Big model only for hard audios: Sample dependent Whisper model selection
for efficient inferences
- URL: http://arxiv.org/abs/2309.12712v1
- Date: Fri, 22 Sep 2023 08:50:58 GMT
- Title: Big model only for hard audios: Sample dependent Whisper model selection
for efficient inferences
- Authors: Hugo Malard, Salah Zaiem, Robin Algayres
- Abstract summary: Several ASR models exist in various sizes, with different inference costs leading to different performance levels.
We propose to train a decision module, that would allow, given an audio sample, to use the smallest sufficient model leading to a good transcription.
By keeping the decision process computationally efficient, we build a decision module that allows substantial computational savings with reduced performance drops.
- Score: 7.592727209806414
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent progress in Automatic Speech Recognition (ASR) has been coupled with a
substantial increase in the model sizes, which may now contain billions of
parameters, leading to slow inferences even with adapted hardware. In this
context, several ASR models exist in various sizes, with different inference
costs leading to different performance levels. Based on the observation that
smaller models perform optimally on large parts of testing corpora, we propose
to train a decision module, that would allow, given an audio sample, to use the
smallest sufficient model leading to a good transcription. We apply our
approach to two Whisper models with different sizes. By keeping the decision
process computationally efficient, we build a decision module that allows
substantial computational savings with reduced performance drops.
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