Merge pull request #2459 from tilmankamp/import_swc
Spoken Wikipedia importer
This commit is contained in:
Tilman Kamp
GitHub
commit
ede4dd6f93
446
bin/import_swc.py
Executable file
446
bin/import_swc.py
Executable file
@ -0,0 +1,446 @@
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#!/usr/bin/env python
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'''
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Downloads and prepares (parts of) the "Spoken Wikipedia Corpora" for DeepSpeech.py
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Use "python3 import_swc.py -h" for help
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'''
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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 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 re
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import csv
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import sox
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import wave
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import shutil
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import random
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import tarfile
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import argparse
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import progressbar
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import unicodedata
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import xml.etree.cElementTree as ET
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from os import path
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from glob import glob
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from collections import Counter
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from multiprocessing.pool import ThreadPool
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from util.text import Alphabet, validate_label
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from util.downloader import maybe_download, SIMPLE_BAR
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SWC_URL = "https://www2.informatik.uni-hamburg.de/nats/pub/SWC/SWC_{language}.tar"
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SWC_ARCHIVE = "SWC_{language}.tar"
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LANGUAGES = ['dutch', 'english', 'german']
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FIELDNAMES = ['wav_filename', 'wav_filesize', 'transcript']
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CHANNELS = 1
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SAMPLE_RATE = 16000
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AUDIO_PATTERN = 'audio*.ogg'
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WAV_NAME = 'audio.wav'
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ALIGNED_NAME = 'aligned.swc'
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SUBSTITUTIONS = {
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'german': [
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(re.compile(r'\$'), 'dollar'),
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(re.compile(r'€'), 'euro'),
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(re.compile(r'£'), 'pfund'),
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(re.compile(r'ein tausend ([^\s]+) hundert ([^\s]+) er( |$)'), r'\1zehnhundert \2er '),
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(re.compile(r'ein tausend (acht|neun) hundert'), r'\1zehnhundert'),
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(re.compile(r'eins punkt null null null punkt null null null punkt null null null'), 'eine milliarde'),
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(re.compile(r'punkt null null null punkt null null null punkt null null null'), 'milliarden'),
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(re.compile(r'eins punkt null null null punkt null null null'), 'eine million'),
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(re.compile(r'punkt null null null punkt null null null'), 'millionen'),
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(re.compile(r'eins punkt null null null'), 'ein tausend'),
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(re.compile(r'punkt null null null'), 'tausend'),
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(re.compile(r'punkt null'), None)
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]
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}
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DONT_NORMALIZE = {
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'german': 'ÄÖÜäöüß'
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}
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PRE_FILTER = str.maketrans(dict.fromkeys('/()[]{}<>:'))
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class Sample:
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def __init__(self, wav_path, start, end, text, speaker, sub_set=None):
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self.wav_path = wav_path
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self.start = start
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self.end = end
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self.text = text
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self.speaker = speaker
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self.sub_set = sub_set
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def fail(message):
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print(message)
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sys.exit(1)
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def group(lst, get_key):
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groups = {}
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for obj in lst:
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key = get_key(obj)
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if key in groups:
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groups[key].append(obj)
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else:
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groups[key] = [obj]
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return groups
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def get_sample_size(population_size):
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margin_of_error = 0.01
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fraction_picking = 0.50
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z_score = 2.58 # Corresponds to confidence level 99%
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numerator = (z_score ** 2 * fraction_picking * (1 - fraction_picking)) / (margin_of_error ** 2)
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sample_size = 0
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for train_size in range(population_size, 0, -1):
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denominator = 1 + (z_score ** 2 * fraction_picking * (1 - fraction_picking)) / \
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(margin_of_error ** 2 * train_size)
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sample_size = int(numerator / denominator)
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if 2 * sample_size + train_size <= population_size:
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break
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return sample_size
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def maybe_download_language(language):
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lang_upper = language[0].upper() + language[1:]
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return maybe_download(SWC_ARCHIVE.format(language=lang_upper),
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CLI_ARGS.base_dir,
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SWC_URL.format(language=lang_upper))
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def maybe_extract(data_dir, extracted_data, archive):
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extracted = path.join(data_dir, extracted_data)
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if path.isdir(extracted):
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print('Found directory "{}" - not extracting.'.format(extracted))
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else:
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print('Extracting "{}"...'.format(archive))
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with tarfile.open(archive) as tar:
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members = tar.getmembers()
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bar = progressbar.ProgressBar(max_value=len(members), widgets=SIMPLE_BAR)
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for member in bar(members):
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tar.extract(member=member, path=extracted)
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return extracted
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def ignored(node):
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if node is None:
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return False
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if node.tag == 'ignored':
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return True
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return ignored(node.find('..'))
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def read_token(token):
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texts, start, end = [], None, None
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notes = token.findall('n')
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if len(notes) > 0:
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for note in notes:
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attributes = note.attrib
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if start is None and 'start' in attributes:
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start = int(attributes['start'])
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if 'end' in attributes:
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token_end = int(attributes['end'])
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if end is None or token_end > end:
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end = token_end
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if 'pronunciation' in attributes:
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t = attributes['pronunciation']
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texts.append(t)
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elif 'text' in token.attrib:
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texts.append(token.attrib['text'])
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return start, end, ' '.join(texts)
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def in_alphabet(alphabet, c):
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return True if alphabet is None else alphabet.has_char(c)
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ALPHABETS = {}
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def get_alphabet(language):
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if language in ALPHABETS:
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return ALPHABETS[language]
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alphabet_path = getattr(CLI_ARGS, language + '_alphabet')
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alphabet = Alphabet(alphabet_path) if alphabet_path else None
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ALPHABETS[language] = alphabet
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return alphabet
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def label_filter(label, language):
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label = label.translate(PRE_FILTER)
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label = validate_label(label)
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if label is None:
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return None, 'validation'
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substitutions = SUBSTITUTIONS[language] if language in SUBSTITUTIONS else []
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for pattern, replacement in substitutions:
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if replacement is None:
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if pattern.match(label):
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return None, 'substitution rule'
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else:
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label = pattern.sub(replacement, label)
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chars = []
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dont_normalize = DONT_NORMALIZE[language] if language in DONT_NORMALIZE else ''
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alphabet = get_alphabet(language)
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for c in label:
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if CLI_ARGS.normalize and c not in dont_normalize and not in_alphabet(alphabet, c):
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c = unicodedata.normalize("NFKD", c).encode("ascii", "ignore").decode("ascii", "ignore")
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for sc in c:
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if not in_alphabet(alphabet, sc):
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return None, 'illegal character'
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chars.append(sc)
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label = ''.join(chars)
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label = validate_label(label)
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return label, 'validation' if label is None else None
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def collect_samples(base_dir, language):
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roots = []
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for root, _, files in os.walk(base_dir):
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if ALIGNED_NAME in files and WAV_NAME in files:
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roots.append(root)
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samples = []
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reasons = Counter()
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def add_sample(p_wav_path, p_speaker, p_start, p_end, p_text, p_reason='complete'):
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if p_start is not None and p_end is not None and p_text is not None:
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duration = p_end - p_start
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text, filter_reason = label_filter(p_text, language)
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skip = False
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if filter_reason is not None:
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skip = True
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p_reason = filter_reason
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elif duration > CLI_ARGS.max_duration > 0 and CLI_ARGS.ignore_too_long:
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skip = True
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p_reason = 'exceeded duration'
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elif int(duration / 20) < len(text):
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skip = True
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p_reason = 'too short to decode'
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elif duration / len(text) < 10:
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skip = True
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p_reason = 'length duration ratio'
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if skip:
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reasons[p_reason] += 1
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else:
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samples.append(Sample(p_wav_path, p_start, p_end, text, p_speaker))
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elif p_start is None or p_end is None:
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reasons['missing timestamps'] += 1
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else:
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reasons['missing text'] += 1
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print('Collecting samples...')
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bar = progressbar.ProgressBar(max_value=len(roots), widgets=SIMPLE_BAR)
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for root in bar(roots):
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wav_path = path.join(root, WAV_NAME)
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aligned = ET.parse(path.join(root, ALIGNED_NAME))
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speaker = '<unknown>'
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for prop in aligned.iter('prop'):
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attributes = prop.attrib
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if 'key' in attributes and 'value' in attributes and attributes['key'] == 'reader.name':
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speaker = attributes['value']
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break
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for sentence in aligned.iter('s'):
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if ignored(sentence):
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continue
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split = False
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tokens = list(map(read_token, sentence.findall('t')))
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sample_start, sample_end, token_texts, sample_texts = None, None, [], []
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for token_start, token_end, token_text in tokens:
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if CLI_ARGS.exclude_numbers and any(c.isdigit() for c in token_text):
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add_sample(wav_path, speaker, sample_start, sample_end, ' '.join(sample_texts),
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p_reason='has numbers')
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sample_start, sample_end, token_texts, sample_texts = None, None, [], []
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continue
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if sample_start is None:
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sample_start = token_start
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if sample_start is None:
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continue
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token_texts.append(token_text)
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if token_end is not None:
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if token_start != sample_start and token_end - sample_start > CLI_ARGS.max_duration > 0:
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add_sample(wav_path, speaker, sample_start, sample_end, ' '.join(sample_texts),
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p_reason='split')
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sample_start = sample_end
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sample_texts = []
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split = True
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sample_end = token_end
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sample_texts.extend(token_texts)
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token_texts = []
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add_sample(wav_path, speaker, sample_start, sample_end, ' '.join(sample_texts),
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p_reason='split' if split else 'complete')
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print('Skipped samples:')
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for reason, n in reasons.most_common():
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print(' - {}: {}'.format(reason, n))
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return samples
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def maybe_convert_one_to_wav(entry):
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root, _, files = entry
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transformer = sox.Transformer()
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transformer.convert(samplerate=SAMPLE_RATE, n_channels=CHANNELS)
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combiner = sox.Combiner()
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combiner.convert(samplerate=SAMPLE_RATE, n_channels=CHANNELS)
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output_wav = path.join(root, WAV_NAME)
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if path.isfile(output_wav):
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return
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files = sorted(glob(path.join(root, AUDIO_PATTERN)))
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try:
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if len(files) == 1:
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transformer.build(files[0], output_wav)
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elif len(files) > 1:
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wav_files = []
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for i, file in enumerate(files):
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wav_path = path.join(root, 'audio{}.wav'.format(i))
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transformer.build(file, wav_path)
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wav_files.append(wav_path)
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combiner.set_input_format(file_type=['wav'] * len(wav_files))
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combiner.build(wav_files, output_wav, 'concatenate')
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except sox.core.SoxError:
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return
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def maybe_convert_to_wav(base_dir):
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roots = list(os.walk(base_dir))
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print('Converting and joining source audio files...')
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bar = progressbar.ProgressBar(max_value=len(roots), widgets=SIMPLE_BAR)
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tp = ThreadPool()
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for _ in bar(tp.imap_unordered(maybe_convert_one_to_wav, roots)):
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pass
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tp.close()
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tp.join()
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def assign_sub_sets(samples):
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sample_size = get_sample_size(len(samples))
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speakers = group(samples, lambda sample: sample.speaker).values()
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speakers = list(sorted(speakers, key=len))
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sample_sets = [[], []]
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while any(map(lambda s: len(s) < sample_size, sample_sets)) and len(speakers) > 0:
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for sample_set in sample_sets:
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if len(sample_set) < sample_size and len(speakers) > 0:
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sample_set.extend(speakers.pop(0))
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train_set = sum(speakers, [])
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if len(train_set) == 0:
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print('WARNING: Unable to build dev and test sets without speaker bias as there is no speaker meta data')
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random.seed(42) # same source data == same output
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random.shuffle(samples)
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for index, sample in enumerate(samples):
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if index < sample_size:
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sample.sub_set = 'dev'
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elif index < 2 * sample_size:
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sample.sub_set = 'test'
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else:
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sample.sub_set = 'train'
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else:
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for sub_set, sub_set_samples in [('train', train_set), ('dev', sample_sets[0]), ('test', sample_sets[1])]:
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for sample in sub_set_samples:
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sample.sub_set = sub_set
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for sub_set, sub_set_samples in group(samples, lambda s: s.sub_set).items():
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t = sum(map(lambda s: s.end - s.start, sub_set_samples)) / (1000 * 60 * 60)
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print('Sub-set "{}" with {} samples (duration: {:.2f} h)'
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.format(sub_set, len(sub_set_samples), t))
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def create_sample_dirs(language):
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print('Creating sample directories...')
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for set_name in ['train', 'dev', 'test']:
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dir_path = path.join(CLI_ARGS.base_dir, language + '-' + set_name)
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if not path.isdir(dir_path):
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os.mkdir(dir_path)
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def split_audio_files(samples, language):
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print('Splitting audio files...')
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sub_sets = Counter()
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src_wav_files = group(samples, lambda s: s.wav_path).items()
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bar = progressbar.ProgressBar(max_value=len(src_wav_files), widgets=SIMPLE_BAR)
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for wav_path, file_samples in bar(src_wav_files):
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file_samples = sorted(file_samples, key=lambda s: s.start)
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with wave.open(wav_path, 'r') as src_wav_file:
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rate = src_wav_file.getframerate()
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for sample in file_samples:
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index = sub_sets[sample.sub_set]
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sample_wav_path = path.join(CLI_ARGS.base_dir,
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language + '-' + sample.sub_set,
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'sample-{0:06d}.wav'.format(index))
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sample.wav_path = sample_wav_path
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sub_sets[sample.sub_set] += 1
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src_wav_file.setpos(int(sample.start * rate / 1000.0))
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data = src_wav_file.readframes(int((sample.end - sample.start) * rate / 1000.0))
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with wave.open(sample_wav_path, 'w') as sample_wav_file:
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sample_wav_file.setnchannels(src_wav_file.getnchannels())
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sample_wav_file.setsampwidth(src_wav_file.getsampwidth())
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sample_wav_file.setframerate(rate)
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sample_wav_file.writeframes(data)
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def write_csvs(samples, language):
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for sub_set, set_samples in group(samples, lambda s: s.sub_set).items():
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set_samples = sorted(set_samples, key=lambda s: s.wav_path)
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base_dir = path.abspath(CLI_ARGS.base_dir)
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csv_path = path.join(base_dir, language + '-' + sub_set + '.csv')
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print('Writing "{}"...'.format(csv_path))
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with open(csv_path, 'w') as csv_file:
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writer = csv.DictWriter(csv_file, fieldnames=FIELDNAMES)
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writer.writeheader()
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bar = progressbar.ProgressBar(max_value=len(set_samples), widgets=SIMPLE_BAR)
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for sample in bar(set_samples):
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writer.writerow({
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'wav_filename': path.relpath(sample.wav_path, base_dir),
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'wav_filesize': path.getsize(sample.wav_path),
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'transcript': sample.text
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})
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def cleanup(archive, language):
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if not CLI_ARGS.keep_archive:
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print('Removing archive "{}"...'.format(archive))
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os.remove(archive)
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language_dir = path.join(CLI_ARGS.base_dir, language)
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if not CLI_ARGS.keep_intermediate and path.isdir(language_dir):
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print('Removing intermediate files in "{}"...'.format(language_dir))
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shutil.rmtree(language_dir)
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def prepare_language(language):
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archive = maybe_download_language(language)
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extracted = maybe_extract(CLI_ARGS.base_dir, language, archive)
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maybe_convert_to_wav(extracted)
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samples = collect_samples(extracted, language)
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assign_sub_sets(samples)
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create_sample_dirs(language)
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split_audio_files(samples, language)
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write_csvs(samples, language)
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cleanup(archive, language)
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def handle_args():
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parser = argparse.ArgumentParser(description='Import Spoken Wikipedia Corpora')
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parser.add_argument('base_dir', help='Directory containing all data')
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parser.add_argument('--language', default='all', help='One of (all|{})'.format('|'.join(LANGUAGES)))
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parser.add_argument('--exclude_numbers', type=bool, default=True,
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help='If sequences with non-transliterated numbers should be excluded')
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parser.add_argument('--max_duration', type=int, default=10000, help='Maximum sample duration in milliseconds')
|
||||
parser.add_argument('--ignore_too_long', type=bool, default=False,
|
||||
help='If samples exceeding max_duration should be removed')
|
||||
parser.add_argument('--normalize', action='store_true', help='Converts diacritic characters to their base ones')
|
||||
for language in LANGUAGES:
|
||||
parser.add_argument('--{}_alphabet'.format(language),
|
||||
help='Exclude {} samples with characters not in provided alphabet file'.format(language))
|
||||
parser.add_argument('--keep_archive', type=bool, default=True,
|
||||
help='If downloaded archives should be kept')
|
||||
parser.add_argument('--keep_intermediate', type=bool, default=False,
|
||||
help='If intermediate files should be kept')
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
CLI_ARGS = handle_args()
|
||||
if CLI_ARGS.language == 'all':
|
||||
for lang in LANGUAGES:
|
||||
prepare_language(lang)
|
||||
elif CLI_ARGS.language in LANGUAGES:
|
||||
prepare_language(CLI_ARGS.language)
|
||||
else:
|
||||
fail('Wrong language id')
|
||||
@ -36,6 +36,9 @@ class Alphabet(object):
|
||||
'then add all these to data/alphabet.txt.'.format(string)
|
||||
).with_traceback(e.__traceback__)
|
||||
|
||||
def has_char(self, char):
|
||||
return char in self._str_to_label
|
||||
|
||||
def encode(self, string):
|
||||
res = []
|
||||
for char in string:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user