Files
tpro/transcript_processing/converters/amazon.py

87 lines
2.6 KiB
Python

import json
from ..converter import TranscriptConverter
from .. import helpers
class AmazonConverter(TranscriptConverter):
name = 'amazon'
def __init__(self, json_data):
super().__init__(json_data)
def get_word_objects(self, json_data):
return json_data['results']['items']
@staticmethod
def get_word_start(word_object):
return float(word_object['start_time'])
@staticmethod
def get_word_end(word_object):
return float(word_object['end_time'])
@staticmethod
def get_word_confidence(word_object):
return float(word_object['alternatives'][0]['confidence'])
@staticmethod
def get_word_word(word_object) -> str:
word_word = word_object['alternatives'][0]['content']
if word_word == 'i':
# weird Amazon quirk
word_word = 'I'
return word_word
@staticmethod
def get_speaker_id(word_object):
return None
def convert_words(self, word_objects, words, tagged_words=None):
converted_words = []
punc_before = False
punc_after = False
for i, w in enumerate(word_objects):
if w['type'] == 'punctuation':
continue
next_word_punc_after = None
word_obj = self.get_word_object(w, i, tagged_words, word_objects)
if word_obj.next_word:
next_word = self.get_word_word(word_obj.next_word)
next_word_type = word_obj.next_word['type']
if next_word in ['.', ',']:
punc_after = next_word
elif next_word_punc_after:
punc_after = next_word_punc_after
next_word_punc_after = None
if word_obj.word.lower() == 'you' and next_word == 'know':
prev_word = word_objects[i - 1]
if prev_word['type'] != 'punctuation':
converted_words[-1]['punc_after'] = ','
if next_word_type != 'punctuation':
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,
'speaker_id': word_obj.speaker_id,
})
punc_after = False
return converted_words