Ethanfel
5df2abd6dd
remove_parametrizations() stores weight as a plain __dict__ tensor (not nn.Parameter), making it invisible to _parameters iteration. Also, buffers (Activation1d anti-aliasing filters) are inference tensors that break the backward graph mid-network. Fix all three categories: 1. _parameters: clone().detach(), wrap as Parameter 2. plain __dict__ tensors: clone(), register_parameter (also makes trainable) 3. _buffers: clone() to strip inference flag without parametrizing Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
379 lines
16 KiB
Python
379 lines
16 KiB
Python
"""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
|
||
import threading
|
||
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
|
||
|
||
def _load_wav(path):
|
||
"""Load audio file to [channels, samples] float32 tensor.
|
||
|
||
Tries torchaudio first; falls back to soundfile for wav/flac when the
|
||
ffmpeg/torchcodec backend is unavailable (e.g. libavutil soname mismatch).
|
||
"""
|
||
try:
|
||
return torchaudio.load(str(path))
|
||
except Exception:
|
||
pass
|
||
# soundfile fallback — handles wav, flac, ogg natively without ffmpeg
|
||
import soundfile as sf
|
||
data, sr = sf.read(str(path), dtype="float32", always_2d=True)
|
||
wav = torch.from_numpy(data.T) # [channels, samples]
|
||
return wav, sr
|
||
|
||
|
||
# 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. "
|
||
"Supports both 16k (BigVGAN) and 44k (BigVGANv2) models. "
|
||
"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
|
||
|
||
device = get_device()
|
||
mode = model["mode"]
|
||
dtype = model["dtype"]
|
||
feature_utils = model["feature_utils"]
|
||
mel_converter = feature_utils.mel_converter
|
||
strategy = model["strategy"]
|
||
|
||
if mode == "16k":
|
||
vocoder = feature_utils.tod.vocoder.vocoder
|
||
sample_rate = 16_000
|
||
elif mode == "44k":
|
||
vocoder = feature_utils.tod.vocoder
|
||
sample_rate = 44_100
|
||
else:
|
||
raise ValueError(f"[BigVGAN] Unknown mode: {mode}")
|
||
|
||
# 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 = _load_wav(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.cpu())
|
||
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)
|
||
|
||
pbar = comfy.utils.ProgressBar(steps)
|
||
|
||
# -----------------------------------------------------------------------
|
||
# Run the entire training in a fresh thread.
|
||
#
|
||
# ComfyUI executes nodes inside torch.inference_mode(). Even with an inner
|
||
# inference_mode(False) context, factory functions and operations may still
|
||
# produce inference tensors in some environments (e.g. when the outer
|
||
# context is set via an async wrapper or a third-party hook).
|
||
#
|
||
# torch.inference_mode is THREAD-LOCAL. A new thread always starts with
|
||
# 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,
|
||
steps, lr, batch_size, save_every, seed,
|
||
out_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],)
|
||
|
||
|
||
def _do_train(vocoder, mel_converter, clips,
|
||
device, dtype, strategy, feature_utils,
|
||
segment_samples, sample_rate,
|
||
steps, lr, batch_size, save_every, seed,
|
||
out_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, start 0
|
||
ref_wav = clips[0][:segment_samples].to(device, dtype) # [T]
|
||
ref_mel = mel_converter(ref_wav.unsqueeze(0)) # [1, n_mels, T_mel]
|
||
|
||
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"
|
||
torchaudio.save(str(wav_path), wav.squeeze(0), sample_rate)
|
||
print(f"[BigVGAN] Sample saved: {wav_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()
|
||
|
||
optimizer = torch.optim.AdamW(vocoder.parameters(), 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]
|
||
|
||
pred_mel = mel_converter(pred_t.squeeze(1)) # [B, n_mels, 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])
|
||
|
||
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)
|
||
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)
|