feat: add experiment configs for logit+cosine combo and BigVGAN decoder fine-tuning

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-04-10 16:48:21 +02:00
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{
"name": "bigvgan_optimized_dataset",
"description": "BigVGAN fine-tuning on optimized dataset (134 clips, 44.1kHz, LUFS-normalized). Standard mode (no LoRA) — trains decoder to faithfully reconstruct target domain audio from mel spectrograms. Uses optimal config from prior sweeps.",
"data_dir": "/media/unraid/davinci/Selva/BJ/features_v2_improved/",
"output_root": "/media/unraid/davinci/Selva/BJ/experiment/bigvgan_optimized_dataset",
"base": {
"train_mode": "snake_alpha_only",
"steps": 5000,
"lr": 1e-4,
"batch_size": 8,
"segment_seconds": 0.5,
"lambda_l2sp": 1e-3,
"use_gafilter": true,
"gafilter_kernel_size": 9,
"lambda_phase": 1.0,
"save_every": 1000,
"seed": 42
},
"experiments": [
{
"id": "standard_5k",
"description": "Standard mode: mel from clean FLAC → BigVGAN → reconstruct FLAC. No LoRA. Directly improves VAE roundtrip quality."
},
{
"id": "disc_fm_5k",
"description": "Standard mode + discriminator feature matching. Tests if perceptual loss helps on clean audio reconstruction.",
"discriminator_path": "/media/unraid/davinci/Selva/BJ/experiment/bigvgan_discriminator_optimizer.pt"
},
{
"id": "standard_10k",
"description": "Extended 10k steps. More data passes on 134 clips may extract more from the optimized dataset.",
"steps": 10000
}
]
}
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{
"name": "lora_logit_cosine_combo",
"description": "Combine the two best findings from optimized dataset sweep: logit-normal timestep sampling + cosine LR schedule. Both individually outperformed baseline by large margins (56% and 68% lower loss). Tests if gains stack.",
"data_dir": "/media/unraid/davinci/Selva/BJ/features_v2_improved/",
"output_root": "/media/unraid/davinci/Selva/BJ/experiment/lora_logit_cosine_combo",
"base": {
"rank": 128,
"lr": 3e-4,
"steps": 5000,
"batch_size": 16,
"warmup_steps": 100,
"save_every": 1000,
"seed": 42,
"init_mode": "pissa",
"use_rslora": true,
"target": "attn.qkv",
"timestep_mode": "uniform",
"lr_schedule": "constant"
},
"experiments": [
{
"id": "logit_normal_cosine",
"description": "Logit-normal timesteps + cosine LR decay. Combining the two best individual improvements.",
"timestep_mode": "logit_normal",
"lr_schedule": "cosine"
},
{
"id": "logit_normal_control",
"description": "Control: logit-normal only (constant LR). Reproduces previous winner for direct comparison.",
"timestep_mode": "logit_normal"
}
]
}