Pith. sign in

REVIEW 2 cited by

Reverb: Open-Source ASR and Diarization from Rev

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.03930 v3 pith:OHOJAM7E submitted 2024-10-04 cs.CL cs.SDeess.AS

Nishchal Bhandari , Danny Chen , Miguel Ángel del Río Fernández , Natalie Delworth , Jennifer Drexler Fox , Migüel Jetté , Quinten McNamara , Corey Miller
show 5 more authors
Ondřej Novotný Ján Profant Nan Qin Martin Ratajczak Jean-Philippe Robichaud
This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords modelsrecognitionspeechdiarizationresearchtodayacrosscore
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Today, we are open-sourcing our core speech recognition and diarization models for non-commercial use. We are releasing both a full production pipeline for developers as well as pared-down research models for experimentation. Rev hopes that these releases will spur research and innovation in the fast-moving domain of voice technology. The speech recognition models released today outperform all existing open source speech recognition models across a variety of long-form speech recognition domains.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Mode-tag conditioning on paired verbatim/intended data makes Whisper produce either verbatim or intended transcripts on demand, with cross-lingual disfluency control and improved word timestamps.

  2. Smooth Operators: LLMs Translating Imperfect Hints into Disfluency-Rich Transcripts

    cs.SD 2025-06 conditional novelty 4.0 of 10

    An 8B LLaMa decoder with a Conformer audio encoder generates disfluency tokens and timestamps, and works even when the text hints come from imperfect phoneme or word aligners.

Pith tools