公开(公告)号：US20240161730A1

公开(公告)日：2024-05-16

申请号：US18421116

申请日：2024-01-24

Applicant: Google LLC

Inventor： Isaac Elias ,  Jonathan Shen ,  Yu Zhang ,  Ye Jia ,  Ron J. Weiss ,  Yonghui Wu ,  Byungha Chun

IPC: G10L13/08 ,  G06F40/126 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  G10L13/047 ,  G10L21/10

CPC classification number: G10L13/08 ,  G06F40/126 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  G10L13/047 ,  G10L21/10 ,  G06N3/048

Abstract: A method for training a non-autoregressive TTS model includes receiving training data that includes a reference audio signal and a corresponding input text sequence. The method also includes encoding the reference audio signal into a variational embedding that disentangles the style/prosody information from the reference audio signal and encoding the input text sequence into an encoded text sequence. The method also includes predicting a phoneme duration for each phoneme in the input text sequence and determining a phoneme duration loss based on the predicted phoneme durations and a reference phoneme duration. The method also includes generating one or more predicted mel-frequency spectrogram sequences for the input text sequence and determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence. The method also includes training the TTS model based on the final spectrogram loss and the corresponding phoneme duration loss.

公开(公告)号：US20240062743A1

公开(公告)日：2024-02-22

申请号：US18499031

申请日：2023-10-31

Applicant: Google LLC

Inventor： Isaac Elias ,  Byungha Chun ,  Jonathan Shen ,  Ye Jia ,  Yu Zhang ,  Yonghui Wu

IPC: G10L13/08 ,  G10L13/04

CPC classification number: G10L13/08 ,  G10L13/04

Abstract: A method for training a non-autoregressive TTS model includes obtaining a sequence representation of an encoded text sequence concatenated with a variational embedding. The method also includes using a duration model network to predict a phoneme duration for each phoneme represented by the encoded text sequence. Based on the predicted phoneme durations, the method also includes learning an interval representation and an auxiliary attention context representation. The method also includes upsampling, using the interval representation and the auxiliary attention context representation, the sequence representation into an upsampled output specifying a number of frames. The method also includes generating, based on the upsampled output, one or more predicted mel-frequency spectrogram sequences for the encoded text sequence. The method also includes determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence and training the TTS model based on the final spectrogram loss.

公开(公告)号：US20220310059A1

公开(公告)日：2022-09-29

申请号：US17643684

申请日：2021-12-10

Applicant: Google LLC

Inventor： Ye Jia ,  Byungha Chun ,  Yu Zhang ,  Jonathan Shen ,  Yonghui Wu

IPC: G10L13/08 ,  G06F40/279 ,  G06F40/263 ,  G06N3/08

Abstract: A method includes receiving a text input including a sequence of words represented as an input encoder embedding. The input encoder embedding includes a plurality of tokens, with the plurality of tokens including a first set of grapheme tokens representing the text input as respective graphemes and a second set of phoneme tokens representing the text input as respective phonemes. The method also includes, for each respective phoneme token of the second set of phoneme tokens: identifying a respective word of the sequence of words corresponding to the respective phoneme token and determining a respective grapheme token representing the respective word of the sequence of words corresponding to the respective phoneme token. The method also includes generating an output encoder embedding based on a relationship between each respective phoneme token and the corresponding grapheme token determined to represent a same respective word as the respective phoneme token.

公开(公告)号：US20220108680A1

公开(公告)日：2022-04-07

申请号：US17492543

申请日：2021-10-01

Applicant: Google LLC

Inventor： Yu Zhang ,  Isaac Elias ,  Byungha Chun ,  Ye Jia ,  Yonghui Wu ,  Mike Chrzanowski ,  Jonathan Shen

IPC: G10L13/027 ,  G10L13/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, synthesizing audio data from text data using duration prediction. One of the methods includes processing an input text sequence that includes a respective text element at each of multiple input time steps using a first neural network to generate a modified input sequence comprising, for each input time step, a representation of the corresponding text element in the input text sequence; processing the modified input sequence using a second neural network to generate, for each input time step, a predicted duration of the corresponding text element in the output audio sequence; upsampling the modified input sequence according to the predicted durations to generate an intermediate sequence comprising a respective intermediate element at each of a plurality of intermediate time steps; and generating an output audio sequence using the intermediate sequence.

公开(公告)号：US20210295858A1

公开(公告)日：2021-09-23

申请号：US17222736

申请日：2021-04-05

Applicant: Google LLC

Inventor： Yonghui Wu ,  Jonathan Shen ,  Ruoming Pang ,  Ron J. Weiss ,  Michael Schuster ,  Navdeep Jaitly ,  Zongheng Yang ,  Zhifeng Chen ,  Yu Zhang ,  Yuxuan Wang ,  Russell John Wyatt Skerry-Ryan ,  Ryan M. Rifkin ,  Ioannis Agiomyrgiannakis

IPC: G10L25/30 ,  G10L13/047 ,  G10L13/08 ,  G06N7/00 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G10L25/18

Abstract: Methods, systems, and computer program products for generating, from an input character sequence, an output sequence of audio data representing the input character sequence. The output sequence of audio data includes a respective audio output sample for each of a number of time steps. One example method includes, for each of the time steps: generating a mel-frequency spectrogram for the time step by processing a representation of a respective portion of the input character sequence using a decoder neural network; generating a probability distribution over a plurality of possible audio output samples for the time step by processing the mel-frequency spectrogram for the time step using a vocoder neural network; and selecting the audio output sample for the time step from the possible audio output samples in accordance with the probability distribution.