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    Cascaded encoders for simplified streaming and non-streaming ASR
    1.
    发明授权
    Cascaded encoders for simplified streaming and non-streaming ASR 有权

    公开(公告)号：US12154581B2

    公开(公告)日：2024-11-26

    申请号：US17237021

    申请日：2021-04-21

    Applicant: Google LLC

    Inventor： Arun Narayanan ,  Tara Sainath ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Rohit Prabhavalkar ,  Jiahui Yu ,  Ehsan Variani ,  Trevor Strohman

    IPC: G10L19/16 ,  G06N3/08 ,  G10L15/00 ,  G10L15/16 ,  G10L15/32 ,  G10L25/30

    Abstract: An automated speech recognition (ASR) model includes a first encoder, a second encoder, and a decoder. The first encoder receives, as input, a sequence of acoustic frames, and generates, at each of a plurality of output steps, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The second encoder receives, as input, the first higher order feature representation generated by the first encoder at each of the plurality of output steps, and generates, at each of the plurality of output steps, a second higher order feature representation for a corresponding first higher order feature frame. The decoder receives, as input, the second higher order feature representation generated by the second encoder at each of the plurality of output steps, and generates, at each of the plurality of time steps, a first probability distribution over possible speech recognition hypotheses.

    Optimizing inference performance for conformer
    4.
    发明授权
    Optimizing inference performance for conformer 有权

    公开(公告)号：US12190869B2

    公开(公告)日：2025-01-07

    申请号：US17936547

    申请日：2022-09-29

    Applicant: Google LLC

    Inventor： Tara N. Sainath ,  Rami Botros ,  Anmol Gulati ,  Krzysztof Choromanski ,  Ruoming Pang ,  Trevor Strohman ,  Weiran Wang ,  Jiahui Yu

    IPC: G10L15/16 ,  G10L15/06 ,  G10L15/22

    Abstract: A computer-implemented method includes receiving a sequence of acoustic frames as input to an automatic speech recognition (ASR) model. Here, the ASR model includes a causal encoder and a decoder. The method also includes generating, by the causal encoder, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method also includes generating, by the decoder, a first probability distribution over possible speech recognition hypotheses. Here, the causal encoder includes a stack of causal encoder layers each including a Recurrent Neural Network (RNN) Attention-Performer module that applies linear attention.

    Systems and Methods for Training Dual-Mode Machine-Learned Speech Recognition Models
    6.
    发明公开
    Systems and Methods for Training Dual-Mode Machine-Learned Speech Recognition Models 审中-公开

    公开(公告)号：US20230237993A1

    公开(公告)日：2023-07-27

    申请号：US18011571

    申请日：2021-10-01

    Applicant: Google LLC

    Inventor： Jiahui Yu ,  Ruoming Pang ,  Wei Han ,  Anmol Gulati ,  Chung-Cheng Chiu ,  Bo Li ,  Tara N. Sainath ,  Yonghui Hu

    IPC: G10L15/16 ,  G10L15/32 ,  G10L15/22

    CPC classification number: G10L15/16 ,  G10L15/32 ,  G10L15/22

    Abstract: Systems and methods of the present disclosure are directed to a computing system, including one or more processors and a machine-learned multi-mode speech recognition model configured to operate in a streaming recognition mode or a contextual recognition mode. The computing system can perform operations including obtaining speech data and a ground truth label and processing the speech data using the contextual recognition mode to obtain contextual prediction data. The operations can include evaluating a difference between the contextual prediction data and the ground truth label and processing the speech data using the streaming recognition mode to obtain streaming prediction data. The operations can include evaluating a difference between the streaming prediction data and the ground truth label and the contextual and streaming prediction data. The operations can include adjusting parameters of the speech recognition model.

    Co-Training of Action Recognition Machine Learning Models
    8.
    发明申请
    Co-Training of Action Recognition Machine Learning Models 有权

    公开(公告)号：US20250037426A1

    公开(公告)日：2025-01-30

    申请号：US18716912

    申请日：2022-12-09

    Applicant: Google LLC

    Inventor： Bowen Zhang ,  Jiahui Yu ,  Christopher Fifty ,  Wei Han ,  Andrew M. Dai ,  Ruoming Pang ,  Fei Sha

    IPC: G06V10/764 ,  G06V10/774

    Abstract: A method includes obtaining video datasets each including pairs of a training video and a ground-truth action classification of the training video. The method also includes generating an action recognition model that includes a shared encoder model and action classification heads. A number of the action classifications heads may be equal to a number of the video datasets, and each action classification head may be configured to, based on an output of the shared encoder model, classify training videos sampled from a corresponding video dataset. The method also includes determining, by the action recognition model and for each training video sampled from the video datasets, an inferred action classification. The method further includes determining a loss value based on the inferred action classifications and the ground-truth action classifications, and adjusting parameters of the action recognition model based on the loss value.

    Vector-Quantized Image Modeling
    10.
    发明公开
    Vector-Quantized Image Modeling 审中-公开

    公开(公告)号：US20240112088A1

    公开(公告)日：2024-04-04

    申请号：US18520083

    申请日：2023-11-27

    Applicant: Google LLC

    Inventor： Jiahui Yu ,  Xin Li ,  Han Zhang ,  Vijay Vasudevan ,  Alexander Yeong-Shiuh Ku ,  Jason Michael Baldridge ,  Yuanzhong Xu ,  Jing Yu Koh ,  Thang Minh Luong ,  Gunjan Baid ,  Zirui Wang ,  Yonghui Wu

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Systems and methods are provided for vector-quantized image modeling using vision transformers and improved codebook handling. In particular, the present disclosure provides a Vector-quantized Image Modeling (VIM) approach that involves pretraining a machine learning model (e.g., Transformer model) to predict rasterized image tokens autoregressively. The discrete image tokens can be encoded from a learned Vision-Transformer-based VQGAN (example implementations of which can be referred to as ViT-VQGAN). The present disclosure proposes multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional image generation, conditioned image generation (e.g., class-conditioned image generation), and unsupervised representation learning.

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