Ethanfel
eece79ccae
Two bugs: 1. _DiscriminatorR used channels=32 but the BigVGAN pretrained discriminator checkpoint has channels=128. All convs in _DiscriminatorR now use 128, matching the checkpoint architecture so state_dict loads without error. 2. BigVGAN trainer OOM: SelVA generator and other ComfyUI models remain in VRAM during training (~90 GiB used). Add unload_all_models() + cache flush before the training loop to reclaim VRAM headroom. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
714 lines
31 KiB
Python
714 lines
31 KiB
Python
"""SelVA BigVGAN Vocoder Fine-tuner.
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Tier-1 approach based on research: snake alpha fine-tuning + L2-SP anchor
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regularization + optional frozen discriminator feature matching.
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Root cause of harmonic smearing with plain mel/STFT losses:
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Spectral L1 minimizes expected reconstruction error — averaging over
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high-variance harmonics. This is a loss-function topology problem, not
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an LR/step-count problem. The fix is either (a) restrict trainable params
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so the model lacks capacity to smear, or (b) use a perceptual loss that
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penalizes harmonic averaging.
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Tier-1 implementation:
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1. snake_alpha_only mode — only tune ~5K per-channel α parameters in
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Snake/SnakeBeta activations. These control harmonic periodicity per
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channel. With only 5K trainable params, the model physically cannot
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reshape the spectrum enough to cause the "green smear".
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2. L2-SP anchor loss — penalizes parameter drift from pretrained values
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(strictly better than weight decay, which anchors to zero).
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3. Frozen discriminator feature matching — if a BigVGAN discriminator
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checkpoint is provided, the pretrained MPD+MRD networks are used as
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fixed perceptual feature extractors. Feature matching loss penalizes
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harmonic smearing directly without any GAN instability.
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Save format: {'generator': vocoder.state_dict()} — same as the original
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BigVGAN checkpoint so it can be loaded with SelVA BigVGAN Loader.
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"""
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import random
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import threading
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from pathlib import Path
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchaudio
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import comfy.utils
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import comfy.model_management
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import folder_paths
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from .utils import SELVA_CATEGORY, get_device, soft_empty_cache
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# ---------------------------------------------------------------------------
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# Minimal MPD + MRD discriminators matching BigVGAN pretrained checkpoint keys
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# ---------------------------------------------------------------------------
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def _get_pad(kernel_size, dilation=1):
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return int((kernel_size * dilation - dilation) / 2)
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class _DiscriminatorP(nn.Module):
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"""Multi-Period Discriminator sub-module (HiFi-GAN / BigVGAN style)."""
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def __init__(self, period):
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super().__init__()
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self.period = period
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from torch.nn.utils.parametrizations import weight_norm
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norm = weight_norm
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self.convs = nn.ModuleList([
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norm(nn.Conv2d(1, 32, (5, 1), (3, 1), (_get_pad(5, 1), 0))),
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norm(nn.Conv2d(32, 128, (5, 1), (3, 1), (_get_pad(5, 1), 0))),
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norm(nn.Conv2d(128, 512, (5, 1), (3, 1), (_get_pad(5, 1), 0))),
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norm(nn.Conv2d(512, 1024,(5, 1), (3, 1), (_get_pad(5, 1), 0))),
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norm(nn.Conv2d(1024,1024,(5, 1), 1, (_get_pad(5, 1), 0))),
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])
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self.conv_post = norm(nn.Conv2d(1024, 1, (3, 1), 1, (1, 0)))
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def forward(self, x):
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fmap = []
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b, c, t = x.shape
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if t % self.period != 0:
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, 0.1)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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return fmap
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class _MultiPeriodDiscriminator(nn.Module):
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def __init__(self):
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super().__init__()
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self.discriminators = nn.ModuleList([
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_DiscriminatorP(p) for p in [2, 3, 5, 7, 11]
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])
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def forward(self, y):
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fmaps = []
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for d in self.discriminators:
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fmaps.extend(d(y))
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return fmaps
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class _DiscriminatorR(nn.Module):
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"""Multi-Resolution Discriminator sub-module."""
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def __init__(self, fft_size, shift_size, win_length):
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super().__init__()
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self.fft_size = fft_size
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self.shift_size = shift_size
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self.win_length = win_length
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from torch.nn.utils.parametrizations import weight_norm
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norm = weight_norm
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self.convs = nn.ModuleList([
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norm(nn.Conv2d(1, 128, (3, 9), padding=(1, 4))),
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norm(nn.Conv2d(128, 128, (3, 9), stride=(1, 2), padding=(1, 4))),
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norm(nn.Conv2d(128, 128, (3, 9), stride=(1, 2), padding=(1, 4))),
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norm(nn.Conv2d(128, 128, (3, 9), stride=(1, 2), padding=(1, 4))),
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norm(nn.Conv2d(128, 128, (3, 3), padding=(1, 1))),
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])
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self.conv_post = norm(nn.Conv2d(128, 1, (3, 3), padding=(1, 1)))
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def spectrogram(self, x):
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"""x: [B, 1, T] → [B, 1, freq, time]"""
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n, hop, win = self.fft_size, self.shift_size, self.win_length
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window = torch.hann_window(win, device=x.device)
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x = x.squeeze(1) # [B, T]
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pad = (win - hop) // 2
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x = F.pad(x, (pad, pad + (win - hop) % 2), mode="reflect")
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x = torch.stft(x, n, hop, win, window, center=False, return_complex=True)
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x = x.abs().unsqueeze(1) # [B, 1, freq, time]
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return x
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def forward(self, x):
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fmap = []
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x = self.spectrogram(x)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, 0.1)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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return fmap
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class _MultiResolutionDiscriminator(nn.Module):
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def __init__(self):
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super().__init__()
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resolutions = [(1024, 120, 600), (2048, 240, 1200), (512, 50, 240)]
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self.discriminators = nn.ModuleList([
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_DiscriminatorR(*r) for r in resolutions
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])
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def forward(self, y):
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fmaps = []
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for d in self.discriminators:
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fmaps.extend(d(y))
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return fmaps
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def _feature_matching_loss(fmaps_real, fmaps_gen):
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"""L1 between paired feature map lists (both already detach-safe for real)."""
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loss = torch.zeros(1, device=fmaps_gen[0].device)
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for fr, fg in zip(fmaps_real, fmaps_gen):
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T = min(fr.shape[-1], fg.shape[-1])
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loss = loss + F.l1_loss(fg[..., :T], fr[..., :T].detach())
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return loss / len(fmaps_real)
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# ---------------------------------------------------------------------------
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# Utility helpers
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# ---------------------------------------------------------------------------
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def _save_spectrogram(path, mel_tensor):
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"""Save mel spectrogram [1, n_mels, T] as a PNG using PIL (no matplotlib dep)."""
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try:
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from PIL import Image
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import numpy as np
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mel = mel_tensor.squeeze(0).float().cpu().numpy() # [n_mels, T]
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mel = mel[::-1] # low freq at bottom
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lo, hi = mel.min(), mel.max()
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if hi > lo:
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mel = (mel - lo) / (hi - lo)
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else:
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mel = mel - lo
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img_u8 = (mel * 255).clip(0, 255).astype(np.uint8)
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# Simple blue→green→yellow colour map (viridis-ish) via LUT
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lut_r = np.array([int(max(0, min(255, 255 * (v * 2 - 1)))) for v in np.linspace(0, 1, 256)], dtype=np.uint8)
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lut_g = np.array([int(max(0, min(255, 255 * (1 - abs(v * 2 - 1))))) for v in np.linspace(0, 1, 256)], dtype=np.uint8)
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lut_b = np.array([int(max(0, min(255, 255 * (1 - v * 2)))) for v in np.linspace(0, 1, 256)], dtype=np.uint8)
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r = Image.fromarray(lut_r[img_u8])
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g = Image.fromarray(lut_g[img_u8])
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b = Image.fromarray(lut_b[img_u8])
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Image.merge("RGB", (r, g, b)).save(str(path))
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except Exception as e:
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print(f"[BigVGAN] Spectrogram save failed: {e}", flush=True)
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def _save_wav(path, wav_tensor, sample_rate):
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"""Save [channels, samples] float32 tensor to .wav.
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Tries torchaudio first; falls back to soundfile when the ffmpeg/torchcodec
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backend is unavailable (same environment constraint as _load_wav).
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"""
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try:
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torchaudio.save(str(path), wav_tensor, sample_rate)
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return
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except Exception:
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pass
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import soundfile as sf
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data = wav_tensor.numpy()
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if data.ndim == 2:
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data = data.T # soundfile expects [samples, channels]
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sf.write(str(path), data, sample_rate)
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def _load_wav(path):
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"""Load audio file to [channels, samples] float32 tensor.
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Tries torchaudio first; falls back to soundfile for wav/flac when the
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ffmpeg/torchcodec backend is unavailable (e.g. libavutil soname mismatch).
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"""
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try:
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return torchaudio.load(str(path))
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except Exception:
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pass
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# soundfile fallback — handles wav, flac, ogg natively without ffmpeg
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import soundfile as sf
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data, sr = sf.read(str(path), dtype="float32", always_2d=True)
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wav = torch.from_numpy(data.T) # [channels, samples]
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return wav, sr
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# Multi-resolution STFT windows — same three resolutions as BigVGAN discriminator config.
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_STFT_RESOLUTIONS = [
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(1024, 120, 600),
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(2048, 240, 1200),
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(512, 50, 240),
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]
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def _stft_mag(wav, n_fft, hop_length, win_length, device):
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"""Magnitude STFT. wav: [B, T] → [B, n_fft//2+1, T']"""
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window = torch.hann_window(win_length, device=device)
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spec = torch.stft(
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wav, n_fft=n_fft, hop_length=hop_length, win_length=win_length,
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window=window, center=True, return_complex=True,
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)
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return spec.abs()
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def _multi_resolution_stft_loss(pred_wav, target_wav, device):
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"""Average L1 mag loss across three STFT resolutions. inputs: [B, 1, T]"""
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pred = pred_wav.squeeze(1) # [B, T]
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target = target_wav.squeeze(1)
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loss = torch.zeros(1, device=device)
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for n_fft, hop, win in _STFT_RESOLUTIONS:
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pm = _stft_mag(pred, n_fft, hop, win, device)
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tm = _stft_mag(target, n_fft, hop, win, device)
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T = min(pm.shape[-1], tm.shape[-1])
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loss = loss + F.l1_loss(pm[..., :T], tm[..., :T])
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return loss / len(_STFT_RESOLUTIONS)
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# ---------------------------------------------------------------------------
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# Node
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# ---------------------------------------------------------------------------
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class SelvaBigvganTrainer:
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OUTPUT_NODE = True
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CATEGORY = SELVA_CATEGORY
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FUNCTION = "train"
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("checkpoint_path",)
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OUTPUT_TOOLTIPS = ("Path to saved vocoder checkpoint — load with SelVA BigVGAN Loader.",)
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DESCRIPTION = (
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"Fine-tunes the BigVGAN vocoder (mel→waveform) on BJ audio clips. "
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"Default mode (snake_alpha_only) tunes only the ~5K Snake activation α "
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"parameters — cannot cause harmonic smearing. Add a discriminator path "
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"for perceptual feature matching loss. DiT and VAE stay frozen."
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)
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("SELVA_MODEL",),
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"data_dir": ("STRING", {
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"default": "",
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"tooltip": "Directory with BJ audio files (.wav/.flac/.mp3). Searched recursively.",
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}),
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"output_path": ("STRING", {
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"default": "bigvgan_bj.pt",
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"tooltip": "Where to save the fine-tuned vocoder. Relative paths → ComfyUI output dir.",
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}),
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"train_mode": (["snake_alpha_only", "all_params"], {
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"default": "snake_alpha_only",
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"tooltip": (
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"snake_alpha_only: only tune ~5K per-channel α parameters in Snake/SnakeBeta "
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"activations. These control harmonic periodicity. Cannot cause spectral smearing. "
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"all_params: tune all vocoder weights — set lambda_l2sp>0 to prevent drift."
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),
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}),
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"steps": ("INT", {
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"default": 2000, "min": 100, "max": 50000,
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"tooltip": "Training steps. 1000–2000 is a good first experiment with snake_alpha_only.",
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}),
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"lr": ("FLOAT", {
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"default": 1e-4, "min": 1e-6, "max": 1e-2, "step": 1e-5,
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"tooltip": "Learning rate. 1e-4 for snake_alpha_only, 1e-5 for all_params.",
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}),
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"batch_size": ("INT", {"default": 4, "min": 1, "max": 32}),
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"segment_seconds": ("FLOAT", {
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"default": 1.0, "min": 0.25, "max": 4.0, "step": 0.25,
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"tooltip": "Audio segment length per training sample in seconds.",
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}),
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"lambda_l2sp": ("FLOAT", {
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"default": 1e-3, "min": 0.0, "max": 0.1, "step": 1e-4,
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"tooltip": (
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"L2-SP anchor regularization: penalizes parameter drift from pretrained values. "
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"0 = disabled. 1e-3 is good for snake_alpha_only. "
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"Increase to 1e-2 for all_params to prevent catastrophic forgetting."
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),
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}),
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"save_every": ("INT", {"default": 500, "min": 50, "max": 10000}),
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"seed": ("INT", {"default": 42, "min": 0, "max": 0xFFFFFFFF}),
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},
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"optional": {
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"discriminator_path": ("STRING", {
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"default": "",
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"tooltip": (
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"Optional path to BigVGAN discriminator checkpoint "
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"(bigvgan_discriminator_optimizer.pt from the BigVGAN pretrained release). "
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"When provided, frozen MPD+MRD feature matching replaces mel L1 — "
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"the key fix for harmonic smearing. Leave empty to use mel+STFT losses only."
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),
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}),
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},
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}
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def train(self, model, data_dir, output_path, train_mode, steps, lr, batch_size,
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segment_seconds, lambda_l2sp, save_every, seed, discriminator_path=""):
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import traceback
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device = get_device()
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mode = model["mode"]
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dtype = model["dtype"]
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feature_utils = model["feature_utils"]
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mel_converter = feature_utils.mel_converter
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strategy = model["strategy"]
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if mode == "16k":
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vocoder = feature_utils.tod.vocoder.vocoder
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sample_rate = 16_000
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elif mode == "44k":
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vocoder = feature_utils.tod.vocoder
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sample_rate = 44_100
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else:
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raise ValueError(f"[BigVGAN] Unknown mode: {mode}")
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# Resolve paths
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data_dir = Path(data_dir.strip())
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if not data_dir.is_absolute():
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data_dir = Path(folder_paths.models_dir) / data_dir
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if not data_dir.exists():
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raise FileNotFoundError(f"[BigVGAN] data_dir not found: {data_dir}")
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out_path = Path(output_path.strip())
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if not out_path.is_absolute():
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out_path = Path(folder_paths.get_output_directory()) / out_path
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out_path.parent.mkdir(parents=True, exist_ok=True)
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disc_path = None
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if discriminator_path and discriminator_path.strip():
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disc_path = Path(discriminator_path.strip())
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if not disc_path.is_absolute():
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disc_path = Path(folder_paths.get_output_directory()) / disc_path
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if not disc_path.exists():
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raise FileNotFoundError(f"[BigVGAN] Discriminator checkpoint not found: {disc_path}")
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# Find and pre-load audio clips
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segment_samples = int(segment_seconds * sample_rate)
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audio_files = []
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for ext in ("*.wav", "*.flac", "*.mp3", "*.ogg", "*.aac"):
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audio_files.extend(data_dir.rglob(ext))
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if not audio_files:
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raise FileNotFoundError(f"[BigVGAN] No audio files found in {data_dir}")
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print(f"[BigVGAN] Loading {len(audio_files)} audio files...", flush=True)
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clips = []
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for af in audio_files:
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try:
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wav, sr = _load_wav(af)
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if wav.shape[0] > 1:
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wav = wav.mean(0, keepdim=True)
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if sr != sample_rate:
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wav = torchaudio.functional.resample(wav, sr, sample_rate)
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wav = wav.squeeze(0) # [L]
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if wav.shape[0] >= segment_samples:
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clips.append(wav.cpu())
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else:
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print(f" [BigVGAN] Skip {af.name}: shorter than {segment_seconds}s", flush=True)
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except Exception as e:
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print(f" [BigVGAN] Failed {af.name}: {e}", flush=True)
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traceback.print_exc()
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if not clips:
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raise RuntimeError(
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f"[BigVGAN] No usable clips found (need audio >= {segment_seconds}s)"
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)
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print(f"[BigVGAN] {len(clips)} clips ready mode={train_mode} "
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f"segment={segment_seconds}s steps={steps} lr={lr} "
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f"batch={batch_size} lambda_l2sp={lambda_l2sp}\n", flush=True)
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# Unload all other ComfyUI models (SelVA generator, etc.) to free VRAM
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# before starting training. BigVGAN + discriminator need the headroom.
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comfy.model_management.unload_all_models()
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soft_empty_cache()
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if strategy == "offload_to_cpu":
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feature_utils.to(device)
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soft_empty_cache()
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mel_converter.to(device)
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pbar = comfy.utils.ProgressBar(steps)
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# -----------------------------------------------------------------------
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# Run the entire training in a fresh thread.
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#
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# ComfyUI executes nodes inside torch.inference_mode(). Even with an inner
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# inference_mode(False) context, factory functions and operations may still
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# produce inference tensors in some environments (e.g. when the outer
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# context is set via an async wrapper or a third-party hook).
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#
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# torch.inference_mode is THREAD-LOCAL. A new thread always starts with
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# inference_mode disabled, so all tensor operations in the worker thread
|
||
# produce normal, autograd-compatible tensors — no flags to fight.
|
||
# -----------------------------------------------------------------------
|
||
_result = [None]
|
||
_exc = [None]
|
||
|
||
def _worker():
|
||
try:
|
||
_result[0] = _do_train(
|
||
vocoder, mel_converter, clips,
|
||
device, dtype, strategy, feature_utils,
|
||
segment_samples, sample_rate,
|
||
train_mode, steps, lr, batch_size, lambda_l2sp,
|
||
save_every, seed, out_path, disc_path, pbar,
|
||
)
|
||
except Exception as e:
|
||
_exc[0] = e
|
||
traceback.print_exc()
|
||
|
||
t = threading.Thread(target=_worker, daemon=True)
|
||
t.start()
|
||
t.join()
|
||
|
||
if _exc[0] is not None:
|
||
raise _exc[0]
|
||
return (_result[0],)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Training worker
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def _do_train(vocoder, mel_converter, clips,
|
||
device, dtype, strategy, feature_utils,
|
||
segment_samples, sample_rate,
|
||
train_mode, steps, lr, batch_size, lambda_l2sp,
|
||
save_every, seed, out_path, disc_path, pbar):
|
||
"""Execute training. Called in a fresh thread — no inference_mode active.
|
||
|
||
Even though inference_mode is off here, tensors created in the calling
|
||
thread's inference_mode carry the inference flag on the object itself.
|
||
Operations on inference tensors produce inference tensors regardless of
|
||
the current context. The ONLY way to strip the flag is to call .clone()
|
||
from outside inference_mode — which is exactly where we are now.
|
||
"""
|
||
import torch.nn as nn_mod
|
||
|
||
# ── Strip inference flag from all inputs that came from the main thread ──
|
||
# 1. Audio clips (loaded in ComfyUI's inference_mode).
|
||
clips = [c.clone() for c in clips]
|
||
|
||
# 2. mel_converter buffers (mel_basis, hann_window) — same origin.
|
||
for name, buf in list(mel_converter._buffers.items()):
|
||
if buf is not None:
|
||
mel_converter._buffers[name] = buf.clone()
|
||
|
||
# 3. Vocoder parameters are handled below with clone().detach().
|
||
# ─────────────────────────────────────────────────────────────────────────
|
||
|
||
torch.manual_seed(seed)
|
||
random.seed(seed)
|
||
|
||
# Reference segment for eval samples — always clip 0, full length
|
||
ref_wav = clips[0].to(device, dtype) # full first clip [T]
|
||
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):
|
||
try:
|
||
voc_device = next(vocoder.parameters()).device
|
||
mel = ref_mel.to(voc_device)
|
||
with torch.no_grad():
|
||
wav = vocoder(mel)
|
||
if wav.dim() == 2:
|
||
wav = wav.unsqueeze(1)
|
||
wav = wav.float().cpu().clamp(-1, 1)
|
||
wav_path = out_path.parent / f"{out_path.stem}_{label}.wav"
|
||
spec_path = out_path.parent / f"{out_path.stem}_{label}_spec.png"
|
||
_save_wav(wav_path, wav.squeeze(0), sample_rate)
|
||
with torch.no_grad():
|
||
pred_mel = mel_converter(wav.squeeze(1).to(mel_converter.mel_basis.device))
|
||
_save_spectrogram(spec_path, pred_mel)
|
||
print(f"[BigVGAN] Sample: {wav_path} spec: {spec_path}", flush=True)
|
||
except Exception as e:
|
||
print(f"[BigVGAN] Sample save failed ({label}): {e}", flush=True)
|
||
|
||
_save_sample("baseline")
|
||
|
||
# Sanitize all inference tensors in the vocoder.
|
||
# Three categories to handle (all loaded in ComfyUI's inference_mode):
|
||
#
|
||
# 1. Registered parameters (_parameters): covers bias, alpha, etc.
|
||
#
|
||
# 2. Plain tensor attributes in __dict__: torch.nn.utils.parametrize.
|
||
# remove_parametrizations() calls setattr(module, name, tensor) with
|
||
# a raw tensor, NOT nn.Parameter. Module.__setattr__ stores raw tensors
|
||
# in __dict__ (not _parameters), so our parameter loop misses them.
|
||
# This is how BigVGAN's conv.weight ends up invisible to _parameters.
|
||
# Fix: re-register as Parameter, which also makes them trainable.
|
||
#
|
||
# 3. Registered buffers (_buffers): Activation1d's anti-aliasing filter
|
||
# tensors. Not trainable, but operations on inference buffers produce
|
||
# inference tensor outputs — which breaks the backward graph mid-network.
|
||
# Fix: clone to strip the inference flag (not registered as parameters).
|
||
for module in vocoder.modules():
|
||
# Category 1: registered parameters
|
||
for pname, param in list(module._parameters.items()):
|
||
if param is not None:
|
||
module._parameters[pname] = nn_mod.Parameter(
|
||
param.data.clone().detach(), requires_grad=True
|
||
)
|
||
|
||
# Category 2: plain tensor attributes (e.g. weight left by remove_parametrizations)
|
||
for name, val in list(module.__dict__.items()):
|
||
if (isinstance(val, torch.Tensor)
|
||
and not isinstance(val, nn_mod.Parameter)
|
||
and name not in module._buffers
|
||
and name not in module._modules):
|
||
module.register_parameter(name, nn_mod.Parameter(val.clone()))
|
||
|
||
# Category 3: buffers (Activation1d filter, etc.) — clone, don't parametrize
|
||
for bname, buf in list(module._buffers.items()):
|
||
if buf is not None:
|
||
module._buffers[bname] = buf.clone()
|
||
|
||
# ── Training mode: select which parameters to train ──────────────────────
|
||
if train_mode == "snake_alpha_only":
|
||
alpha_params = []
|
||
for name, param in vocoder.named_parameters():
|
||
if "alpha" in name:
|
||
param.requires_grad_(True)
|
||
alpha_params.append(param)
|
||
else:
|
||
param.requires_grad_(False)
|
||
n_trainable = sum(p.numel() for p in alpha_params)
|
||
print(f"[BigVGAN] snake_alpha_only: {n_trainable} trainable params "
|
||
f"({len(alpha_params)} alpha tensors)", flush=True)
|
||
trainable_params = alpha_params
|
||
else: # all_params
|
||
for param in vocoder.parameters():
|
||
param.requires_grad_(True)
|
||
n_trainable = sum(p.numel() for p in vocoder.parameters())
|
||
print(f"[BigVGAN] all_params: {n_trainable} trainable params", flush=True)
|
||
trainable_params = list(vocoder.parameters())
|
||
|
||
# ── L2-SP: cache reference parameter values (before any gradient steps) ──
|
||
ref_params = {}
|
||
if lambda_l2sp > 0.0:
|
||
for name, param in vocoder.named_parameters():
|
||
if param.requires_grad:
|
||
ref_params[name] = param.data.clone().detach()
|
||
print(f"[BigVGAN] L2-SP anchor: {len(ref_params)} params λ={lambda_l2sp}", flush=True)
|
||
|
||
# ── Optional: load pretrained discriminator for feature matching ──────────
|
||
mpd = mrd = None
|
||
if disc_path is not None:
|
||
try:
|
||
ckpt_d = torch.load(str(disc_path), map_location="cpu", weights_only=False)
|
||
mpd = _MultiPeriodDiscriminator()
|
||
mrd = _MultiResolutionDiscriminator()
|
||
# Try common key names used by different BigVGAN releases
|
||
for mpd_key in ("mpd", "discriminator_mpd", "MPD"):
|
||
if mpd_key in ckpt_d:
|
||
mpd.load_state_dict(ckpt_d[mpd_key], strict=False)
|
||
print(f"[BigVGAN] Loaded MPD from key '{mpd_key}'", flush=True)
|
||
break
|
||
for mrd_key in ("mrd", "discriminator_mrd", "MRD", "msd", "discriminator_msd"):
|
||
if mrd_key in ckpt_d:
|
||
mrd.load_state_dict(ckpt_d[mrd_key], strict=False)
|
||
print(f"[BigVGAN] Loaded MRD from key '{mrd_key}'", flush=True)
|
||
break
|
||
mpd.to(device).eval()
|
||
mrd.to(device).eval()
|
||
for p in mpd.parameters():
|
||
p.requires_grad_(False)
|
||
for p in mrd.parameters():
|
||
p.requires_grad_(False)
|
||
print(f"[BigVGAN] Frozen discriminators ready for feature matching", flush=True)
|
||
except Exception as e:
|
||
print(f"[BigVGAN] WARNING: Could not load discriminator ({e}), "
|
||
f"falling back to mel+STFT losses", flush=True)
|
||
mpd = mrd = None
|
||
|
||
optimizer = torch.optim.AdamW(trainable_params, lr=lr, betas=(0.8, 0.99))
|
||
vocoder.train()
|
||
|
||
try:
|
||
for step in range(steps):
|
||
# Sample random batch — clips are CPU floats, move to device
|
||
batch = []
|
||
for _ in range(batch_size):
|
||
clip = random.choice(clips)
|
||
start = random.randint(0, clip.shape[0] - segment_samples)
|
||
batch.append(clip[start : start + segment_samples])
|
||
|
||
target_flat = torch.stack(batch).to(device, dtype) # [B, T]
|
||
target_wav = target_flat.unsqueeze(1) # [B, 1, T]
|
||
|
||
with torch.no_grad():
|
||
target_mel = mel_converter(target_flat) # [B, n_mels, T_mel]
|
||
|
||
pred_wav = vocoder(target_mel) # [B, 1, T_wav]
|
||
|
||
T = min(pred_wav.shape[-1], target_wav.shape[-1])
|
||
pred_t = pred_wav[..., :T]
|
||
target_t = target_wav[..., :T]
|
||
|
||
# ── Compute loss ─────────────────────────────────────────────────
|
||
if mpd is not None and mrd is not None:
|
||
# Perceptual feature matching via frozen discriminators
|
||
with torch.no_grad():
|
||
fmaps_real_mpd = mpd(target_t)
|
||
fmaps_real_mrd = mrd(target_t)
|
||
fmaps_gen_mpd = mpd(pred_t)
|
||
fmaps_gen_mrd = mrd(pred_t)
|
||
fm_loss = (
|
||
_feature_matching_loss(fmaps_real_mpd, fmaps_gen_mpd) +
|
||
_feature_matching_loss(fmaps_real_mrd, fmaps_gen_mrd)
|
||
)
|
||
# Keep a small mel loss for stable frequency alignment
|
||
pred_mel = mel_converter(pred_t.squeeze(1))
|
||
T_mel = min(pred_mel.shape[-1], target_mel.shape[-1])
|
||
mel_loss = F.l1_loss(pred_mel[..., :T_mel], target_mel[..., :T_mel])
|
||
primary_loss = 2.0 * fm_loss + 0.1 * mel_loss
|
||
loss_desc = f"fm={fm_loss.item():.4f} mel={mel_loss.item():.4f}"
|
||
else:
|
||
# Fallback: mel L1 + multi-resolution STFT L1
|
||
pred_mel = mel_converter(pred_t.squeeze(1))
|
||
T_mel = min(pred_mel.shape[-1], target_mel.shape[-1])
|
||
mel_loss = F.l1_loss(pred_mel[..., :T_mel], target_mel[..., :T_mel])
|
||
stft_loss = _multi_resolution_stft_loss(pred_t, target_t, device)
|
||
primary_loss = mel_loss + stft_loss
|
||
loss_desc = f"mel={mel_loss.item():.4f} stft={stft_loss.item():.4f}"
|
||
|
||
# ── L2-SP regularization ─────────────────────────────────────────
|
||
l2sp_loss = torch.zeros(1, device=device)
|
||
if lambda_l2sp > 0.0 and ref_params:
|
||
for name, param in vocoder.named_parameters():
|
||
if name in ref_params and param.requires_grad:
|
||
l2sp_loss = l2sp_loss + F.mse_loss(
|
||
param, ref_params[name], reduction="sum"
|
||
)
|
||
l2sp_loss = l2sp_loss * lambda_l2sp
|
||
|
||
loss = primary_loss + l2sp_loss
|
||
optimizer.zero_grad()
|
||
loss.backward()
|
||
torch.nn.utils.clip_grad_norm_(trainable_params, 1.0)
|
||
optimizer.step()
|
||
|
||
pbar.update(1)
|
||
|
||
if (step + 1) % max(1, steps // 20) == 0 or step == steps - 1:
|
||
l2sp_str = f" l2sp={l2sp_loss.item():.4e}" if lambda_l2sp > 0 else ""
|
||
print(f"[BigVGAN] {step+1}/{steps} {loss_desc}"
|
||
f" total={loss.item():.4f}{l2sp_str}", flush=True)
|
||
|
||
if (step + 1) % save_every == 0 and (step + 1) < steps:
|
||
step_path = out_path.parent / f"{out_path.stem}_step{step+1}{out_path.suffix}"
|
||
torch.save({"generator": vocoder.state_dict()}, str(step_path))
|
||
print(f"[BigVGAN] Checkpoint: {step_path}", flush=True)
|
||
vocoder.eval()
|
||
_save_sample(f"step{step+1}")
|
||
vocoder.train()
|
||
|
||
finally:
|
||
vocoder.requires_grad_(False)
|
||
vocoder.eval()
|
||
if strategy == "offload_to_cpu":
|
||
feature_utils.to("cpu")
|
||
soft_empty_cache()
|
||
|
||
torch.save({"generator": vocoder.state_dict()}, str(out_path))
|
||
print(f"\n[BigVGAN] Saved: {out_path}", flush=True)
|
||
_save_sample("final")
|
||
return str(out_path)
|