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| import logging | |
| from json import loads | |
| from torch import load, FloatTensor | |
| from numpy import float32 | |
| import librosa | |
| class HParams(): | |
| def __init__(self, **kwargs): | |
| for k, v in kwargs.items(): | |
| if type(v) == dict: | |
| v = HParams(**v) | |
| self[k] = v | |
| def keys(self): | |
| return self.__dict__.keys() | |
| def items(self): | |
| return self.__dict__.items() | |
| def values(self): | |
| return self.__dict__.values() | |
| def __len__(self): | |
| return len(self.__dict__) | |
| def __getitem__(self, key): | |
| return getattr(self, key) | |
| def __setitem__(self, key, value): | |
| return setattr(self, key, value) | |
| def __contains__(self, key): | |
| return key in self.__dict__ | |
| def __repr__(self): | |
| return self.__dict__.__repr__() | |
| def load_checkpoint(checkpoint_path, model): | |
| checkpoint_dict = load(checkpoint_path, map_location='cpu') | |
| iteration = checkpoint_dict['iteration'] | |
| saved_state_dict = checkpoint_dict['model'] | |
| if hasattr(model, 'module'): | |
| state_dict = model.module.state_dict() | |
| else: | |
| state_dict = model.state_dict() | |
| new_state_dict = {} | |
| for k, v in state_dict.items(): | |
| try: | |
| new_state_dict[k] = saved_state_dict[k] | |
| except: | |
| logging.info("%s is not in the checkpoint" % k) | |
| new_state_dict[k] = v | |
| pass | |
| if hasattr(model, 'module'): | |
| model.module.load_state_dict(new_state_dict) | |
| else: | |
| model.load_state_dict(new_state_dict) | |
| logging.info("Loaded checkpoint '{}' (iteration {})".format( | |
| checkpoint_path, iteration)) | |
| return | |
| def get_hparams_from_file(config_path): | |
| with open(config_path, "r") as f: | |
| data = f.read() | |
| config = loads(data) | |
| hparams = HParams(**config) | |
| return hparams | |
| def load_audio_to_torch(full_path, target_sampling_rate): | |
| audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True) | |
| return FloatTensor(audio.astype(float32)) | |