NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC. NGC collection of pre-trained speech processing models.

Features

  • Python version 3.6, 3.7 or 3.8
  • Pytorch version 1.8.1
  • You must have access to an NVIDIA GPU for training
  • Text Classification (Sentiment Analysis)
  • NeMo voice swap demo
  • Build a nemo container with Dockerfile from a branch

Project Samples

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License

Apache License V2.0

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NVIDIA NeMo Web Site

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