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Turning Whisper into Real-Time Transcription System

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arxiv 2307.14743 v2 pith:RTBB62GF submitted 2023-07-27 cs.CL

Dominik Macháček , Raj Dabre , Ondřej Bojar This is my paper
classification cs.CL
keywords transcriptionspeechwhisperwhisper-streaminglatencymodelsmultilingualreal-time
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SimulS2ST-Omni: Data-Efficient Streaming Speech-to-Speech Translation via Explicit Trajectory Supervision

    cs.SD 2026-07 conditional novelty 6.0 of 10

    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.

  2. WhisperRT -- Turning Whisper into a Causal Streaming Model

    cs.CL 2025-08 conditional novelty 6.0 of 10

    WhisperRT converts Whisper to a causal streaming ASR model via encoder causality, decoder synchronization on partial states, and fine-tuning, achieving better performance than non-fine-tuned streaming methods on sub-3...

  3. WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition

    cs.CL 2026-04 unverdicted novelty 5.0 of 10

    WhisperPipe delivers 89 ms median latency and 48% lower peak GPU memory than standard Whisper while keeping word error rate within 2% of the offline model.

  4. WhisperKit: On-device Real-time ASR with Billion-Scale Transformers

    cs.SD 2025-07 conditional novelty 5.0 of 10

    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.

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