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wav2letter is an automatic speech recognition toolkit and deep learning framework designed to convert audio speech signals into written text. It functions as a distributed training system and an inference engine for building and deploying neural network architectures. The system enables the training of large-scale speech models across multiple compute nodes using custom architecture files and structured recipes. It includes an inference engine that allows these trained models to be executed within Python workflows to transform audio sequences into text. The framework covers the full speech r
Filter design, periodograms, window functions, and other digital signal processing functionality
A package to deal with graphs built on images.
The main features of biosignalsplux/biosignalsnotebooks are: Computer Vision and Signal Processing.
Projects with overlapping indexed features include: clementfarabet/videograph — This package extends imgraph, for videos... facebookresearch/wav2letter — wav2letter is an automatic speech recognition toolkit and deep learning framework designed to convert audio speech… juliadsp/dsp.jl — Filter design, periodograms, window functions, and other digital signal processing functionality. juliaimages/images.jl — An image library for Julia. koraykv/fex — feature extractor library for torch7. clementfarabet/lua---imgraph — A package to deal with graphs built on images.