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BACH: Bar-level AI Composing Helper

> "Via Score to Performance: Efficient Human-Controllable Long Song Generation with Bar-Level Symbolic Notation"
> ICASSP 2026 Submission – Pending Review

> Read it on: https://arxiv.org/abs/2508.01394


🎼 One-sentence Summary

BACH is the first human-editable, bar-level symbolic song generator:
LLM writes lyrics β†’ Transformer emits ABC score β†’ off-the-shelf renderers give minutes-long, Suno-level music.
1 B params, minute-level inference, SOTA open-source.


πŸ“¦ What is inside this repo (preview release)

Path Description
README.md This file
code/ inference code
example.mp3 an example song
fig/ Architecture figure

πŸ—οΈ Model Architecture (one glance)

User prompt Qwen3 β€” lyrics & style tags BACH-1B Decoder-Only Transformer ABC score (Dual-NTP + Chain-of-Score) ABC β†’ MIDI β†’ FluidSynth + VOCALOID Stereo mix

Component Key idea
Dual-NTP Predict {vocal_patch, accomp_patch} jointly every step
Chain-of-Score Section tags [START:Chorus] ... [END:Chorus] for long coherence
Bar-stream patch 16-char non-overlapping patches per bar

πŸ§ͺ Quick start (CPU friendly)

# 1. Clone
git clone https://github.com/your-github/BACH.git
cd BACH

# 2. Install
pip install -r requirements.txt        # transformers>=4.41 mido abcpy fluidsynth

# 3. Generate ABC
python bach/generate.py \
    --prompt "A rainy-day lo-fi hip-hop song about missing the last train" \
    --out_abc demo/rainy_lofi.abc

# 4. Render audio

🎧 Listen now

example.mp3 is ready for you, it's a whole song. You can compare it with SunoπŸ™‚

Full release upon related paper acceptance

  • Complete training set (ABC + lyrics + structure labels)
  • BACH-1B weights (Transformers format)
  • Training scripts (multiphase + multitask + ICL)
  • Complete Code

πŸ“Ž Citation

Paper is released on Arxiv,

@misc{wang2025scoreperformanceefficienthumancontrollable,
      title={Via Score to Performance: Efficient Human-Controllable Long Song Generation with Bar-Level Symbolic Notation}, 
      author={Tongxi Wang and Yang Yu and Qing Wang and Junlang Qian},
      year={2025},
      eprint={2508.01394},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2508.01394}, 
}

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A Music GenAI (ICASSP 2026 Submission)

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