TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful features. Here, we survey TorchAudio's development principles and contents and highlight key features we include in its latest version (2.1): self-supervised learning pre-trained pipelines and training recipes, high-performance CTC decoders, speech recognition models and training recipes, advanced media I/O capabilities, and tools for performing forced alignment, multi-channel speech enhancement, and reference-less speech assessment. For a selection of these features, through empirical studies, we demonstrate their efficacy and show that they achieve competitive or state-of-the-art performance

Spatial and Temporal Variations in the Rainy Season Onset over the Qinghai–Tibet Plateau

Precipitation on the Qinghai–Tibet Plateau (TP) in southwestern China is subject to interactions between the complex and variable terrain and the sensitive climate. The regional climate is mainly affected by three circulations: westerlies, the South Asian monsoon, and the East Asian monsoon. Spatial and temporal variations in the rainy season onset were characterised based on daily precipitation from 106 meteorological stations on the TP from 1971 to 2015. Using the Theil–Sen Median trend analysis, Mann–Kendall test and mutation detection, the characteristics and reasons for the variations during the rainy season over the plateau over the past 45 years were investigated. The following results were obtained from the analysis: (1) There were obvious regional differences in the rainy season onset over the TP, and the rainy season began on the southeastern plateau and moved northwestward. (2) The TP rainy season underwent a significant mutation in approximately 1997, and following this mutation, the area affected by the delayed rainy season increased. (3) Against the background of global warming, the rainy season trend over the TP was advanced; however, there were still several multiple contiguous concentrated areas on the plateau. (4) Before the rainy season mutation, there were two centres of delayed precipitation on the plateau, which existed primarily due to their location at the end of the plateau water vapour transport channel. After the mutation, the number of delayed precipitation centres on the plateau increased to three and presented a spatially expanding trend, which may be related to the weakening trend in atmospheric circulation

The Identification and Validation of Hub Genes Associated with Acute Myocardial Infarction Using Weighted Gene Co-Expression Network Analysis

Acute myocardial infarction (AMI), one of the most severe and fatal cardiovascular diseases, remains the main cause of mortality and morbidity worldwide. The objective of this study is to investigate the potential biomarkers for AMI based on bioinformatics analysis. A total of 2102 differentially expressed genes (DEGs) were screened out from the data obtained from the gene expression omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) explored the co-expression network of DEGs and determined the key module. The brown module was selected as the key one correlated with AMI. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses demonstrated that genes in the brown module were mainly enriched in ‘ribosomal subunit’ and ‘Ribosome’. Gene Set Enrichment Analysis revealed that ‘TNFA_SIGNALING_VIA_NFKB’ was remarkably enriched in AMI. Based on the protein–protein interaction network, ribosomal protein L9 (RPL9) and ribosomal protein L26 (RPL26) were identified as the hub genes. Additionally, the polymerase chain reaction (PCR) results indicated that the expression levels of RPL9 and RPL26 were both downregulated in AMI patients compared with controls, in accordance with the bioinformatics analysis. In summary, the identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of AMI