Last week, NVIDIA announced the NeMo model for the development of speech and language models and to create a conversational AI. NeMo is an open-source toolkit based on the PyTorch backend. The neural modules form the building blocks of these NeMo models. With NeMo, users can compose and train state-of-the-art neural network architectures.

How Can NeMo Help

NVIDIA NeMo allows to quickly build, train, and fine-tune conversational AI. It consists of NeMo core and NeMo collections. While NeMo core helps in getting the common look and feel for all models, NeMo collections act as groups of domain-specific modules and models.

There are main parts of NeMo: model, neural module, and neural type.

The models contain all necessary information regarding training, fine-tuning, data augmentation, and infrastructure details.  The models of NeMo consists of:

• Neural network implementation where all the neural models are connected for training and evaluation
• All pre- and post-processing activities such as tokenisation and augmentation
• The dataset classes to be used with this model
• The optimisation algorithm and the learning rate schedule
• Other infrastructure details

The neural modules are encoder-decoder architectures consisting of conceptual building blocks responsible for different tasks. At its core, Neural Module is the logical part of the neural network, which takes a set of inputs and computes a set of outputs.k.

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