Omkar2118/datasets huggingface dataset:
MoisesDB

In this paper, we introduce the MoisesDB dataset for musical source separation. It consists of 240 tracks from 45 artists, covering twelve musical genres. For each song, we provide its individual audio sources, organized in a two-level hierarchical taxonomy of stems. This will facilitate building and evaluating fine-grained source separation systems that go beyond the limitation of using four stems (drums, bass, other, and vocals) due to lack of data. To facilitate the adoption of this dataset, we publish an easy-to-use Python library to download, process and use MoisesDB. Alongside a thorough documentation and analysis of the dataset contents, this work provides baseline results for open-source separation models for varying separation granularities (four, five, and six stems), and discuss their results.

Introducing paper: Moisesdb: A dataset for source separation beyond 4-stems
XStoryCloze

XStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. This dataset is released by Meta AI.

Introducing paper: Few-shot Learning with Multilingual Language Models
TDMD

**TDMD** contains eight reference DCM objects with six typical distortions. Using processed video sequences (PVS) derived from the DCM, the authors conducted a large-scale subjective experiment that resulted in 303 distorted DCM samples with mean opinion scores, making the TDMD the largest available DCM database to our knowledge.

Introducing paper: TDMD: A Database for Dynamic Color Mesh Subjective and Objective Quality Explorations