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