Automatic Lyrics Transcription
7 papers with code • 5 benchmarks • 1 datasets
Automatic Lyrics Transcription is the task of transcribing singing voice from audio into text.
Benchmarks
These leaderboards are used to track progress in Automatic Lyrics Transcription
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Libraries
Use these libraries to find Automatic Lyrics Transcription models and implementations
Jam-ALT
Most implemented papers
Automatic Lyrics Transcription using Dilated Convolutional Neural Networks with Self-Attention
Speech recognition is a well developed research field so that the current state of the art systems are being used in many applications in the software industry, yet as by today, there still does not exist such robust system for the recognition of words and sentences from singing voice.
MSTRE-Net: Multistreaming Acoustic Modeling for Automatic Lyrics Transcription
This paper makes several contributions to automatic lyrics transcription (ALT) research.
Computational Pronunciation Analysis in Sung Utterances
Recent automatic lyrics transcription (ALT) approaches focus on building stronger acoustic models or in-domain language models, while the pronunciation aspect is seldom touched upon.
Music-robust Automatic Lyrics Transcription of Polyphonic Music
To improve the robustness of lyrics transcription to the background music, we propose a strategy of combining the features that emphasize the singing vocals, i. e. music-removed features that represent singing vocal extracted features, and the features that capture the singing vocals as well as the background music, i. e. music-present features.
LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT
We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets, even in challenging genres such as rock and metal.
Adapting pretrained speech model for Mandarin lyrics transcription and alignment
With the use of data augmentation and source separation model, results show that the proposed method achieves a character error rate of less than 18% on a Mandarin polyphonic dataset for lyrics transcription, and a mean absolute error of 0. 071 seconds for lyrics alignment.
Jam-ALT: A Formatting-Aware Lyrics Transcription Benchmark
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.
