Search Results for author:
Found 18 papers, 7 papers with code
NVIDIA NeMo’s Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21
This paper provides an overview of NVIDIA NeMo’s neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks.
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022
Our cascade system consists of 1) Conformer RNN-T automatic speech recognition model, 2) punctuation-capitalization model based on pre-trained T5 encoder, 3) ensemble of Transformer neural machine translation models fine-tuned on TED talks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation
We present a novel Speech Augmented Language Model (SALM) with {\em multitask} and {\em in-context} learning capabilities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
Conformer-based models have become the dominant end-to-end architecture for speech processing tasks.
Ranked #1 on
Speech Recognition
on LibriSpeech test-other
Leveraging Synthetic Targets for Machine Translation
In this work, we provide a recipe for training machine translation models in a limited resource setting by leveraging synthetic target data generated using a large pre-trained model.
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation
General translation models often still struggle to generate accurate translations in specialized domains.
NVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21
This paper provides an overview of NVIDIA NeMo's neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks.
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition
We propose Citrinet - a new end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Tensorized Embedding Layers
The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments
We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay.
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model
In this work, we introduce a simple yet efficient post-processing model for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
NeMo: a toolkit for building AI applications using Neural Modules
NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition.
Ranked #1 on
Speech Recognition
on Common Voice Spanish
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks
We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay.
Catalyst.RL: A Distributed Framework for Reproducible RL Research
Despite the recent progress in deep reinforcement learning field (RL), and, arguably because of it, a large body of work remains to be done in reproducing and carefully comparing different RL algorithms.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Generalized Tensor Models for Recurrent Neural Networks
Such networks, however, are not very often applied to real life tasks.
Tensorized Embedding Layers for Efficient Model Compression
The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.
Riemannian Optimization for Skip-Gram Negative Sampling
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent.
