mbrukman / nemo

NeMo: a toolkit for conversational AI

NVIDIA NeMo

Introduction

NeMo is a toolkit for creating Conversational AI applications.

NeMo toolkit makes it possible for researchers to easily compose complex neural network architectures for conversational AI using reusable components - Neural Modules. Neural Modules are 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.

The toolkit comes with extendable collections of pre-built modules and ready-to-use models for:

• Automatic Speech Recognition (ASR)
• Natural Language Processing (NLP)
• Speech synthesis, or Text-To-Speech (TTS)

Built for speed, NeMo can utilize NVIDIA's Tensor Cores and scale out training to multiple GPUs and multiple nodes.

NeMo product page.

Introductory video.

Requirements

NeMo's works with:

1. Python 3.6 or 3.7
2. Pytorch 1.6 or above

Docker containers:

The easiest way to start training with NeMo is by using NeMo's container.

It has all requirements and NeMo 1.0.0b2 already installed.

docker run --gpus all -it --rm -v <nemo_github_folder>:/NeMo --shm-size=8g \
-p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
stack=67108864 --device=/dev/snd nvcr.io/nvidia/nemo:1.0.0b3

If you chose to work with main branch, we recommend using NVIDIA's PyTorch container version 20.09-py3.

docker run --gpus all -it --rm -v <nemo_github_folder>:/NeMo --shm-size=8g \
-p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:20.09-py3

Installation

If you are not inside the NVIDIA docker container, please install Cython first. If you wish to either use the ASR or TTS collection, please install libsndfile1 and ffmpeg as well.

• pip install Cython
• apt-get update && apt-get install -y libsndfile1 ffmpeg (If you want to install the TTS or ASR collections)

Once requirements are satisfied, simply install using pip:

• pip install nemo_toolkit[all]==1.0.0b2 (latest version)

Or if you want the latest (or particular) version from GitHub:

• python -m pip install git+https://github.com/NVIDIA/NeMo.git@{BRANCH}#egg=nemo_toolkit[all] - where {BRANCH} should be replaced with the branch you want. This is recommended route if you are testing out the latest WIP version of NeMo.
• ./reinstall.sh - from NeMo's git root. This will install the version from current branch in developement mode.

Examples

<nemo_github_folder>/examples/ folder contains various example scripts. Many of them look very similar and have the same arguments because we used Facebook's Hydra for configuration.

Here is an example command which trains ASR model (QuartzNet15x5) on LibriSpeech, using 4 GPUs and mixed precision training. (It assumes you are inside the container with NeMo installed)

root@987b39669a7e:/NeMo# python examples/asr/speech_to_text.py --config-name=quartznet_15x5 \
model.train_ds.manifest_filepath=<PATH_TO_DATA>/librispeech-train-all.json \
model.validation_ds.manifest_filepath=<PATH_TO_DATA>/librispeech-dev-other.json \
trainer.gpus=4 trainer.max_epochs=128 model.train_ds.batch_size=64 \
+trainer.precision=16 +trainer.amp_level=O1  \
+model.validation_ds.num_workers=16  \
+model.train_ds.num_workers=16 \
+model.train_ds.pin_memory=True

#(Optional) Tensorboard:
tensorboard --bind_all --logdir nemo_experiments

Documentation

Version	Status	Description
Latest		Documentation of the latest (i.e. main) branch
Stable		Documentation of the stable (i.e. 0.11.1) branch
Main		Documentation of the main branch
v0.11.1		Documentation of the v0.11.1 release
v0.11.0		Documentation of the v0.11.0 release

Tutorials

The best way to get started with NeMo is to checkout one of our tutorials.

Most NeMo tutorials can be run on Google's Colab.

To run tutorials:

• Click on Colab link (see table below)
• Connect to an instance with a GPU (Runtime -> Change runtime type -> select "GPU" for hardware accelerator)

Tutorials

Domain	Title	GitHub URL
NeMo	Simple Application with NeMo	Voice swap app
NeMo	Exploring NeMo Fundamentals	NeMo primer
NeMo Models	Exploring NeMo Model Construction	NeMo models
ASR	ASR with NeMo	ASR with NeMo
ASR	Speech Commands	Speech commands
ASR	Speaker Recognition and Verification	Speaker Recognition and Verification
ASR	Online Noise Augmentation	Online noise augmentation
ASR	Beam Search and External Language Model Rescoring	Beam search and external language model rescoring
NLP	Using Pretrained Language Models for Downstream Tasks	Pretrained language models for downstream tasks
NLP	Exploring NeMo NLP Tokenizers	NLP tokenizers
NLP	Text Classification (Sentiment Analysis) with BERT	Text Classification (Sentiment Analysis)
NLP	Question answering with SQuAD	Question answering Squad
NLP	Token Classification (Named Entity Recognition)	Token classification: named entity recognition
NLP	Joint Intent Classification and Slot Filling	Joint Intent and Slot Classification
NLP	GLUE Benchmark	GLUE benchmark
NLP	Punctuation and Capitialization	Punctuation and capitalization
NLP	Named Entity Recognition - BioMegatron	Named Entity Recognition - BioMegatron
NLP	Relation Extraction - BioMegatron	Relation Extraction - BioMegatron
TTS	Speech Synthesis	TTS inference
Tools	CTC Segmentation	CTC Segmentation
Tools	Text Normalization for Text To Speech	Text Normalization

Contributing

We welcome community contributions! Please refer to the CONTRIBUTING.md for the process.

License

NeMo is under Apache 2.0 license.