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Merge pull request #2818 from lissyx/validate_label_locale+multiprocessing.notDummy 

Validate label locale+multiprocessing.not dummy
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lissyx 2020-03-19 10:14:06 +01:00 committed by GitHub
parent 29a2ac37f0 7b2a409f9f
commit ff9a720764
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GPG Key ID: 4AEE18F83AFDEB23
22 changed files with 480 additions and 398 deletions
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4
bin/import_aidatatang.py
View File

@ -7,7 +7,7 @@ import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import argparse
from util.importers import get_importers_parser
import glob
import pandas
import tarfile
@ -81,7 +81,7 @@ def preprocess_data(tgz_file, target_dir):
def main():
# https://www.openslr.org/62/
parser = argparse.ArgumentParser(description='Import aidatatang_200zh corpus')
parser = get_importers_parser(description='Import aidatatang_200zh corpus')
parser.add_argument('tgz_file', help='Path to aidatatang_200zh.tgz')
parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to same folder as the main archive.')
params = parser.parse_args()

4
bin/import_aishell.py
View File

@ -7,7 +7,7 @@ import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import argparse
from util.importers import get_importers_parser
import glob
import tarfile
import pandas
@ -80,7 +80,7 @@ def preprocess_data(tgz_file, target_dir):
def main():
# http://www.openslr.org/33/
parser = argparse.ArgumentParser(description='Import AISHELL corpus')
parser = get_importers_parser(description='Import AISHELL corpus')
parser.add_argument('aishell_tgz_file', help='Path to data_aishell.tgz')
parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to same folder as the main archive.')
params = parser.parse_args()

69
bin/import_cv.py
View File

@ -15,10 +15,8 @@ import progressbar
from glob import glob
from os import path
from threading import RLock
from multiprocessing.dummy import Pool
from multiprocessing import cpu_count
from util.text import validate_label
from multiprocessing import Pool
from util.importers import validate_label_eng as validate_label, get_counter, get_imported_samples, print_import_report
from util.downloader import maybe_download, SIMPLE_BAR
FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
@ -53,27 +51,8 @@ def _maybe_convert_sets(target_dir, extracted_data):
for source_csv in glob(path.join(extracted_dir, '*.csv')):
_maybe_convert_set(extracted_dir, source_csv, path.join(target_dir, os.path.split(source_csv)[-1]))
def _maybe_convert_set(extracted_dir, source_csv, target_csv):
print()
if path.exists(target_csv):
print('Found CSV file "%s" - not importing "%s".' % (target_csv, source_csv))
return
print('No CSV file "%s" - importing "%s"...' % (target_csv, source_csv))
samples = []
with open(source_csv) as source_csv_file:
reader = csv.DictReader(source_csv_file)
for row in reader:
samples.append((row['filename'], row['text']))
# Mutable counters for the concurrent embedded routine
counter = { 'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0 }
lock = RLock()
num_samples = len(samples)
rows = []
def one_sample(sample):
mp3_filename = path.join(*(sample[0].split('/')))
mp3_filename = path.join(extracted_dir, mp3_filename)
mp3_filename = sample[0]
# Storing wav files next to the mp3 ones - just with a different suffix
wav_filename = path.splitext(mp3_filename)[0] + ".wav"
_maybe_convert_wav(mp3_filename, wav_filename)
@ -83,7 +62,8 @@ def _maybe_convert_set(extracted_dir, source_csv, target_csv):
file_size = path.getsize(wav_filename)
frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
label = validate_label(sample[1])
with lock:
rows = []
counter = get_counter()
if file_size == -1:
# Excluding samples that failed upon conversion
counter['failed'] += 1
@ -100,11 +80,32 @@ def _maybe_convert_set(extracted_dir, source_csv, target_csv):
# This one is good - keep it for the target CSV
rows.append((wav_filename, file_size, label))
counter['all'] += 1
counter['total_time'] += frames
return (counter, rows)
def _maybe_convert_set(extracted_dir, source_csv, target_csv):
print()
if path.exists(target_csv):
print('Found CSV file "%s" - not importing "%s".' % (target_csv, source_csv))
return
print('No CSV file "%s" - importing "%s"...' % (target_csv, source_csv))
samples = []
with open(source_csv) as source_csv_file:
reader = csv.DictReader(source_csv_file)
for row in reader:
samples.append((os.path.join(extracted_dir, row['filename']), row['text']))
# Mutable counters for the concurrent embedded routine
counter = get_counter()
num_samples = len(samples)
rows = []
print('Importing mp3 files...')
pool = Pool(cpu_count())
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
pool.close()
@ -118,15 +119,11 @@ def _maybe_convert_set(extracted_dir, source_csv, target_csv):
for filename, file_size, transcript in bar(rows):
writer.writerow({ 'wav_filename': filename, 'wav_filesize': file_size, 'transcript': transcript })
print('Imported %d samples.' % (counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long']))
if counter['failed'] > 0:
print('Skipped %d samples that failed upon conversion.' % counter['failed'])
if counter['invalid_label'] > 0:
print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
if counter['too_short'] > 0:
print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
if counter['too_long'] > 0:
print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], MAX_SECS))
imported_samples = get_imported_samples(counter)
assert counter['all'] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def _maybe_convert_wav(mp3_filename, wav_filename):
if not path.exists(wav_filename):

81
bin/import_cv2.py
View File

@ -16,18 +16,15 @@ sys.path.insert(1, os.path.join(sys.path[0], '..'))
import csv
import sox
import argparse
import subprocess
import progressbar
import unicodedata
from os import path
from threading import RLock
from multiprocessing.dummy import Pool
from multiprocessing import cpu_count
from multiprocessing import Pool
from util.downloader import SIMPLE_BAR
from util.text import Alphabet, validate_label
from util.helpers import secs_to_hours
from util.text import Alphabet
from util.importers import get_importers_parser, get_validate_label, get_counter, get_imported_samples, print_import_report
FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
@ -35,34 +32,16 @@ SAMPLE_RATE = 16000
MAX_SECS = 10
def _preprocess_data(tsv_dir, audio_dir, label_filter, space_after_every_character=False):
def _preprocess_data(tsv_dir, audio_dir, space_after_every_character=False):
for dataset in ['train', 'test', 'dev', 'validated', 'other']:
input_tsv = path.join(path.abspath(tsv_dir), dataset+".tsv")
if os.path.isfile(input_tsv):
print("Loading TSV file: ", input_tsv)
_maybe_convert_set(input_tsv, audio_dir, label_filter, space_after_every_character)
def _maybe_convert_set(input_tsv, audio_dir, label_filter, space_after_every_character=None):
output_csv = path.join(audio_dir, os.path.split(input_tsv)[-1].replace('tsv', 'csv'))
print("Saving new DeepSpeech-formatted CSV file to: ", output_csv)
# Get audiofile path and transcript for each sentence in tsv
samples = []
with open(input_tsv, encoding='utf-8') as input_tsv_file:
reader = csv.DictReader(input_tsv_file, delimiter='\t')
for row in reader:
samples.append((row['path'], row['sentence']))
# Keep track of how many samples are good vs. problematic
counter = {'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0, 'total_time': 0}
lock = RLock()
num_samples = len(samples)
rows = []
_maybe_convert_set(input_tsv, audio_dir, space_after_every_character)
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
mp3_filename = path.join(audio_dir, sample[0])
mp3_filename = sample[0]
if not path.splitext(mp3_filename.lower())[1] == '.mp3':
mp3_filename += ".mp3"
# Storing wav files next to the mp3 ones - just with a different suffix
@ -73,8 +52,9 @@ def _maybe_convert_set(input_tsv, audio_dir, label_filter, space_after_every_cha
if path.exists(wav_filename):
file_size = path.getsize(wav_filename)
frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
label = label_filter(sample[1])
with lock:
label = label_filter_fun(sample[1])
rows = []
counter = get_counter()
if file_size == -1:
# Excluding samples that failed upon conversion
counter['failed'] += 1
@ -93,10 +73,29 @@ def _maybe_convert_set(input_tsv, audio_dir, label_filter, space_after_every_cha
counter['all'] += 1
counter['total_time'] += frames
return (counter, rows)
def _maybe_convert_set(input_tsv, audio_dir, space_after_every_character=None):
output_csv = path.join(audio_dir, os.path.split(input_tsv)[-1].replace('tsv', 'csv'))
print("Saving new DeepSpeech-formatted CSV file to: ", output_csv)
# Get audiofile path and transcript for each sentence in tsv
samples = []
with open(input_tsv, encoding='utf-8') as input_tsv_file:
reader = csv.DictReader(input_tsv_file, delimiter='\t')
for row in reader:
samples.append((path.join(audio_dir, row['path']), row['sentence']))
counter = get_counter()
num_samples = len(samples)
rows = []
print("Importing mp3 files...")
pool = Pool(cpu_count())
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
pool.close()
@ -113,16 +112,11 @@ def _maybe_convert_set(input_tsv, audio_dir, label_filter, space_after_every_cha
else:
writer.writerow({'wav_filename': filename, 'wav_filesize': file_size, 'transcript': transcript})
print('Imported %d samples.' % (counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long']))
if counter['failed'] > 0:
print('Skipped %d samples that failed upon conversion.' % counter['failed'])
if counter['invalid_label'] > 0:
print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
if counter['too_short'] > 0:
print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
if counter['too_long'] > 0:
print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], MAX_SECS))
print('Final amount of imported audio: %s.' % secs_to_hours(counter['total_time'] / SAMPLE_RATE))
imported_samples = get_imported_samples(counter)
assert counter['all'] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def _maybe_convert_wav(mp3_filename, wav_filename):
@ -136,7 +130,7 @@ def _maybe_convert_wav(mp3_filename, wav_filename):
if __name__ == "__main__":
PARSER = argparse.ArgumentParser(description='Import CommonVoice v2.0 corpora')
PARSER = get_importers_parser(description='Import CommonVoice v2.0 corpora')
PARSER.add_argument('tsv_dir', help='Directory containing tsv files')
PARSER.add_argument('--audio_dir', help='Directory containing the audio clips - defaults to "<tsv_dir>/clips"')
PARSER.add_argument('--filter_alphabet', help='Exclude samples with characters not in provided alphabet')
@ -144,6 +138,7 @@ if __name__ == "__main__":
PARSER.add_argument('--space_after_every_character', action='store_true', help='To help transcript join by white space')
PARAMS = PARSER.parse_args()
validate_label = get_validate_label(PARAMS)
AUDIO_DIR = PARAMS.audio_dir if PARAMS.audio_dir else os.path.join(PARAMS.tsv_dir, 'clips')
ALPHABET = Alphabet(PARAMS.filter_alphabet) if PARAMS.filter_alphabet else None
@ -161,4 +156,4 @@ if __name__ == "__main__":
label = None
return label
_preprocess_data(PARAMS.tsv_dir, AUDIO_DIR, label_filter_fun, PARAMS.space_after_every_character)
_preprocess_data(PARAMS.tsv_dir, AUDIO_DIR, PARAMS.space_after_every_character)

2
bin/import_fisher.py
View File

@ -19,7 +19,7 @@ import unicodedata
import librosa
import soundfile # <= Has an external dependency on libsndfile
from util.text import validate_label
from util.importers import validate_label_eng as validate_label
def _download_and_preprocess_data(data_dir):
# Assume data_dir contains extracted LDC2004S13, LDC2004T19, LDC2005S13, LDC2005T19

4
bin/import_freestmandarin.py
View File

@ -7,7 +7,7 @@ import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import argparse
from util.importers import get_importers_parser
import glob
import numpy as np
import pandas
@ -81,7 +81,7 @@ def preprocess_data(tgz_file, target_dir):
def main():
# https://www.openslr.org/38/
parser = argparse.ArgumentParser(description='Import Free ST Chinese Mandarin corpus')
parser = get_importers_parser(description='Import Free ST Chinese Mandarin corpus')
parser.add_argument('tgz_file', help='Path to ST-CMDS-20170001_1-OS.tar.gz')
parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to same folder as the main archive.')
params = parser.parse_args()

13
bin/import_gram_vaani.py
View File

@ -1,12 +1,16 @@
#!/usr/bin/env python
# Make sure we can import stuff from util/
# This script needs to be run from the root of the DeepSpeech repository
import os
import csv
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import csv
import math
import urllib
import logging
import argparse
from util.importers import get_importers_parser, get_validate_label
import subprocess
from os import path
from pathlib import Path
@ -15,8 +19,6 @@ import swifter
import pandas as pd
from sox import Transformer
from util.text import validate_label
__version__ = "0.1.0"
_logger = logging.getLogger(__name__)
@ -38,7 +40,7 @@ def parse_args(args):
Returns:
:obj:`argparse.Namespace`: command line parameters namespace
"""
parser = argparse.ArgumentParser(
parser = get_importers_parser(
description="Imports GramVaani data for Deep Speech"
)
parser.add_argument(
@ -286,6 +288,7 @@ def main(args):
args ([str]): command line parameter list
"""
args = parse_args(args)
validate_label = get_validate_label(args)
setup_logging(args.loglevel)
_logger.info("Starting GramVaani importer...")
_logger.info("Starting loading GramVaani csv...")

89
bin/import_lingua_libre.py
View File

@ -3,13 +3,13 @@ from __future__ import absolute_import, division, print_function
# Make sure we can import stuff from util/
# This script needs to be run from the root of the DeepSpeech repository
import argparse
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from util.importers import get_importers_parser, get_validate_label, get_counter, get_imported_samples, print_import_report
import argparse
import csv
import re
import sox
@ -18,17 +18,14 @@ import subprocess
import progressbar
import unicodedata
from threading import RLock
from multiprocessing.dummy import Pool
from multiprocessing import cpu_count
from multiprocessing import Pool
from util.downloader import SIMPLE_BAR
from os import path
from glob import glob
from util.downloader import maybe_download
from util.text import Alphabet, validate_label
from util.helpers import secs_to_hours
from util.text import Alphabet
FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
SAMPLE_RATE = 16000
@ -61,32 +58,9 @@ def _maybe_extract(target_dir, extracted_data, archive_path):
else:
print('Found directory "%s" - not extracting it from archive.' % archive_path)
def _maybe_convert_sets(target_dir, extracted_data):
extracted_dir = path.join(target_dir, extracted_data)
# override existing CSV with normalized one
target_csv_template = os.path.join(target_dir, ARCHIVE_DIR_NAME + '_' + ARCHIVE_NAME.replace('.zip', '_{}.csv'))
if os.path.isfile(target_csv_template):
return
ogg_root_dir = os.path.join(extracted_dir, ARCHIVE_NAME.replace('.zip', ''))
# Get audiofile path and transcript for each sentence in tsv
samples = []
glob_dir = os.path.join(ogg_root_dir, '**/*.ogg')
for record in glob(glob_dir, recursive=True):
record_file = record.replace(ogg_root_dir + os.path.sep, '')
if record_filter(record_file):
samples.append((record_file, os.path.splitext(os.path.basename(record_file))[0]))
# Keep track of how many samples are good vs. problematic
counter = {'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0, 'total_time': 0}
lock = RLock()
num_samples = len(samples)
rows = []
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
ogg_filename = path.join(ogg_root_dir, sample[0])
ogg_filename = sample[0]
# Storing wav files next to the ogg ones - just with a different suffix
wav_filename = path.splitext(ogg_filename)[0] + ".wav"
_maybe_convert_wav(ogg_filename, wav_filename)
@ -96,7 +70,9 @@ def _maybe_convert_sets(target_dir, extracted_data):
file_size = path.getsize(wav_filename)
frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
label = label_filter(sample[1])
with lock:
rows = []
counter = get_counter()
if file_size == -1:
# Excluding samples that failed upon conversion
counter['failed'] += 1
@ -115,10 +91,35 @@ def _maybe_convert_sets(target_dir, extracted_data):
counter['all'] += 1
counter['total_time'] += frames
return (counter, rows)
def _maybe_convert_sets(target_dir, extracted_data):
extracted_dir = path.join(target_dir, extracted_data)
# override existing CSV with normalized one
target_csv_template = os.path.join(target_dir, ARCHIVE_DIR_NAME + '_' + ARCHIVE_NAME.replace('.zip', '_{}.csv'))
if os.path.isfile(target_csv_template):
return
ogg_root_dir = os.path.join(extracted_dir, ARCHIVE_NAME.replace('.zip', ''))
# Get audiofile path and transcript for each sentence in tsv
samples = []
glob_dir = os.path.join(ogg_root_dir, '**/*.ogg')
for record in glob(glob_dir, recursive=True):
record_file = record.replace(ogg_root_dir + os.path.sep, '')
if record_filter(record_file):
samples.append((os.path.join(ogg_root_dir, record_file), os.path.splitext(os.path.basename(record_file))[0]))
counter = get_counter()
num_samples = len(samples)
rows = []
print("Importing ogg files...")
pool = Pool(cpu_count())
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
pool.close()
@ -152,16 +153,11 @@ def _maybe_convert_sets(target_dir, extracted_data):
transcript=transcript,
))
print('Imported %d samples.' % (counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long']))
if counter['failed'] > 0:
print('Skipped %d samples that failed upon conversion.' % counter['failed'])
if counter['invalid_label'] > 0:
print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
if counter['too_short'] > 0:
print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
if counter['too_long'] > 0:
print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], MAX_SECS))
print('Final amount of imported audio: %s.' % secs_to_hours(counter['total_time'] / SAMPLE_RATE))
imported_samples = get_imported_samples(counter)
assert counter['all'] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def _maybe_convert_wav(ogg_filename, wav_filename):
if not path.exists(wav_filename):
@ -173,7 +169,7 @@ def _maybe_convert_wav(ogg_filename, wav_filename):
print('SoX processing error', ex, ogg_filename, wav_filename)
def handle_args():
parser = argparse.ArgumentParser(description='Importer for LinguaLibre dataset. Check https://lingualibre.fr/wiki/Help:Download_from_LinguaLibre for details.')
parser = get_importers_parser(description='Importer for LinguaLibre dataset. Check https://lingualibre.fr/wiki/Help:Download_from_LinguaLibre for details.')
parser.add_argument(dest='target_dir')
parser.add_argument('--qId', type=int, required=True, help='LinguaLibre language qId')
parser.add_argument('--iso639-3', type=str, required=True, help='ISO639-3 language code')
@ -186,6 +182,7 @@ def handle_args():
if __name__ == "__main__":
CLI_ARGS = handle_args()
ALPHABET = Alphabet(CLI_ARGS.filter_alphabet) if CLI_ARGS.filter_alphabet else None
validate_label = get_validate_label(CLI_ARGS)
bogus_regexes = []
if CLI_ARGS.bogus_records:

97
bin/import_m-ailabs.py
View File

@ -4,29 +4,27 @@ from __future__ import absolute_import, division, print_function
# Make sure we can import stuff from util/
# This script needs to be run from the root of the DeepSpeech repository
import argparse
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from util.importers import get_importers_parser, get_validate_label, get_counter, get_imported_samples, print_import_report
import csv
import subprocess
import progressbar
import unicodedata
import tarfile
from threading import RLock
from multiprocessing.dummy import Pool
from multiprocessing import cpu_count
from multiprocessing import Pool
from util.downloader import SIMPLE_BAR
from os import path
from glob import glob
from util.downloader import maybe_download
from util.text import Alphabet, validate_label
from util.helpers import secs_to_hours
from util.text import Alphabet
FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
SAMPLE_RATE = 16000
@ -62,6 +60,38 @@ def _maybe_extract(target_dir, extracted_data, archive_path):
print('Found directory "%s" - not extracting it from archive.' % archive_path)
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
wav_filename = sample[0]
file_size = -1
frames = 0
if path.exists(wav_filename):
file_size = path.getsize(wav_filename)
frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
label = label_filter(sample[1])
counter = get_counter()
rows = []
if file_size == -1:
# Excluding samples that failed upon conversion
print("conversion failure", wav_filename)
counter['failed'] += 1
elif label is None:
# Excluding samples that failed on label validation
counter['invalid_label'] += 1
elif int(frames/SAMPLE_RATE*1000/15/2) < len(str(label)):
# Excluding samples that are too short to fit the transcript
counter['too_short'] += 1
elif frames/SAMPLE_RATE > MAX_SECS:
# Excluding very long samples to keep a reasonable batch-size
counter['too_long'] += 1
else:
# This one is good - keep it for the target CSV
rows.append((wav_filename, file_size, label))
counter['all'] += 1
counter['total_time'] += frames
return (counter, rows)
def _maybe_convert_sets(target_dir, extracted_data):
extracted_dir = path.join(target_dir, extracted_data)
# override existing CSV with normalized one
@ -84,44 +114,16 @@ def _maybe_convert_sets(target_dir, extracted_data):
transcript = re[2]
samples.append((audio, transcript))
# Keep track of how many samples are good vs. problematic
counter = {'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0, 'total_time': 0}
lock = RLock()
counter = get_counter()
num_samples = len(samples)
rows = []
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
wav_filename = sample[0]
file_size = -1
frames = 0
if path.exists(wav_filename):
file_size = path.getsize(wav_filename)
frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
label = label_filter(sample[1])
with lock:
if file_size == -1:
# Excluding samples that failed upon conversion
counter['failed'] += 1
elif label is None:
# Excluding samples that failed on label validation
counter['invalid_label'] += 1
elif int(frames/SAMPLE_RATE*1000/15/2) < len(str(label)):
# Excluding samples that are too short to fit the transcript
counter['too_short'] += 1
elif frames/SAMPLE_RATE > MAX_SECS:
# Excluding very long samples to keep a reasonable batch-size
counter['too_long'] += 1
else:
# This one is good - keep it for the target CSV
rows.append((wav_filename, file_size, label))
counter['all'] += 1
counter['total_time'] += frames
print("Importing WAV files...")
pool = Pool(cpu_count())
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
pool.close()
@ -155,20 +157,14 @@ def _maybe_convert_sets(target_dir, extracted_data):
transcript=transcript,
))
print('Imported %d samples.' % (counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long']))
if counter['failed'] > 0:
print('Skipped %d samples that failed upon conversion.' % counter['failed'])
if counter['invalid_label'] > 0:
print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
if counter['too_short'] > 0:
print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
if counter['too_long'] > 0:
print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], MAX_SECS))
print('Final amount of imported audio: %s.' % secs_to_hours(counter['total_time'] / SAMPLE_RATE))
imported_samples = get_imported_samples(counter)
assert counter['all'] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def handle_args():
parser = argparse.ArgumentParser(description='Importer for M-AILABS dataset. https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/.')
parser = get_importers_parser(description='Importer for M-AILABS dataset. https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/.')
parser.add_argument(dest='target_dir')
parser.add_argument('--filter_alphabet', help='Exclude samples with characters not in provided alphabet')
parser.add_argument('--normalize', action='store_true', help='Converts diacritic characters to their base ones')
@ -181,6 +177,7 @@ if __name__ == "__main__":
CLI_ARGS = handle_args()
ALPHABET = Alphabet(CLI_ARGS.filter_alphabet) if CLI_ARGS.filter_alphabet else None
SKIP_LIST = filter(None, CLI_ARGS.skiplist.split(','))
validate_label = get_validate_label(CLI_ARGS)
def label_filter(label):
if CLI_ARGS.normalize:

4
bin/import_magicdata.py
View File

@ -7,7 +7,7 @@ import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import argparse
from util.importers import get_importers_parser
import glob
import pandas
import tarfile
@ -99,7 +99,7 @@ def preprocess_data(folder_with_archives, target_dir):
def main():
# https://openslr.org/68/
parser = argparse.ArgumentParser(description='Import MAGICDATA corpus')
parser = get_importers_parser(description='Import MAGICDATA corpus')
parser.add_argument('folder_with_archives', help='Path to folder containing magicdata_{train,dev,test}.tar.gz')
parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to a folder called magicdata next to the archives')
params = parser.parse_args()

4
bin/import_primewords.py
View File

@ -7,7 +7,7 @@ import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
import argparse
from util.importers import get_importers_parser
import glob
import json
import numpy as np
@ -93,7 +93,7 @@ def preprocess_data(tgz_file, target_dir):
def main():
# https://www.openslr.org/47/
parser = argparse.ArgumentParser(description='Import Primewords Chinese corpus set 1')
parser = get_importers_parser(description='Import Primewords Chinese corpus set 1')
parser.add_argument('tgz_file', help='Path to primewords_md_2018_set1.tar.gz')
parser.add_argument('--target_dir', default='', help='Target folder to extract files into and put the resulting CSVs. Defaults to same folder as the main archive.')
params = parser.parse_args()

97
bin/import_slr57.py
View File

@ -3,13 +3,12 @@ from __future__ import absolute_import, division, print_function
# Make sure we can import stuff from util/
# This script needs to be run from the root of the DeepSpeech repository
import argparse
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from util.importers import get_importers_parser, get_validate_label, get_counter, get_imported_samples, print_import_report
import csv
import re
import sox
@ -19,16 +18,14 @@ import progressbar
import unicodedata
import tarfile
from threading import RLock
from multiprocessing.dummy import Pool
from multiprocessing import cpu_count
from multiprocessing import Pool
from util.downloader import SIMPLE_BAR
from os import path
from glob import glob
from util.downloader import maybe_download
from util.text import Alphabet, validate_label
from util.text import Alphabet
from util.helpers import secs_to_hours
FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
@ -63,6 +60,37 @@ def _maybe_extract(target_dir, extracted_data, archive_path):
else:
print('Found directory "%s" - not extracting it from archive.' % archive_path)
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
wav_filename = sample[0]
file_size = -1
frames = 0
if path.exists(wav_filename):
file_size = path.getsize(wav_filename)
frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
label = label_filter(sample[1])
counter = get_counter()
rows = []
if file_size == -1:
# Excluding samples that failed upon conversion
counter['failed'] += 1
elif label is None:
# Excluding samples that failed on label validation
counter['invalid_label'] += 1
elif int(frames/SAMPLE_RATE*1000/15/2) < len(str(label)):
# Excluding samples that are too short to fit the transcript
counter['too_short'] += 1
elif frames/SAMPLE_RATE > MAX_SECS:
# Excluding very long samples to keep a reasonable batch-size
counter['too_long'] += 1
else:
# This one is good - keep it for the target CSV
rows.append((wav_filename, file_size, label))
counter['all'] += 1
counter['total_time'] += frames
return (counter, rows)
def _maybe_convert_sets(target_dir, extracted_data):
extracted_dir = path.join(target_dir, extracted_data)
# override existing CSV with normalized one
@ -113,43 +141,16 @@ def _maybe_convert_sets(target_dir, extracted_data):
samples.append((record, transcripts[record_file]))
# Keep track of how many samples are good vs. problematic
counter = {'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0, 'total_time': 0}
lock = RLock()
counter = get_counter()
num_samples = len(samples)
rows = []
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
wav_filename = sample[0]
file_size = -1
frames = 0
if path.exists(wav_filename):
file_size = path.getsize(wav_filename)
frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
label = label_filter(sample[1])
with lock:
if file_size == -1:
# Excluding samples that failed upon conversion
counter['failed'] += 1
elif label is None:
# Excluding samples that failed on label validation
counter['invalid_label'] += 1
elif int(frames/SAMPLE_RATE*1000/15/2) < len(str(label)):
# Excluding samples that are too short to fit the transcript
counter['too_short'] += 1
elif frames/SAMPLE_RATE > MAX_SECS:
# Excluding very long samples to keep a reasonable batch-size
counter['too_long'] += 1
else:
# This one is good - keep it for the target CSV
rows.append((wav_filename, file_size, label))
counter['all'] += 1
counter['total_time'] += frames
print("Importing WAV files...")
pool = Pool(cpu_count())
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
pool.close()
@ -183,19 +184,14 @@ def _maybe_convert_sets(target_dir, extracted_data):
transcript=transcript,
))
print('Imported %d samples.' % (counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long']))
if counter['failed'] > 0:
print('Skipped %d samples that failed upon conversion.' % counter['failed'])
if counter['invalid_label'] > 0:
print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
if counter['too_short'] > 0:
print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
if counter['too_long'] > 0:
print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], MAX_SECS))
print('Final amount of imported audio: %s.' % secs_to_hours(counter['total_time'] / SAMPLE_RATE))
imported_samples = get_imported_samples(counter)
assert counter['all'] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def handle_args():
parser = argparse.ArgumentParser(description='Importer for African Accented French dataset. More information on http://www.openslr.org/57/.')
parser = get_importers_parser(description='Importer for African Accented French dataset. More information on http://www.openslr.org/57/.')
parser.add_argument(dest='target_dir')
parser.add_argument('--filter_alphabet', help='Exclude samples with characters not in provided alphabet')
parser.add_argument('--normalize', action='store_true', help='Converts diacritic characters to their base ones')
@ -204,6 +200,7 @@ def handle_args():
if __name__ == "__main__":
CLI_ARGS = handle_args()
ALPHABET = Alphabet(CLI_ARGS.filter_alphabet) if CLI_ARGS.filter_alphabet else None
validate_label = get_validate_label(CLI_ARGS)
def label_filter(label):
if CLI_ARGS.normalize:

2
bin/import_swb.py
View File

@ -20,7 +20,7 @@ import wave
import codecs
import tarfile
import requests
from util.text import validate_label
from util.importers import validate_label_eng as validate_label
import librosa
import soundfile # <= Has an external dependency on libsndfile

3
bin/import_swc.py
View File

@ -27,7 +27,8 @@ from os import path
from glob import glob
from collections import Counter
from multiprocessing.pool import ThreadPool
from util.text import Alphabet, validate_label
from util.text import Alphabet
from util.importers import validate_label_eng as validate_label
from util.downloader import maybe_download, SIMPLE_BAR
SWC_URL = "https://www2.informatik.uni-hamburg.de/nats/pub/SWC/SWC_{language}.tar"

90
bin/import_ts.py
View File

@ -3,14 +3,13 @@ from __future__ import absolute_import, division, print_function
# Make sure we can import stuff from util/
# This script needs to be run from the root of the DeepSpeech repository
import argparse
import os
import re
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from util.importers import get_importers_parser, get_validate_label, get_counter, get_imported_samples, print_import_report
import csv
import unidecode
import zipfile
@ -18,16 +17,12 @@ import sox
import subprocess
import progressbar
from threading import RLock
from multiprocessing.dummy import Pool
from multiprocessing import cpu_count
from multiprocessing import Pool
from util.downloader import SIMPLE_BAR
from os import path
from util.downloader import maybe_download
from util.text import validate_label
from util.helpers import secs_to_hours
FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
SAMPLE_RATE = 16000
@ -61,30 +56,9 @@ def _maybe_extract(target_dir, extracted_data, archive_path):
print('Found directory "%s" - not extracting it from archive.' % archive_path)
def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
extracted_dir = path.join(target_dir, extracted_data)
# override existing CSV with normalized one
target_csv_template = os.path.join(target_dir, 'ts_' + ARCHIVE_NAME + '_{}.csv')
if os.path.isfile(target_csv_template):
return
path_to_original_csv = os.path.join(extracted_dir, 'data.csv')
with open(path_to_original_csv) as csv_f:
data = [
d for d in csv.DictReader(csv_f, delimiter=',')
if float(d['duration']) <= MAX_SECS
]
# Keep track of how many samples are good vs. problematic
counter = {'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0, 'total_time': 0}
lock = RLock()
num_samples = len(data)
rows = []
wav_root_dir = extracted_dir
def one_sample(sample):
""" Take a audio file, and optionally convert it to 16kHz WAV """
orig_filename = path.join(wav_root_dir, sample['path'])
orig_filename = sample['path']
# Storing wav files next to the wav ones - just with a different suffix
wav_filename = path.splitext(orig_filename)[0] + ".converted.wav"
_maybe_convert_wav(orig_filename, wav_filename)
@ -94,7 +68,11 @@ def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
file_size = path.getsize(wav_filename)
frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
label = sample['text']
with lock:
rows = []
# Keep track of how many samples are good vs. problematic
counter = get_counter()
if file_size == -1:
# Excluding samples that failed upon conversion
counter['failed'] += 1
@ -113,10 +91,35 @@ def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
counter['all'] += 1
counter['total_time'] += frames
print("Importing wav files...")
pool = Pool(cpu_count())
return (counter, rows)
def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
extracted_dir = path.join(target_dir, extracted_data)
# override existing CSV with normalized one
target_csv_template = os.path.join(target_dir, 'ts_' + ARCHIVE_NAME + '_{}.csv')
if os.path.isfile(target_csv_template):
return
path_to_original_csv = os.path.join(extracted_dir, 'data.csv')
with open(path_to_original_csv) as csv_f:
data = [
d for d in csv.DictReader(csv_f, delimiter=',')
if float(d['duration']) <= MAX_SECS
]
for line in data:
line['path'] = os.path.join(extracted_dir, line['path'])
num_samples = len(data)
rows = []
counter = get_counter()
print("Importing {} wav files...".format(num_samples))
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, _ in enumerate(pool.imap_unordered(one_sample, data), start=1):
for i, processed in enumerate(pool.imap_unordered(one_sample, data), start=1):
counter += processed[0]
rows += processed[1]
bar.update(i)
bar.update(num_samples)
pool.close()
@ -133,7 +136,6 @@ def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
test_writer.writeheader()
for i, item in enumerate(rows):
print('item', item)
transcript = validate_label(cleanup_transcript(item[2], english_compatible=english_compatible))
if not transcript:
continue
@ -151,16 +153,11 @@ def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
transcript=transcript,
))
print('Imported %d samples.' % (counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long']))
if counter['failed'] > 0:
print('Skipped %d samples that failed upon conversion.' % counter['failed'])
if counter['invalid_label'] > 0:
print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
if counter['too_short'] > 0:
print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
if counter['too_long'] > 0:
print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], MAX_SECS))
print('Final amount of imported audio: %s.' % secs_to_hours(counter['total_time'] / SAMPLE_RATE))
imported_samples = get_imported_samples(counter)
assert counter['all'] == num_samples
assert len(rows) == imported_samples
print_import_report(counter, SAMPLE_RATE, MAX_SECS)
def _maybe_convert_wav(orig_filename, wav_filename):
if not path.exists(wav_filename):
@ -186,7 +183,7 @@ def cleanup_transcript(text, english_compatible=False):
def handle_args():
parser = argparse.ArgumentParser(description='Importer for TrainingSpeech dataset.')
parser = get_importers_parser(description='Importer for TrainingSpeech dataset.')
parser.add_argument(dest='target_dir')
parser.add_argument('--english-compatible', action='store_true', dest='english_compatible', help='Remove diactrics and other non-ascii chars.')
return parser.parse_args()
@ -194,4 +191,5 @@ def handle_args():
if __name__ == "__main__":
cli_args = handle_args()
validate_label = get_validate_label(cli_args)
_download_and_preprocess_data(cli_args.target_dir, cli_args.english_compatible)

3
bin/import_tuda.py
View File

@ -21,7 +21,8 @@ import xml.etree.cElementTree as ET
from os import path
from collections import Counter
from util.text import Alphabet, validate_label
from util.text import Alphabet
from util.importers import validate_label_eng as validate_label
from util.downloader import maybe_download, SIMPLE_BAR
TUDA_VERSION = 'v2'

48
bin/import_vctk.py
View File

@ -14,13 +14,14 @@ import sys
sys.path.insert(1, os.path.join(sys.path[0], ".."))
from util.importers import get_counter, get_imported_samples, print_import_report
import re
import librosa
import progressbar
from os import path
from multiprocessing.dummy import Pool
from multiprocessing import cpu_count
from multiprocessing import Pool
from util.downloader import maybe_download, SIMPLE_BAR
from zipfile import ZipFile
@ -61,23 +62,27 @@ def _maybe_convert_sets(target_dir, extracted_data):
extracted_dir = path.join(target_dir, extracted_data, "wav48")
txt_dir = path.join(target_dir, extracted_data, "txt")
cnt = 1
directory = os.path.expanduser(extracted_dir)
srtd = len(sorted(os.listdir(directory)))
all_samples = []
for target in sorted(os.listdir(directory)):
print(f"\nSpeaker {cnt} of {srtd}")
_maybe_convert_set(path.join(extracted_dir, os.path.split(target)[-1]))
cnt += 1
all_samples += _maybe_prepare_set(path.join(extracted_dir, os.path.split(target)[-1]))
num_samples = len(all_samples)
print(f"Converting wav files to {SAMPLE_RATE}hz...")
pool = Pool()
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, _ in enumerate(pool.imap_unordered(one_sample, all_samples), start=1):
bar.update(i)
bar.update(num_samples)
pool.close()
pool.join()
_write_csv(extracted_dir, txt_dir, target_dir)
def _maybe_convert_set(target_csv):
def one_sample(sample):
if is_audio_file(sample):
sample = os.path.join(target_csv, sample)
y, sr = librosa.load(sample, sr=16000)
# Trim the beginning and ending silence
@ -89,19 +94,14 @@ def _maybe_convert_set(target_csv):
else:
librosa.output.write_wav(sample, yt, sr)
def _maybe_prepare_set(target_csv):
samples = sorted(os.listdir(target_csv))
num_samples = len(samples)
print(f"Converting wav files to {SAMPLE_RATE}hz...")
pool = Pool(cpu_count())
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
bar.update(i)
bar.update(num_samples)
pool.close()
pool.join()
new_samples = []
for s in samples:
new_samples.append(os.path.join(target_csv, s))
samples = new_samples
return samples
def _write_csv(extracted_dir, txt_dir, target_dir):
print(f"Writing CSV file")
@ -196,8 +196,8 @@ def load_txts(directory):
AUDIO_EXTENSIONS = [".wav", "WAV"]
def is_audio_file(filename):
return any(filename.endswith(extension) for extension in AUDIO_EXTENSIONS)
def is_audio_file(filepath):
return any(os.path.basename(filepath).endswith(extension) for extension in AUDIO_EXTENSIONS)
if __name__ == "__main__":

2
requirements_tests.txt
View File

@ -1 +1,3 @@
absl-py
argparse
semver

77
util/importers.py Normal file
View File

@ -0,0 +1,77 @@
import argparse
import importlib
import os
import re
import sys
from util.helpers import secs_to_hours
from collections import Counter
def get_counter():
return Counter({'all': 0, 'failed': 0, 'invalid_label': 0, 'too_short': 0, 'too_long': 0, 'total_time': 0})
def get_imported_samples(counter):
return counter['all'] - counter['failed'] - counter['too_short'] - counter['too_long'] - counter['invalid_label']
def print_import_report(counter, sample_rate, max_secs):
print('Imported %d samples.' % (get_imported_samples(counter)))
if counter['failed'] > 0:
print('Skipped %d samples that failed upon conversion.' % counter['failed'])
if counter['invalid_label'] > 0:
print('Skipped %d samples that failed on transcript validation.' % counter['invalid_label'])
if counter['too_short'] > 0:
print('Skipped %d samples that were too short to match the transcript.' % counter['too_short'])
if counter['too_long'] > 0:
print('Skipped %d samples that were longer than %d seconds.' % (counter['too_long'], max_secs))
print('Final amount of imported audio: %s.' % secs_to_hours(counter['total_time'] / sample_rate))
def get_importers_parser(description):
parser = argparse.ArgumentParser(description=description)
parser.add_argument('--validate_label_locale', help='Path to a Python file defining a |validate_label| function for your locale. WARNING: THIS WILL ADD THIS FILE\'s DIRECTORY INTO PYTHONPATH.')
return parser
def get_validate_label(args):
"""
Expects an argparse.Namespace argument to search for validate_label_locale parameter.
If found, this will modify Python's library search path and add the directory of the
file pointed by the validate_label_locale argument.
:param args: The importer's CLI argument object
:type args: argparse.Namespace
:return: The user-supplied validate_label function
:type: function
"""
if 'validate_label_locale' not in args or (args.validate_label_locale is None):
print('WARNING: No --validate_label_locale specified, your might end with inconsistent dataset.')
return validate_label_eng
if not os.path.exists(os.path.abspath(args.validate_label_locale)):
print('ERROR: Inexistent --validate_label_locale specified. Please check.')
return None
module_dir = os.path.abspath(os.path.dirname(args.validate_label_locale))
sys.path.insert(1, module_dir)
fname = os.path.basename(args.validate_label_locale).replace('.py', '')
locale_module = importlib.import_module(fname, package=None)
return locale_module.validate_label
# Validate and normalize transcriptions. Returns a cleaned version of the label
# or None if it's invalid.
def validate_label_eng(label):
# For now we can only handle [a-z ']
if re.search(r"[0-9]|[(<\[\]&*{]", label) is not None:
return None
label = label.replace("-", " ")
label = label.replace("_", " ")
label = re.sub("[ ]{2,}", " ", label)
label = label.replace(".", "")
label = label.replace(",", "")
label = label.replace(";", "")
label = label.replace("?", "")
label = label.replace("!", "")
label = label.replace(":", "")
label = label.replace("\"", "")
label = label.strip()
label = label.lower()
return label if label else None

2
util/test_data/validate_locale_fra.py Normal file
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@ -0,0 +1,2 @@
def validate_label(label):
return label

38
util/test_importers.py Normal file
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@ -0,0 +1,38 @@
import unittest
from argparse import Namespace
from .importers import validate_label_eng, get_validate_label
class TestValidateLabelEng(unittest.TestCase):
def test_numbers(self):
label = validate_label_eng("this is a 1 2 3 test")
self.assertEqual(label, None)
class TestGetValidateLabel(unittest.TestCase):
def test_no_validate_label_locale(self):
f = get_validate_label(Namespace())
self.assertEqual(f('toto'), 'toto')
self.assertEqual(f('toto1234'), None)
self.assertEqual(f('toto1234[{[{[]'), None)
def test_validate_label_locale_default(self):
f = get_validate_label(Namespace(validate_label_locale=None))
self.assertEqual(f('toto'), 'toto')
self.assertEqual(f('toto1234'), None)
self.assertEqual(f('toto1234[{[{[]'), None)
def test_get_validate_label_missing(self):
args = Namespace(validate_label_locale='util/test_data/validate_locale_ger.py')
f = get_validate_label(args)
self.assertEqual(f, None)
def test_get_validate_label(self):
args = Namespace(validate_label_locale='util/test_data/validate_locale_fra.py')
f = get_validate_label(args)
l = f('toto')
self.assertEqual(l, 'toto')
if __name__ == '__main__':
unittest.main()

23
util/text.py
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@ -1,7 +1,6 @@
from __future__ import absolute_import, division, print_function
import numpy as np
import re
import struct
from six.moves import range
@ -166,25 +165,3 @@ def levenshtein(a, b):
current[j] = min(add, delete, change)
return current[n]
# Validate and normalize transcriptions. Returns a cleaned version of the label
# or None if it's invalid.
def validate_label(label):
# For now we can only handle [a-z ']
if re.search(r"[0-9]|[(<\[\]&*{]", label) is not None:
return None
label = label.replace("-", " ")
label = label.replace("_", " ")
label = re.sub("[ ]{2,}", " ", label)
label = label.replace(".", "")
label = label.replace(",", "")
label = label.replace(";", "")
label = label.replace("?", "")
label = label.replace("!", "")
label = label.replace(":", "")
label = label.replace("\"", "")
label = label.strip()
label = label.lower()
return label if label else None