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WhisperKit-0.7.0 VAD Chunking Strategy Evaluation Results
This is an evaluation study to verify that the Voice Activity Detection (VAD) based chunk-and-batch strategy introduced in WhisperKit-0.7.0 does not decrease transcription quality. In order to measure the impact of chunking, we picked a random 10% subset of the earnings22 dataset which comprises corporate earnings call recordings in English with various accents. The long-form nature (>1hr/clip) and the density of speech in these audio clips are intended to stress test VAD accuracy. If VAD is inaccurate, WhisperKit will present speech segments to the Whisper model that start middle-of-speech and cause Whisper to hallucinate at increased rates.
Dataset: earnings22-12hours
Long-Form Audio (>1hr/clip) - ~12 hours of earnings call recordings in English with various accents
with VAD
| WER (↓) | QoI (↑) | File Size (MB) | Code Commit | |
|---|---|---|---|---|
| large-v3_turbo | 11.97 | 100 | 3100 | Link |
| large-v2 | 12.4 | 38.5 | 3100 | Link |
| distil-large-v3 | 12.32 | 23.1 | 1510 | Link |
| small.en | 13.08 | 15.4 | 483 | Link |
| small | 13.27 | 15.4 | 483 | Link |
| base.en | 15.34 | 7.7 | 145 | Link |
| base | 16.62 | 7.7 | 145 | Link |
| tiny.en | 19.02 | 0 | 66 | Link |
| tiny | 21.21 | 0 | 66 | Link |
without VAD
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