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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))
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