Add M-AILABS importer
This commit is contained in:
Alexandre Lissy
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#!/usr/bin/env python3
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from __future__ import absolute_import, division, print_function
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# Make sure we can import stuff from util/
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# This script needs to be run from the root of the DeepSpeech repository
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import argparse
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import os
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import sys
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sys.path.insert(1, os.path.join(sys.path[0], '..'))
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import csv
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import re
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import sox
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import zipfile
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import subprocess
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import progressbar
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import unicodedata
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import tarfile
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from threading import RLock
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from multiprocessing.dummy import Pool
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from multiprocessing import cpu_count
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from util.downloader import SIMPLE_BAR
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from os import path
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from glob import glob
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from util.downloader import maybe_download
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from util.text import Alphabet, validate_label
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from util.feeding import secs_to_hours
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FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
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SAMPLE_RATE = 16000
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MAX_SECS = 15
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ARCHIVE_DIR_NAME = '{language}'
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ARCHIVE_NAME = '{language}.tgz'
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ARCHIVE_URL = 'http://www.caito.de/data/Training/stt_tts/' + ARCHIVE_NAME
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SKIP_LIST = []
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def _download_and_preprocess_data(target_dir):
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# Making path absolute
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target_dir = path.abspath(target_dir)
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# Conditionally download data
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archive_path = maybe_download(ARCHIVE_NAME, target_dir, ARCHIVE_URL)
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# Conditionally extract data
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_maybe_extract(target_dir, ARCHIVE_DIR_NAME, archive_path)
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# Produce CSV files
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_maybe_convert_sets(target_dir, ARCHIVE_DIR_NAME)
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def _maybe_extract(target_dir, extracted_data, archive_path):
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# If target_dir/extracted_data does not exist, extract archive in target_dir
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extracted_path = path.join(target_dir, extracted_data)
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if not path.exists(extracted_path):
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print('No directory "%s" - extracting archive...' % extracted_path)
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if not os.path.isdir(extracted_path):
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os.mkdir(extracted_path)
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tar = tarfile.open(archive_path)
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tar.extractall(extracted_path)
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tar.close()
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else:
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print('Found directory "%s" - not extracting it from archive.' % archive_path)
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def _maybe_convert_sets(target_dir, extracted_data):
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extracted_dir = path.join(target_dir, extracted_data)
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# override existing CSV with normalized one
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target_csv_template = os.path.join(target_dir, ARCHIVE_DIR_NAME, ARCHIVE_NAME.replace('.tgz', '_{}.csv'))
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if os.path.isfile(target_csv_template):
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return
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wav_root_dir = os.path.join(extracted_dir)
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# Get audiofile path and transcript for each sentence in tsv
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samples = []
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glob_dir = os.path.join(wav_root_dir, '**/metadata.csv')
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for record in glob(glob_dir, recursive=True):
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for sk in SKIP_LIST:
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if not (sk in record):
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with open(record, 'r') as rec:
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for re in rec.readlines():
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re = re.strip().split('|')
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audio = os.path.join(os.path.dirname(record), 'wavs', re[0] + '.wav')
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transcript = re[2]
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samples.append((audio, transcript))
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# Keep track of how many samples are good vs. problematic
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counter = {'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0, 'total_time': 0}
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lock = RLock()
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num_samples = len(samples)
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rows = []
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def one_sample(sample):
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""" Take a audio file, and optionally convert it to 16kHz WAV """
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wav_filename = sample[0]
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file_size = -1
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frames = 0
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if path.exists(wav_filename):
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file_size = path.getsize(wav_filename)
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frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
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label = label_filter(sample[1])
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with lock:
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if file_size == -1:
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# Excluding samples that failed upon conversion
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counter['failed'] += 1
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elif label is None:
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# Excluding samples that failed on label validation
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counter['invalid_label'] += 1
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elif int(frames/SAMPLE_RATE*1000/15/2) < len(str(label)):
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# Excluding samples that are too short to fit the transcript
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counter['too_short'] += 1
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elif frames/SAMPLE_RATE > MAX_SECS:
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# Excluding very long samples to keep a reasonable batch-size
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counter['too_long'] += 1
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else:
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# This one is good - keep it for the target CSV
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rows.append((wav_filename, file_size, label))
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counter['all'] += 1
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counter['total_time'] += frames
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print("Importing WAV files...")
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pool = Pool(cpu_count())
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bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
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for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
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bar.update(i)
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bar.update(num_samples)
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pool.close()
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pool.join()
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with open(target_csv_template.format('train'), 'w') as train_csv_file: # 80%
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with open(target_csv_template.format('dev'), 'w') as dev_csv_file: # 10%
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with open(target_csv_template.format('test'), 'w') as test_csv_file: # 10%
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train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
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train_writer.writeheader()
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dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)
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dev_writer.writeheader()
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test_writer = csv.DictWriter(test_csv_file, fieldnames=FIELDNAMES)
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test_writer.writeheader()
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for i, item in enumerate(rows):
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transcript = validate_label(item[2])
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if not transcript:
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continue
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wav_filename = item[0]
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i_mod = i % 10
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if i_mod == 0:
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writer = test_writer
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elif i_mod == 1:
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writer = dev_writer
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else:
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writer = train_writer
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writer.writerow(dict(
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wav_filename=wav_filename,
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wav_filesize=os.path.getsize(wav_filename),
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transcript=transcript,
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))
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print('Imported %d samples.' % (counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long']))
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if counter['failed'] > 0:
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print('Skipped %d samples that failed upon conversion.' % counter['failed'])
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if counter['invalid_label'] > 0:
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print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
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if counter['too_short'] > 0:
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print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
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if counter['too_long'] > 0:
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print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], MAX_SECS))
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print('Final amount of imported audio: %s.' % secs_to_hours(counter['total_time'] / SAMPLE_RATE))
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def handle_args():
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parser = argparse.ArgumentParser(description='Importer for M-AILABS dataset. https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/.')
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parser.add_argument(dest='target_dir')
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parser.add_argument('--filter_alphabet', help='Exclude samples with characters not in provided alphabet')
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parser.add_argument('--normalize', action='store_true', help='Converts diacritic characters to their base ones')
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parser.add_argument('--skiplist', type=str, help='Directories / books to skip, comma separated')
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parser.add_argument('--language', required=True, type=str, help='Dataset language to use')
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return parser.parse_args()
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if __name__ == "__main__":
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CLI_ARGS = handle_args()
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ALPHABET = Alphabet(CLI_ARGS.filter_alphabet) if CLI_ARGS.filter_alphabet else None
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SKIP_LIST = CLI_ARGS.skiplist.split(',')
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def label_filter(label):
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if CLI_ARGS.normalize:
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label = unicodedata.normalize("NFKD", label.strip()) \
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.encode("ascii", "ignore") \
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.decode("ascii", "ignore")
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label = validate_label(label)
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if ALPHABET and label:
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try:
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[ALPHABET.label_from_string(c) for c in label]
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except KeyError:
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label = None
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return label
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ARCHIVE_DIR_NAME = ARCHIVE_DIR_NAME.format(language=CLI_ARGS.language)
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ARCHIVE_NAME = ARCHIVE_NAME.format(language=CLI_ARGS.language)
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ARCHIVE_URL = ARCHIVE_URL.format(language=CLI_ARGS.language)
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_download_and_preprocess_data(target_dir=CLI_ARGS.target_dir)
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