Ethanfel/ComfyUI-SelVA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat: save ground truth spectrogram once for direct comparison

Browse Source 
Writes _gt_spec.png from ref_mel before training starts so each step's
_spec.png can be compared against the unmodified vocoder roundtrip target.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-04-09 03:05:47 +02:00
parent 304d9d01bf
commit f17f6f0863
1 changed files with 7 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files
nodes/selva_bigvgan_trainer.py
+7 -3
View File
@@ -305,10 +305,15 @@ def _do_train(vocoder, mel_converter, clips,
torch.manual_seed(seed) torch.manual_seed(seed)
random.seed(seed) random.seed(seed)
# Reference segment for eval samples — always clip 0, start 0 # Reference segment for eval samples — always clip 0, full length
ref_wav = clips[0].to(device, dtype) # full first clip [T] ref_wav = clips[0].to(device, dtype) # full first clip [T]
ref_mel = mel_converter(ref_wav.unsqueeze(0)) # [1, n_mels, T_mel] ref_mel = mel_converter(ref_wav.unsqueeze(0)) # [1, n_mels, T_mel]
# Ground-truth spectrogram — saved once alongside baseline for comparison
gt_spec_path = out_path.parent / f"{out_path.stem}_gt_spec.png"
_save_spectrogram(gt_spec_path, ref_mel)
print(f"[BigVGAN] GT spectrogram: {gt_spec_path}", flush=True)
def _save_sample(label): def _save_sample(label):
try: try:
voc_device = next(vocoder.parameters()).device voc_device = next(vocoder.parameters()).device
@@ -321,11 +326,10 @@ def _do_train(vocoder, mel_converter, clips,
wav_path = out_path.parent / f"{out_path.stem}_{label}.wav" wav_path = out_path.parent / f"{out_path.stem}_{label}.wav"
spec_path = out_path.parent / f"{out_path.stem}_{label}_spec.png" spec_path = out_path.parent / f"{out_path.stem}_{label}_spec.png"
_save_wav(wav_path, wav.squeeze(0), sample_rate) _save_wav(wav_path, wav.squeeze(0), sample_rate)
# Compute mel of the vocoded output for visual comparison
with torch.no_grad(): with torch.no_grad():
pred_mel = mel_converter(wav.squeeze(1).to(mel_converter.mel_basis.device)) pred_mel = mel_converter(wav.squeeze(1).to(mel_converter.mel_basis.device))
_save_spectrogram(spec_path, pred_mel) _save_spectrogram(spec_path, pred_mel)
print(f"[BigVGAN] Sample saved: {wav_path} spec: {spec_path}", flush=True) print(f"[BigVGAN] Sample: {wav_path} spec: {spec_path}", flush=True)
except Exception as e: except Exception as e:
print(f"[BigVGAN] Sample save failed ({label}): {e}", flush=True) print(f"[BigVGAN] Sample save failed ({label}): {e}", flush=True)