134 lines
3.8 KiB
Python
134 lines
3.8 KiB
Python
from collections import namedtuple
|
|
import json
|
|
|
|
from ..converter import TranscriptConverter
|
|
from .. import helpers
|
|
|
|
|
|
|
|
class SpeechmaticsConverter(TranscriptConverter):
|
|
|
|
name = 'speechmatics'
|
|
|
|
def __init__(self, path):
|
|
super().__init__(path)
|
|
|
|
def get_word_objects(self, json_data):
|
|
return json_data['words']
|
|
|
|
@staticmethod
|
|
def get_word_start(word_object):
|
|
return float(word_object['time'])
|
|
|
|
@staticmethod
|
|
def get_word_end(word_object):
|
|
return (SpeechmaticsConverter.get_word_start(word_object)
|
|
+ float(word_object['duration']))
|
|
|
|
@staticmethod
|
|
def get_word_confidence(word_object):
|
|
return float(word_object['confidence'])
|
|
|
|
@staticmethod
|
|
def get_word_word(word_object):
|
|
return word_object['name']
|
|
|
|
def convert_words(self, word_objects, words, tagged_words=None):
|
|
converted_words = []
|
|
punc_before = False
|
|
punc_after = False
|
|
num_words = len(words)
|
|
|
|
for i, w in enumerate(word_objects):
|
|
word_obj = self.get_word_object(w, i, tagged_words, word_objects)
|
|
if word_obj.word == '.':
|
|
continue
|
|
|
|
if word_obj.next_word:
|
|
next_word = self.get_word_word(word_obj.next_word)
|
|
if next_word == '.':
|
|
punc_after = '.'
|
|
|
|
converted_words.append({
|
|
'start': word_obj.start,
|
|
'end': word_obj.end,
|
|
'confidence': word_obj.confidence,
|
|
'word': word_obj.word,
|
|
'always_capitalized': self.check_if_always_capitalized(
|
|
word_obj.word,
|
|
i,
|
|
tagged_words),
|
|
'punc_after': punc_after,
|
|
'punc_before': punc_before,
|
|
})
|
|
|
|
punc_after = False
|
|
|
|
return converted_words
|
|
|
|
|
|
def speechmatics_aligned_text_converter(data):
|
|
data = data.readlines()[0]
|
|
|
|
class Exhausted(Exception):
|
|
pass
|
|
|
|
Word = namedtuple('Word', 'start end word')
|
|
|
|
def get_time(transcript, index):
|
|
time_index = transcript.find('time=', index)
|
|
if time_index == -1:
|
|
raise Exhausted
|
|
close_index = transcript.find('>', time_index)
|
|
return float(transcript[time_index + 5: close_index]), close_index
|
|
|
|
def find_next_word(transcript, start_index):
|
|
start, end_of_start_index = get_time(transcript, start_index)
|
|
|
|
word_start_index = end_of_start_index + 1
|
|
word_end_index = transcript.find('<', word_start_index)
|
|
word = transcript[word_start_index: word_end_index]
|
|
|
|
end, close_index = get_time(transcript, word_end_index)
|
|
|
|
return Word(start, end, word), close_index
|
|
|
|
words = []
|
|
next_index = 0
|
|
word = None
|
|
|
|
while True:
|
|
try:
|
|
word, next_index = find_next_word(data, next_index)
|
|
except Exhausted:
|
|
break
|
|
else:
|
|
words.append(word)
|
|
|
|
tagged_words = helpers.tag_words([w.word for w in words])
|
|
converted_words = []
|
|
|
|
for i, word in enumerate(words):
|
|
is_proper_noun = tagged_words[i][1] in helpers.PROPER_NOUN_TAGS
|
|
punc_before = helpers.get_punc_before(word.word)
|
|
punc_after = helpers.get_punc_after(word.word)
|
|
the_word = word.word
|
|
if punc_before or punc_after:
|
|
for p in helpers.PUNCTUATION:
|
|
the_word = the_word.replace(p, '')
|
|
converted_words.append({
|
|
'start': word.start,
|
|
'end': word.end,
|
|
'confidence': 1,
|
|
'word': the_word,
|
|
'always_capitalized': self.check_if_always_capitalized(
|
|
word.word,
|
|
i,
|
|
tagged_words),
|
|
'index': i,
|
|
'punc_before': punc_before,
|
|
'punc_after': punc_after,
|
|
})
|
|
|
|
return converted_words
|