Search Results for author:
Found 24 papers, 5 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)
+5
Llama-Nemotron: Efficient Reasoning Models
We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an open license for enterprise use.
OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs
Large Language Models (LLMs) have transformed software development by enabling code generation, automated debugging, and complex reasoning.
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
We introduce Nemotron-H, a family of 8B and 56B/47B hybrid Mamba-Transformer models designed to reduce inference cost for a given accuracy level.
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
We then perform analysis on the data sources used to construct our dataset, the impact of code execution filtering, and the importance of instruction/solution diversity.
Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning
We also propose multiple metrics to measure different aspects of the synthetic verifiers with the proposed benchmarks.
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
We present Dynamic-SUPERB Phase-2, an open and evolving benchmark for the comprehensive evaluation of instruction-based universal speech models.
Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models
Our proposed approach is highly parallelizable and effective even with a small seed data and weaker generator models.
Instruction Data Generation and Unsupervised Adaptation for Speech Language Models
In this paper, we propose three methods for generating synthetic samples to train and evaluate multimodal large language models capable of processing both text and speech inputs.
Nemotron-4 340B Technical Report
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward.
Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition
We also showed that training a model with multiple latencies can achieve better accuracy than single latency models while it enables us to support multiple latencies with a single model.
Investigating End-to-End ASR Architectures for Long Form Audio Transcription
This paper presents an overview and evaluation of some of the end-to-end ASR models on long-form audios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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 Common Voice English
(using extra training data)
SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services
In this paper, we propose SGD-QA, a simple and extensible model for schema-guided dialogue state tracking based on a question answering approach.
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 #3 on
Speech Recognition
on SPGISpeech
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
I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications
In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings.
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset
Dialog State Tracking (DST) is one of the most crucial modules for goal-oriented dialogue systems.
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings
However, real-world data is different and translations are yielding varying results.
Leveraging Semi-Supervised Learning for Fairness using Neural Networks
There has been a growing concern about the fairness of decision-making systems based on machine learning.
Semi-supervised Deep Representation Learning for Multi-View Problems
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied.
Dimensionality Reduction
Learning Representation Of Multi-View Data
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks.
SEVEN: Deep Semi-supervised Verification Networks
The model consists of two complementary components.
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item.
