Towards Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription
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Automatic speech recognition (ASR) has progressed significantly in recent years due to large-scale datasets and the paradigm of self-supervised learning (SSL) methods. However, as its counterpart problem in the singing domain, automatic lyric transcription (ALT) suffers from limited data and degraded intelligibility of sung lyrics, which has caused it to develop at a slower pace. To fill in the performance gap between ALT and ASR, we attempt to exploit the similarities between speech and singing. In this work, we propose a transfer-learning-based ALT solution that takes advantage of these similarities by adapting wav2vec 2.0, an SSL ASR model, to the singing domain. We maximize the effectiveness of transfer learning by exploring the influence of different transfer starting points. We further enhance the performance by extending the original CTC model to a hybrid CTC/attention model. Our method surpasses previous approaches by a large margin on various ALT benchmark datasets. Further experiment shows that, with even a tiny proportion of training data, our method still achieves competitive performance.
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