To demonstrate how easy it is to use NeMo and Lightning to train conversational AI, we’ll build an end 2 end speech recognition model that can be used to transcribe voice commands. With tight integration with PyTorch Lightning, NeMo is guaranteed to run across many research environments and allow researchers to focus on what matters. This allowed the NeMo team to focus on building the AI models, and allows NeMo users to make use of the Lightning Trainer, which includes many features to speedup your training. Every NeMo model is actually a LightningModule. Instead of building support for multiple GPUs and multiple nodes from scratch, NeMo team decided to use PyTorch Lightning under the hood to handle all the engineering details.
Large list of SOTA pre-trained models available in NGC. 
Optimizations via TensorRT and deployment using NVIDIA Jarvis. Exporting models using ONNX or PyTorch TorchScript. NeMo is built on top of PyTorch, PyTorch Lightning, and many other open-source libraries, which offers many other highlight features such as: It provides researchers the ability to extend the scale of their experiments and build upon existing implementations of models, datasets, and training procedures without having to worry about scaling, boiler-plate code, or unnecessary engineering. NeMo also has out of the box support for various Speech Recognition models providing pre-trained models for easier deployment and fine-tuning, or providing the ability to train from scratch with easy to modify configurations which we delve into detail below. NeMo comes out of the box with examples to train popular models from scratch such as the infamous Speech Synthesis Tactotron2 model published by Google Research, as well as the ability to fine-tune pre-trained transformer models such as Megatron-LM for downstream NLP tasks such as text classification and question answering. NeMo provides a light wrapper to develop models across various domains, in particular ASR (Automatic speech recognition), TTS (text to speech) and NLP. In this article we’ll highlight some of the great features within NeMo, steps to building your own ASR model on LibriSpeech and how to to fine-tune models on your own datasets across different languages. Continue reading to learn how to use NeMo and Lightning to train an end-2-end speech recognition model on multiple GPUs and how you can extend the NeMo models for your own use case, like fine-tuning strong pre-trained ASR models on Spanish audio data. NeMo models can be trained on multi-GPU and multi-node, with or without Mixed Precision, in just 3 lines of code. 
NeMo (Neural Modules) is a powerful framework from NVIDIA, built for easy training, building and manipulating of state-of-the-art conversational AI models.
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