def make_feature(self, txt):
syllables = preprocessing.syllable_tokenize(txt)
sy2ix, ch2ix = self.sy_dict, self.ch_dict
ch_ix, ch_type_ix, syllable_ix = [], [], []
for syllable in syllables:
six = preprocessing.syllable2ix(sy2ix, syllable)
characters = list(syllable)
chs = list(
map(
lambda ch: preprocessing.character2ix(ch2ix, ch),
characters,
)
)
ch_ix.extend(chs)
ch_type_ix.extend(char_type.get_char_type_ix(characters))
syllable_ix.extend([six]*len(chs))
features = np.stack((ch_ix, ch_type_ix, syllable_ix), axis=0) \
.reshape((1, 3, -1)) \
.astype(np.int64)
seq_lengths = np.array([features.shape[-1]], dtype=np.int64)
return list(txt), (torch.from_numpy(features), torch.from_numpy(seq_lengths))
def _process_training_line(self, syllables, w_bi_labels, output_scheme):
sy_ix = list(map(lambda s: preprocessing.syllable2ix(self.sy_dict, s), syllables))
x = np.array(sy_ix)
y = w_bi_labels
assert len(sy_ix) == len(y)
return (x, len(y)), y
def make_feature(self, txt):
syllables = preprocessing.syllable_tokenize(txt)
sy2ix = self.sy_dict
syllable_ix = []
for syllable in syllables:
six = preprocessing.syllable2ix(sy2ix, syllable)
syllable_ix.append(six)
# dims: (len,)
features = np.array(syllable_ix)\
.astype(np.int64)\
.reshape(-1)
seq_lengths = np.array([features.shape[-1]], dtype=np.int64)
features = torch.from_numpy(features)
return syllables, (features, torch.from_numpy(seq_lengths))
def _process_training_line(self, syllables, w_bi_labels, output_scheme):
assert len(syllables) == len(w_bi_labels)
characters, syllable_indices, labels = [], [], []
# we get syllable and its label here
for syllable, label in zip(syllables, w_bi_labels):
_len = len(syllable)
if _len == 0:
_len, _chr = 1, [""]
_label = [label]
else:
_chr = list(syllable)
_label = [label] + [0] * (_len - 1)
assert len(_chr) == len(_label) == _len, "%d vs %d vs %d" % (len(_chr), len(_label), _len)
characters.extend(_chr)
labels.extend(_label)
sy_ix = preprocessing.syllable2ix(self.sy_dict, syllable)
syllable_indices.extend([sy_ix]*_len)
y = np.array(list(labels)).astype(int)
ch_ix = np.array(
list(map(lambda ch: preprocessing.character2ix(self.ch_dict, ch), characters))
).astype(int)
ct_ix = np.array(char_type.get_char_type_ix(characters)).astype(int)
x = np.stack((ch_ix, ct_ix, syllable_indices), axis=0)
y = output_scheme.encode(y, syllable_indices)
assert len(y) == len(ch_ix)
assert len(y) == len(ct_ix)
assert len(y) == len(syllable_indices)
return (x, len(y)), y
def prepare_syllable_charater_seq_data(files,
ch2ix,
sy2ix,
sampling=10,
output_dir=""):
training, validation = files
if sampling:
training = training[:sampling]
validation = validation[:sampling]
output_dir = "%s/best-syllable-crf-and-character-seq-feature-sampling-%d" % (
output_dir, sampling)
print("Saving data to %s" % output_dir)
utils.maybe_create_dir(output_dir)
for name, dataset in zip(("training", "val"), (training, validation)):
print("working on : %s" % name)
fout_txt = open("%s/%s.txt" % (output_dir, name), "w")
try:
for path in dataset:
count = 0
with open(path,
"r") as fin, open(path.replace(".txt", ".label"),
"r") as flab:
has_space_problem = False
for txt, label in zip(fin, flab):
txt = txt.strip().replace("~~", "~")
if not txt:
continue
label = label.strip()
syllables = txt.split("~")
chars_idx = []
char_labels = []
syllable_idx = []
syllable_indices = list(
map(
lambda sy: preprocessing.syllable2ix(
sy2ix, sy), syllables))
if len(syllables) != len(label):
print(txt, path)
print(len(syllables), len(label))
print(syllables)
print(label)
raise SystemExit("xx")
label = list(label)
for ii, (syllable, six, l) in enumerate(
zip(syllables, syllable_indices, label)):
if not syllable:
continue
if syllable == " ":
# next syllable is B, then we should also split this space
if label[ii + 1] == "1":
l = "1"
else:
l = "0"
chs = list(
map(
lambda c: preprocessing.character2ix(
ch2ix, c), list(syllable)))
total_chs = len(chs)
syllable_idx.extend([six] * total_chs)
chars_idx.extend(chs)
if l == "1":
char_labels.extend(["1"] + ["0"] *
(total_chs - 1))
else:
char_labels.extend(["0"] * total_chs)
assert len(char_labels) == len(chars_idx)
# check space problem
if not has_space_problem:
for cix, clb in zip(chars_idx, char_labels):
if cix == 3 and clb == "0":
has_space_problem = True
print(txt)
break
fout_txt.write("%s::%s::%s\n" % (
"".join(char_labels),
" ".join(np.array(chars_idx).astype(str)),
" ".join(np.array(syllable_idx).astype(str)),
))
if has_space_problem:
print("problem with space in %s" % path)
finally:
fout_txt.close()