{"repo":"nyrahealth/CrisperWhisper","free":true,"listed":false,"github":"https://github.com/nyrahealth/CrisperWhisper","clone":"git clone https://github.com/nyrahealth/CrisperWhisper.git","description":"Controllable Transcription. Verbatim ( every, filler, pause, stutter, vocal sound) , or intended ( what the speaker meant to say, optimized for readability) with word-level timestamps.","language":"Python","stars":1327,"topics":["asr","audio","detection","filler","recognition","speech","speech-recognition","timestamps","transcription","verbatim"],"license":null,"category":"media-processing","readme_excerpt":"CrisperWhisper 2.0 The most accurate verbatim speech recognition you can run in production: controllable, multilingual, and timed to the word. Release post · Paper · Full documentation · Models · Benchmark Most speech-to-text systems never actually decide whether to write down what was said or what was meant . They inherit that choice from their training data and apply it inconsistently. CrisperWhisper 2.0 makes it an explicit, controllable choice. One recording, two transcripts: Verbatim , exactly what was said, in one consistent format: [um] so we we need to, to reschedule the th- thursday meeting to [uh] march third at nine thirty [laughter] Intended , the clean version the speaker meant, with numbers, dates, and emails formatted the way you'd write them: So we need to reschedule the Thursday meeting to March 3 at 9:30. On top of that: - Word-level timings. Around 30 ms mean boundary error on read speech and 41 ms on conversational speech, the most precise word timing of any system we benchmarked, on both. - Verbatimize. Upgrade transcripts you already have: given audio plus a trusted clean transcript, the model reproduces your content word-for-word and inserts only the disfluencies and vocal events actually present in the audio (rare-word recall jumps from 6.8% to 96.1% vs. re-transcribing). This turns the world's abundant clean corpora into verbatim ones, ready for TTS data, clinical speech analysis, and dataset construction. - Multilingual. Verbatim and intended modes w","default_branch":null,"files":null,"tree":[],"storefront":"/r/nyrahealth","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/nyrahealth/CrisperWhisper/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}