feat: add BigVGAN vocoder fine-tuner and loader nodes
Spectral-loss-only fine-tuning of the BigVGAN vocoder (mel→waveform)
on BJ audio clips. DiT and VAE are completely frozen. Losses: mel L1
reconstruction + multi-resolution STFT magnitude L1 (same three
resolutions as the BigVGAN discriminator config). Saves in
{'generator': state_dict} format compatible with the original BigVGAN
checkpoint. Loader replaces vocoder weights in the loaded SELVA_MODEL
in-place so no full model reload is needed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -0,0 +1,240 @@
|
||||
"""SelVA BigVGAN Vocoder Fine-tuner.
|
||||
|
||||
Fine-tunes only the BigVGAN vocoder (mel → waveform) on BJ audio clips using
|
||||
spectral reconstruction losses. The DiT and VAE are completely untouched.
|
||||
|
||||
Loss: L1 mel reconstruction + multi-resolution STFT magnitude L1.
|
||||
No GAN discriminator — this is a proof-of-concept to verify that the vocoder
|
||||
can absorb BJ timbral characteristics before investing in full adversarial training.
|
||||
|
||||
Save format: {'generator': vocoder.state_dict()} — same as the original BigVGAN
|
||||
checkpoint so it can be loaded with SelVA BigVGAN Loader.
|
||||
"""
|
||||
|
||||
import random
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import comfy.utils
|
||||
import folder_paths
|
||||
|
||||
from .utils import SELVA_CATEGORY, get_device, soft_empty_cache
|
||||
|
||||
# Multi-resolution STFT windows — same three resolutions as BigVGAN discriminator config.
|
||||
_STFT_RESOLUTIONS = [
|
||||
(1024, 120, 600),
|
||||
(2048, 240, 1200),
|
||||
(512, 50, 240),
|
||||
]
|
||||
|
||||
|
||||
def _stft_mag(wav, n_fft, hop_length, win_length, device):
|
||||
"""Magnitude STFT. wav: [B, T] → [B, n_fft//2+1, T']"""
|
||||
window = torch.hann_window(win_length, device=device)
|
||||
spec = torch.stft(
|
||||
wav, n_fft=n_fft, hop_length=hop_length, win_length=win_length,
|
||||
window=window, center=True, return_complex=True,
|
||||
)
|
||||
return spec.abs()
|
||||
|
||||
|
||||
def _multi_resolution_stft_loss(pred_wav, target_wav, device):
|
||||
"""Average L1 mag loss across three STFT resolutions. inputs: [B, 1, T]"""
|
||||
pred = pred_wav.squeeze(1) # [B, T]
|
||||
target = target_wav.squeeze(1)
|
||||
loss = torch.zeros(1, device=device)
|
||||
for n_fft, hop, win in _STFT_RESOLUTIONS:
|
||||
pm = _stft_mag(pred, n_fft, hop, win, device)
|
||||
tm = _stft_mag(target, n_fft, hop, win, device)
|
||||
T = min(pm.shape[-1], tm.shape[-1])
|
||||
loss = loss + F.l1_loss(pm[..., :T], tm[..., :T])
|
||||
return loss / len(_STFT_RESOLUTIONS)
|
||||
|
||||
|
||||
class SelvaBigvganTrainer:
|
||||
OUTPUT_NODE = True
|
||||
CATEGORY = SELVA_CATEGORY
|
||||
FUNCTION = "train"
|
||||
RETURN_TYPES = ("STRING",)
|
||||
RETURN_NAMES = ("checkpoint_path",)
|
||||
OUTPUT_TOOLTIPS = ("Path to saved vocoder checkpoint — load with SelVA BigVGAN Loader.",)
|
||||
DESCRIPTION = (
|
||||
"Fine-tunes the BigVGAN vocoder (mel→waveform) on BJ audio clips using "
|
||||
"spectral losses (mel L1 + multi-resolution STFT L1). DiT and VAE stay frozen. "
|
||||
"16k mode only. Load the result with SelVA BigVGAN Loader."
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("SELVA_MODEL",),
|
||||
"data_dir": ("STRING", {
|
||||
"default": "",
|
||||
"tooltip": "Directory with BJ audio files (.wav/.flac/.mp3). Searched recursively.",
|
||||
}),
|
||||
"output_path": ("STRING", {
|
||||
"default": "bigvgan_bj.pt",
|
||||
"tooltip": "Where to save the fine-tuned vocoder. Relative paths → ComfyUI output dir.",
|
||||
}),
|
||||
"steps": ("INT", {
|
||||
"default": 2000, "min": 100, "max": 50000,
|
||||
"tooltip": "Training steps. 1000–2000 is a good first experiment.",
|
||||
}),
|
||||
"lr": ("FLOAT", {
|
||||
"default": 1e-4, "min": 1e-6, "max": 1e-2, "step": 1e-5,
|
||||
"tooltip": "Learning rate. BigVGAN default is 1e-4.",
|
||||
}),
|
||||
"batch_size": ("INT", {"default": 4, "min": 1, "max": 32}),
|
||||
"segment_seconds": ("FLOAT", {
|
||||
"default": 1.0, "min": 0.25, "max": 4.0, "step": 0.25,
|
||||
"tooltip": "Audio segment length per training sample in seconds.",
|
||||
}),
|
||||
"save_every": ("INT", {"default": 500, "min": 50, "max": 10000}),
|
||||
"seed": ("INT", {"default": 42, "min": 0, "max": 0xFFFFFFFF}),
|
||||
},
|
||||
}
|
||||
|
||||
def train(self, model, data_dir, output_path, steps, lr, batch_size,
|
||||
segment_seconds, save_every, seed):
|
||||
import traceback
|
||||
|
||||
if model["mode"] != "16k":
|
||||
raise NotImplementedError(
|
||||
"[BigVGAN] Only 16k mode is supported. "
|
||||
"44k uses BigVGANv2 which requires a different training setup."
|
||||
)
|
||||
|
||||
device = get_device()
|
||||
feature_utils = model["feature_utils"]
|
||||
mel_converter = feature_utils.mel_converter
|
||||
sample_rate = 16_000
|
||||
strategy = model["strategy"]
|
||||
|
||||
# BigVGANVocoder nn.Module — bypass the @inference_mode wrapper on BigVGAN.forward
|
||||
vocoder = feature_utils.tod.vocoder.vocoder
|
||||
|
||||
# Resolve paths
|
||||
data_dir = Path(data_dir.strip())
|
||||
if not data_dir.is_absolute():
|
||||
data_dir = Path(folder_paths.models_dir) / data_dir
|
||||
if not data_dir.exists():
|
||||
raise FileNotFoundError(f"[BigVGAN] data_dir not found: {data_dir}")
|
||||
|
||||
out_path = Path(output_path.strip())
|
||||
if not out_path.is_absolute():
|
||||
out_path = Path(folder_paths.get_output_directory()) / out_path
|
||||
out_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Find and pre-load audio clips
|
||||
segment_samples = int(segment_seconds * sample_rate)
|
||||
audio_files = []
|
||||
for ext in ("*.wav", "*.flac", "*.mp3", "*.ogg", "*.aac"):
|
||||
audio_files.extend(data_dir.rglob(ext))
|
||||
if not audio_files:
|
||||
raise FileNotFoundError(f"[BigVGAN] No audio files found in {data_dir}")
|
||||
|
||||
print(f"[BigVGAN] Loading {len(audio_files)} audio files...", flush=True)
|
||||
clips = []
|
||||
for af in audio_files:
|
||||
try:
|
||||
wav, sr = torchaudio.load(str(af))
|
||||
if wav.shape[0] > 1:
|
||||
wav = wav.mean(0, keepdim=True)
|
||||
if sr != sample_rate:
|
||||
wav = torchaudio.functional.resample(wav, sr, sample_rate)
|
||||
wav = wav.squeeze(0) # [L]
|
||||
if wav.shape[0] >= segment_samples:
|
||||
clips.append(wav)
|
||||
else:
|
||||
print(f" [BigVGAN] Skip {af.name}: shorter than {segment_seconds}s", flush=True)
|
||||
except Exception as e:
|
||||
print(f" [BigVGAN] Failed {af.name}: {e}", flush=True)
|
||||
traceback.print_exc()
|
||||
|
||||
if not clips:
|
||||
raise RuntimeError(
|
||||
f"[BigVGAN] No usable clips found (need audio >= {segment_seconds}s)"
|
||||
)
|
||||
print(f"[BigVGAN] {len(clips)} clips ready segment={segment_seconds}s "
|
||||
f"steps={steps} lr={lr} batch={batch_size}\n", flush=True)
|
||||
|
||||
if strategy == "offload_to_cpu":
|
||||
feature_utils.to(device)
|
||||
soft_empty_cache()
|
||||
|
||||
mel_converter.to(device)
|
||||
vocoder.requires_grad_(True)
|
||||
optimizer = torch.optim.AdamW(vocoder.parameters(), lr=lr, betas=(0.8, 0.99))
|
||||
|
||||
torch.manual_seed(seed)
|
||||
random.seed(seed)
|
||||
|
||||
pbar = comfy.utils.ProgressBar(steps)
|
||||
|
||||
try:
|
||||
with torch.inference_mode(False):
|
||||
with torch.enable_grad():
|
||||
vocoder.train()
|
||||
|
||||
for step in range(steps):
|
||||
# Sample random batch
|
||||
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) # [B, T]
|
||||
target_wav = target_flat.unsqueeze(1) # [B, 1, T]
|
||||
|
||||
# Fixed target mel (no grad needed here)
|
||||
with torch.no_grad():
|
||||
target_mel = mel_converter(target_flat) # [B, 80, T_mel]
|
||||
|
||||
# Vocoder forward: mel → waveform
|
||||
pred_wav = vocoder(target_mel) # [B, 1, T_wav]
|
||||
|
||||
# Align lengths
|
||||
T = min(pred_wav.shape[-1], target_wav.shape[-1])
|
||||
pred_t = pred_wav[..., :T]
|
||||
target_t = target_wav[..., :T]
|
||||
|
||||
# Mel reconstruction loss: mel(pred) vs target_mel
|
||||
pred_mel = mel_converter(pred_t.squeeze(1)) # [B, 80, T_mel']
|
||||
T_mel = min(pred_mel.shape[-1], target_mel.shape[-1])
|
||||
mel_loss = F.l1_loss(pred_mel[..., :T_mel], target_mel[..., :T_mel])
|
||||
|
||||
# Multi-resolution STFT loss
|
||||
stft_loss = _multi_resolution_stft_loss(pred_t, target_t, device)
|
||||
|
||||
loss = mel_loss + stft_loss
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(vocoder.parameters(), 1.0)
|
||||
optimizer.step()
|
||||
|
||||
pbar.update(1)
|
||||
|
||||
if (step + 1) % max(1, steps // 20) == 0 or step == steps - 1:
|
||||
print(f"[BigVGAN] {step+1}/{steps} "
|
||||
f"mel={mel_loss.item():.4f} stft={stft_loss.item():.4f} "
|
||||
f"total={loss.item():.4f}", 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)
|
||||
|
||||
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)
|
||||
return (str(out_path),)
|
||||
Reference in New Issue
Block a user