def _load(self, path):
corpus = audiomate.Corpus(path=path)
meta_file = os.path.join(path, META_FILENAME)
records = textfile.read_separated_lines_generator(meta_file,
separator='\t',
max_columns=4)
for record in records:
idx = record[0]
speaker_idx = record[1]
language = record[2]
transcript = record[3]
file_path = os.path.join(path, 'audio', language,
'{}.mp3'.format(idx))
corpus.new_file(file_path, idx)
if speaker_idx not in corpus.issuers.keys():
issuer = assets.Speaker(speaker_idx)
corpus.import_issuers(issuer)
utterance = corpus.new_utterance(idx, idx, speaker_idx)
utterance.set_label_list(
assets.LabelList.create_single(
transcript, idx=audiomate.corpus.LL_WORD_TRANSCRIPT_RAW))
return corpus
def read_issuers(file_path, corpus):
if not os.path.isfile(file_path):
return
data = jsonfile.read_json_file(file_path)
for issuer_idx, issuer_data in data.items():
issuer_type = issuer_data.get('type', None)
issuer_info = issuer_data.get('info', {})
if issuer_type == 'speaker':
gender = assets.Gender(
issuer_data.get('gender', 'unknown').lower())
age_group = assets.AgeGroup(
issuer_data.get('age_group', 'unknown').lower())
native_language = issuer_data.get('native_language', None)
issuer = assets.Speaker(issuer_idx,
gender=gender,
age_group=age_group,
native_language=native_language,
info=issuer_info)
elif issuer_type == 'artist':
name = issuer_data.get('name', None)
issuer = assets.Artist(issuer_idx, name=name, info=issuer_info)
else:
issuer = assets.Issuer(issuer_idx, info=issuer_info)
corpus.import_issuers(issuer)
def load_subset(corpus, path, subset_idx):
""" Load subset into corpus. """
csv_file = os.path.join(path, '{}.csv'.format(subset_idx))
utt_ids = []
for entry in textfile.read_separated_lines_generator(
csv_file,
separator=',',
max_columns=8,
ignore_lines_starting_with=['filename']):
rel_file_path = entry[0]
filename = os.path.split(rel_file_path)[1]
basename = os.path.splitext(filename)[0]
transcription = entry[1]
age = CommonVoiceReader.map_age(entry[4])
gender = CommonVoiceReader.map_gender(entry[5])
idx = '{}-{}'.format(subset_idx, basename)
file_path = os.path.join(path, rel_file_path)
corpus.new_file(file_path, idx)
issuer = assets.Speaker(idx, gender=gender, age_group=age)
corpus.import_issuers(issuer)
utterance = corpus.new_utterance(idx, idx, issuer.idx)
utterance.set_label_list(
assets.LabelList.create_single(
transcription, idx=audiomate.corpus.LL_WORD_TRANSCRIPT))
utt_ids.append(idx)
filter = subset.MatchingUtteranceIdxFilter(utterance_idxs=set(utt_ids))
subview = subset.Subview(corpus, filter_criteria=[filter])
corpus.import_subview(subset_idx, subview)
def load_file(folder_path, idx, corpus):
"""
Load speaker, file, utterance, labels for the file with the given id.
"""
xml_path = os.path.join(folder_path, '{}.xml'.format(idx))
wav_path = os.path.join(folder_path,
'{}{}.wav'.format(idx, WAV_SUFFIX))
xml_file = open(xml_path, 'r', encoding='utf-8')
soup = BeautifulSoup(xml_file, 'lxml')
transcription = soup.recording.cleaned_sentence.string
transcription_raw = soup.recording.sentence.string
gender = soup.recording.gender.string
is_native = soup.recording.muttersprachler.string
age_class = soup.recording.ageclass.string
speaker_idx = soup.recording.speaker_id.string
if speaker_idx not in corpus.issuers.keys():
start_age_class = int(age_class.split('-')[0])
if start_age_class < 12:
age_group = assets.AgeGroup.CHILD
elif start_age_class < 18:
age_group = assets.AgeGroup.YOUTH
elif start_age_class < 65:
age_group = assets.AgeGroup.ADULT
else:
age_group = assets.AgeGroup.SENIOR
native_lang = None
if is_native == 'Ja':
native_lang = 'deu'
issuer = assets.Speaker(speaker_idx,
gender=assets.Gender(gender),
age_group=age_group,
native_language=native_lang)
corpus.import_issuers(issuer)
corpus.new_file(wav_path, idx)
utt = corpus.new_utterance(idx, idx, speaker_idx)
utt.set_label_list(
assets.LabelList.create_single(
transcription, idx=audiomate.corpus.LL_WORD_TRANSCRIPT))
utt.set_label_list(
assets.LabelList.create_single(
transcription_raw,
idx=audiomate.corpus.LL_WORD_TRANSCRIPT_RAW))
def _create_or_get_speech_issuer(corpus, file_idx, annotations):
if file_idx not in annotations:
return None
issuer = assets.Speaker(file_idx)
if file_idx in annotations:
if annotations[file_idx][0] == 'm':
issuer.gender = assets.Gender.MALE
elif annotations[file_idx][0] == 'f':
issuer.gender = assets.Gender.FEMALE
corpus.import_issuers(issuer)
return file_idx
def parse_speaker_info(readme_path):
""" Parse speaker info and return tuple (idx, gender). """
idx = None
gender = assets.Gender.UNKNOWN
age_group = assets.AgeGroup.UNKNOWN
native_lang = None
with open(readme_path, 'r', errors='ignore') as f:
for raw_line in f:
line = raw_line.strip()
if line is not None and line is not '':
line = line.rstrip(';.')
parts = line.split(':', maxsplit=1)
if len(parts) > 1:
key = parts[0].strip().lower()
value = parts[1].strip()
if key == 'user name':
idx = value
value = value.lower()
if key == 'gender':
if value in ['männlich', 'male', 'mnnlich']:
gender = assets.Gender.MALE
elif value in ['weiblich', 'female', '[female]']:
gender = assets.Gender.FEMALE
if key == 'age range':
if value in ['erwachsener', 'adult', '[adult]', '[erwachsener]']:
age_group = assets.AgeGroup.ADULT
elif value in ['senior', '[senior']:
age_group = assets.AgeGroup.SENIOR
elif value in ['youth', 'jugendlicher', '[youth]', '[jugendlicher]']:
age_group = assets.AgeGroup.YOUTH
elif value in ['kind', 'child']:
age_group = assets.AgeGroup.CHILD
if key == 'language':
if value in ['de', 'ger', 'deu', '[de]']:
native_lang = 'deu'
elif value in ['en', 'eng', '[en]']:
native_lang = 'eng'
return assets.Speaker(idx, gender=gender, age_group=age_group, native_language=native_lang)
def _load(self, path):
corpus = audiomate.Corpus(path=path)
for file_path in glob.glob(os.path.join(path, 'recordings', '*.wav')):
file_idx = os.path.splitext(os.path.basename(file_path))[0]
corpus.new_file(file_path, file_idx)
idx_parts = file_idx.split('_')
digit = idx_parts[0]
issuer_idx = '_'.join(idx_parts[1:-1])
if issuer_idx not in corpus.issuers.keys():
issuer = assets.Speaker(issuer_idx)
corpus.import_issuers(issuer)
utterance = corpus.new_utterance(file_idx, file_idx, issuer_idx)
utterance.set_label_list(
assets.LabelList.create_single(
str(digit), idx=audiomate.corpus.LL_WORD_TRANSCRIPT))
return corpus
def _load(self, path):
corpus = audiomate.Corpus(path=path)
for part in ['TEST', 'TRAIN']:
part_path = os.path.join(path, part)
part_utt_ids = set()
for region in os.listdir(part_path):
region_path = os.path.join(part_path, region)
if os.path.isdir(region_path):
for speaker_abbr in os.listdir(region_path):
speaker_path = os.path.join(region_path, speaker_abbr)
speaker_idx = speaker_abbr[1:]
if speaker_idx not in corpus.issuers.keys():
issuer = assets.Speaker(speaker_idx)
if speaker_abbr[:1] == 'M':
issuer.gender = assets.Gender.MALE
elif speaker_abbr[:1] == 'F':
issuer.gender = assets.Gender.FEMALE
corpus.import_issuers(issuer)
for wav_path in glob.glob(
os.path.join(speaker_path, '*.WAV')):
sentence_idx = os.path.splitext(
os.path.basename(wav_path))[0]
utt_idx = '{}-{}-{}'.format(
region, speaker_abbr, sentence_idx).lower()
part_utt_ids.add(utt_idx)
raw_text_path = os.path.join(
speaker_path, '{}.TXT'.format(sentence_idx))
raw_text = textfile.read_separated_lines(
raw_text_path, separator=' ',
max_columns=3)[0][2]
words_path = os.path.join(
speaker_path, '{}.WRD'.format(sentence_idx))
words = textfile.read_separated_lines(
words_path, separator=' ', max_columns=3)
phones_path = os.path.join(
speaker_path, '{}.PHN'.format(sentence_idx))
phones = textfile.read_separated_lines(
phones_path, separator=' ', max_columns=3)
corpus.new_file(wav_path, utt_idx)
utt = corpus.new_utterance(utt_idx, utt_idx,
speaker_idx)
raw_ll = assets.LabelList.create_single(
raw_text,
idx=audiomate.corpus.LL_WORD_TRANSCRIPT_RAW)
utt.set_label_list(raw_ll)
word_ll = assets.LabelList(
idx=audiomate.corpus.LL_WORD_TRANSCRIPT)
for record in words:
start = int(record[0]) / 16000
end = int(record[1]) / 16000
word_ll.append(
assets.Label(record[2],
start=start,
end=end))
utt.set_label_list(word_ll)
phone_ll = assets.LabelList(
idx=audiomate.corpus.LL_PHONE_TRANSCRIPT)
for record in phones:
start = int(record[0]) / 16000
end = int(record[1]) / 16000
phone_ll.append(
assets.Label(record[2],
start=start,
end=end))
utt.set_label_list(phone_ll)
filter = subset.MatchingUtteranceIdxFilter(
utterance_idxs=part_utt_ids)
subview = subset.Subview(corpus, filter_criteria=[filter])
corpus.import_subview(part, subview)
return corpus
def create_dataset():
temp_path = tempfile.mkdtemp()
ds = audiomate.Corpus(temp_path)
wav_1_path = sample_wav_file('wav_1.wav')
wav_2_path = sample_wav_file('wav_2.wav')
wav_3_path = sample_wav_file('wav_3.wav')
wav_4_path = sample_wav_file('wav_4.wav')
file_1 = ds.new_file(wav_1_path, file_idx='wav-1')
file_2 = ds.new_file(wav_2_path, file_idx='wav_2')
file_3 = ds.new_file(wav_3_path, file_idx='wav_3')
file_4 = ds.new_file(wav_4_path, file_idx='wav_4')
issuer_1 = assets.Speaker('spk-1', gender=assets.Gender.MALE)
issuer_2 = assets.Speaker('spk-2', gender=assets.Gender.FEMALE)
issuer_3 = assets.Issuer('spk-3')
ds.import_issuers([issuer_1, issuer_2, issuer_3])
utt_1 = ds.new_utterance('utt-1', file_1.idx, issuer_idx=issuer_1.idx)
utt_2 = ds.new_utterance('utt-2', file_2.idx, issuer_idx=issuer_1.idx)
utt_3 = ds.new_utterance('utt-3',
file_3.idx,
issuer_idx=issuer_2.idx,
start=0,
end=1.5)
utt_4 = ds.new_utterance('utt-4',
file_3.idx,
issuer_idx=issuer_2.idx,
start=1.5,
end=2.5)
utt_5 = ds.new_utterance('utt-5', file_4.idx, issuer_idx=issuer_3.idx)
utt_1.set_label_list(
assets.LabelList(audiomate.corpus.LL_WORD_TRANSCRIPT,
labels=[assets.Label('who am i')]))
utt_2.set_label_list(
assets.LabelList(
audiomate.corpus.LL_WORD_TRANSCRIPT,
labels=[assets.Label('who are you', meta={
'a': 'hey',
'b': 2
})]))
utt_3.set_label_list(
assets.LabelList(audiomate.corpus.LL_WORD_TRANSCRIPT,
labels=[assets.Label('who is he')]))
utt_4.set_label_list(
assets.LabelList(audiomate.corpus.LL_WORD_TRANSCRIPT,
labels=[assets.Label('who are they')]))
utt_5.set_label_list(
assets.LabelList(audiomate.corpus.LL_WORD_TRANSCRIPT,
labels=[assets.Label('who is she')]))
train_filter = subview.MatchingUtteranceIdxFilter(
utterance_idxs={'utt-1', 'utt-2', 'utt-3'})
sv_train = subview.Subview(ds, filter_criteria=[train_filter])
dev_filter = subview.MatchingUtteranceIdxFilter(
utterance_idxs={'utt-4', 'utt-5'})
sv_dev = subview.Subview(ds, filter_criteria=[dev_filter])
ds.import_subview('train', sv_train)
ds.import_subview('dev', sv_dev)
ds.new_feature_container('mfcc', '/some/dummy/path')
ds.new_feature_container('mel', '/some/dummy/path_mel')
return ds
