| """Pretrained mel-conditioned vocoder helpers (NVIDIA BigVGAN).""" | |
| import json | |
| import os | |
| import torch | |
| from bigvgan.env import AttrDict | |
| from bigvgan.bigvgan import BigVGAN | |
| VOC_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "vocoder") | |
| BUILTIN = { | |
| "bigvgan_base": ("bigvgan_base_24k_100band.json", | |
| "bigvgan_base_24k_100band.pt"), | |
| "bigvgan_v2": ("bigvgan_v2_24k_100band.json", | |
| "bigvgan_v2_24k_100band.pt"), | |
| } | |
| _cache = {} | |
| def load_bigvgan(name="bigvgan_base", device="cpu"): | |
| """Load a pretrained BigVGAN generator (frozen, eval mode).""" | |
| if name in _cache: | |
| return _cache[name] | |
| if name not in BUILTIN: | |
| raise ValueError(f"Unknown vocoder '{name}', choose from {list(BUILTIN)}") | |
| cfg_file, ckpt_file = BUILTIN[name] | |
| h = AttrDict(json.load(open(os.path.join(VOC_DIR, cfg_file)))) | |
| model = BigVGAN(h) | |
| ckpt = torch.load(os.path.join(VOC_DIR, ckpt_file), map_location="cpu", | |
| weights_only=True) | |
| model.load_state_dict(ckpt["generator"]) | |
| model.remove_weight_norm() | |
| model.eval().to(device) | |
| for p in model.parameters(): | |
| p.requires_grad = False | |
| _cache[name] = model | |
| return model | |