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codes, audio, data, deep, scribe, automatic speech recognition, incorporates", "timecode_offset": null, "timezone": null, "title": "ASR Timed Text Format Test 2", "transcript_updated_at": 1716258713, "unshared": false, "upload_finished": true, "word_clouds": [{"word": "format", "score": "0.060", "variants": ["formats", "format"]}, {"word": "services", "score": "0.035", "variants": ["services", "service"]}, {"word": "features", "score": "0.023", "variants": ["features"]}, {"word": "word", "score": "0.022", "variants": ["word", "words"]}, {"word": "whisper", "score": "0.021", "variants": ["whisper"]}, {"word": "ai", "score": "0.020", "variants": ["ai"]}, {"word": "gentle", "score": "0.020", "variants": ["gentles", "gentle"]}, {"word": "compare", "score": "0.019", "variants": ["comparing", "compare"]}, {"word": "translates", "score": "0.019", "variants": ["translates", "translating"]}, {"word": "assembly", "score": "0.018", "variants": ["assembly"]}, {"word": "api", "score": "0.017", 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5079370651, "start_offset": 0, "end_offset": 1915200, "transcript": "This audio recording is intended for use with various automatic speech recognition services to generate machine transcripts. We will submit the same audio to multiple services to obtain word level time codes indicating when each word in the recording begins and ends. By comparing the word accuracy and timecode accuracy provided by each service, we can analyze the performance of each service on a per word basis. Since the spoken words in the source audio submitted to all ASR services are consistent in wording and timing, we can directly compare how each service formats their word timecode data, understanding the structure of each time text file format, will allow us to develop a converter that translates between them. Initially, we will compare formats from YouTube's automatic captions deep Graham's Nova assembly API is format, gentles format, and a variant of open API's whisper whisper X. We may also consider formats from otter AI, happy scribe, and descript. Although these are paid services and may not be included immediately, our goal is to create a superset timed text format that incorporates all features from the individual formats. This includes translating exclusive features of each format to the syntax of others, even if the original tools for each source format cannot read the extra features by default. For example, whisper deep gram, and assembly AI provide a confidence score for each word, while gentle and YouTube do not. Conversely, gentle offers per phoneme timing data, whereas others only provide per word time codes. Thank you for your attention. We hope this data will shed light on the features of each format", "label": "0", "speaker_id": null, "created_at": "2026-02-08 16:06:42", "speaker_model_label": "1", "speech_id": "25KXE4GTA2IKBAOG", "speaker_edited_at": null, "alignment": [{"word": "This", "start": 0.0, "end": 0.3, "startOffset": 0, "endOffset": 4}, {"word": "audio", "start": 0.33, "end": 0.69, "startOffset": 5, "endOffset": 10}, {"word": "recording", "start": 0.69, "end": 1.2, "startOffset": 11, "endOffset": 20}, {"word": "is", "start": 1.2, "end": 1.35, "startOffset": 21, "endOffset": 23}, {"word": "intended", "start": 1.35, "end": 1.77, "startOffset": 24, "endOffset": 32}, {"word": "for", "start": 1.77, "end": 1.92, "startOffset": 33, "endOffset": 36}, {"word": "use", "start": 1.92, "end": 2.34, "startOffset": 37, "endOffset": 40}, {"word": "with", "start": 2.34, "end": 2.52, "startOffset": 41, "endOffset": 45}, {"word": "various", "start": 2.52, "end": 3.03, "startOffset": 46, "endOffset": 53}, {"word": 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