BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21023@www.clsp.jhu.edu DTSTAMP:20240328T165022Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nSpeech data is notoriously difficult to work with due to a variety of codecs\, length s of recordings\, and meta-data formats. We present Lhotse\, a speech data representation library that draws upon lessons learned from Kaldi speech recognition toolkit and brings its concepts into the modern deep learning ecosystem. Lhotse provides a common JSON description format with correspon ding Python classes and data preparation recipes for over 30 popular speec h corpora. Various datasets can be easily combined together and re-purpose d for different tasks. The library handles multi-channel recordings\, long recordings\, local and cloud storage\, lazy and on-the-fly operations amo ngst other features. We introduce Cut and CutSet concepts\, which simplify common data wrangling tasks for audio and help incorporate acoustic conte xt of speech utterances. Finally\, we show how Lhotse leverages PyTorch da ta API abstractions and adopts them to handle speech data for deep learnin g.
\nBiography
\nPiotr Zelasko is an a ssistant research scientist in the Center for Language and Speech Processi ng (CLSP) who specializes in automatic speech recognition (ASR) and spoken language understanding (SLU). His current research focuses on applying mu ltilingual and crosslingual speech recognition systems to categorize the p honetic inventory of a previously unknown language and on improving defens es against adversarial attacks on both speaker identification and automati c speech recognition systems. He is also addressing the question of how to structure a spontaneous conversation into high-level semantic units such as dialog acts or topics. Finally\, he is working on Lhotse + K2\, the nex t-generation speech processing research software ecosystem. Before joining Johns Hopkins\, Zelasko worked as a machine learning consultant for Avaya (2017-2019)\, and as a machine learning engineer for Techmo (2015-2017). Zelasko received his PhD (2019) in electronics engineering\, as well as hi s master’s (2014) and undergraduate degrees (2013) in acoustic engineering from AGH University of Science and Technology in Kraków\, Poland.
DTSTART;TZID=America/New_York:20211029T120000 DTEND;TZID=America/New_York:20211029T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore MD 21218 SEQUENCE:0 SUMMARY:Piotr Zelasko (CLSP at JHU) “Lhotse: a speech data representation l ibrary for the modern deep learning ecosystem” URL:https://www.clsp.jhu.edu/events/piotr-zelasko-clsp-at-jhu-lhotse-a-spee ch-data-representation-library-for-the-modern-deep-learning-ecosystem/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2021\,October\,Zelasko END:VEVENT BEGIN:VEVENT UID:ai1ec-24465@www.clsp.jhu.edu DTSTAMP:20240328T165022Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nLarge Language Models (LLM s) have demonstrated remarkable capabilities across various domains. Howev er\, it is still very challenging to build highly-reliable applications wi th LLMs that support specialized use cases. LLMs trained on web data often excel at capturing general language patterns\, but they could struggle to support specialized domains and personalized user needs. Moreover\, LLMs can produce errors that are deceptively plausible\, making them potentiall y dangerous for high-trust scenarios. In this talk\, I will discuss some o f our recent efforts in addressing these challenges with data-efficient tu ning methods and a novel factuality evaluation framework. Specifically\, m y talk will focus on building multilingual applications\, one crucial use case often characterized by limited tuning and evaluation data.
\nBio
Xinyi(Cindy) Wang is a research scientist at Go ogle DeepMind working on Large Language Models(LLM) and its application to generative question-answering. She has worked on multilingual instruction -tuning for Gemini and multilingual generative models used in Google searc h. Before Google DeepMind\, Cindy Wang obtained her PhD degree in Language Technologies at Carnegie Mellon University. During her PhD\, she mainly w orked on developing data-efficient natural language processing~(NLP) syste ms. She has made several contributions in data selection\, data representa tion\, and model adaptation for multilingual NLP.
DTSTART;TZID=America/New_York:20240308T120000 DTEND;TZID=America/New_York:20240308T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Cindy Wang (Google DeepMind) “Building Data-Efficient and Reliable Applications with Large Language Models” URL:https://www.clsp.jhu.edu/events/cindy-wang-google-deepmind-building-dat a-efficient-and-reliable-applications-with-large-language-models/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,March\,Wang END:VEVENT END:VCALENDAR