Introduction#

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) synthesis 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.

Pre-trained NeMo models.

For more information and questions, visit the NVIDIA NeMo Discussion Board.

Prerequisites#

Before you begin using NeMo, it’s assumed you meet the following prerequisites.

You have Python version 3.6, 3.7 or 3.8.
You have Pytorch version 1.8.1.
You have access to an NVIDIA GPU for training.

Quick Start Guide#

This NeMo Quick Start Guide is a starting point for users who want to try out NeMo; specifically, this guide enables users to quickly get started with the NeMo fundamentals by walking you through an example audio translator and voice swap.

If you’re new to NeMo, the best way to get started is to take a look at the following tutorials:

Text Classification (Sentiment Analysis) - demonstrates the Text Classification model using the NeMo NLP collection.
NeMo Primer - introduces NeMo, PyTorch Lightning, and OmegaConf, and shows how to use, modify, save, and restore NeMo models.
NeMo Models - explains the fundamental concepts of the NeMo model.
NeMo voice swap demo - demonstrates how to swap a voice in the audio fragment with a computer generated one using NeMo.

Below we is the code snippet of Audio Translator application.

# Import NeMo and it's ASR, NLP and TTS collections
import nemo
# Import Speech Recognition collection
import nemo.collections.asr as nemo_asr
# Import Natural Language Processing colleciton
import nemo.collections.nlp as nemo_nlp
# Import Speech Synthesis collection
import nemo.collections.tts as nemo_tts

# Next, we instantiate all the necessary models directly from NVIDIA NGC
# Speech Recognition model - QuartzNet trained on Russian part of MCV 6.0
quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_ru_quartznet15x5").cuda()
# Neural Machine Translation model
nmt_model = nemo_nlp.models.MTEncDecModel.from_pretrained(model_name='nmt_ru_en_transformer6x6').cuda()
# Spectrogram generator which takes text as an input and produces spectrogram
spectrogram_generator = nemo_tts.models.FastPitchModel.from_pretrained(model_name="tts_en_fastpitch").cuda()
# Vocoder model which takes spectrogram and produces actual audio
vocoder = nemo_tts.models.HifiGanModel.from_pretrained(model_name="tts_en_hifigan").cuda()
# Transcribe an audio file
# IMPORTANT: The audio must be mono with 16Khz sampling rate
# Get example from: https://nemo-public.s3.us-east-2.amazonaws.com/mcv-samples-ru/common_voice_ru_19034087.wav
russian_text = quartznet.transcribe(['Path_to_audio_file'])
print(russian_text)
# You should see russian text here. Let's translate it to English
english_text = nmt_model.translate(russian_text)
print(english_text)
# After this you should see English translation
# Let's convert it into audio
# A helper function which combines FastPitch and HiFiGAN to go directly from
# text to audio
def text_to_audio(text):
  parsed = spectrogram_generator.parse(text)
  spectrogram = spectrogram_generator.generate_spectrogram(tokens=parsed)
  audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
  return audio.to('cpu').numpy()
audio = text_to_audio(english_text[0])

Installation#

Pip#

Use this installation mode if you want the latest released version.

apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython
pip install nemo_toolkit[all]

Pip from source#

Use this installation mode if you want the version from a particular GitHub branch (for example, main).

apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython
python -m pip install git+https://github.com/NVIDIA/NeMo.git@{BRANCH}#egg=nemo_toolkit[all]
# For v1.0.2, replace {BRANCH} with v1.0.2 like so:
# python -m pip install git+https://github.com/NVIDIA/NeMo.git@v1.0.2#egg=nemo_toolkit[all]

From source#

Use this installation mode if you are contributing to NeMo.

apt-get update && apt-get install -y libsndfile1 ffmpeg
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh

Docker containers#

To build a nemo container with Dockerfile from a branch, please run

DOCKER_BUILDKIT=1 docker build -f Dockerfile -t nemo:latest.

If you chose to work with the main branch, we recommend using NVIDIA’s PyTorch container version 21.05-py3, then install from GitHub.

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:21.05-py3

Mac computers with Apple silicon#

To install NeMo on Mac with Apple M-Series GPU:

install Homebrew package manager
create a new Conda environment
install PyTorch 2.0 or higher
run the following code:

# install mecab using Homebrew, required for sacrebleu for NLP collection
brew install mecab

# install pynini using Conda, required for text normalization
conda install -c conda-forge pynini

# install Cython manually
pip install cython

# clone the repo and install in development mode
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh

FAQ #

Have a look at our discussions board and feel free to post a question or start a discussion.

Contributing#

We welcome community contributions! Refer to the CONTRIBUTING.md file for the process.

License#

NeMo is under Apache 2.0 license.

Introduction

Contents