brought over changes from when transcript_processing was nested inside transcribely's back_end package. started refactoring converters into OOP

converters/speechmatics.py

@@ -0,0 +1,113 @@
+from collections import namedtuple
+import json
+
+from transcript_processing import helpers
+
+
+Word = namedtuple('Word', 'start end word')
+
+
+def speechmatics_converter(data):
+    data = json.load(data)
+    converted_words = []
+    words = data['words']
+    tagged_words = helpers.tag_words([w['name'] for w in words])
+    punc_before = False
+    punc_after = False
+    num_words = len(words)
+    index = 0
+
+    for i, w in enumerate(words):
+        word_start = float(w['time'])
+        word_end = word_start + float(w['duration'])
+        confidence = float(w['confidence'])
+        word = w['name']
+        if word == '.':
+            continue
+        is_proper_noun = tagged_words[i][1] in helpers.PROPER_NOUN_TAGS
+
+        next_word = None
+        if i < num_words - 1:
+            next_word = words[i + 1]['name']
+        if next_word == '.':
+            punc_after = '.'
+
+        converted_words.append({
+            'start': word_start,
+            'end': word_end,
+            'confidence': confidence,
+            'word': word,
+            'always_capitalized': is_proper_noun or word == 'I',
+            'index': index,
+            'punc_after': punc_after,
+            'punc_before': punc_before,
+        })
+
+        index += 1
+        punc_after = False
+
+    return converted_words
+
+
+def speechmatics_aligned_text_converter(data):
+    data = data.readlines()[0]
+
+    class Exhausted(Exception):
+        pass
+
+    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': is_proper_noun or word == 'I',
+            'index': i,
+            'punc_before': punc_before,
+            'punc_after': punc_after,
+        })
+
+    return converted_words
+
+
+def gentle_converter