QUANTIZATION AND SPARSITY AWARE FINE-TUNING FOR SPEECH RECOGNITION WITH UNIVERSAL SPEECH MODELS
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United States of America
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Mar 6, 2025
app pub date -
Sep 5, 2024
filing date -
Sep 5, 2024
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Abstract
A method includes obtaining a plurality of training samples that each include a respective speech utterance and a respective textual utterance representing a transcription of the respective speech utterance. The method also includes fine-tuning, using quantization and sparsity aware training with native integer operations, a pre-trained automatic speech recognition (ASR) model on the plurality of training samples. Here, the pre-trained ASR model includes a plurality of weights and the fine-tuning includes pruning one or more weights of the plurality of weights using a sparsity mask and quantizing each weight of the plurality of weights based on an integer with a fixed-bit width. The method also includes providing the fine-tuned ASR model to a user device.
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