177 citations · 225 across the 6 of their papers we have counts for
6 papers
RULER: What's the Real Context Size of Your Long-Context Language Models?
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman +5
The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of information (the "needle") from long distractor texts (the "haystack"), has been widely adop…
Investigating End-to-End ASR Architectures for Long Form Audio Transcription
Nithin Rao Koluguri, Samuel Kriman, Georgy Zelenfroind +5
This paper presents an overview and evaluation of some of the end-to-end ASR models on long-form audios. We study three categories of Automatic Speech Recognition(ASR) models based…
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
Dima Rekesh, Nithin Rao Koluguri, Samuel Kriman +8
Conformer-based models have become the dominant end-to-end architecture for speech processing tasks. With the objective of enhancing the conformer architecture for efficient traini…
Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models
Travis M. Bartley, Fei Jia, Krishna C. Puvvada +2
In this paper, we extend previous self-supervised approaches for language identification by experimenting with Conformer based architecture in a multilingual pre-training paradigm.…
QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions
Samuel Kriman, Stanislav Beliaev, Boris Ginsburg +6
We propose a new end-to-end neural acoustic model for automatic speech recognition. The model is composed of multiple blocks with residual connections between them. Each block cons…
NeMo: a toolkit for building AI applications using Neural Modules
Oleksii Kuchaiev, Jason Li, Huyen Nguyen +11
NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition. NeMo is built around neural modules, c…