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A10G
| """Utility for loading the models from HF.""" | |
| from pathlib import Path | |
| import typing as tp | |
| from omegaconf import OmegaConf | |
| from huggingface_hub import hf_hub_download | |
| import torch | |
| from audiocraft.models import builders, MusicGen | |
| MODEL_CHECKPOINTS_MAP = { | |
| "small": "facebook/musicgen-small", | |
| "medium": "facebook/musicgen-medium", | |
| "large": "facebook/musicgen-large", | |
| "melody": "facebook/musicgen-melody", | |
| } | |
| def _get_state_dict(file_or_url: tp.Union[Path, str], | |
| filename="state_dict.bin", device='cpu'): | |
| # Return the state dict either from a file or url | |
| print("loading", file_or_url, filename) | |
| file_or_url = str(file_or_url) | |
| assert isinstance(file_or_url, str) | |
| return torch.load( | |
| hf_hub_download(repo_id=file_or_url, filename=filename), map_location=device) | |
| def load_compression_model(file_or_url: tp.Union[Path, str], device='cpu'): | |
| pkg = _get_state_dict(file_or_url, filename="compression_state_dict.bin") | |
| cfg = OmegaConf.create(pkg['xp.cfg']) | |
| cfg.device = str(device) | |
| model = builders.get_compression_model(cfg) | |
| model.load_state_dict(pkg['best_state']) | |
| model.eval() | |
| model.cfg = cfg | |
| return model | |
| def load_lm_model(file_or_url: tp.Union[Path, str], device='cpu'): | |
| pkg = _get_state_dict(file_or_url) | |
| cfg = OmegaConf.create(pkg['xp.cfg']) | |
| cfg.device = str(device) | |
| if cfg.device == 'cpu': | |
| cfg.transformer_lm.memory_efficient = False | |
| cfg.transformer_lm.custom = True | |
| cfg.dtype = 'float32' | |
| else: | |
| cfg.dtype = 'float16' | |
| model = builders.get_lm_model(cfg) | |
| model.load_state_dict(pkg['best_state']) | |
| model.eval() | |
| model.cfg = cfg | |
| return model | |
| def get_pretrained(name: str = 'small', device='cuda'): | |
| model_id = MODEL_CHECKPOINTS_MAP[name] | |
| compression_model = load_compression_model(model_id, device=device) | |
| lm = load_lm_model(model_id, device=device) | |
| return MusicGen(name, compression_model, lm) | |