Showing 55 open source projects for "python samples"

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    Deepvoice3_pytorch

    Deepvoice3_pytorch

    PyTorch implementation of convolutional neural networks

    An open source implementation of Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.
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  • 2
    Siamese and triplet learning

    Siamese and triplet learning

    Siamese and triplet networks with online triplet mining in PyTorch

    Siamese and triplet learning is a PyTorch implementation of Siamese and triplet neural network architectures designed for learning embedding representations in machine learning tasks. These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train...
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  • 3
    DC-TTS

    DC-TTS

    TensorFlow Implementation of DC-TTS: yet another text-to-speech model

    DC-TTS is a TensorFlow implementation of the DC-TTS architecture, a fully convolutional text-to-speech system designed to be efficiently trainable while producing natural speech. It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN...
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  • 4

    Training Image Operators from Samples

    Tools to train Image Operators automatically from a set of samples.

    TRIOS - Training Image Operators from Samples is a set of tools to bring Image Processing closer to scientists in general. It is capable of estimating an operator between two images using only pairs of samples that contain an input image and the desired output. The operator is saved to a file and can be applied to any image.
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  • 5
    This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
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