Varley, James Richard

The systems, methods and apparatus described herein are directed towards at least the use of a two-stage augmentation process for vocal audio samples to generate a training dataset for a machine-learning embedding model, the training of machine-learning embedding models on such training data, and the use of such a trained machine-learning embedding models to generate embedding of audio snippets for downstream tasks. The two-stage augmentation process has a first augmentation stage, in which strong augmentations are applied at an audio sample level, and a second augmentation stage, in which weak augmentations are applied at an audio snippet level, where an audio snippet is a section of audio extracted from an audio sample after the first augmentations are applied.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for updating a local operating state of a first user device. In one aspect, a method includes identifying a context of the first user device; identifying a second user device within a proximity of the first user device; determining relevant aspects of a local operating state of the first user device that are relevant to the context; performing an automated data exchange session between a first agent executing on the first user device and a second agent executing on the second user device to determine one or more actions that each define an update to the local operating state; and performing the one or more actions by the first user device to update the local operating state of the first user device.

This specification relates to processing data using machine learning models. A computer implemented method is provided comprising: receiving an input query; and generating an output. The generating comprises, for a plurality of iterations: generating, using a machine-learning model, a data element for the iteration based on the input query, wherein the data element comprises: (i) an output data element indicative of an output of the machine-learning model; and/or (ii) an interrupt data element indicative of one or more interrupt options. For one or more of the plurality of iterations, the method comprises: in response to determining that the data element for the iteration and/or a previous iteration is an output data element, causing the output indicated by the output data element to be output; and in response to determining that the generative data element for the iteration and/or a previous iteration is an interrupt data element, causing the one or more interrupt options indicated by the interrupt data to be exposed via an interface. The method further comprises, during generation of the output: receiving, via the interface, data indicative of a selection of at least one interrupt option of the one or more interrupt options; and adapting the generation of the output based on the at least one selected interrupt option.

Computer implemented method comprising: obtaining an audio stem sample, wherein the audio stem sample comprises audio originating from one or more sources of a particular type in an audio sample; and generating, using a machine-learning embedding model, an embedding of the audio stem sample in an embedding space. Computer implemented method comprising: obtaining an embedding of an audio stem sample, wherein the audio stem sample comprises audio originating from one or more sources of a particular type in an audio sample, inputting the embedding of the audio stem sample into a machine-learning generative model; and generating, by the machine-learning generative model and conditioned on the embedding of the audio stem sample, output audio data comprising an output audio stem, wherein the output audio stem has one or more audio properties in common with the audio stem sample.

Implementations relate to depersonalizing and/or anonymizing generative model inputs and/or outputs to remove sensitive and/or private information, such as PII (personally identifiable information). In various implementations, a generative model input prompt may be retrieved from local memory of the edge computing device and further processed using one or more generative models to generate generative model output. The generative model input prompt may be submitted to an edge-based anonymization process including assembling, as an anonymization prompt and processing the anonymization prompt using one or more on-device generative models of the edge computing device to generate an anonymized version of the portion of the generative model input prompt that is stripped of PII. The anonymized version may further be inspected of any remaining PII and the fully anonymized version may be surfaced without concern of leaking PII.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for increasing sparsity to improve neural network efficiency. In some implementations, a system stores parameter values of parameter matrices of one or more layers of a neural network. The parameter values of the parameter matrices include (i) weight values of the one or more layers of the neural network, and (ii) predictor values that have been trained to predict levels of importance of items processed by the neural network. The system generates an output, including: determining a value for each of multiple items using the predictor values, selecting a proper subset of the items based on the values in the vector based on a threshold, and generating output of the one or more layers limiting computation based on the selected proper subset.

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