import logging import os 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 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', encoding='utf-8') 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)) def clean_folder(folder_path): for filename in os.listdir(folder_path): file_path = os.path.join(folder_path, filename) # 如果是文件,则删除文件 if os.path.isfile(file_path): os.remove(file_path) # is none -> True, is not none -> False def check_is_none(s): return s is None or (isinstance(s, str) and str(s).isspace()) or str(s) == ""