公开(公告)号：US20240347043A1

公开(公告)日：2024-10-17

申请号：US18632237

申请日：2024-04-10

Applicant: Google LLC

Inventor： David Qiu ,  David Rim ,  Shaojin Ding ,  Yanzhang He

IPC: G10L15/06

CPC classification number: G10L15/063

Abstract: A method includes obtaining a plurality of training samples, determining a minimum integer fixed-bit width representing a maximum quantization of an automatic speech recognition (ASR) model, and training the ASR model on the plurality of training samples using a quantity of random noise. The ASR model includes a plurality of weights that each include a respective float value. The quantity of random noise is based on the minimum integer fixed-bit value. After training the ASR model, the method also includes selecting a target integer fixed-bit width greater than or equal to the minimum integer fixed-bit width, and for each respective weight of the plurality of weights, quantizing the respective weight from the respective float value to a respective integer associated with a value of the selected target integer fixed-bit width. The operations also include providing the quantized trained ASR model to a user device.

公开(公告)号：US20250078815A1

公开(公告)日：2025-03-06

申请号：US18826135

申请日：2024-09-05

Applicant: Google LLC

Inventor： Shaojin Ding ,  David Qiu ,  David Rim ,  Amir Yazdanbakhsh ,  Yanzhang He ,  Zhonglin Han ,  Rohit Prakash Prabhavalkar ,  Weiran Wang ,  Bo Li ,  Jian Li ,  Tara N. Sainath ,  Shivani Agrawal ,  Oleg Rybakov

IPC: G10L15/06

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.