args)
elif args.mode == 'download_wavs':
download_wavs(args)
elif args.mode == 'pack_waveforms_to_hdf5':
pack_waveforms_to_hdf5(args)
else:
raise Exception('Incorrect arguments!') |