Files
tpro/transcript_processing/converter.py

126 lines
3.1 KiB
Python

import abc
from collections import namedtuple
import os
from . import helpers
from . import converters
Word = namedtuple(
'Word',
'start end confidence word always_capitalized next_word speaker_id')
class TranscriptConverter:
__metaclass__ = abc.ABCMeta
def __init__(self, json_data: dict, language_code='en-US'):
self.json_data = json_data
self.language_code = language_code
def convert(self):
tagged_words = None
word_objects = self.get_word_objects(self.json_data)
words = self.get_words(word_objects)
if self.language_code != 'en-US':
tagged_words = None
else:
tagged_words = helpers.tag_words(words)
self.converted_words = self.convert_words(
word_objects,
words,
tagged_words
)
@property
@abc.abstractmethod
def transcript_type(self):
pass
@abc.abstractmethod
def get_word_objects(self, json_data):
pass
def get_words(self, word_objects):
return [self.get_word_word(w)
for w in word_objects]
@abc.abstractmethod
def convert_words(self, word_objects, words, tagged_words=None):
pass
@staticmethod
@abc.abstractmethod
def get_word_start(word_object):
pass
@staticmethod
@abc.abstractmethod
def get_word_end(word_object):
pass
@staticmethod
@abc.abstractmethod
def get_word_confidence(word_object):
pass
@staticmethod
@abc.abstractmethod
def get_speaker_id(word_object, speaker_segments=None):
pass
@staticmethod
@abc.abstractmethod
def get_word_word(word_object):
pass
@staticmethod
def check_if_always_capitalized(word, index, tagged_words):
if tagged_words is None:
return False
else:
if word.upper() == 'I':
return True
word_category = tagged_words[index][1]
return word_category in helpers.PROPER_NOUN_TAGS
def get_word_object(
self,
word_object,
index,
tagged_words,
word_objects,
speaker_segments=None,
):
word = self.get_word_word(word_object)
return Word(
self.get_word_start(word_object),
self.get_word_end(word_object),
self.get_word_confidence(word_object),
word,
self.check_if_always_capitalized(word, index, tagged_words),
self.get_next_word(word_objects, index),
self.get_speaker_id(word_object, speaker_segments),
)
def get_next_word(self, word_objects, index):
if index < len(word_objects) - 1:
return word_objects[index + 1]
def save(self, path, output_target):
with open(path, 'w') as fout:
fout.write(getattr(self, output_target)())
return path
from . import outputs
for name, val in outputs.__dict__.items():
if callable(val):
setattr(TranscriptConverter, name, val)