# path, channel, name, noise_type, duration
return {
key: np.array(sorted(val, key=lambda x: x[0]))
for key, val in all_files.items()
}
# ==================== run the validation ==================== #
if CURRENT_STATE == SystemStates.EXTRACT_FEATURES:
ALL_NOISE = validating_noise_data(in_path_raw=PATH_RAW_DATA)
print("Processed noise data:")
for ds_name, noise_list in ALL_NOISE.items():
print(" ", ctext(ds_name, 'yellow'), ':', noise_list.shape)
if len(noise_list) == 0:
continue
for name, count in sorted(freqcount(noise_list[:, 3]).items(),
key=lambda x: x[0]):
print(' ', ctext('%-10s' % name, 'yellow'), ':',
'%s(files)' % ctext('%-6d' % count, 'cyan'))
# ===========================================================================
# Validating the file list of training data
# ===========================================================================
@cache_disk
def validating_training_data(in_path_raw, training_dataset):
file_list = {
ds: sre_file_list[ds]
for ds in training_dataset if ds in sre_file_list
}
# ====== meta info ====== #
def transform(self,
texts,
mode='seq',
dtype='int32',
padding='pre',
truncating='pre',
value=0.,
end_document=None,
maxlen=None,
token_not_found='ignore'):
"""
Parameters
----------
texts: iterator of unicode
iterator, generator or list (e.g. [u'a', u'b', ...])
of unicode documents.
mode: 'binary', 'tfidf', 'count', 'freq', 'seq'
'binary', abc
'tfidf', abc
'count', abc
'freq', abc
'seq', abc
token_not_found: 'ignore', 'raise', a token string, an integer
pass
"""
# ====== check arguments ====== #
texts = self._validate_texts(texts)
# ====== check mode ====== #
mode = str(mode)
if mode not in ('seq', 'binary', 'count', 'freq', 'tfidf'):
raise ValueError('The "mode" argument must be: "seq", "binary", '
'"count", "freq", or "tfidf".')
# ====== check token_not_found ====== #
if not is_number(token_not_found) and \
not is_string(token_not_found) and \
token_not_found not in ('ignore', 'raise'):
raise ValueError('token_not_found can be: "ignore", "raise"'
', an integer of token index, or a string '
'represented a token.')
if token_not_found not in ('ignore', 'raise'):
token_not_found = int(self.dictionary[token_not_found])
elif is_number(token_not_found):
token_not_found = int(token_not_found)
# ====== pick engine ====== #
if self.__engine == 'spacy':
processor = self._preprocess_docs_spacy
elif self.__engine == 'odin':
processor = self._preprocess_docs_odin
# ====== Initialize variables ====== #
dictionary = self.dictionary
results = []
# ====== preprocess arguments ====== #
if isinstance(end_document, str):
end_document = dictionary.index(end_document)
elif is_number(end_document):
end_document = int(end_document)
# ====== processing ====== #
if hasattr(texts, '__len__'):
target_len = len(texts)
auto_adjust_len = False
else:
target_len = 1234
auto_adjust_len = True
prog = Progbar(target=target_len,
name="Tokenize Transform",
print_report=True,
print_summary=True)
for nb_docs, doc in processor(texts, vocabulary=None, keep_order=True):
# found the word in dictionary
vec = []
for x in doc:
idx = dictionary.get(x, -1)
if idx >= 0:
vec.append(idx)
# not found the token in dictionary
elif token_not_found == 'ignore':
continue
elif token_not_found == 'raise':
raise RuntimeError(
'Cannot find token: "%s" in dictionary' % x)
elif isinstance(token_not_found, int):
vec.append(token_not_found)
# append ending document token
if end_document is not None:
vec.append(end_document)
# add the final results
results.append(vec)
# print progress
if self.print_progress:
prog['#Docs'] = nb_docs
prog.add(1)
if auto_adjust_len and prog.seen_so_far >= 0.8 * prog.target:
prog.target = 1.2 * prog.target
# end the process
# if self.print_progress and auto_adjust_len:
# prog.target = nb_docs; prog.update(nb_docs)
# ====== pad the sequence ====== #
# just transform into sequence of tokens
if mode == 'seq':
maxlen = self.longest_document_length if maxlen is None \
else int(maxlen)
results = pad_sequences(results,
maxlen=maxlen,
dtype=dtype,
padding=padding,
truncating=truncating,
value=value)
# transform into one-hot matrix
else:
X = np.zeros(shape=(len(results), self.nb_words))
for i, seq in enumerate(results):
if mode == 'binary':
X[i, seq] = 1
elif mode == 'freq':
length = len(seq)
count = freqcount(seq)
for tok, n in count.items():
X[i, tok] = n / float(length)
elif mode == 'count':
count = freqcount(seq)
for tok, n in count.items():
X[i, tok] = n
elif mode == 'tfidf':
count = freqcount(seq)
for tok, n in count.items():
tf = 1 + np.log(n)
docs_freq = self._word_dictionary_info.get(
tok, (0, 0))[-1]
idf = np.log(1 + self.nb_docs / (1 + docs_freq))
X[i, tok] = tf * idf
results = X
return results
for name, (start, end) in indices:
assert end - start > 0
if name.split('_')[0] == 'train':
train.append((name, (start, end)))
else:
test.append((name, (start, end)))
max_length = max(end - start, max_length)
min_length = min(end - start, min_length)
print(ctext("#Train:", 'yellow'), len(train), train[:2])
print(ctext("#Test:", 'yellow'), len(test), test[:2])
print("Min Length:", ctext(min_length, 'cyan'))
print("Max Length:", ctext(max_length, 'cyan'))
# ====== gender and single digit distribution ====== #
gender_digit = lambda x: x[0].split('_')[1] + '-' + x[0].split('_')[-1]
print(
print_dist(d=freqcount(train, key=gender_digit),
show_number=True,
title="Training distribution"))
print(
print_dist(d=freqcount(test, key=gender_digit),
show_number=True,
title="Testing distribution"))
# ====== digits ====== #
f_digits, digits = unique_labels([i[0] for i in train + test],
key_func=lambda x: x.split('_')[-1],
return_labels=True)
print(ctext("All digits:", 'yellow'), ctext(digits, 'cyan'))
# ====== genders ====== #
f_genders, genders = unique_labels([i[0] for i in train + test],
key_func=lambda x: x.split('_')[1],
return_labels=True)
nb_classes = 10 # 10 digits (0-9)
# ===========================================================================
# Create feeder
# ===========================================================================
indices = [(name, start, end) for name, (start, end) in ds['indices']]
longest_utterances = max(
int(end) - int(start) - 1 for i, start, end in indices)
longest_vad = max(end - start for name, vad in ds['vadids']
for (start, end) in vad)
print("Longest Utterance:", longest_utterances)
print("Longest Vad:", longest_vad)
np.random.shuffle(indices)
train, valid, test = train_valid_test_split(indices, train=0.6, inc_test=True)
print('Nb train:', len(train), freqcount([int(i[0][0]) for i in train]))
print('Nb valid:', len(valid), freqcount([int(i[0][0]) for i in valid]))
print('Nb test:', len(test), freqcount([int(i[0][0]) for i in test]))
train_feeder = F.Feeder(ds['mspec'], train, ncpu=1)
test_feeder = F.Feeder(ds['mspec'], test, ncpu=2)
valid_feeder = F.Feeder(ds['mspec'], valid, ncpu=2)
recipes = [
F.recipes.Name2Trans(converter_func=lambda x: int(x[0])),
F.recipes.Normalization(mean=ds['mspec_mean'],
std=ds['mspec_std'],
local_normalize=False),
F.recipes.Sequencing(frame_length=longest_utterances,
hop_length=1,
end='pad',
def transform(self, texts, mode='seq', dtype='int32',
padding='pre', truncating='pre', value=0.,
end_document=None, maxlen=None,
token_not_found='ignore'):
"""
Parameters
----------
texts: iterator of unicode
iterator, generator or list (e.g. [u'a', u'b', ...])
of unicode documents.
mode: 'binary', 'tfidf', 'count', 'freq', 'seq'
'binary', abc
'tfidf', abc
'count', abc
'freq', abc
'seq', abc
token_not_found: 'ignore', 'raise', a token string, an integer
pass
"""
# ====== check arguments ====== #
texts = self._validate_texts(texts)
# ====== check mode ====== #
mode = str(mode)
if mode not in ('seq', 'binary', 'count', 'freq', 'tfidf'):
raise ValueError('The "mode" argument must be: "seq", "binary", '
'"count", "freq", or "tfidf".')
# ====== check token_not_found ====== #
if not is_number(token_not_found) and \
not is_string(token_not_found) and \
token_not_found not in ('ignore', 'raise'):
raise ValueError('token_not_found can be: "ignore", "raise"'
', an integer of token index, or a string '
'represented a token.')
if token_not_found not in ('ignore', 'raise'):
token_not_found = int(self.dictionary[token_not_found])
elif is_number(token_not_found):
token_not_found = int(token_not_found)
# ====== pick engine ====== #
if self.__engine == 'spacy':
processor = self._preprocess_docs_spacy
elif self.__engine == 'odin':
processor = self._preprocess_docs_odin
# ====== Initialize variables ====== #
dictionary = self.dictionary
results = []
# ====== preprocess arguments ====== #
if isinstance(end_document, str):
end_document = dictionary.index(end_document)
elif is_number(end_document):
end_document = int(end_document)
# ====== processing ====== #
if hasattr(texts, '__len__'):
target_len = len(texts)
auto_adjust_len = False
else:
target_len = 1208
auto_adjust_len = True
prog = Progbar(target=target_len, name="Tokenize Transform",
print_report=True, print_summary=True)
for nb_docs, doc in processor(texts, vocabulary=None, keep_order=True):
# found the word in dictionary
vec = []
for x in doc:
idx = dictionary.get(x, -1)
if idx >= 0: vec.append(idx)
# not found the token in dictionary
elif token_not_found == 'ignore':
continue
elif token_not_found == 'raise':
raise RuntimeError('Cannot find token: "%s" in dictionary' % x)
elif isinstance(token_not_found, int):
vec.append(token_not_found)
# append ending document token
if end_document is not None:
vec.append(end_document)
# add the final results
results.append(vec)
# print progress
if self.print_progress:
prog['#Docs'] = nb_docs
prog.add(1)
if auto_adjust_len and prog.seen_so_far >= 0.8 * prog.target:
prog.target = 1.2 * prog.target
# end the process
# if self.print_progress and auto_adjust_len:
# prog.target = nb_docs; prog.update(nb_docs)
# ====== pad the sequence ====== #
# just transform into sequence of tokens
if mode == 'seq':
maxlen = self.longest_document_length if maxlen is None \
else int(maxlen)
results = pad_sequences(results, maxlen=maxlen, dtype=dtype,
padding=padding, truncating=truncating,
value=value)
# transform into one-hot matrix
else:
X = np.zeros(shape=(len(results), self.nb_words))
for i, seq in enumerate(results):
if mode == 'binary':
X[i, seq] = 1
elif mode == 'freq':
length = len(seq)
count = freqcount(seq)
for tok, n in count.items():
X[i, tok] = n / float(length)
elif mode == 'count':
count = freqcount(seq)
for tok, n in count.items():
X[i, tok] = n
elif mode == 'tfidf':
count = freqcount(seq)
for tok, n in count.items():
tf = 1 + np.log(n)
docs_freq = self._word_dictionary_info.get(tok, (0, 0))[-1]
idf = np.log(1 + self.nb_docs / (1 + docs_freq))
X[i, tok] = tf * idf
results = X
return results
((name, start, end) for name, (start, end) in ds['indices'].iteritems()),
key=lambda x: x[0])
all_labels = list(set(i[0].split("_")[0] for i in indices))
print("Labels:", all_labels)
np.random.shuffle(indices)
np.random.shuffle(indices)
longest_utterance = max(int(end - start) for name, start, end in indices)
print("Longest Utterance:", longest_utterance)
nb_files = len(indices)
train_indices = indices[:int(0.6 * nb_files)]
valid_indices = indices[int(0.6 * nb_files):int(0.8 * nb_files)]
test_indices = indices[int(0.8 * nb_files):]
print("Train distribution:", len(train_indices),
freqcount([x[0].split('_')[0] for x in train_indices]).items())
print("Valid distribution:", len(valid_indices),
freqcount([x[0].split('_')[0] for x in valid_indices]).items())
print("Test distribution:", len(test_indices),
freqcount([x[0].split('_')[0] for x in test_indices]).items())
train = F.Feeder(ds[FEAT], train_indices, ncpu=1)
valid = F.Feeder(ds[FEAT], valid_indices, ncpu=1)
test = F.Feeder(ds[FEAT], test_indices, ncpu=1)
recipes = [
F.recipes.Name2Trans(
converter_func=lambda x: all_labels.index(x.split("_")[0])),
F.recipes.Normalization(mean=ds[FEAT + "_mean"],
std=ds[FEAT + "_std"],
local_normalize=False),
# ====== return ====== #
# Header:
# 0 1 2 3 4
# path, channel, name, noise_type, duration
return {key: np.array(sorted(val, key=lambda x: x[0]))
for key, val in all_files.items()}
# ==================== run the validation ==================== #
if CURRENT_STATE == SystemStates.EXTRACT_FEATURES:
ALL_NOISE = validating_noise_data(
in_path_raw=PATH_RAW_DATA)
print("Processed noise data:")
for ds_name, noise_list in ALL_NOISE.items():
print(" ", ctext(ds_name, 'yellow'), ':', noise_list.shape)
if len(noise_list) == 0:
continue
for name, count in sorted(freqcount(noise_list[:, 3]).items(),
key=lambda x: x[0]):
print(' ', ctext('%-10s' % name, 'yellow'), ':',
'%s(files)' % ctext('%-6d' % count, 'cyan'))
# ===========================================================================
# Validating the file list of training data
# ===========================================================================
@cache_disk
def validating_training_data(in_path_raw, training_dataset):
file_list = {ds: sre_file_list[ds]
for ds in training_dataset
if ds in sre_file_list}
# ====== meta info ====== #
all_files = []
non_exist_files = []
extension_count = defaultdict(int)
