CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptions

• URL: http://arxiv.org/abs/2408.16589v1
• Date: Thu, 29 Aug 2024 14:52:42 GMT
• Title: CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptions
• Authors: Laurin Wagner, Bernhard Thallinger, Mario Zusag,
• Abstract summary: We fine-tune the model to produce more verbatim speech transcriptions. We employ several techniques to increase robustness against multiple speakers and background noise.
• Score: 0.5120567378386615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We demonstrate that carefully adjusting the tokenizer of the Whisper speech recognition model significantly improves the precision of word-level timestamps when applying dynamic time warping to the decoder's cross-attention scores. We fine-tune the model to produce more verbatim speech transcriptions and employ several techniques to increase robustness against multiple speakers and background noise. These adjustments achieve state-of-the-art performance on benchmarks for verbatim speech transcription, word segmentation, and the timed detection of filler events, and can further mitigate transcription hallucinations. The code is available open https://github.com/nyrahealth/CrisperWhisper.

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