Search Results for author:
Found 16 papers, 9 papers with code
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
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.
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.
SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition
In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models.
Ranked #1 on
Speech Recognition
on SPGISpeech
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.
QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions
We propose a new end-to-end neural acoustic model for automatic speech recognition.
Ranked #29 on
Speech Recognition
on LibriSpeech test-clean
Speech Recognition
Audio and Speech Processing
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.
Jasper: An End-to-End Convolutional Neural Acoustic Model
In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data.
Ranked #3 on
Speech Recognition
on Hub5'00 SwitchBoard
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models
We present OpenSeq2Seq {--} an open-source toolkit for training sequence-to-sequence models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
We present OpenSeq2Seq - a TensorFlow-based toolkit for training sequence-to-sequence models that features distributed and mixed-precision training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Training Deep AutoEncoders for Recommender Systems
Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.
Mixed Precision Training
Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x.
Training Deep AutoEncoders for Collaborative Filtering
Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.
Factorization tricks for LSTM networks
We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and states into the independent groups.
Ranked #20 on
Language Modelling
on One Billion Word
