363 lines
15 KiB
Python
363 lines
15 KiB
Python
"""SelVA BigVGAN Vocoder Fine-tuner.
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Fine-tunes only the BigVGAN vocoder (mel → waveform) on BJ audio clips using
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spectral reconstruction losses. The DiT and VAE are completely untouched.
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Loss: L1 mel reconstruction + multi-resolution STFT magnitude L1.
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No GAN discriminator — this is a proof-of-concept to verify that the vocoder
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can absorb BJ timbral characteristics before investing in full adversarial training.
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Save format: {'generator': vocoder.state_dict()} — same as the original BigVGAN
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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.functional as F
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import torchaudio
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import comfy.utils
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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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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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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 using "
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"spectral losses (mel L1 + multi-resolution STFT L1). DiT and VAE stay frozen. "
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"Supports both 16k (BigVGAN) and 44k (BigVGANv2) models. "
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"Load the result with SelVA BigVGAN Loader."
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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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"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.",
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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. BigVGAN default is 1e-4.",
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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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"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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}
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def train(self, model, data_dir, output_path, steps, lr, batch_size,
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segment_seconds, save_every, seed):
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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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# 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 segment={segment_seconds}s "
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f"steps={steps} lr={lr} batch={batch_size}\n", flush=True)
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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
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# produce normal, autograd-compatible tensors — no flags to fight.
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# -----------------------------------------------------------------------
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_result = [None]
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_exc = [None]
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def _worker():
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try:
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_result[0] = _do_train(
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vocoder, mel_converter, clips,
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device, dtype, strategy, feature_utils,
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segment_samples, sample_rate,
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steps, lr, batch_size, save_every, seed,
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out_path, pbar,
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)
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except Exception as e:
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_exc[0] = e
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traceback.print_exc()
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t = threading.Thread(target=_worker, daemon=True)
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t.start()
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t.join()
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if _exc[0] is not None:
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raise _exc[0]
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return (_result[0],)
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def _do_train(vocoder, mel_converter, clips,
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device, dtype, strategy, feature_utils,
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segment_samples, sample_rate,
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steps, lr, batch_size, save_every, seed,
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out_path, pbar):
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"""Execute training. Called in a fresh thread — no inference_mode active.
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Even though inference_mode is off here, tensors created in the calling
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thread's inference_mode carry the inference flag on the object itself.
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Operations on inference tensors produce inference tensors regardless of
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the current context. The ONLY way to strip the flag is to call .clone()
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from outside inference_mode — which is exactly where we are now.
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"""
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import torch.nn as nn_mod
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# ── Strip inference flag from all inputs that came from the main thread ──
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# 1. Audio clips (loaded in ComfyUI's inference_mode).
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clips = [c.clone() for c in clips]
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# 2. mel_converter buffers (mel_basis, hann_window) — same origin.
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for name, buf in list(mel_converter._buffers.items()):
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if buf is not None:
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mel_converter._buffers[name] = buf.clone()
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# 3. Vocoder parameters are handled below with clone().detach().
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# ─────────────────────────────────────────────────────────────────────────
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torch.manual_seed(seed)
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random.seed(seed)
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# Reference segment for eval samples — always clip 0, start 0
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ref_wav = clips[0][:segment_samples].to(device, dtype) # [T]
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ref_mel = mel_converter(ref_wav.unsqueeze(0)) # [1, n_mels, T_mel]
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def _save_sample(label):
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try:
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voc_device = next(vocoder.parameters()).device
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mel = ref_mel.to(voc_device)
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with torch.no_grad():
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wav = vocoder(mel)
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if wav.dim() == 2:
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wav = wav.unsqueeze(1)
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wav = wav.float().cpu().clamp(-1, 1)
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wav_path = out_path.parent / f"{out_path.stem}_{label}.wav"
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torchaudio.save(str(wav_path), wav.squeeze(0), sample_rate)
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print(f"[BigVGAN] Sample saved: {wav_path}", flush=True)
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except Exception as e:
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print(f"[BigVGAN] Sample save failed ({label}): {e}", flush=True)
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_save_sample("baseline")
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# Replace inference-tensor parameters with fresh trainable parameters.
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# (Vocoder was loaded by ComfyUI before this thread existed; its parameters
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# may carry the inference flag from that context. Here we're in a clean thread
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# so all new tensors are normal.)
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for module in vocoder.modules():
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for pname, param in list(module._parameters.items()):
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if param is not None:
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fresh = param.data.clone().detach().requires_grad_(True)
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module._parameters[pname] = nn_mod.Parameter(fresh)
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optimizer = torch.optim.AdamW(vocoder.parameters(), lr=lr, betas=(0.8, 0.99))
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vocoder.train()
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try:
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for step in range(steps):
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# Sample random batch — clips are CPU floats, move to device
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batch = []
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for _ in range(batch_size):
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clip = random.choice(clips)
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start = random.randint(0, clip.shape[0] - segment_samples)
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batch.append(clip[start : start + segment_samples])
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target_flat = torch.stack(batch).to(device, dtype) # [B, T]
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target_wav = target_flat.unsqueeze(1) # [B, 1, T]
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with torch.no_grad():
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target_mel = mel_converter(target_flat) # [B, n_mels, T_mel]
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if step == 0:
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print(f"[BigVGAN DEBUG] inference_mode={torch.is_inference_mode_enabled()}", flush=True)
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print(f"[BigVGAN DEBUG] clips[0].is_inference()={clips[0].is_inference()}", flush=True)
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print(f"[BigVGAN DEBUG] mel_basis.is_inference()={mel_converter.mel_basis.is_inference()}", flush=True)
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print(f"[BigVGAN DEBUG] target_flat.is_inference()={target_flat.is_inference()}", flush=True)
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print(f"[BigVGAN DEBUG] target_mel.is_inference()={target_mel.is_inference()}", flush=True)
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cp = vocoder.conv_pre
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print(f"[BigVGAN DEBUG] conv_pre.weight.is_inference()={cp.weight.is_inference()}", flush=True)
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if cp.bias is not None:
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print(f"[BigVGAN DEBUG] conv_pre.bias.is_inference()={cp.bias.is_inference()}", flush=True)
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pred_wav = vocoder(target_mel) # [B, 1, T_wav]
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T = min(pred_wav.shape[-1], target_wav.shape[-1])
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pred_t = pred_wav[..., :T]
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target_t = target_wav[..., :T]
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pred_mel = mel_converter(pred_t.squeeze(1)) # [B, n_mels, T_mel']
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T_mel = min(pred_mel.shape[-1], target_mel.shape[-1])
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mel_loss = F.l1_loss(pred_mel[..., :T_mel], target_mel[..., :T_mel])
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stft_loss = _multi_resolution_stft_loss(pred_t, target_t, device)
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loss = mel_loss + stft_loss
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optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_norm_(vocoder.parameters(), 1.0)
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optimizer.step()
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pbar.update(1)
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if (step + 1) % max(1, steps // 20) == 0 or step == steps - 1:
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print(f"[BigVGAN] {step+1}/{steps} "
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f"mel={mel_loss.item():.4f} stft={stft_loss.item():.4f} "
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f"total={loss.item():.4f}", flush=True)
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if (step + 1) % save_every == 0 and (step + 1) < steps:
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step_path = out_path.parent / f"{out_path.stem}_step{step+1}{out_path.suffix}"
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torch.save({"generator": vocoder.state_dict()}, str(step_path))
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print(f"[BigVGAN] Checkpoint: {step_path}", flush=True)
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vocoder.eval()
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_save_sample(f"step{step+1}")
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vocoder.train()
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finally:
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vocoder.requires_grad_(False)
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vocoder.eval()
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if strategy == "offload_to_cpu":
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feature_utils.to("cpu")
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soft_empty_cache()
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torch.save({"generator": vocoder.state_dict()}, str(out_path))
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print(f"\n[BigVGAN] Saved: {out_path}", flush=True)
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_save_sample("final")
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return str(out_path)
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