Scaling Audio Models Efficiently: Joint Optimization of Scale, Resolution, Adaptation, Precision, and Sparsity
-cross Abstract: Large automatic speech recognition (ASR) models such as Whisper must be deployed across hardware with widely varying memory and…
Arxiv 2 versions
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The headline was rewritten. 4 new sentences, beginning: “We present a compression framework that jointly parametrizes Whisper deployment along \emph{six} dimensions: model size,…”
Scaling Audio Models Efficiently: Joint Optimization of Scale, Resolution, Adaptation, Precision, and Sparsity
-cross Abstract: Large automatic speech recognition (ASR) models such as Whisper must be deployed across hardware with widely varying memory and inference-speed constraints. We present a compression framework that jointly parametrizes Whisper deployment along \emph{six} dimensions: model size, temporal resolution, encoder token stride, low-rank adaptation capacity, weight precision and sparsity pattern. All axes are jointly optimized using NSGA-III with respect to three deployment objectives: word error rate (WER), inference FLOPs, and memory footprint. Across 50 of the 1,680 candidate configurations evaluated, we characterize the conditional effect of each axis and identify compression combinations that dominate naive single-axis scaling, while finding that 1:4 structured sparsity fails to recover acceptable accuracy under the tested recovery budgets. We report measured WER and resident memory, use analytical EffFLOPs as the search-time compute surrogate, and separately validate representative inference configurations using measured real-time factor (RTF).
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Scaling Audio Models Efficiently: A Joint Study of Compute Constraints and Optimization Behavior
-cross Abstract: Large automatic speech recognition (ASR) models such as Whisper must be deployed across hardware with widely varying memory and inference-speed constraints. We present a compression framework that jointly parametrizes Whisper deployment along \emph{six} dimensions: model size $x_N$, temporal resolution $x_T$, encoder token stride $x_V$, low-rank adaptation capacity $x_R$, weight precision $x_Q$ and sparsity pattern $x_P$. All axes are jointly optimized against three deployment objectives (word error rate, inference FLOPs, and memory footprint) using a non-dominated sorting genetic evolutionary search (NSGA). Across 50 of the 1,680 candidate configurations evaluated, we measure the marginal effect of each axis on the three objectives and identify compression combinations that dominate naive single-axis scaling, and report a consistent negative result: 1:4 structured sparsity fails to recover acceptable accuracy under any tested recovery budget. We report real, measured memory and accuracy figures for genuinely quantized deployment artifacts, and provide a lookup table mapping deployment scenarios (cloud, server, edge, ultra-constrained) to specific axis configurations with their measured accuracy/memory/compute trade-offs
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What happened
-cross Abstract: Large automatic speech recognition (ASR) models such as Whisper must be deployed across hardware with widely varying memory and inference-speed constraints. We present a compression framework that jointly parametrizes Whisper deployment along \emph{six} dimensions: model size, temporal resolution, encoder token stride, low-rank adaptation capacity, weight precision and sparsity pattern. All axes are jointly optimized using NSGA-III with respect to three deployment objectives: word error rate (WER), inference FLOPs, and memory footprint.
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