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librispeech.py
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import os
import wget
import tarfile
import argparse
import subprocess
from utils import create_manifest
import shutil
parser = argparse.ArgumentParser(description='Processes and downloads LibriSpeech dataset.')
parser.add_argument("--target-dir", default='LibriSpeech_dataset/', type=str, help="Directory to store the dataset.")
parser.add_argument('--sample-rate', default=16000, type=int, help='Sample rate')
parser.add_argument('--files-to-use', default="train-clean-100.tar.gz,"
"train-clean-360.tar.gz,train-other-500.tar.gz,"
"dev-clean.tar.gz,dev-other.tar.gz,"
"test-clean.tar.gz,test-other.tar.gz", type=str,
help='list of file names to download')
parser.add_argument('--min-duration', default=1, type=int,
help='Prunes training samples shorter than the min duration (given in seconds, default 1)')
parser.add_argument('--max-duration', default=15, type=int,
help='Prunes training samples longer than the max duration (given in seconds, default 15)')
args = parser.parse_args()
LIBRI_SPEECH_URLS = {
"train": ["http://www.openslr.org/resources/12/train-clean-100.tar.gz",
"http://www.openslr.org/resources/12/train-clean-360.tar.gz",
"http://www.openslr.org/resources/12/train-other-500.tar.gz"],
"val": ["http://www.openslr.org/resources/12/dev-clean.tar.gz",
"http://www.openslr.org/resources/12/dev-other.tar.gz"],
"test_clean": ["http://www.openslr.org/resources/12/test-clean.tar.gz"],
"test_other": ["http://www.openslr.org/resources/12/test-other.tar.gz"]
}
def _preprocess_transcript(phrase):
return phrase.strip().upper()
def _process_file(wav_dir, txt_dir, base_filename, root_dir):
full_recording_path = os.path.join(root_dir, base_filename)
assert os.path.exists(full_recording_path) and os.path.exists(root_dir)
wav_recording_path = os.path.join(wav_dir, base_filename.replace(".flac", ".wav"))
subprocess.call(["sox {} -r {} -b 16 -c 1 {}".format(full_recording_path, str(args.sample_rate),
wav_recording_path)], shell=True)
# process transcript
txt_transcript_path = os.path.join(txt_dir, base_filename.replace(".flac", ".txt"))
transcript_file = os.path.join(root_dir, "-".join(base_filename.split('-')[:-1]) + ".trans.txt")
assert os.path.exists(transcript_file), "Transcript file {} does not exist.".format(transcript_file)
transcriptions = open(transcript_file).read().strip().split("\n")
transcriptions = {t.split()[0].split("-")[-1]: " ".join(t.split()[1:]) for t in transcriptions}
with open(txt_transcript_path, "w") as f:
key = base_filename.replace(".flac", "").split("-")[-1]
assert key in transcriptions, "{} is not in the transcriptions".format(key)
f.write(_preprocess_transcript(transcriptions[key]))
f.flush()
def main():
target_dl_dir = args.target_dir
if not os.path.exists(target_dl_dir):
os.makedirs(target_dl_dir)
files_to_dl = args.files_to_use.strip().split(',')
for split_type, lst_libri_urls in LIBRI_SPEECH_URLS.items():
split_dir = os.path.join(target_dl_dir, split_type)
if not os.path.exists(split_dir):
os.makedirs(split_dir)
split_wav_dir = os.path.join(split_dir, "wav")
if not os.path.exists(split_wav_dir):
os.makedirs(split_wav_dir)
split_txt_dir = os.path.join(split_dir, "txt")
if not os.path.exists(split_txt_dir):
os.makedirs(split_txt_dir)
extracted_dir = os.path.join(split_dir, "LibriSpeech")
if os.path.exists(extracted_dir):
shutil.rmtree(extracted_dir)
for url in lst_libri_urls:
# check if we want to dl this file
dl_flag = False
for f in files_to_dl:
if url.find(f) != -1:
dl_flag = True
if not dl_flag:
print("Skipping url: {}".format(url))
continue
filename = url.split("/")[-1]
target_filename = os.path.join(split_dir, filename)
if not os.path.exists(target_filename):
wget.download(url, split_dir)
print("Unpacking {}...".format(filename))
tar = tarfile.open(target_filename)
tar.extractall(split_dir)
tar.close()
os.remove(target_filename)
print("Converting flac files to wav and extracting transcripts...")
assert os.path.exists(extracted_dir), "Archive {} was not properly uncompressed.".format(filename)
for root, subdirs, files in os.walk(extracted_dir):
for f in files:
if f.find(".flac") != -1:
_process_file(wav_dir=split_wav_dir, txt_dir=split_txt_dir,
base_filename=f, root_dir=root)
print("Finished {}".format(url))
shutil.rmtree(extracted_dir)
if split_type == 'train': # Prune to min/max duration
create_manifest(split_dir, 'libri_' + split_type + '_manifest.csv', args.min_duration, args.max_duration)
else:
create_manifest(split_dir, 'libri_' + split_type + '_manifest.csv')
if __name__ == "__main__":
main()