Multilingual automatic lyrics transcription (ALT) is a challenging task due
to the limited availability of labelled data and the challenges introduced by
singing, compared to multilingual automatic speech recognition. Although some
multilingual singing datasets have been released recently, English continues to
dominate these collections. Multilingual ALT remains underexplored due to the
scale of data and annotation quality. In this paper, we aim to create a
multilingual ALT system with available datasets. Inspired by architectures that
have been proven effective for English ALT, we adapt these techniques to the
multilingual scenario by expanding the target vocabulary set. We then evaluate
the performance of the multilingual model in comparison to its monolingual
counterparts. Additionally, we explore various conditioning methods to
incorporate language information into the model. We apply analysis by language
and combine it with the language classification performance. Our findings
reveal that the multilingual model performs consistently better than the
monolingual models trained on the language subsets. Furthermore, we demonstrate
that incorporating language information significantly enhances performance.

基于可用数据集，本文旨在创建一个能够进行多语言自动歌词转录的系统，并通过扩展目标词汇集合将已被证明在英语自动歌词转录中有效的技术应用于多语言场景。通过语言分析结合语言分类性能，我们的研究发现多语言模型的性能连续优于仅针对语言子集进行训练的单语言模型，并且将语言信息纳入模型中显著提高性能。

构建端到端多语言自动歌词转录模型

Towards Building an End-to-End Multilingual Automatic Lyrics  Transcription Model

Few-shot learning is a promising way for reducing the label cost in new
categories adaptation with the guidance of a small, well labeled support set.
But for few-shot semantic segmentation, the pixel-level annotations of support
images are still expensive. In this paper, we propose an innovative solution to
tackle the challenge of few-shot semantic segmentation using only language
information, i.e.image-level text labels. Our approach involves a
vision-language-driven mask distillation scheme, which contains a
vision-language pretraining (VLP) model and a mask refiner, to generate high
quality pseudo-semantic masks from text prompts. We additionally introduce a
distributed prototype supervision method and complementary correlation matching
module to guide the model in digging precise semantic relations among support
and query images. The experiments on two benchmark datasets demonstrate that
our method establishes a new baseline for language-guided few-shot semantic
segmentation and achieves competitive results to recent vision-guided methods.

通过语言信息进行语言指导的少样本语义分割，使用视觉 - 语言预训练模型和遮罩优化来生成高质量伪语义遮罩，引入分布式原型监督方法和互补相关匹配模块来指导模型挖掘支持和查询图像的精确语义关系。在两个基准数据集上的实验表明，我们的方法为语言指导的少样本语义分割建立了新的基准，并达到了与最近的视觉指导方法竞争的结果。