데이터셋 탐색

The Leeds Sports Pose (LSP) dataset is widely used as the benchmark for human pose estimation. The original LSP dataset contains 2,000 images of sportspersons gathered from Flickr, 1000 for training and 1000 for testing. Each image is annotated with 14 joint locations, where left and right joints are consistently labelled from a person-centric viewpoint. The extended LSP dataset contains additional 10,000 images labeled for training. Source: Deep Deformation Network for Object Landmark Localization Image: Sumer et al

PartialSpoof is a dataset of partially-spoofed data to evaluate detection of partially-spoofed speech data. It has been built based on the ASVspoof 2019 LA database since the latter covers 17 types of spoofed data produced by advanced speech synthesizers, voice converters, and hybrids. The authors used the same set of bona fide data from the ASVspoof 2019 LA database but created partially spoofed audio from the ASVspoof 2019 LA data.

SkillSpan is a dataset for Skill Extraction (SE). It is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, the authors introduce SkillSpan, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans.

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match") for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites.

Konzil dataset was created by specialists of the University of Greifswald. It contains manuscripts written in modern German. Train sample consists of 353 lines, validation - 29 lines and test - 87 lines.

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A Brazilian Portuguese TTS dataset featuring a female voice recorded with high quality in a controlled environment, with neutral emotion and more than 20 hours of recordings. with neutral emotion and more than 20 hours of recordings. Our dataset aims to facilitate transfer learning for researchers and developers working on TTS applications: a highly professional neutral female voice can serve as a good warm-up stage for learning language-specific structures, pronunciation and other non-individual characteristics of speech, leaving to further training procedures only to learn the specific adaptations needed (e.g. timbre, emotion and prosody). This can surely help enabling the accommodation of a more diverse range of female voices in Brazilian Portuguese. By doing so, we also hope to contribute to the development of accessible and high-quality TTS systems for several use cases such as virtual assistants, audiobooks, language learning tools and accessibility solutions. Possible use cases: TTS; Voice Conversion; ASR; Speech Enhancement

A database containing high sampling rate recordings of a single speaker reading sentences in Brazilian Portuguese with neutral voice, along with the corresponding text corpus. Intended for speech synthesis and automatic speech recognition applications, the dataset contains text extracted from a popular Brazilian news TV program, totalling roughly 20 h of audio spoken by a trained individual in a controlled environment. The text was normalized in the recording process and special textual occurrences (e.g. acronyms, numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental sounds and background noise has been kept to a minimum in all audio samples. Intended for: TTS ASR Speech Enhancement Voice Conversion

A collection of test sets for evaluating base and chat LLMs (incl. VLMs) on Greek generation and understanding capabilities.

All existing databases of spoofed speech contain attack data that is spoofed in its entirety. In practice, it is entirely plausible that successful attacks can be mounted with utterances that are only partially spoofed. By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with entirely spoofed utterances. This hypothesis raises the obvious question: ‘Can we detect partially spoofed audio?’ This paper introduces a new database of partially-spoofed data, named PartialSpoof, to help address this question. This new database enables to investigate and compare the performance of countermeasures on both utterance- and segmental- level labels. Source: https://zenodo.org/record/4817532#.YMi9-jZKgox