def with_alignment_from_ctm(self, ctm_file: Pathlike, type: str = 'word', match_channel: bool = False) -> 'SupervisionSet':
"""
Add alignments from CTM file to the supervision set.
:param ctm: Path to CTM file.
:param type: Alignment type (optional, default = `word`).
:param match_channel: if True, also match channel between CTM and SupervisionSegment
:return: A new SupervisionSet with AlignmentItem objects added to the segments.
"""
ctm_words = []
with open(ctm_file) as f:
for line in f:
reco_id, channel, start, duration, symbol = line.strip().split()
ctm_words.append((reco_id, int(channel), float(start), float(duration), symbol))
ctm_words = sorted(ctm_words, key=lambda x:(x[0], x[2]))
reco_to_ctm = defaultdict(list, {k: list(v) for k,v in groupby(ctm_words, key=lambda x:x[0])})
segments = []
num_total = len(ctm_words)
num_overspanned = 0
for reco_id in set([s.recording_id for s in self]):
if reco_id in reco_to_ctm:
for seg in self.find(recording_id=reco_id):
alignment = [AlignmentItem(symbol=word[4], start=word[2], duration=word[3]) for word in reco_to_ctm[reco_id]
if overspans(seg, TimeSpan(word[2], word[2] + word[3]))
and (seg.channel == word[1] or not match_channel)
]
num_overspanned += len(alignment)
segments.append(fastcopy(seg, alignment={type: alignment}))
else:
segments.append([s for s in self.find(recording_id=reco_id)])
logging.info(f"{num_overspanned} alignments added out of {num_total} total. If there are several"
" missing, there could be a mismatch problem.")
return SupervisionSet.from_segments(segments)
def test_overspans(lhs, rhs, expected):
assert overspans(lhs, rhs) == expected
