pytorch-speech-commands - Speech commands recognition with PyTorch

  •        83

Convolutional neural networks for Google speech commands data set with PyTorch. We, xuyuan and tugstugi, have participated in the Kaggle competition TensorFlow Speech Recognition Challenge and reached the 10-th place. This repository contains a simplified and cleaned up version of our team's code.



Related Projects

deep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

  •    Jupyter

Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

LightNet - LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

  •    Python

This repository contains the code (in PyTorch) for: "LightNet: Light-weight Networks for Semantic Image Segmentation " (underway) by Huijun Liu @ TU Braunschweig. Semantic Segmentation is a significant part of the modern autonomous driving system, as exact understanding the surrounding scene is very important for the navigation and driving decision of the self-driving car. Nowadays, deep fully convolutional networks (FCNs) have a very significant effect on semantic segmentation, but most of the relevant researchs have focused on improving segmentation accuracy rather than model computation efficiency. However, the autonomous driving system is often based on embedded devices, where computing and storage resources are relatively limited. In this paper we describe several light-weight networks based on MobileNetV2, ShuffleNet and Mixed-scale DenseNet for semantic image segmentation task, Additionally, we introduce GAN for data augmentation[17] (pix2pixHD) concurrent Spatial-Channel Sequeeze & Excitation (SCSE) and Receptive Field Block (RFB) to the proposed network. We measure our performance on Cityscapes pixel-level segmentation, and achieve up to 70.72% class mIoU and 88.27% cat. mIoU. We evaluate the trade-offs between mIoU, and number of operations measured by multiply-add (MAdd), as well as the number of parameters.

tensorflow-speech-recognition - 🎙Speech recognition using the tensorflow deep learning framework, sequence-to-sequence neural networks

  •    Python

Speech recognition using google's tensorflow deep learning framework, sequence-to-sequence neural networks. Replaces caffe-speech-recognition, see there for some background.

NCRFpp - NCRF++, an Open-source Neural Sequence Labeling Toolkit

  •    Python

Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.

ResNeXt-DenseNet - PyTorch Implementation for ResNet, Pre-Activation ResNet, ResNeXt, DenseNet, and Group Normalisation

  •    Python

PyTorch Implementation for ResNet, Pre-Activation ResNet, ResNeXt, DenseNet, and Group Normalisation

Kur - Descriptive Deep Learning

  •    Python

Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.

grokking-pytorch - The Hitchiker's Guide to PyTorch


PyTorch is a flexible deep learning framework that allows automatic differentiation through dynamic neural networks (i.e., networks that utilise dynamic control flow like if statements and while loops). It supports GPU acceleration, distributed training, various optimisations, and plenty more neat features. These are some notes on how I think about using PyTorch, and don't encompass all parts of the library or every best practice, but may be helpful to others. Neural networks are a subclass of computation graphs. Computation graphs receive input data, and data is routed to and possibly transformed by nodes which perform processing on the data. In deep learning, the neurons (nodes) in neural networks typically transform data with parameters and differentiable functions, such that the parameters can be optimised to minimise a loss via gradient descent. More broadly, the functions can be stochastic, and the structure of the graph can be dynamic. So while neural networks may be a good fit for dataflow programming, PyTorch's API has instead centred around imperative programming, which is a more common way for thinking about programs. This makes it easier to read code and reason about complex programs, without necessarily sacrificing much performance; PyTorch is actually pretty fast, with plenty of optimisations that you can safely forget about as an end user (but you can dig in if you really want to).

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

  •    Python

PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.

Facial-Similarity-with-Siamese-Networks-in-Pytorch - Implementing Siamese networks with a contrastive loss for similarity learning

  •    Jupyter

The goal is to teach a siamese network to be able to distinguish pairs of images. This project uses pytorch. Any dataset can be used. Each class must be in its own folder. This is the same structure that PyTorch's own image folder dataset uses.

pytorch-cnn-finetune - Fine-tune pretrained Convolutional Neural Networks with PyTorch

  •    Python

VGG and AlexNet models use fully-connected layers, so you have to additionally pass the input size of images when constructing a new model. This information is needed to determine the input size of fully-connected layers. See examples/ file (requires PyTorch 0.4).

espnet - End-to-End Speech Processing Toolkit

  •    Shell

ESPnet is an end-to-end speech processing toolkit, mainly focuses on end-to-end speech recognition, and end-to-end text-to-speech. ESPnet uses chainer and pytorch as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. To use cuda (and cudnn), make sure to set paths in your .bashrc or .bash_profile appropriately.

awesome-very-deep-learning - 🔥A curated list of papers and code about very deep neural networks


awesome-very-deep-learning is a curated list for papers and code about implementing and training very deep neural networks. Value Iteration Networks are very deep networks that have tied weights and perform approximate value iteration. They are used as an internal (model-based) planning module.

awesome-deep-learning-music - List of articles related to deep learning applied to music

  •    TeX

By Yann Bayle (Website, GitHub) from LaBRI (Website, Twitter), Univ. Bordeaux (Website, Twitter), CNRS (Website, Twitter) and SCRIME (Website). The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the How To Contribute section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for music generation, speech separation and speaker identification. However, these surveys do not cover music information retrieval tasks that are included in this repository.

ResNeXt - Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks

  •    Lua

This repository contains a Torch implementation for the ResNeXt algorithm for image classification. The code is based on fb.resnet.torch. ResNeXt is a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width.

onnx - Open Neural Network Exchange

  •    PureBasic

Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Initially we focus on the capabilities needed for inferencing (evaluation). Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. Enabling interoperability between different frameworks and streamlining the path from research to production will increase the speed of innovation in the AI community. We are an early stage and we invite the community to submit feedback and help us further evolve ONNX.

video-classification-3d-cnn-pytorch - Video classification tools using 3D ResNet

  •    Python

This is a pytorch code for video (action) classification using 3D ResNet trained by this code. The 3D ResNet is trained on the Kinetics dataset, which includes 400 action classes. This code uses videos as inputs and outputs class names and predicted class scores for each 16 frames in the score mode. In the feature mode, this code outputs features of 512 dims (after global average pooling) for each 16 frames. Torch (Lua) version of this code is available here.

ignite - High-level library to help with training neural networks in PyTorch

  •    Python

Ignite is a high-level library to help with training neural networks in PyTorch. As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of your code when "rolling your own" training loop.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.