Current automatic lyrics transcription (ALT) benchmarks focus exclusively on
word content and ignore the finer nuances of written lyrics including
formatting and punctuation, which leads to a potential misalignment with the
creative products of musicians and songwriters as well as listeners'
experiences. For example, line breaks are important in conveying information
about rhythm, emotional emphasis, rhyme, and high-level structure. To address
this issue, we introduce Jam-ALT, a new lyrics transcription benchmark based on
the JamendoLyrics dataset. Our contribution is twofold. Firstly, a complete
revision of the transcripts, geared specifically towards ALT evaluation by
following a newly created annotation guide that unifies the music industry's
guidelines, covering aspects such as punctuation, line breaks, spelling,
background vocals, and non-word sounds. Secondly, a suite of evaluation metrics
designed, unlike the traditional word error rate, to capture such phenomena. We
hope that the proposed benchmark contributes to the ALT task, enabling more
precise and reliable assessments of transcription systems and enhancing the
user experience in lyrics applications such as subtitle renderings for live
captioning or karaoke.

为了解决自动歌词转录中忽略了格式和标点等细微差别的问题，我们引入了 Jam-ALT，这是一个新的歌词转录基准，基于 JamendoLyrics 数据集，包括修订的转录以及用于评估的指标，旨在提高转录系统的准确性和可靠性。

Jam-ALT: 一份注重格式的歌词转录基准

Jam-ALT: A Formatting-Aware Lyrics Transcription Benchmark

Emotion recognition from a given music track has heavily relied on acoustic
features, social tags, and metadata but is seldom focused on lyrics. There are
no datasets of Indian language songs that contain both valence and arousal
manual ratings of lyrics. We present a new manually annotated dataset of Telugu
songs' lyrics collected from Spotify with valence and arousal annotated on a
discrete scale. A fairly high inter-annotator agreement was observed for both
valence and arousal. Subsequently, we create two music emotion recognition
models by using two classification techniques to identify valence, arousal and
respective emotion quadrant from lyrics. Support vector machine (SVM) with term
frequency-inverse document frequency (TF-IDF) features and fine-tuning the
pre-trained XLMRoBERTa (XLM-R) model were used for valence, arousal and
quadrant classification tasks. Fine-tuned XLMRoBERTa performs better than the
SVM by improving macro-averaged F1-scores of 54.69%, 67.61%, 34.13% to 77.90%,
80.71% and 58.33% for valence, arousal and quadrant classifications,
respectively, on 10-fold cross-validation. In addition, we compare our lyrics
annotations with Spotify's annotations of valence and energy (same as arousal),
which are based on entire music tracks. The implications of our findings are
discussed. Finally, we make the dataset publicly available with lyrics,
annotations and Spotify IDs.

通过使用分类技术和 XLMRoBERTa 模型，为印度语歌曲的歌词建立了一个手动注释的数据集，并进行了音乐情感识别模型的测试。