Soundboard

Quick facts

Usage

Load with the Locutius training package:

import torch
from huggingface_hub import hf_hub_download
from locutius_train.config import TrainConfig
from locutius_train.network import AttnUNetF, SinusoidalTemporalEmbedding
from locutius_train.diffusion import Diffusion
from locutius_train.representation import WaveformToInput, InputToWaveform
from locutius_train.restore import restore_spectrogram

ckpt_path = hf_hub_download(repo_id="protodotdesign/Soundboard", filename="model.pt")
sd = torch.load(ckpt_path, map_location="cuda", weights_only=False)

cfg = TrainConfig()
model = AttnUNetF(
    n_updown_levels=cfg.model.n_updown_levels,
    in_channels=cfg.model.in_channels,
    hidden_channels=list(cfg.model.hidden_channels),
    out_channels=cfg.model.out_channels,
    emb_channels=cfg.diffusion.n_timestep_channels,
    band_embedding_dim=cfg.model.band_embedding_dim,
    n_attn_heads=cfg.model.n_attn_heads,
    attention_levels=list(cfg.model.attention_levels),
    use_attn_input_norm=cfg.model.use_attn_input_norm,
    num_res_blocks=cfg.model.num_res_blocks,
).to("cuda").eval()
model.load_state_dict(sd["model"])

See restore.py in the Locutius repo for a complete CLI that takes a clean source, applies the calibrated festival-corruption profile, and runs the reverse Schrödinger Bridge to produce a restored output.

Calibrated corruption profile

This model was trained against a single calibrated profile recovered from a real (studio FLAC, festival M4A) pair via per-kick local Wiener deconvolution. The profile is bundled in profile.json:

{
"name": "edc_festival",
"ir_path": "../impulses/EchoThief/Brutalism/San Diego Supercomputer Center Outdoor Patio California.wav",
"delay_ms_range": [
15.0,
25.0
],
"studio_gain_range": [
0.6,
0.7
],
"room_gain_range": [
0.55,
0.65
]
}

Each training-step corruption draws fresh values from these ranges, so the model has been exposed to ~50,000 distinct delay/blend combinations within the same venue character.

Training data

Trained on a focused subset of electronic music FLACs. No festival recordings or other licensed audio were stored or distributed — only the studio source material was used; festival-corrupted versions were synthesized on-the-fly from the calibrated profile during each training step.

Limitations

• Single profile: trained against one calibrated venue (edc_festival). Performance on festival recordings from very different venues / mix chains will degrade.
• Electronic music bias: training set was EDM-heavy. Restoration quality on rock, classical, or vocal-led material may be uneven.
• No crowd-noise model: the calibrated profile didn't include additive crowd-noise (no real crowd recordings were available during calibration). Recordings with heavy crowd vocals may have residual artifacts.
• Non-commercial use only — see the license below.

License

Dual non-commercial license:

• NVIDIA Source Code License for A2SB (the upstream license inherited from the A2SB base checkpoint)
• PolyForm Noncommercial 1.0.0 (additional terms on top, source-availability + patent retaliation)

You must comply with both licenses. Use is restricted to research and evaluation only — no commercial use is permitted. See LICENSING.md for the full plain-English breakdown.

Citation

If you use this model in research, please cite the upstream A2SB paper and reference this fine-tune:

@misc{soundboard,
  title={Soundboard: festival audio restoration via profile-calibrated Schrödinger Bridge fine-tuning},
  author={Locutius},
  year={2026},
  howpublished={\url{https://huggingface.co/protodotdesign/Soundboard}},
}