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affectscore-ace-step-r32-20260629
HiiragiLee/affectscore-ace-step-r32-20260629
affectscore-ace-step-r32-20260629 is a machine learning model from HiiragiLee. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
LoRA adapter for ACE-Step v1 3.5B fine-tuned for real-time affect-conditioned music generation in narrative-driven games. This is the primary checkpoint selected by held-out MER accuracy.
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At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Jun 30, 2026
- Updated
- Aug 7, 2026
- SHA
- 021c499e
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Model card
From the Hugging Face model README
AffectScore -- LoRA r=32 (full, selected)
LoRA adapter for ACE-Step v1 3.5B fine-tuned for real-time affect-conditioned music generation in narrative-driven games. This is the primary checkpoint selected by held-out MER accuracy.
Part of the AffectScore system. Code and Colab notebooks: github.com/LeeTgk/affectscore (link active after publication)
What this model does
AffectScore conditions music generation on a two-layer signal:
- Layer 1 -- Designer intent (authored per scene):
scene_valence,scene_arousal,arc_positionon the Russell circumplex. Converted to mood-word text prompts (e.g. "melancholic, tense") via a lookup table. - Layer 2 -- Player engagement (runtime behavioral):
choice_latency_norm,dwell_deviation_norm,interaction_rate_norm. Applied as an inference-time text suffix that modulates intensity and texture around the Layer 1 anchor without overriding it.
LoRA targets cross_attn.q_proj and cross_attn.v_proj only. Self-attention and FFN stay frozen.
Training
| Parameter | Value |
|---|---|
| Base model | ACE-Step/ACE-Step-v1-3.5B |
| LoRA rank | 32 |
| LoRA targets | cross_attn.q_proj, cross_attn.v_proj |
| use_rslora | True |
| lora_dropout | 0.05 |
| Trainable parameters | 7.86M (0.237% of total) |
| Training clips | 1,571 (CC0/CC-BY, CLAP-quality filtered) |
| Epochs | 50 |
| Learning rate | 1e-4 |
| Batch size | 8 |
| Quadrant weighting | max-quadrant-weight 5.0 (compensates Q2 scarcity) |
| Hardware | NVIDIA A100 (Google Colab) |
Training dataset: https://zenodo.org/records/21830658
Evaluation
All metrics computed on 200 held-out clips (50 per V-A quadrant) at 40 diffusion steps.
Audio quality (EVAL-02)
| FAD-MERT ↓ | CLAP-score ↑ | KLD ↓ | PCE ↑ |
|---|---|---|---|
| 33.06 | −0.45 | 1.17 | 3.57 |
Note: FAD-MERT is higher than the no-lora baseline because LoRA fine-tuning specializes the model distribution toward the training corpus. This is expected and not evidence of degraded quality -- see the paper for full discussion.
Emotion controllability (EVAL-03)
Pearson r between designer-intended and music2emo-predicted V-A on 200 held-out clips. No variant achieves significant positive correlation (critical value |r| ≥ 0.138 at α=0.05); null result attributed to Q3-heavy training distribution (78.1% melancholic clips) and text-space ceiling.
| r_valence | RMSE_valence | r_arousal | RMSE_arousal |
|---|---|---|---|
| −0.171 | 0.520 | −0.087 | 0.292 |
How to use
This adapter is designed to run inside the AffectScore FastAPI server, which handles conditioning, generation, and WAV encoding end-to-end. Direct PEFT loading is possible but the conditioning pipeline is non-trivial -- refer to server/affectscore_server.py in the code repository.
python server/affectscore_server.py \
--lora HiiragiLee/affectscore-ace-step-r32-20260629
The server exposes a /generate endpoint accepting a 6-float affect embedding (Layer 1 + Layer 2), style prompt, chunk duration, and step count.
Ablation variants
| Repo | Description |
|---|---|
| This model | Full two-layer conditioning, r=32 (selected) |
| affectscore-ace-step-r16-20260629 | Lower capacity, r=16 |
| affectscore-ace-step-r64-20260629 | r=64, diverged at epoch 40 -- not recommended |
| affectscore-ace-step-r32-20260629-no-style | No Layer 2 style descriptor (ablation) |
| affectscore-ace-step-r32-20260629-no-affect | No Layer 1 V-A conditioning (ablation) |
| affectscore-ace-step-r32-20260629-no-lora | Vanilla ACE-Step base (reconstruction reference) |
Citation
Citation will be added once the paper is published. If you use this model before then, please link to this repository.
- Library
- peft
- License
- mit
- Created
- Jun 30, 2026
- Updated
- Aug 7, 2026