# SongPrep: A Preprocessing Framework and End-to-end Model for Full-song Structure Parsing and Lyrics Transcription

arXiv: https://arxiv.org/abs/2509.17404 · HTML: https://mlsanity.com/paper/2509.17404/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 16 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Zipformer: A faster and better encoder for automatic speech recognition](https://mlsanity.com/paper/2310.11230/) (2023)
- [MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization](https://mlsanity.com/paper/2501.01108/) (2025)
- [GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks](https://mlsanity.com/paper/2409.13832/) (2024)
- [All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio](https://mlsanity.com/paper/2307.16425/) (2023)
- [LeVo: High-Quality Song Generation with Multi-Preference Alignment](https://mlsanity.com/paper/2506.07520/) (2025)
- [SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement](https://mlsanity.com/paper/2506.07634/) (2025)
- [AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data](https://mlsanity.com/paper/2309.13905/) (2023)
- [Towards Hallucination-Free Music: A Reinforcement Learning Preference Optimization Framework for Reliable Song Generation](https://mlsanity.com/paper/2508.05011/) (2025)

## Built on by (most cited first, 5 of 5)

- [SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment](https://mlsanity.com/paper/2604.25937/) (2026)
- [LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training](https://mlsanity.com/paper/2606.30642/) (2026)
- [Tutti: Expressive Multi-Singer Synthesis via Structure-Level Timbre Control and Vocal Texture Modeling](https://mlsanity.com/paper/2602.08233/) (2026)
- [VocalParse: Towards Unified and Scalable Singing Voice Transcription with Large Audio Language Models](https://mlsanity.com/paper/2605.04613/) (2026)
- [MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation](https://mlsanity.com/paper/2607.26698/) (2026)

## Abstract (arXiv)

Artificial Intelligence Generated Content (AIGC) is currently a popular research area. Among its various branches, song generation has attracted growing interest. Despite the abundance of available songs, effective data preparation remains a significant challenge. Converting these songs into training-ready datasets typically requires extensive manual labeling, which is both time consuming and costly. To address this issue, we propose SongPrep, an automated preprocessing pipeline designed specifically for song data. This framework streamlines key processes such as source separation, structure analysis, and lyric recognition, producing structured data that can be directly used to train song generation models. Furthermore, we introduce SongPrepE2E, an end-to-end structured lyrics recognition model based on pretrained language models. Without the need for additional source separation, SongPrepE2E is able to analyze the structure and lyrics of entire songs and provide precise timestamps. By leveraging context from the whole song alongside pretrained semantic knowledge, SongPrepE2E achieves low Diarization Error Rate (DER) and Word Error Rate (WER) on the proposed SSLD-200 dataset. Downstream tasks demonstrate that training song generation models with the data output by SongPrepE2E enables the generated songs to closely resemble those produced by humans.