diff --git a/experiments/vocoder_finetune.json b/experiments/vocoder_finetune.json
new file mode 100644
index 0000000..c85d5ba
--- /dev/null
+++ b/experiments/vocoder_finetune.json
@@ -0,0 +1,30 @@
+{
+  "name": "vocoder_finetune",
+  "description": "Single run with fine-tuned BJ BigVGAN vocoder injected. Validates vocoder integration with LoRA training. Best known config: lr=3e-4, rank=128.",
+  "data_dir": "/media/unraid/davinci/Selva/BJ/features",
+  "output_root": "/media/unraid/davinci/Selva/BJ/experiment/vocoder_finetune",
+  "base": {
+    "steps": 10000,
+    "rank": 128,
+    "alpha": 0.0,
+    "lr": 3e-4,
+    "batch_size": 16,
+    "warmup_steps": 200,
+    "grad_accum": 1,
+    "save_every": 2000,
+    "seed": 42,
+    "target": "attn.qkv",
+    "timestep_mode": "uniform",
+    "logit_normal_sigma": 1.0,
+    "curriculum_switch": 0.6,
+    "lora_dropout": 0.0,
+    "lora_plus_ratio": 1.0,
+    "lr_schedule": "constant"
+  },
+  "experiments": [
+    {
+      "id": "r128_lr_3e4_bj_vocoder",
+      "description": "lr=3e-4 rank=128 with fine-tuned BJ BigVGAN vocoder. Direct comparison baseline against previous best g1_r128_lr_3e4."
+    }
+  ]
+}
diff --git a/nodes/selva_dataset_pipeline.py b/nodes/selva_dataset_pipeline.py
index 86772f2..f71434d 100644
--- a/nodes/selva_dataset_pipeline.py
+++ b/nodes/selva_dataset_pipeline.py
@@ -24,6 +24,19 @@ from .utils import SELVA_CATEGORY
 AUDIO_DATASET = "AUDIO_DATASET"
 
 _AUDIO_EXTS = {".wav", ".flac", ".mp3", ".ogg", ".aac", ".m4a"}
+_SOUNDFILE_EXTS = {".wav", ".flac", ".ogg"}  # handled natively without FFmpeg
+
+
+def _load_audio(path: Path):
+    """Load audio file. Uses soundfile for WAV/FLAC/OGG to avoid torchcodec/FFmpeg issues."""
+    if path.suffix.lower() in _SOUNDFILE_EXTS:
+        import soundfile as sf
+        wav_np, sr = sf.read(str(path), dtype="float32", always_2d=True)  # [L, C]
+        wav = torch.from_numpy(wav_np).T.unsqueeze(0)                      # [1, C, L]
+    else:
+        wav, sr = torchaudio.load(str(path))   # [C, L]
+        wav = wav.unsqueeze(0).float()          # [1, C, L]
+    return wav, sr
 
 
 class SelvaDatasetLoader:
@@ -58,8 +71,7 @@ class SelvaDatasetLoader:
         dataset = []
         for f in sorted(files):
             try:
-                wav, sr = torchaudio.load(str(f))          # [C, L]
-                wav = wav.unsqueeze(0).float()              # [1, C, L]
+                wav, sr = _load_audio(f)
                 dataset.append({"waveform": wav, "sample_rate": sr, "name": f.stem})
             except Exception as e:
                 print(f"[DatasetLoader] Skipping {f.name}: {e}", flush=True)