Search Results for "git:/git.code.sf.net/p/docfetcher/code"
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Showing 11 open source projects for "git:/git.code.sf.net/p/docfetcher/code"
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whisper.cpp
Port of OpenAI's Whisper model in C/C++
whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently. -
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Omnilingual ASR
Omnilingual ASR Open-Source Multilingual SpeechRecognition
...It emphasizes modularity: acoustic modeling, language modeling, tokenization, and decoding are separable pieces you can swap or ablate. The repo is aimed at pushing practical multilingual ASR—robust to accents, code-switching, and domain shifts—rather than language-by-language systems. For practitioners, it’s a starting point to study transfer, zero-shot behavior, and trade-offs between model size, compute cost, and coverage. -
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wav2letter++
Facebook AI research's automatic speech recognition toolkit
...All results reproduction must use Flashlight <= 0.3.2 for exact reproducibility. At least one of LZMA, BZip2, or Z is required for LM compression with KenLM. It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. After installing, run export KENLM_ROOT_DIR=... so that wav2letter++ can find it. This is needed because KenLM doesn't support a make install step.wav2letter++ expects audio and transcription data to be prepared in a specific format so that they can be read from the pipelines. Each dataset (test/valid/train) needs to be in a separate file with one sample per line. ... -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results. Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. ... -
Keep company data safe with Chrome EnterpriseMake AI work your way with Chrome Enterprise. Block unapproved sites and set custom data controls that align with your company's policies.
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
...To be able to follow the exercises, you are going to need a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed. The following instruction would work as is for Mac or Ubuntu Linux users, Windows users would need to install and work in the Git BASH terminal. JupyterLab has a built-in selectable dark theme, so you only need to install something if you want to use the classic notebook interface. -
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
Tensorpack is a neural network training interface based on TensorFlow v1. Uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. ... -
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NASH OS
Nash Operating System for Modern Ecommerce
The all-built-in-one, automatic, ready-to-go out-of-box, easy-to-use state-of-the-art, and really awesome NASH OS! Over 25,000+ flexible features and controls and all scalable!! The most powerful solution ever built to instantly deliver new heights of online ecommerce enterprise to you. -
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Lip Reading
Cross Audio-Visual Recognition using 3D Architectures
The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. -
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C# Speech Recognition Tutorial
C# Speech Recognition Tutorial
This is an easy (as can be) tutorial to show how speech recognition is done with in C#. On the form the button is pressed, and within 5 seconds say your speech. In this example Q and B act as commands. The code filters the recognised words looking for the letter Q and B. File contains the source code-use this to make the simple form with the named elements in the image-in a new winforms program. The pdf file in the zip file explains how to link the voice recognition to a database. -
Desktop and Mobile Device Management SoftwareDesktop Central is a unified endpoint management (UEM) solution that helps in managing servers, laptops, desktops, smartphones, and tablets from a central location.
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Speech Recognition System
Speech Recognition System - Matlab source code
Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these... -
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InproTK
An Incremental Spoken Dialogue Processing Toolkit
InproTK is an Incremental Spoken Dialogue Processing Toolkit, that is, a toolkit to help you build dialogue systems that listen and talk incrementally, allowing for advanced interactional behaviour. Please see our Wiki for more information: http://sourceforge.net/p/inprotk/wiki/
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