NeMo: a toolkit for building AI applications using Neural Modules
NVIDIA（英伟达）
Oleksii Kuchaiev, Jason Li, Huyen Nguyen, Oleksii Hrinchuk, Ryan Leary, Boris Ginsburg, Samuel Kriman, Stanislav Beliaev, Vitaly Lavrukhin, Jack Cook, Patrice C… 展开作者
Oleksii Kuchaiev, Jason Li, Huyen Nguyen, Oleksii Hrinchuk, Ryan Leary, Boris Ginsburg, Samuel Kriman, Stanislav Beliaev, Vitaly Lavrukhin, Jack Cook, Patrice Castonguay, Mariya Popova, Jocelyn Huang, Jonathan M. Cohen
NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition. NeMo is built around neural modules, conceptual blocks of neural networks that take typed inputs and produce typed outputs. Such modules typically represent data layers, encoders, decoders, language models, loss functions, or methods of combining activations. NeMo makes it easy to combine and re-use these building blocks while providing a level of semantic correctness checking via its neural type system. The toolkit comes with extendable collections of pre-built modules for automatic speech recognition and natural language processing. Furthermore, NeMo provides built-in support for distributed training and mixed precision on latest NVIDIA GPUs. NeMo is open-source https://github.com/NVIDIA/NeMo
We introduce PyKaldi2 speech recognition toolkit implemented based on Kaldi and PyTorch. While similar toolkits are available built on top of the two, a key feature of PyKaldi2 is sequence training with criteria such as MMI, sMBR and MPE. In particular, we implemented the sequence training module with on-the-fly lattice generation during model training in order to simplify the training pipeline. To address the challenging acoustic environments in real applications, PyKaldi2 also supports on-the-fly noise and reverberation simulation to improve the model robustness. With this feature, it is pos…
ESPnet-SpeechLM: An Open Speech Language Model Toolkit
Carnegie Mellon University（卡内基梅隆大学）；Mitsubishi Electric Research Laboratories（三菱电机研究所）；LY Corporation（LY公司）；Renmin University of China（中国人民大学）；Brno University of Technology（布尔诺理工大学）；NVIDIA Research（英伟达研究院）
NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
State Key Laboratory of Complex \& Critical Software Environment, Beihang University Beijing China；Peng Cheng Laboratory Shenzhen China；National University of Singapore Singapore；Beihang University Beijing China；Hangzhou Innovation Institute of Beihang University Hangzhou China；State Key Laboratory of Complex \& Critical Software Environment, Beihang University；Peng Cheng Laboratory；National University of Singapore；Beihang University；Hangzhou Innovation Institute of Beihang University
In general, speech processing models consist of a language model along with an acoustic model. Regardless of the language model's complexity and variants, three critical pre-processing steps are needed in language models: cleaning, normalization, and tokenization. Among mentioned steps, the normalization step is so essential to format unification in pure textual applications. However, for embedded language models in speech processing modules, normalization is not limited to format unification. Moreover, it has to convert each readable symbol, number, etc., to how they are pronounced. To the be…
We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modu…
CANNs: A Toolkit for Research on Continuous Attractor Neural Networks
School of Psychological and Cognitive Sciences, Peking–Tsinghua Center for Life Sciences, PKU-IDG/McGovern Institute for Brain Research, Center of Quantitative Biology（心理与认知科学学院，北京清华大学生命科学中心，北京大学IDG/麦克戈文脑科学研究院，定量生物学中心）
aeon: a Python toolkit for learning from time series
University of Southampton（南安普顿大学）；Université de Haute-Alsace（上阿尔萨斯大学）；ENSTA Paris（巴黎国立高等先进技术学院）；University of East Anglia（东英吉利大学）；University of Córdoba（科尔多瓦大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images
University of Edinburgh（爱丁堡大学）；Institute for Regeneration and Repair, University of Edinburgh（爱丁堡大学再生与修复研究所）；Centre for Medical Informatics, University of Edinburgh（爱丁堡大学医学信息学中心）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Clinical Research Facility and Imaging, University of Edinburgh（爱丁堡大学临床研究设施与影像中心）；School of Mathematics, University of Edinburgh（爱丁堡大学数学学院）；Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh（爱丁堡大学信息学院自适应与神经计算研究所）；Centre for Clinical Brain Sciences, University of Edinburgh（爱丁堡大学临床脑科学中心）
A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs w…
