REVIEW 3 cited by
Turning Whisper into Real-Time Transcription System
Not yet reviewed by Pith; the record is open.
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
Signed reviews
read the original abstract
Whisper is one of the recent state-of-the-art multilingual speech recognition and translation models, however, it is not designed for real time transcription. In this paper, we build on top of Whisper and create Whisper-Streaming, an implementation of real-time speech transcription and translation of Whisper-like models. Whisper-Streaming uses local agreement policy with self-adaptive latency to enable streaming transcription. We show that Whisper-Streaming achieves high quality and 3.3 seconds latency on unsegmented long-form speech transcription test set, and we demonstrate its robustness and practical usability as a component in live transcription service at a multilingual conference.
Forward citations
Cited by 3 Pith papers
-
SimulS2ST-Omni: Data-Efficient Streaming Speech-to-Speech Translation via Explicit Trajectory Supervision
A joint text-code trajectory supervision recipe lets a two-stream speech LM achieve competitive long-form streaming S2ST with ~2k hours of paired speech.
-
WhisperFlow: speech foundation models in real time
WhisperFlow combines a learned 'hush word', beam pruning, and CPU/GPU pipelining to cut streaming Whisper latency by 1.6x-4.7x on client devices with near-unchanged accuracy.
-
WhisperKit: On-device Real-time ASR with Billion-Scale Transformers
WhisperKit's optimized on-device Whisper Large v3 Turbo streaming system reportedly achieves 0.46 s per-word latency and 2.2% WER, beating cloud baselines in its benchmark.
Discussion (0). Continue with ORCID to comment.