def reorg_noncollapsed(f):
padding = 0.1
print(f)
tg_path = os.path.join(noncollapsed_dir, f)
tg = TextGrid()
tg.read(tg_path)
new_tg = TextGrid(maxTime=tg.maxTime)
new_tg_path = tg_path.replace(noncollapsed_dir, data_dir)
for tier in tg.tiers:
new_tier = IntervalTier(name=tier.name, maxTime=tg.maxTime)
for i in tier:
new_mark = sub_pattern.sub(' ', i.mark).strip()
if not new_mark:
continue
new_begin = i.minTime - padding
if new_begin < 0:
new_begin = 0
new_end = i.maxTime + padding
if new_end > tg.maxTime:
new_end = tg.maxTime
try:
new_tier.add(new_begin, new_end, new_mark)
except ValueError:
new_tier[-1].maxTime = new_end
new_tier[-1].mark += ' ' + new_mark
print(len(new_tier))
new_tg.append(new_tier)
new_tg.write(new_tg_path)
def export_segments(self, output_directory):
from decimal import Decimal
from textgrid import TextGrid, IntervalTier
file_dict = {}
for utt, segment in self.corpus.vad_segments.items():
filename, utt_begin, utt_end = segment
utt_begin = Decimal(utt_begin)
utt_end = Decimal(utt_end)
if filename not in file_dict:
file_dict[filename] = {}
speaker = 'segments'
text = 'speech'
if speaker not in file_dict[filename]:
file_dict[filename][speaker] = []
file_dict[filename][speaker].append([utt_begin, utt_end, text])
for filename, speaker_dict in file_dict.items():
try:
speaker_directory = os.path.join(
output_directory,
self.corpus.file_directory_mapping[filename])
except KeyError:
speaker_directory = output_directory
os.makedirs(speaker_directory, exist_ok=True)
max_time = self.corpus.get_wav_duration(filename)
tg = TextGrid(maxTime=max_time)
for speaker in sorted(speaker_dict.keys()):
words = speaker_dict[speaker]
tier = IntervalTier(name=speaker, maxTime=max_time)
for w in words:
if w[1] > max_time:
w[1] = max_time
tier.add(*w)
tg.append(tier)
tg.write(os.path.join(speaker_directory, filename + '.TextGrid'))
def convert_ctm_to_textgrid(ctm, textgrid):
words = []
phonemes = []
with open(ctm, encoding='utf-8') as f:
for l in f:
tok = l.strip().split()
text = tok[4]
beg = float(tok[2])
dur = float(tok[3])
if tok[0][0] == '@':
if besi.match(text):
text = text[:-2]
phonemes.append((text, beg, dur))
else:
words.append((text, beg, dur))
tw = IntervalTier(name='words')
tp = IntervalTier(name='phonemes')
for seg in words:
try:
tw.add(round(seg[1], 2), round(seg[1] + seg[2], 2), seg[0])
except ValueError:
print("Error in word seg: " + seg[0])
for seg in phonemes:
try:
tp.add(round(seg[1], 2), round(seg[1] + seg[2], 2), seg[0])
except ValueError:
print("Error in phoneme seg: " + seg[0])
tg = TextGrid()
tg.append(tw)
tg.append(tp)
tg.write(textgrid)
def ctm_to_textgrid(phone_ctm, out_directory, utt2dur, frameshift=0.01):
textgrid_write_errors = {}
frameshift = Decimal(str(frameshift))
if not os.path.exists(out_directory):
os.makedirs(out_directory)
utt2dur_mapping = generate_utt2dur(utt2dur)
for i, (k, v) in enumerate(sorted(phone_ctm.items())):
maxtime = Decimal(str(utt2dur_mapping[k]))
try:
tg = TextGrid(maxTime=maxtime)
phonetier = IntervalTier(name='phones', maxTime=maxtime)
for interval in v:
if maxtime - interval[1] < frameshift:
interval[1] = maxtime
#remove B E I and stress (0,1) information from phoneme
interval[2] = re.sub("\d+", "", interval[2].split('_')[0])
phonetier.add(*interval)
tg.append(phonetier)
outpath = os.path.join(out_directory, k + '.TextGrid')
tg.write(outpath)
except Exception as e:
exc_type, exc_value, exc_traceback = sys.exc_info()
textgrid_write_errors[k] = '\n'.join(
traceback.format_exception(exc_type, exc_value, exc_traceback))
if textgrid_write_errors:
error_log = os.path.join(out_directory, 'output_errors.txt')
with io_open(error_log, 'w', encoding='utf-8') as f:
f.write(
u'The following exceptions were encountered during the ouput of the alignments to TextGrids:\n\n'
)
for k, v in textgrid_write_errors.items():
f.write(u'{}:\n'.format(k))
f.write(u'{}\n\n'.format(v))
def loadOrGenerate(self):
fname = self.app.Data.checkFileLevel('.TextGrid', shoulderror=False)
if fname:
self.TextGrid = self.fromFile(fname)
else:
minTime = 0.
if not hasattr(self.app.Audio, 'duration'):
self.app.Audio.reset()
try:
maxTime = self.app.Audio.duration
except:
warn(
'Audio has no duration attribute after calling reset(), defaulting to 1 second'
)
maxTime = 1.
self.TextGrid = TextGridFile(maxTime=maxTime)
keys = self.app.Data.getFileLevel('all')
if not ('.ult' in keys and '.txt' in keys):
sentenceTier = IntervalTier("text")
sentenceTier.add(minTime, maxTime, "text")
self.TextGrid.append(sentenceTier)
fname = self.app.Data.unrelativize(
self.app.Data.getCurrentFilename() + '.TextGrid')
self.app.Data.setFileLevel('.TextGrid', fname)
names = self.TextGrid.getNames()
for i, n in enumerate(names):
if n in ALIGNMENT_TIER_NAMES:
if len(self.TextGrid[i]) == 0:
self.TextGrid.pop(i)
break
else:
self.frameTierName = n
return
self.genFramesTier()
def loadOrGenerate(self):
fname = self.app.Data.checkFileLevel('.TextGrid', shoulderror=False)
if fname:
self.TextGrid = self.fromFile(fname)
else:
minTime = 0.
if not hasattr(self.app.Audio, 'duration'):
self.app.Audio.reset()
maxTime = self.app.Audio.duration
self.TextGrid = TextGridFile(maxTime=maxTime)
sentenceTier = IntervalTier("text")
sentenceTier.add(minTime, maxTime, "text")
self.TextGrid.tiers.append(sentenceTier)
fname = self.app.Data.unrelativize(
self.app.Data.getCurrentFilename() + '.TextGrid')
self.app.Data.setFileLevel('.TextGrid', fname)
names = self.TextGrid.getNames()
for i, n in enumerate(names):
if n in ALIGNMENT_TIER_NAMES:
if len(self.TextGrid[i]) == 0:
self.TextGrid.pop(i)
break
else:
return
self.genFramesTier()
def parseFile(path, fn):
filename= fn.split(".")[0] #just name of file
with open(path, "r") as f1:
lines = f1.readlines()
SAM = getSAM(lines)
allSegs = getMAU(lines, SAM, filename)
if allSegs is None:
return
segs = []
for seg in allSegs:
#print("%f %f %s %s"%(seg.start, seg.end, seg.segment, seg.index))
tup = getSegInfo(seg)
segs.append(tup)
words = getWords(lines, allSegs, filename)
maxtime = getMaxTime(allSegs)
if maxtime == -1:
return
tg = TextGrid(maxTime = maxtime)
wordtier = IntervalTier(name = 'words', maxTime = maxtime)
phonetier = IntervalTier(name = 'phones', maxTime = maxtime)
for interval in words:
wordtier.add(*interval)
for interval in segs:
phonetier.add(*interval)
tg.append(wordtier)
tg.append(phonetier)
outpath = "/Users/elias/Desktop/TextGrids/%s.TextGrid"%filename
tg.write(outpath)
def createTextGrid(data, tierName = "words"): tier = IntervalTier(tierName) txtgrid = TextGrid() prevTime = 0 for (name, time, dur, words) in data: tier.add(prevTime, prevTime+dur, makeSentence(words)) prevTime += dur txtgrid.append(tier) return txtgrid
def ctm_to_textgrid(word_ctm,
phone_ctm,
out_directory,
corpus,
dictionary,
frameshift=0.01):
textgrid_write_errors = {}
frameshift = Decimal(str(frameshift))
if not os.path.exists(out_directory):
os.makedirs(out_directory, exist_ok=True)
silences = {dictionary.optional_silence, dictionary.nonoptional_silence}
for i, (filename, speaker_dict) in enumerate(sorted(word_ctm.items())):
maxtime = corpus.get_wav_duration(filename)
try:
speaker_directory = os.path.join(
out_directory, corpus.file_directory_mapping[filename])
tg = TextGrid(maxTime=maxtime)
for speaker in corpus.speaker_ordering[filename]:
words = speaker_dict[speaker]
word_tier_name = '{} - words'.format(speaker)
phone_tier_name = '{} - phones'.format(speaker)
word_tier = IntervalTier(name=word_tier_name, maxTime=maxtime)
phone_tier = IntervalTier(name=phone_tier_name,
maxTime=maxtime)
for w in words:
word_tier.add(*w)
for p in phone_ctm[filename][speaker]:
if len(phone_tier) > 0 and phone_tier[
-1].mark in silences and p[2] in silences:
phone_tier[-1].maxTime = p[1]
else:
if len(phone_tier) > 0 and p[2] in silences and p[
0] < phone_tier[-1].maxTime:
p = phone_tier[-1].maxTime, p[1], p[2]
elif len(phone_tier) > 0 and p[2] not in silences and p[0] < phone_tier[-1].maxTime and \
phone_tier[-1].mark in silences:
phone_tier[-1].maxTime = p[0]
phone_tier.add(*p)
tg.append(word_tier)
tg.append(phone_tier)
tg.write(os.path.join(speaker_directory, filename + '.TextGrid'))
except Exception as e:
exc_type, exc_value, exc_traceback = sys.exc_info()
textgrid_write_errors[filename] = '\n'.join(
traceback.format_exception(exc_type, exc_value, exc_traceback))
if textgrid_write_errors:
error_log = os.path.join(out_directory, 'output_errors.txt')
with open(error_log, 'w', encoding='utf8') as f:
f.write(
'The following exceptions were encountered during the ouput of the alignments to TextGrids:\n\n'
)
for k, v in textgrid_write_errors.items():
f.write('{}:\n'.format(k))
f.write('{}\n\n'.format(v))
def export_classification(self, output_directory):
if self.cluster:
self.cluster_utterances()
else:
self.get_classification_stats()
from decimal import Decimal
from textgrid import TextGrid, IntervalTier
spk2utt_path = os.path.join(self.classify_directory, 'spk2utt')
utt2spk_path = os.path.join(self.classify_directory, 'utt2spk')
if self.corpus.segments:
utt2spk = load_scp(utt2spk_path)
file_dict = {}
for utt, segment in self.corpus.segments.items():
filename, utt_begin, utt_end = segment.split(' ')
utt_begin = Decimal(utt_begin)
utt_end = Decimal(utt_end)
if filename not in file_dict:
file_dict[filename] = {}
speaker = utt2spk[utt]
text = self.corpus.text_mapping[utt]
if speaker not in file_dict[filename]:
file_dict[filename][speaker] = []
file_dict[filename][speaker].append([utt_begin, utt_end, text])
for filename, speaker_dict in file_dict.items():
try:
speaker_directory = os.path.join(
output_directory,
self.corpus.file_directory_mapping[filename])
except KeyError:
speaker_directory = output_directory
max_time = self.corpus.get_wav_duration(filename)
tg = TextGrid(maxTime=max_time)
for speaker in sorted(speaker_dict.keys()):
words = speaker_dict[speaker]
tier = IntervalTier(name=speaker, maxTime=max_time)
for w in words:
if w[1] > max_time:
w[1] = max_time
tier.add(*w)
tg.append(tier)
tg.write(
os.path.join(speaker_directory, filename + '.TextGrid'))
else:
spk2utt = load_scp(spk2utt_path)
for speaker, utts in spk2utt.items():
speaker_dir = os.path.join(output_directory, speaker)
os.makedirs(speaker_dir, exist_ok=True)
with open(os.path.join(speaker_dir, 'utterances.txt'),
'w',
encoding='utf8') as f:
for u in utts:
f.write('{}\n'.format(u))
def test_time_to_frame_interval_tier_short_seg(self):
tier = IntervalTier('test', 0, 0.01)
tier.add(0, 0.003, "a")
tier.add(0.003, 0.01, "b")
frame_tier = utterance.time_to_frame_interval_tier(tier, 5)
self.assertEqual(frame_tier.minTime, 0)
self.assertEqual(frame_tier.maxTime, 2)
self.assertEqual(frame_tier.intervals[0].minTime, 0)
self.assertEqual(frame_tier.intervals[0].maxTime, 1)
self.assertEqual(frame_tier.intervals[1].minTime, 1)
self.assertEqual(frame_tier.intervals[1].maxTime, 2)
def generator_textgrid(maxtime, lines, output):
# Download Praat: https://www.fon.hum.uva.nl/praat/
interval = maxtime / (len(lines) + 1)
margin = 0.0001
tg = TextGrid(maxTime=maxtime)
linetier = IntervalTier(name="line", maxTime=maxtime)
i = 0
for l in lines:
s, e, w = l.split()
linetier.add(minTime=float(s) + margin, maxTime=float(e), mark=w)
tg.append(linetier)
print("successfully generator {}".format(output))
tg.write(output)
def createNew(textgrid, tier_name, VERBOSE=False):
tiers = textgrid.getList(tier_name)
tier = tiers[0]
new_tier = IntervalTier(tier_name+'_clean')
new_txtgrid = TextGrid()
if VERBOSE == True:
print ("Old tier: %s" % tier)
for interval in tier:
if isPause(interval.mark) == True:
new_tier.add(interval.minTime, interval.maxTime, '')
else:
new_tier.add(interval.minTime, interval.maxTime, fixString(interval.mark))
new_txtgrid.append(new_tier)
if VERBOSE == True:
print ("New tier: %s" % new_tier)
return new_txtgrid
def convert_ctm_to_textgrid(ctms, textgrid):
for ctm in ctms:
tiername = ctm.stem
ret = []
with open(ctm, encoding='utf-8') as f:
for l in f:
tok = l.strip().split()
word = tok[4]
beg = float(tok[2])
dur = float(tok[3])
ret.append((word, beg, dur))
t = IntervalTier(name=tiername)
for seg in ret:
try:
t.add(round(seg[1], 2), round(seg[1] + seg[2], 2), seg[0])
except ValueError:
print("Error in seg: " + seg[0])
tg = TextGrid()
tg.append(t)
tg.write(textgrid)
def read_segment(val: Segment) -> IntervalTier:
"""Read a Segment message and save it to an IntervalTier object.
Args:
val: A Segment message as defined in data_utterance.pb.
Returns:
interval: The Segment message saved in an IntervalTier object.
"""
symbols = val.symbol
start_time = mat_to_numpy(val.start_time)
end_time = mat_to_numpy(val.end_time)
num_items = val.num_item
if not (len(symbols) == len(start_time) == len(end_time) == num_items):
raise ValueError("Interval item number is not consistent!")
interval = IntervalTier(minTime=start_time[0], maxTime=end_time[-1])
for sym, min_time, max_time in zip(symbols, start_time, end_time):
interval.add(min_time, max_time, sym)
return interval
def time_to_frame_interval_tier(time_tier: IntervalTier,
shift) -> IntervalTier:
"""Convert an IntervalTier in time to frame.
Args:
time_tier: IntervalTier represented in seconds.
shift: Window shift in ms.
Returns:
frame_tier: IntervalTier represented in frames.
"""
max_frame = time_to_frame(time_tier.maxTime, shift)
frame_tier = IntervalTier(time_tier.name, 0, max_frame)
# Deal with (occasionally) very short segments -- less than a frame shift
# If we have consecutive very small segments then the function will raise a
# ValueError
start_shift = 0
for each_interval in time_tier.intervals:
curr_min_frame = time_to_frame(each_interval.minTime, shift)
if start_shift > 0:
logging.warning("Last segment is too short, have to cut the %d "
"frame(s) from the beginning of the current "
"segment.", start_shift)
curr_min_frame += start_shift
start_shift = 0
curr_max_frame = time_to_frame(each_interval.maxTime, shift)
if curr_min_frame >= curr_max_frame:
curr_max_frame = curr_min_frame + 1
start_shift = curr_max_frame - curr_min_frame
logging.warning("The current segment is too short, extend it for "
"%d frame(s).", start_shift)
if curr_max_frame > frame_tier.maxTime:
raise ValueError("Extreme short segments in the tier, please fix "
"these.")
frame_tier.add(curr_min_frame, curr_max_frame, each_interval.mark)
return frame_tier
def genFramesTier(self):
debug('generating frames tier for %s' %
self.app.Data.getCurrentFilename())
self.frameTierName = 'frames'
times = self.app.Dicom.getFrameTimes()
self.app.Data.setFileLevel("NumberOfFrames", len(times))
try:
maxTime = max(self.app.Audio.duration, times[-1])
except AttributeError:
maxTime = times[-1]
tier = PointTier('frames', maxTime=maxTime)
for f, t in enumerate(times):
tier.addPoint(Point(t, str(f)))
if not self.TextGrid.maxTime or maxTime > self.TextGrid.maxTime:
self.TextGrid.maxTime = maxTime
self.TextGrid.append(tier)
keys = self.app.Data.getFileLevel('all')
if '.ult' in keys and '.txt' in keys:
fname = self.app.Data.unrelativize(
self.app.Data.getFileLevel('.txt'))
f = open(fname, 'rb')
s = util.decode_bytes(f.read())
f.close()
if s:
line = s.splitlines()[0]
sentenceTier = IntervalTier("sentence")
sentenceTier.add(0, self.app.Audio.duration, line)
self.TextGrid.append(sentenceTier)
self.TextGrid.tiers = [self.TextGrid.tiers[-1]
] + self.TextGrid.tiers[:-1]
path = self.app.Data.unrelativize(
self.app.Data.getFileLevel('.TextGrid'))
self.TextGrid.write(path)
self.TextGrid = TextGridFile.fromFile(path)
def ctm_to_textgrid(word_ctm, phone_ctm, out_directory, corpus, dictionary, frameshift=0.01):
textgrid_write_errors = {}
frameshift = Decimal(str(frameshift))
if not os.path.exists(out_directory):
os.makedirs(out_directory, exist_ok=True)
if not corpus.segments:
for i, (k, v) in enumerate(sorted(word_ctm.items())):
maxtime = Decimal(str(corpus.get_wav_duration(k)))
speaker = list(v.keys())[0]
v = list(v.values())[0]
try:
tg = TextGrid(maxTime=maxtime)
wordtier = IntervalTier(name='words', maxTime=maxtime)
phonetier = IntervalTier(name='phones', maxTime=maxtime)
for interval in v:
if maxtime - interval[1] < frameshift: # Fix rounding issues
interval[1] = maxtime
wordtier.add(*interval)
for interval in phone_ctm[k][speaker]:
if maxtime - interval[1] < frameshift:
interval[1] = maxtime
phonetier.add(*interval)
tg.append(wordtier)
tg.append(phonetier)
relative = corpus.file_directory_mapping[k]
if relative:
speaker_directory = os.path.join(out_directory, relative)
else:
speaker_directory = out_directory
os.makedirs(speaker_directory, exist_ok=True)
outpath = os.path.join(speaker_directory, k + '.TextGrid')
tg.write(outpath)
except Exception as e:
exc_type, exc_value, exc_traceback = sys.exc_info()
textgrid_write_errors[k] = '\n'.join(traceback.format_exception(exc_type, exc_value, exc_traceback))
else:
silences = {dictionary.optional_silence, dictionary.nonoptional_silence}
for i, (filename, speaker_dict) in enumerate(sorted(word_ctm.items())):
maxtime = corpus.get_wav_duration(filename)
try:
speaker_directory = os.path.join(out_directory, corpus.file_directory_mapping[filename])
tg = TextGrid(maxTime=maxtime)
for speaker in corpus.speaker_ordering[filename]:
words = speaker_dict[speaker]
word_tier_name = '{} - words'.format(speaker)
phone_tier_name = '{} - phones'.format(speaker)
word_tier = IntervalTier(name=word_tier_name, maxTime=maxtime)
phone_tier = IntervalTier(name=phone_tier_name, maxTime=maxtime)
for w in words:
word_tier.add(*w)
for p in phone_ctm[filename][speaker]:
if len(phone_tier) > 0 and phone_tier[-1].mark in silences and p[2] in silences:
phone_tier[-1].maxTime = p[1]
else:
if len(phone_tier) > 0 and p[2] in silences and p[0] < phone_tier[-1].maxTime:
p = phone_tier[-1].maxTime, p[1], p[2]
elif len(phone_tier) > 0 and p[2] not in silences and p[0] < phone_tier[-1].maxTime and \
phone_tier[-1].mark in silences:
phone_tier[-1].maxTime = p[0]
phone_tier.add(*p)
tg.append(word_tier)
tg.append(phone_tier)
tg.write(os.path.join(speaker_directory, filename + '.TextGrid'))
except Exception as e:
exc_type, exc_value, exc_traceback = sys.exc_info()
textgrid_write_errors[filename] = '\n'.join(traceback.format_exception(exc_type, exc_value, exc_traceback))
if textgrid_write_errors:
error_log = os.path.join(out_directory, 'output_errors.txt')
with open(error_log, 'w', encoding='utf8') as f:
f.write('The following exceptions were encountered during the ouput of the alignments to TextGrids:\n\n')
for k,v in textgrid_write_errors.items():
f.write('{}:\n'.format(k))
f.write('{}\n\n'.format(v))
#print(transcription)
if len(transcription) < 3 and u.duration > 25:
continue
if u.duration > 25:
print(d, u.duration)
print([(x._type_node['label'], x.begin, x.duration)
for x in u.word])
error
transcription = ' '.join(x[0] for x in transcription)
begin = u.begin - 0.075
if begin < 0:
begin = 0
end = u.end + 0.075
if end > duration:
end = duration
utterance_tier.add(begin, end, transcription)
utt_duration = end - begin
utt_name = '{}_{}_{}'.format(d, begin, end)
utt_wav_path = os.path.join(lab_dir, utt_name + '.wav')
if not os.path.exists(utt_wav_path):
extract_audio(wav_path, utt_wav_path, begin, end, padding=0)
lab_path = os.path.join(lab_dir, utt_name + '.lab')
with open(lab_path, 'w') as f:
f.write(transcription)
trans_path = os.path.join(lab_dir, utt_name + '.txt')
with open(trans_path, 'w') as f:
f.write('{}\t{}\t0\t{}\t{}'.format(speaker, speaker,
utt_duration,
transcription))
utt_tg_path = os.path.join(lab_dir, utt_name + '.TextGrid')
utt_tg = TextGrid(maxTime=utt_duration)
class TestUtterance(unittest.TestCase):
def setUp(self):
self.utt = utterance.Utterance()
self.float_mat = FloatMatrix()
self.int_mat = Int32Matrix()
self.bool_mat = BinaryMatrix()
self.seg = Segment()
self.tier = IntervalTier('test', 0, 2)
self.tier.add(0, 1, "a")
self.tier.add(1, 2, "b")
def tearDown(self):
pass
def test_empty_init(self):
self.assertTrue(isinstance(self.utt, utterance.Utterance))
def test_full_init(self):
utt = utterance.Utterance(np.array([1, 2, 3, 4, 5]), 16000, "test")
self.assertTrue(isinstance(utt, utterance.Utterance))
def test_invalid_init(self):
try:
utt = utterance.Utterance(np.array([1, 2, 3, 4, 5]), text="test")
except ValueError:
pass
def test_rw_internal(self):
pb = self.utt.write_internal()
self.utt.read_internal(pb)
def test_rw(self):
with tempfile.TemporaryDirectory() as tpf:
output_file = os.path.join(tpf, "test.data")
self.utt.write(output_file)
self.assertTrue(os.path.exists(output_file))
self.utt.read(output_file)
def test_rw_mat_empty(self):
np_mat = np.array([])
utterance.numpy_to_mat(np_mat, self.float_mat)
self.assertEqual(self.float_mat.num_row, 0)
self.assertEqual(self.float_mat.num_col, 0)
np_mat_recover = utterance.mat_to_numpy(self.float_mat)
self.assertEqual(np_mat_recover.shape, np_mat.shape)
self.assertTrue((np_mat == np_mat_recover).all())
def test_rw_mat_scalar(self):
np_mat = np.array([1])
utterance.numpy_to_mat(np_mat, self.float_mat)
self.assertEqual(self.float_mat.num_row, 1)
self.assertEqual(self.float_mat.num_col, 1)
np_mat_recover = utterance.mat_to_numpy(self.float_mat)
self.assertEqual(np_mat_recover.shape, np_mat.shape)
self.assertTrue((np_mat == np_mat_recover).all())
def test_rw_mat_row_vec(self):
np_mat = np.array([1, 2, 3, 4])
num_ele = len(np_mat)
utterance.numpy_to_mat(np_mat, self.float_mat)
self.assertEqual(self.float_mat.num_row, 1)
self.assertEqual(self.float_mat.num_col, num_ele)
np_mat_recover = utterance.mat_to_numpy(self.float_mat)
self.assertEqual(np_mat_recover.shape, np_mat.shape)
self.assertTrue((np_mat == np_mat_recover).all())
def test_rw_mat_col_vec(self):
np_mat = np.array([[1], [2], [3], [4]])
num_row, num_col = np_mat.shape
utterance.numpy_to_mat(np_mat, self.float_mat)
self.assertEqual(self.float_mat.num_row, num_row)
self.assertEqual(self.float_mat.num_col, num_col)
np_mat_recover = utterance.mat_to_numpy(self.float_mat)
self.assertEqual(np_mat_recover.shape, (num_row, num_col))
self.assertTrue((np_mat == np_mat_recover).all())
def test_rw_mat_int_mat(self):
np_mat = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
np_mat.astype(int)
num_row, num_col = np_mat.shape
utterance.numpy_to_mat(np_mat, self.int_mat)
self.assertEqual(self.int_mat.num_row, num_row)
self.assertEqual(self.int_mat.num_col, num_col)
np_mat_recover = utterance.mat_to_numpy(self.int_mat)
self.assertEqual(np_mat_recover.shape, (num_row, num_col))
self.assertTrue((np_mat == np_mat_recover).all())
def test_rw_mat_float_mat(self):
np_mat = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
num_row, num_col = np_mat.shape
utterance.numpy_to_mat(np_mat, self.float_mat)
self.assertEqual(self.float_mat.num_row, num_row)
self.assertEqual(self.float_mat.num_col, num_col)
np_mat_recover = utterance.mat_to_numpy(self.float_mat)
self.assertEqual(np_mat_recover.shape, (num_row, num_col))
self.assertTrue((np_mat == np_mat_recover).all())
def test_rw_mat_binary_mat(self):
np_mat = np.array([[1, 0], [0, 1], [0, 0], [1, 1]])
np_mat.astype(bool)
num_row, num_col = np_mat.shape
utterance.numpy_to_mat(np_mat, self.bool_mat)
self.assertEqual(self.bool_mat.num_row, num_row)
self.assertEqual(self.bool_mat.num_col, num_col)
np_mat_recover = utterance.mat_to_numpy(self.bool_mat)
self.assertEqual(np_mat_recover.shape, (num_row, num_col))
self.assertTrue((np_mat == np_mat_recover).all())
def test_rw_segment(self):
utterance.write_segment(self.tier, self.seg)
self.assertEqual(self.seg.num_item, len(self.tier.intervals))
interval = utterance.read_segment(self.seg)
self.assertTrue(isinstance(interval, IntervalTier))
self.assertEqual(len(self.tier.intervals), len(interval.intervals))
def test_rw_data(self):
data = self.utt.data
self.utt.data = data
def test_rw_wav(self):
input_wav = np.array([1, 2, 3, 4])
self.utt.wav = input_wav
wav = self.utt.wav
self.assertTrue((input_wav == wav).any())
def test_rw_fs(self):
self.utt.fs = 16000
fs = self.utt.fs
self.assertEqual(fs, 16000)
def test_set_invalid_fs(self):
try:
self.utt.fs = 0
except ValueError:
pass
try:
self.utt.fs = -2
except ValueError:
pass
def test_rw_text(self):
self.utt.text = "test"
text = self.utt.text
self.assertEqual(text, "test")
def test_rw_align(self):
align = TextGrid()
align.read("data/test.TextGrid")
self.utt.align = align
align_recover = self.utt.align
self.assertEqual(len(align_recover.tiers), len(align.tiers))
def test_rw_ppg(self):
ppg = np.random.uniform(0, 1, (100, 5816))
self.utt.ppg = ppg
ppg_recover = self.utt.ppg
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((ppg_recover - ppg)**2).sum(), 0)
def test_rw_monophone_ppg(self):
mono_ppg = np.random.uniform(0, 1, (100, 40))
self.utt.monophone_ppg = mono_ppg
mono_ppg_recover = self.utt.monophone_ppg
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((mono_ppg_recover - mono_ppg)**2).sum(), 0)
def test_rw_phone(self):
self.utt.phone = self.tier
interval = self.utt.phone
self.assertEqual(len(interval.intervals), len(self.tier.intervals))
def test_rw_word(self):
self.utt.word = self.tier
interval = self.utt.word
self.assertEqual(len(interval.intervals), len(self.tier.intervals))
def test_rw_lab(self):
lab = np.array([1, 2, 3, 4, 5, 5, 6, 7])
self.utt.lab = lab
lab_recover = self.utt.lab
self.assertTrue((lab == lab_recover).all())
def test_rw_utterance_id(self):
self.utt.utterance_id = "id"
utt_id = self.utt.utterance_id
self.assertEqual(utt_id, "id")
def test_rw_speaker_id(self):
self.utt.speaker_id = "id"
spk_id = self.utt.speaker_id
self.assertEqual(spk_id, "id")
def test_rw_dialect(self):
self.utt.dialect = "EN_CN"
dialect = self.utt.dialect
self.assertEqual(dialect, "EN_CN")
def test_rw_gender(self):
self.utt.gender = "O"
gender = self.utt.gender
self.assertEqual(gender, "O")
def test_rw_original_file(self):
self.utt.original_file = "file.test"
origin_file = self.utt.original_file
self.assertEqual(origin_file, "file.test")
def test_rw_num_channel(self):
self.utt.num_channel = 2
channel = self.utt.num_channel
self.assertEqual(channel, 2)
def test_rw_kaldi_shift(self):
self.utt.kaldi_shift = 10
shift = self.utt.kaldi_shift
self.assertEqual(shift, 10)
def test_rw_kaldi_window_size(self):
self.utt.kaldi_window_size = 25
w_size = self.utt.kaldi_window_size
self.assertEqual(w_size, 25)
def test_rw_kaldi_window_type(self):
self.utt.kaldi_window_type = "hamming"
w_type = self.utt.kaldi_window_type
self.assertEqual(w_type, "hamming")
def test_rw_vocoder(self):
self.utt.vocoder = "WORLD"
vocoder_name = self.utt.vocoder
self.assertEqual(vocoder_name, "WORLD")
def test_rw_spec(self):
spec = np.random.uniform(0, 1, (100, 513))
self.utt.spec = spec
spec_recover = self.utt.spec
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((spec_recover - spec)**2).sum(), 0)
self.assertEqual(self.utt.spec_dim, 513)
def test_rw_mfcc(self):
mfcc = np.random.uniform(0, 1, (100, 25))
self.utt.mfcc = mfcc
mfcc_recover = self.utt.mfcc
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((mfcc_recover - mfcc)**2).sum(), 0)
self.assertEqual(self.utt.mfcc_dim, 25)
def test_rw_mcep(self):
mcep = np.random.uniform(0, 1, (100, 60))
self.utt.mcep = mcep
mcep_recover = self.utt.mcep
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((mcep_recover - mcep)**2).sum(), 0)
self.assertEqual(self.utt.mcep_dim, 60)
def test_rw_f0(self):
f0 = np.array([1, 2, 3, 4, 5])
self.utt.f0 = f0
f0_recover = self.utt.f0
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((f0_recover - f0)**2).sum(), 0)
self.assertEqual(self.utt.num_frame, 5)
def test_rw_ap(self):
ap = np.random.uniform(0, 1, (100, 513))
self.utt.ap = ap
ap_recover = self.utt.ap
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((ap_recover - ap)**2).sum(), 0)
self.assertEqual(self.utt.ap_dim, 513)
def test_rw_bap(self):
bap = np.array([1, 2, 3, 4, 5])
self.utt.bap = bap
bap_recover = self.utt.bap
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((bap_recover - bap)**2).sum(), 0)
self.assertEqual(self.utt.bap_dim, 1)
bap = np.random.uniform(0, 1, (100, 5))
self.utt.bap = bap
bap_recover = self.utt.bap
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((bap_recover - bap)**2).sum(), 0)
self.assertEqual(self.utt.bap_dim, 5)
def test_rw_vuv(self):
vuv = np.array([1, 2, 3, 4, 5])
self.utt.vuv = vuv
vuv_recover = self.utt.vuv
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((vuv_recover - vuv)**2).sum(), 0)
def test_rw_temporal_position(self):
tp = np.array([1, 2, 3, 4, 5])
self.utt.temporal_position = tp
tp_recover = self.utt.temporal_position
# There might be a small difference since the data are saved in float
# while the original values are double precision
self.assertAlmostEqual(((tp_recover - tp)**2).sum(), 0)
def test_rw_vocoder_shift(self):
self.utt.vocoder_shift = 10
shift = self.utt.vocoder_shift
self.assertEqual(shift, 10)
def test_rw_vocoder_window_size(self):
self.utt.vocoder_window_size = 25
w_size = self.utt.vocoder_window_size
self.assertEqual(w_size, 25)
def test_rw_vocoder_window_type(self):
self.utt.vocoder_window_type = "hamming"
w_type = self.utt.vocoder_window_type
self.assertEqual(w_type, "hamming")
def test_rw_num_frame(self):
self.utt.num_frame = 10
num_frame = self.utt.num_frame
self.assertEqual(num_frame, 10)
def test_rw_alpha(self):
self.utt.alpha = 0.42
alpha = self.utt.alpha
self.assertAlmostEqual(alpha, 0.42)
def test_rw_fft_size(self):
self.utt.fft_size = 1024
fft_size = self.utt.fft_size
self.assertEqual(fft_size, 1024)
def test_rw_spec_dim(self):
self.utt.spec_dim = 513
spec_dim = self.utt.spec_dim
self.assertEqual(spec_dim, 513)
def test_rw_mfcc_dim(self):
self.utt.mfcc_dim = 60
mfcc_dim = self.utt.mfcc_dim
self.assertEqual(mfcc_dim, 60)
def test_rw_mcep_dim(self):
self.utt.mcep_dim = 60
mcep_dim = self.utt.mcep_dim
self.assertEqual(mcep_dim, 60)
def test_rw_f0_floor(self):
self.utt.f0_floor = 40
f0_floor = self.utt.f0_floor
self.assertEqual(f0_floor, 40)
def test_rw_f0_ceil(self):
self.utt.f0_ceil = 800
f0_ceil = self.utt.f0_ceil
self.assertEqual(f0_ceil, 800)
def test_rw_timestamp(self):
self.utt.timestamp = '1/25/2019'
timestamp = self.utt.timestamp
self.assertEqual(timestamp, '1/25/2019')
def test_rw_ap_dim(self):
self.utt.ap_dim = 5
ap_dim = self.utt.ap_dim
self.assertEqual(ap_dim, 5)
def test_rw_bap_dim(self):
self.utt.bap_dim = 1
bap_dim = self.utt.bap_dim
self.assertEqual(bap_dim, 1)
def test_rw_pitch_tracker(self):
self.utt.pitch_tracker = 'dio'
pitch_tracker = self.utt.pitch_tracker
self.assertEqual(pitch_tracker, 'dio')
def test_time_to_frame_valid_input(self):
self.assertEqual(utterance.time_to_frame(3.66, 5), 732)
self.assertEqual(utterance.time_to_frame(0, 5), 0)
def test_time_to_frame_invalid_input(self):
try:
utterance.time_to_frame(-10, 5)
except ValueError:
pass
def test_time_to_frame_interval_tier_simple_case(self):
shift = 5
frame_tier = utterance.time_to_frame_interval_tier(self.tier, shift)
self.assertEqual(frame_tier.minTime, 0)
self.assertEqual(frame_tier.maxTime,
utterance.time_to_frame(self.tier.maxTime, shift))
def test_time_to_frame_interval_tier_short_seg(self):
tier = IntervalTier('test', 0, 0.01)
tier.add(0, 0.003, "a")
tier.add(0.003, 0.01, "b")
frame_tier = utterance.time_to_frame_interval_tier(tier, 5)
self.assertEqual(frame_tier.minTime, 0)
self.assertEqual(frame_tier.maxTime, 2)
self.assertEqual(frame_tier.intervals[0].minTime, 0)
self.assertEqual(frame_tier.intervals[0].maxTime, 1)
self.assertEqual(frame_tier.intervals[1].minTime, 1)
self.assertEqual(frame_tier.intervals[1].maxTime, 2)
def test_is_sil(self):
self.assertTrue(utterance.is_sil("sil"))
self.assertTrue(utterance.is_sil("sp"))
self.assertTrue(utterance.is_sil("spn"))
self.assertTrue(utterance.is_sil(""))
self.assertTrue(utterance.is_sil("SIL"))
self.assertTrue(utterance.is_sil("SPN"))
self.assertTrue(utterance.is_sil("SP"))
self.assertFalse(utterance.is_sil("AO"))
self.assertFalse(utterance.is_sil("s"))
def test_normalize_phone(self):
self.assertEqual("ao", utterance.normalize_phone("AO0"))
self.assertEqual("ao", utterance.normalize_phone("AO"))
self.assertEqual("ao", utterance.normalize_phone("AO0, AH, s"))
self.assertEqual("ao,ah,s",
utterance.normalize_phone("AO0, AH, s", False))
self.assertEqual("sil", utterance.normalize_phone("SIL"))
self.assertEqual("sil", utterance.normalize_phone(""))
try:
utterance.normalize_phone("1243")
except ValueError:
pass
def test_normalize_tier_mark(self):
utterance.normalize_tier_mark(self.tier)
utterance.normalize_tier_mark(self.tier, "NormalizePhoneAnnotation")
try:
utterance.normalize_tier_mark(self.tier, "NormalizePhone")
except ValueError:
pass
def test_read_sym_table(self):
sym_table = utterance.read_sym_table("data/phoneme_table")
self.assertEqual(len(sym_table), 40)
def test_get_hardcoded_sym_table(self):
sym_table = utterance.get_hardcoded_sym_table()
correct_sym_table = utterance.read_sym_table("data/phoneme_table")
self.assertTrue(
set(sym_table.items()) == set(correct_sym_table.items()))
def test_get_phone_tier_invalid_0(self):
try:
self.utt.get_phone_tier()
except ValueError:
pass
def test_get_phone_tier_invalid_1(self):
try:
self.utt.kaldi_shift = 5
self.utt.get_phone_tier()
except ValueError:
pass
def test_get_word_tier_invalid_0(self):
try:
self.utt.get_word_tier()
except ValueError:
pass
def test_get_word_tier_invalid_1(self):
try:
self.utt.kaldi_shift = 5
self.utt.get_word_tier()
except ValueError:
pass
def test_get_monophone_ppg_valid(self):
fs, wav = wavfile.read('data/test_mono_channel.wav')
utt = utterance.Utterance(wav, fs)
utt.kaldi_shift = 5
ppgs = utt.get_monophone_ppg()
self.assertEqual(ppgs.shape[1], 40)
def test_get_monophone_ppg_invalid_0(self):
try:
self.utt.get_monophone_ppg()
except ValueError:
pass
def test_get_monophone_ppg_invalid_1(self):
try:
self.utt.kaldi_shift = 5
self.utt.get_monophone_ppg()
except ValueError:
pass
def ctm_to_textgrid(word_ctm, phone_ctm, out_directory, corpus):
if not os.path.exists(out_directory):
os.makedirs(out_directory, exist_ok=True)
if not corpus.segments:
for i,(k,v) in enumerate(word_ctm.items()):
maxtime = corpus.get_wav_duration(k)
try:
tg = TextGrid(maxTime = maxtime)
wordtier = IntervalTier(name = 'words', maxTime = maxtime)
phonetier = IntervalTier(name = 'phones', maxTime = maxtime)
for interval in v:
wordtier.add(*interval)
for interval in phone_ctm[k]:
phonetier.add(*interval)
tg.append(wordtier)
tg.append(phonetier)
if corpus.speaker_directories:
speaker_directory = os.path.join(out_directory, corpus.utt_speak_mapping[k])
else:
speaker_directory = out_directory
os.makedirs(speaker_directory, exist_ok=True)
outpath = os.path.join(speaker_directory, k + '.TextGrid')
tg.write(outpath)
except ValueError as e:
print('Could not write textgrid for {}'.format(k))
print(e)
else:
tgs = {}
for i,(k,v) in enumerate(word_ctm.items()):
rec = corpus.segments[k]
rec, begin, end = rec.split(' ')
maxtime = corpus.get_wav_duration(k)
if rec not in tgs:
tgs[rec] = TextGrid(maxTime = maxtime)
tg = tgs[rec]
begin = float(begin)
speaker = corpus.utt_speak_mapping[k]
word_tier_name = '{} - words'.format(speaker)
phone_tier_name = '{} - phones'.format(speaker)
wordtier = tg.getFirst(word_tier_name)
if wordtier is None:
wordtier = IntervalTier(name = word_tier_name, maxTime = maxtime)
tg.append(wordtier)
phonetier = tg.getFirst(phone_tier_name)
if phonetier is None:
phonetier = IntervalTier(name = phone_tier_name, maxTime = maxtime)
tg.append(phonetier)
for interval in v:
interval = interval[0] + begin, interval[1] + begin, interval[2]
wordtier.add(*interval)
for interval in phone_ctm[k]:
interval = interval[0] + begin, interval[1] + begin, interval[2]
phonetier.add(*interval)
for k,v in tgs.items():
outpath = os.path.join(out_directory, k + '.TextGrid')
try:
v.write(outpath)
except ValueError as e:
print('Could not write textgrid for {}'.format(k))
print(e)
label = ss[4]
result.append([begin, end, label])
return result
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('words_ctm')
parser.add_argument('phones_ctm')
parser.add_argument('output_textgrid')
args = parser.parse_args()
words = read_ctm(args.words_ctm)
phones = read_ctm(args.phones_ctm)
max_time = phones[-1][1]
tg = TextGrid(maxTime=max_time)
word_tier = IntervalTier(name="words", maxTime=max_time)
phone_tier = IntervalTier(name="phones", maxTime=max_time)
for w in words:
word_tier.add(*w)
for p in phones:
phone_tier.add(*p)
tg.append(word_tier)
tg.append(phone_tier)
tg.write(args.output_textgrid)
def ctm_to_textgrid(word_ctm,
phone_ctm,
out_directory,
corpus,
dictionary,
frameshift=0.01):
textgrid_write_errors = {}
frameshift = Decimal(str(frameshift))
if not os.path.exists(out_directory):
os.makedirs(out_directory, exist_ok=True)
if not corpus.segments:
for i, (k, v) in enumerate(sorted(word_ctm.items())):
maxtime = Decimal(str(corpus.get_wav_duration(k)))
speaker = list(v.keys())[0]
v = list(v.values())[0]
try:
tg = TextGrid(maxTime=maxtime)
wordtier = IntervalTier(name='words', maxTime=maxtime)
phonetier = IntervalTier(name='phones', maxTime=maxtime)
for interval in v:
if maxtime - interval[
1] < frameshift: # Fix rounding issues
interval[1] = maxtime
wordtier.add(*interval)
for interval in phone_ctm[k][speaker]:
if maxtime - interval[1] < frameshift:
interval[1] = maxtime
phonetier.add(*interval)
tg.append(wordtier)
tg.append(phonetier)
if corpus.speaker_directories:
speaker_directory = os.path.join(
out_directory, corpus.utt_speak_mapping[k])
else:
speaker_directory = out_directory
os.makedirs(speaker_directory, exist_ok=True)
outpath = os.path.join(speaker_directory, k + '.TextGrid')
tg.write(outpath)
except Exception as e:
exc_type, exc_value, exc_traceback = sys.exc_info()
textgrid_write_errors[k] = '\n'.join(
traceback.format_exception(exc_type, exc_value,
exc_traceback))
else:
silences = {
dictionary.optional_silence, dictionary.nonoptional_silence
}
for i, (filename, speaker_dict) in enumerate(sorted(word_ctm.items())):
maxtime = corpus.get_wav_duration(filename)
try:
tg = TextGrid(maxTime=maxtime)
for speaker, words in speaker_dict.items():
word_tier_name = '{} - words'.format(speaker)
phone_tier_name = '{} - phones'.format(speaker)
word_tier = IntervalTier(name=word_tier_name,
maxTime=maxtime)
phone_tier = IntervalTier(name=phone_tier_name,
maxTime=maxtime)
for w in words:
word_tier.add(*w)
for p in phone_ctm[filename][speaker]:
if len(phone_tier) > 0 and phone_tier[
-1].mark in silences and p[2] in silences:
phone_tier[-1].maxTime = p[1]
else:
if len(phone_tier) > 0 and p[2] in silences and p[
0] < phone_tier[-1].maxTime:
p = phone_tier[-1].maxTime, p[1], p[2]
elif len(phone_tier) > 0 and p[2] not in silences and p[0] < phone_tier[-1].maxTime and \
phone_tier[-1].mark in silences:
phone_tier[-1].maxTime = p[0]
phone_tier.add(*p)
tg.append(word_tier)
tg.append(phone_tier)
tg.write(os.path.join(out_directory, filename + '.TextGrid'))
except Exception as e:
exc_type, exc_value, exc_traceback = sys.exc_info()
textgrid_write_errors[filename] = '\n'.join(
traceback.format_exception(exc_type, exc_value,
exc_traceback))
if textgrid_write_errors:
error_log = os.path.join(out_directory, 'output_errors.txt')
with open(error_log, 'w', encoding='utf8') as f:
f.write(
'The following exceptions were encountered during the ouput of the alignments to TextGrids:\n\n'
)
for k, v in textgrid_write_errors.items():
f.write('{}:\n'.format(k))
f.write('{}\n\n'.format(v))
if interval.mark == '':
continue
print(interval.mark, interval.minTime, interval.maxTime)
outpath = os.path.join(temp_wav_dir, interval.mark + '.wav')
extract_audio(wav_path, outpath, interval.minTime, interval.maxTime, padding = padding)
rep = Mfcc(outpath, freq_lims = (80, 7800), num_coeffs = 12, win_len = 0.025, time_step = 0.01)
rep.is_windowed = True
duration = interval.maxTime - interval.minTime
thresh = unnorm(norm(duration, min_duration, max_duration), min_thresh, max_thresh)
rep.segment(threshold = thresh)
print(sorted(rep._segments.keys()))
padded_begin = interval.minTime - padding
if padded_begin < 0:
padded_begin = 0
for k in sorted(rep._segments.keys()):
with open(os.path.join(temp_mfcc_dir, '{}.mfcc'.format(seg_ind)), 'wb') as fh:
pickle.dump(rep[k[0],k[1]], fh)
with open(os.path.join(temp_mean_dir, '{}.mean'.format(seg_ind)), 'wb') as fh:
pickle.dump(rep._segments[k], fh)
segs.append(str(seg_ind))
seg_ind += 1
begin = round(k[0] + padded_begin, 3)
end = round(k[1] + padded_begin,3)
print(begin, end)
segmentation_tier.add(begin, end, '{}'.format(seg_ind))
with open(os.path.join(temp_align_dir, '{}.seg'.format(f)), 'w') as fa:
fa.write(' '.join(segs))
tg.append(segmentation_tier)
tg.write(textgrid_path.replace(data_dir, temp_textgrid_dir))
wordintervals.append(x)
elif i == 1:
for x in ti:
x.maxTime += cur_dur
x.minTime += cur_dur
phoneintervals.append(x)
cur_dur += maxtime
words = IntervalTier(name='words')
for i in wordintervals:
words.addInterval(i)
phones = IntervalTier(name='phones')
for i in phoneintervals:
phones.addInterval(i)
tg1 = TextGrid(maxTime=cur_dur)
tg1.append(words)
tg1.append(phones)
tg1.write(chapteroutpath1, null='')
speaker_tier = IntervalTier(name=speaker)
for i in range(len(groupedwavfiles)):
if i == 1:
speaker_tier.add(0.0, wavfiletimes[0], groupedlabtext[0])
else:
speaker_tier.add(wavfiletimes[i - 2], wavfiletimes[i - 1],
groupedlabtext[i - 1])
tg2 = TextGrid(maxTime=duration)
tg2.append(speaker_tier)
tg2.write(chapteroutpath2, null='')
num_coeffs=12,
win_len=0.025,
time_step=0.01)
rep.is_windowed = True
duration = interval.maxTime - interval.minTime
thresh = unnorm(norm(duration, min_duration, max_duration), min_thresh,
max_thresh)
rep.segment(threshold=thresh)
print(sorted(rep._segments.keys()))
padded_begin = interval.minTime - padding
if padded_begin < 0:
padded_begin = 0
for k in sorted(rep._segments.keys()):
with open(os.path.join(temp_mfcc_dir, '{}.mfcc'.format(seg_ind)),
'wb') as fh:
pickle.dump(rep[k[0], k[1]], fh)
with open(os.path.join(temp_mean_dir, '{}.mean'.format(seg_ind)),
'wb') as fh:
pickle.dump(rep._segments[k], fh)
segs.append(str(seg_ind))
seg_ind += 1
begin = round(k[0] + padded_begin, 3)
end = round(k[1] + padded_begin, 3)
print(begin, end)
segmentation_tier.add(begin, end, '{}'.format(seg_ind))
with open(os.path.join(temp_align_dir, '{}.seg'.format(f)), 'w') as fa:
fa.write(' '.join(segs))
tg.append(segmentation_tier)
tg.write(textgrid_path.replace(data_dir, temp_textgrid_dir))
elif i == 1: for x in ti: x.maxTime += cur_dur x.minTime += cur_dur phoneintervals.append(x) cur_dur += maxtime words = IntervalTier(name='words') for i in wordintervals: words.addInterval(i) phones = IntervalTier(name='phones') for i in phoneintervals: phones.addInterval(i) tg1 = TextGrid(maxTime = cur_dur) tg1.append(words) tg1.append(phones) tg1.write(chapteroutpath1, null = '') speaker_tier = IntervalTier(name=speaker) for i in range(len(groupedwavfiles)): if i == 1: speaker_tier.add(0.0, wavfiletimes[0], groupedlabtext[0]) else: speaker_tier.add(wavfiletimes[i-2], wavfiletimes[i-1], groupedlabtext[i-1]) tg2 = TextGrid(maxTime = duration) tg2.append(speaker_tier) tg2.write(chapteroutpath2, null = '')
def ctm_to_textgrid(word_ctm,
phone_ctm,
out_directory,
corpus,
dictionary,
frameshift=0.01):
frameshift = Decimal(str(frameshift))
if not os.path.exists(out_directory):
os.makedirs(out_directory, exist_ok=True)
if not corpus.segments:
for i, (k, v) in enumerate(sorted(word_ctm.items())):
maxtime = Decimal(str(corpus.get_wav_duration(k)))
speaker = list(v.keys())[0]
v = list(v.values())[0]
try:
tg = TextGrid(maxTime=maxtime)
wordtier = IntervalTier(name='words', maxTime=maxtime)
phonetier = IntervalTier(name='phones', maxTime=maxtime)
for interval in v:
if maxtime - interval[
1] < frameshift: # Fix rounding issues
interval[1] = maxtime
wordtier.add(*interval)
for interval in phone_ctm[k][speaker]:
if maxtime - interval[1] < frameshift:
interval[1] = maxtime
phonetier.add(*interval)
tg.append(wordtier)
tg.append(phonetier)
if corpus.speaker_directories:
speaker_directory = os.path.join(
out_directory, corpus.utt_speak_mapping[k])
else:
speaker_directory = out_directory
os.makedirs(speaker_directory, exist_ok=True)
outpath = os.path.join(speaker_directory, k + '.TextGrid')
tg.write(outpath)
except ValueError as e:
print(
'There was an error writing the TextGrid for {}, please see below:'
.format(k))
raise
else:
silences = {
dictionary.optional_silence, dictionary.nonoptional_silence
}
for i, (filename, speaker_dict) in enumerate(sorted(word_ctm.items())):
maxtime = corpus.get_wav_duration(filename)
tg = TextGrid(maxTime=maxtime)
for speaker, words in speaker_dict.items():
word_tier_name = '{} - words'.format(speaker)
phone_tier_name = '{} - phones'.format(speaker)
word_tier = IntervalTier(name=word_tier_name, maxTime=maxtime)
phone_tier = IntervalTier(name=phone_tier_name,
maxTime=maxtime)
for w in words:
word_tier.add(*w)
for p in phone_ctm[filename][speaker]:
if len(phone_tier) > 0 and phone_tier[
-1].mark in silences and p[2] in silences:
phone_tier[-1].maxTime = p[1]
else:
if len(phone_tier) > 0 and p[2] in silences and p[
0] < phone_tier[-1].maxTime:
p = phone_tier[-1].maxTime, p[1], p[2]
elif len(phone_tier) > 0 and p[2] not in silences and p[0] < phone_tier[-1].maxTime and \
phone_tier[-1].mark in silences:
phone_tier[-1].maxTime = p[0]
phone_tier.add(*p)
tg.append(word_tier)
tg.append(phone_tier)
tg.write(os.path.join(out_directory, filename + '.TextGrid'))