nyra labs
Research
How we build speech models that keep what others erase — the methods, the open benchmark, the data engine, and the models themselves.
Featured
CrisperWhisper 2.0
The most accurate verbatim speech recognition you can run in production: controllable, multilingual, and timed to the word.
Longform transcription with conditional continuation
How CrisperWhisper 2.0 transcribes audio longer than 30 seconds: each window is prompted with the last words of the previous one and trained to continue them, with no timestamp tokens and predictable batching.
Measuring verbatimness
An open suite that scores not just the words, but the fillers, repetitions, cut-offs, and vocal sounds that make speech real.
Closing the verbatim data gap
How to upgrade existing spontaneous-speech corpora into faithful verbatim datasets while preserving the transcripts you already trust.
All research
How CrisperWhisper 2.0 transcribes audio longer than 30 seconds: each window is prompted with the last words of the previous one and trained to continue them, with no timestamp tokens and predictable batching.
The most accurate verbatim speech recognition you can run in production: controllable, multilingual, and timed to the word.
An open suite that scores not just the words, but the fillers, repetitions, cut-offs, and vocal sounds that make speech real.
How to upgrade existing spontaneous-speech corpora into faithful verbatim datasets while preserving the transcripts you already trust.
How CrisperWhisper 2.0 stops Whisper from inventing text on silence and from looping, and how we made it fast enough for production with a CTranslate2 port and speculative decoding.
How a handful of decoder-prefix tokens turn transcription style into an explicit switch, activating verbatim and intended transcription that Whisper already latently knew, and transferring it across languages zero-shot.
How CrisperWhisper 2.0 reads millisecond-accurate word timings out of an ASR decoder's cross-attention: by making the output policy explicit, then supervising the heads that already lean toward alignment.