99 lines
2.4 KiB
Python
99 lines
2.4 KiB
Python
import abc
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import json
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from collections import namedtuple
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import os
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import helpers
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from transcript_processing.converters import converters
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Word = namedtuple('Word', 'start end confidence word is_proper_noun next_word')
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class TranscriptConverter:
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__metaclass__ = abc.ABCMeta
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def __init__(self, path, output_target):
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self.path = path
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self.output_target = output_target
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def convert(self):
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tagged_words = None
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with open(self.path) as f:
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data = json.load(f)
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word_objects = self.get_word_objects(data)
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words = self.get_words(word_objects)
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if self.output_target == 'interactive_transcript':
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tagged_words = helpers.tag_words(words)
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self.converted_words = self.convert_words(
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word_objects,
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words,
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tagged_words
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)
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@staticmethod
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@abc.abstractmethod
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def get_word_objects(json_data):
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pass
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@staticmethod
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@abc.abstractmethod
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def get_words(word_objects):
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pass
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@staticmethod
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@abc.abstractmethod
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def convert_words(word_objects, words, tagged_words=None):
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pass
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@staticmethod
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@abc.abstractmethod
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def get_word_start(word_object):
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pass
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@staticmethod
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@abc.abstractmethod
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def get_word_end(word_object):
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pass
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@staticmethod
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@abc.abstractmethod
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def get_word_confidence(word_object):
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pass
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@staticmethod
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@abc.abstractmethod
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def get_word_word(word_object):
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pass
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@staticmethod
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def check_if_proper_noun(index, tagged_words):
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return tagged_words[index][1] in helpers.PROPER_NOUN_TAGS
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def get_word_object(self, word_object, index, tagged_words, word_objects):
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return Word(
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self.get_word_start(word_object),
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self.get_word_end(word_object),
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self.get_word_confidence(word_object),
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self.get_word_word(word_object),
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self.check_if_proper_noun(index, tagged_words),
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self.get_next_word(word_objects, index)
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)
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def get_next_word(self, word_objects, index):
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if index < len(word_objects) - 1:
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return word_objects[index + 1]
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def to_json(self):
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return json.dumps(self.converted_words, indent=4)
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def save(self, path):
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with open(path, 'w') as fout:
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fout.write(self.to_json())
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return path
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