Abstract: A speech-processing system receives input data representing text. One or more encoders trained to predict audio properties corresponding to the text process the text to predict those properties. A speech decoder processes phoneme embeddings as well as the predicted properties to create data representing synthesized speech.

Abstract: Devices and techniques are generally described for learned condition text-to-speech synthesis. In some examples, first data representing a selection of a type of prosodic expressivity may be received. In some further examples, a selection of content comprising text data may be received. First audio data may be determined that includes an audio representation of the text data. The first audio data may be generated based at least in part on sampling from a first latent distribution generated using a conditional primary variational autoencoder (VAE). The sampling from the first latent distribution may be conditioned on a first learned distribution associated with the type of prosodic expressivity. In various examples, the first audio data may be sent to a first computing device.

Abstract: Techniques for performing text-to-speech are described. An exemplary method includes receiving a request to generate audio from input text; generating audio from the input text by: generating a first number of vectors from phoneme embeddings representing the input text, predicting one or more spectrograms having the first number of frames using multiple scales wherein a coarser scale influences a finer scale, concatenating the first number of vectors and the predicted one or more spectrograms, generating at least one mel spectrogram from the concatenated vectors and the predicted one or more spectrograms, and converting, with a vocoder, the at least one mel spectrogram frames to audio; and outputting the generated audio according to the request.

Abstract: A speech-processing system receives input data representing text. An input encoder processes the input data to determine first embedding data representing the text. A local attention encoder processes a subset of the first embedding data in accordance with a predicted size to determine second embedding data. An attention encoder processes the second embedding data to determine third embedding data. A decoder processes the third embedding data to determine audio data corresponding to the text.

Abstract: Techniques for utilizing memory for a neural network are described. For example, some techniques utilize a plurality of memory types to respond to a query from a neural network including a short-term memory to store fine-grained information for recent text of a document and receiving a first value in response, an episodic long-term memory to store information discarded from the short-term memory in a compressed form and receiving a second value in response, and a semantic long-term memory to store relevant facts per entity in the document.

Abstract: The present disclosure provides a natural plant preparation, including extracts of the following parts of plants: roots of Salvia miltiorrhiza Bunge, Juglans regia, Aesculus hippocastanum L. and Zingiber officinale Rosc., barks of Cinnamomum cassia Presl, seeds of Sesamum indicum, Carica papaya L., Nigella damascena L. and Punica granatum L., leaves of Centaurea cyanus L., resin of Pistacia Lentiscus, drupes of Cocos nucifera L., beans of Theobroma cacao L., dried pulp and kernels of Phoenix dactylifera L., dried pulp of Olea europaea, Ficus carica Linn., Walnut and Vaccinium Spp, cloves of Allium sativum, and petals of Rosa sp. The present disclosure further provides a method for preparing a plant preparation, including treating the above parts of plants with ethanol.

Abstract: The present disclosure provides a natural plant preparation, including extracts of the following parts of plants: roots of Salvia miltiorrhiza Bunge, Juglans regia, Aesculus hippocastanum L. and Zingiber officinale Rosc., barks of Cinnamomum cassia Presl, seeds of Sesamum indicum, Carica papaya L., Nigella damascena L. and Punica granatum L., leaves of Centaurea cyanus L., resin of Pistacia Lentiscus, drupes of Cocos nucifera L., beans of Theobroma cacao L., dried pulp and kernels of Phoenix dactylifera L., dried pulp of Olea europaea, Ficus carica Linn., Walnut and Vaccinium Spp, cloves of Allium sativum, and petals of Rosa sp. The present disclosure further provides a method for preparing a plant preparation, including treating the above parts of plants with ethanol.

Abstract: A device may receive a request to establish a connection between a first device and a second device. The request may include information identifying a network connectivity type. The device may determine one or more network devices to provision based on the network connectivity type; determine, based on the network connectivity type, provisioning parameters used to provision the one or more network devices; and provision the one or more network devices in accordance with the provisioning parameters to establish the connection between the first device and the second device.

Abstract: An intelligent network interconnect may include a control channel and a plurality of nodes. The plurality of nodes may include a first node coupled to a first network and a second node coupled to a second network. Each of the plurality of nodes is coupled to the control channel. The intelligent network interconnect may also include a control device coupled to the control channel. The intelligent network interconnect may be configured to: collect network data from the first node and the second node, wherein the network data includes traffic data of the first network; obtain metrics based on the collected network data; detect an event based on the metrics and the collected network data; and a rule whose condition matches the event; and send a command over the control channel, to one or more of the nodes, to perform an action associated with the rule.

Abstract: A server may receive flow information from multiple network devices. The flow information may include information regarding multiple data flows received by the multiple network devices and destined for a client device. The server may determine generate an optimization instruction, based on flow information, to re-route one or more of the plurality of data flows or to apply filters to the one or more of the plurality of data flows; provide the optimization instruction to cause one or more of the multiple network devices to re-route one or more of the multiple data flows or to apply filters to the one or more data flows to alleviate overloaded network components or to re-route network resources to or from the client device; receive updated flow information after receiving the flow information; generate an updated optimization instruction based on the updated flow information; and provide the updated optimization instruction.

Abstract: A device may receive a request to establish a connection between a first device and a second device. The request may include information identifying a network connectivity type. The device may determine one or more network devices to provision based on the network connectivity type; determine, based on the network connectivity type, provisioning parameters used to provision the one or more network devices; and provision the one or more network devices in accordance with the provisioning parameters to establish the connection between the first device and the second device.

Abstract: An intelligent network interconnect may include a control channel and a plurality of nodes. The plurality of nodes may include a first node coupled to a first network and a second node coupled to a second network. Each of the plurality of nodes is coupled to the control channel. The intelligent network interconnect may also include a control device coupled to the control channel. The intelligent network interconnect may be configured to: collect network data from the first node and the second node, wherein the network data includes traffic data of the first network; obtain metrics based on the collected network data; detect an event based on the metrics and the collected network data; and a rule whose condition matches the event; and send a command over the control channel, to one or more of the nodes, to perform an action associated with the rule.

Abstract: A server may receive flow information from multiple network devices. The flow information may include information regarding multiple data flows received by the multiple network devices and destined for a client device. The server may determine generate an optimization instruction, based on flow information, to re-route one or more of the plurality of data flows or to apply filters to the one or more of the plurality of data flows; provide the optimization instruction to cause one or more of the multiple network devices to re-route one or more of the multiple data flows or to apply filters to the one or more data flows to alleviate overloaded network components or to re-route network resources to or from the client device; receive updated flow information after receiving the flow information; generate an updated optimization instruction based on the updated flow information; and provide the updated optimization instruction.