CrisperWhisper – Speech to Text Model That Transcribes What You Say Verbatim Nyra Labs released CrisperWhisper 2.0, a multilingual speech-to-text model that transcribes speech verbatim or as intended, with word-level timings averaging 30 ms boundary error on read speech and 41 ms on conversational speech. The model tops the Nyra Verbatim Speech Benchmark with a disfluency F1 of 93.5 for the Pro version, ahead of ElevenLabs Scribe v2 (79.2) and Microsoft MAI-Transcribe-1.5 (77.5). The most accurate verbatim speech recognition you can run in production: controllable, multilingual, and timed to the word. Release post https://www.nyra-labs.com/crisperwhisper · Paper https://arxiv.org/abs/2607.18934 · Full documentation /nyrahealth/CrisperWhisper/blob/main/DOCS.md · Models https://huggingface.co/nyralabs · Benchmark https://www.nyra-labs.com/research/nyra-verbatim-speech-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 work across most languages Whisper supports. CrisperWhisper 2.0 tops the Nyra Verbatim Speech Benchmark https://www.nyra-labs.com/research/nyra-verbatim-speech-benchmark leaderboard for disfluency F1 across ten languages, ahead of every closed-source alternative we tested. - Seamless longform. Audio of any length, transcribed without the usual chunk-boundary artifacts: each window continues from the words already transcribed conditional continuation , so there are no duplicated or dropped words at the seams and no fragile timestamp-token bookkeeping. - Production inference. A CTranslate2 runtime with speculative decoding and built-in mitigation of Whisper's looping-hallucination failure mode. The Nyra Verbatim Speech Benchmark https://www.nyra-labs.com/research/nyra-verbatim-speech-benchmark scores fillers, repetitions, cut-offs, and vocal sounds as separate, typed metrics. Its headline number is disfluency F1 : how reliably a system writes down the disfluencies that were actually spoken, without inventing ones that weren't. Averaged over ten languages: | | System | Disfluency F1 | |---|---|---| | 1 | CrisperWhisper 2.0 Pro | 93.5 | | 2 | CrisperWhisper 2.0 | 87.8 | | 3 | ElevenLabs Scribe v2 | 79.2 | | 4 | Microsoft MAI-Transcribe-1.5 | 77.5 | | 5 | CrisperWhisper 1.0 | 64.8 | | 6 | Inworld STT | 59.5 | | 7 | xAI Grok Speech-to-Text | 42.8 | | 8 | Deepgram Nova-3 | 37.8 | | 9 | Fish Audio ASR | 35.0 | | 10 | AssemblyAI Universal-3 Pro | 30.5 |