Ethanfel
614a2e02aa
PyTorch 2.6 changed the default to weights_only=True. SelVA checkpoints contain non-tensor types (numpy scalars etc.) that fail strict unpickling. All weights come from trusted sources (jnwnlee/selva HF repo). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
33 lines
990 B
Python
33 lines
990 B
Python
from pathlib import Path
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import torch
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import torch.nn as nn
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from omegaconf import OmegaConf
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from selva_core.ext.bigvgan.models import BigVGANVocoder
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_bigvgan_vocoder_path = Path(__file__).parent / 'bigvgan_vocoder.yml'
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class BigVGAN(nn.Module):
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def __init__(self, ckpt_path, config_path=_bigvgan_vocoder_path):
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super().__init__()
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vocoder_cfg = OmegaConf.load(config_path)
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self.vocoder = BigVGANVocoder(vocoder_cfg).eval()
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vocoder_ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=False)['generator']
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self.vocoder.load_state_dict(vocoder_ckpt)
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self.weight_norm_removed = False
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self.remove_weight_norm()
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@torch.inference_mode()
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def forward(self, x):
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assert self.weight_norm_removed, 'call remove_weight_norm() before inference'
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return self.vocoder(x)
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def remove_weight_norm(self):
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self.vocoder.remove_weight_norm()
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self.weight_norm_removed = True
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return self
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