CrisperWhisper2.0_large

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 · Benchmark repo

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 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.

Performance

The 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:

two languages. English and German use human-labeled evaluation sets; the other eight languages use synthetic verbatim sets. Per-language breakdowns and how the metric is computed are in the benchmark post.

Word-timing accuracy

Mean absolute word-boundary error on read speech (TIMIT), lower is better:

are extracted from supervised cross-attention, plus results on conversational speech, are in the aligner post.

Install

# NVIDIA GPU (Linux): fastest, includes speculative decoding.
# An NVIDIA driver is all you need; CUDA libraries arrive via pip.
pip install "crisperwhisper[ct2]"

# Pure PyTorch: runs anywhere torch does (macOS, Windows, CPU)
pip install "crisperwhisper[transformers]"

Quickstart

from crisperwhisper import CrisperWhisperModel

model = CrisperWhisperModel()          # nyralabs/CrisperWhisper2.0_large
# or pick a size: CrisperWhisperModel("turbo")  # turbo / medium / small

# Verbatim transcription (default): every filler, repetition, stutter,
# false start, and vocal event
result = model.transcribe("meeting.wav", language="en")
print(result.text)

# Intended: the clean, readable version
clean = model.transcribe("meeting.wav", language="en", mode="intended")

# Word-level timestamps
result = model.transcribe("meeting.wav", language="en", word_timestamps=True)
for w in result.words:
    print(f"{w.start:6.2f}-{w.end:6.2f}  {w.word}")

# Verbatimize: upgrade an existing clean transcript with the
# disfluencies that are actually in the audio
result = model.verbatimize("clip.wav", "I think we should ship it Friday.")

Audio longer than 30 seconds is handled automatically (see longform below). The first load of a model downloads it from HuggingFace and, on the ct2 backend, converts it once into a local cache.

Models