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From wearables to powerful smart devices, modern automatic speech recognition(ASR) models run on a variety of edge devices with different computationalbudgets. To navigate the Pareto front of model accuracy vs model size,researchers are trapped in a dilemma of optimizing model accuracy by trainingand fine-tuning models for each individual edge device while keeping thetraining GPU-hours tractable. In this paper, we propose Omni-sparsity DNN,where a single neural network can be pruned to generate optimized model for alarge range of model sizes. We develop training strategies for Omni-sparsityDNN that allows it to find models along the Pareto front of word-error-rate(WER) vs model size while keeping the training GPU-hours to no more than thatof training one singular model. We demonstrate the Omni-sparsity DNN withstreaming E2E ASR models. Our results show great saving on training time andresources with similar or better accuracy on LibriSpeech compared toindividually pruned sparse models: 2%-6.6% better WER on Test-other.

Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet