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import os
import glob
import json
import torch
import argparse
import numpy as np
from scipy.io.wavfile import read
def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
def go(model, bkey):
saved_state_dict = checkpoint_dict[bkey]
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]
if saved_state_dict[k].shape != state_dict[k].shape:
print(
"shape-%s-mismatch. need: %s, get: %s",
k,
state_dict[k].shape,
saved_state_dict[k].shape,
)
raise KeyError
except:
print("%s is not in the checkpoint", k)
new_state_dict[k] = v
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
return model
go(combd, "combd")
model = go(sbd, "sbd")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if optimizer is not None and load_opt == 1:
optimizer.load_state_dict(checkpoint_dict["optimizer"])
print("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
return model, optimizer, learning_rate, iteration
def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
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]
if saved_state_dict[k].shape != state_dict[k].shape:
print(
"shape-%s-mismatch|need-%s|get-%s",
k,
state_dict[k].shape,
saved_state_dict[k].shape,
)
raise KeyError
except:
print("%s is not in the checkpoint", k)
new_state_dict[k] = v
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if optimizer is not None and load_opt == 1:
optimizer.load_state_dict(checkpoint_dict["optimizer"])
print(f"Loaded checkpoint '{checkpoint_path}' (epoch {iteration})")
return model, optimizer, learning_rate, iteration
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
print(f"Saved model '{checkpoint_path}' (epoch {iteration})")
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save(
{
"model": state_dict,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
checkpoint_path,
)
def summarize(
writer,
global_step,
scalars={},
histograms={},
images={},
audios={},
audio_sampling_rate=22050,
):
for k, v in scalars.items():
writer.add_scalar(k, v, global_step)
for k, v in histograms.items():
writer.add_histogram(k, v, global_step)
for k, v in images.items():
writer.add_image(k, v, global_step, dataformats="HWC")
for k, v in audios.items():
writer.add_audio(k, v, global_step, audio_sampling_rate)
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
f_list = glob.glob(os.path.join(dir_path, regex))
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
x = f_list[-1]
return x
def plot_spectrogram_to_numpy(spectrogram):
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
plt.colorbar(im, ax=ax)
plt.xlabel("Frames")
plt.ylabel("Channels")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def load_filepaths_and_text(filename, split="|"):
with open(filename, encoding="utf-8") as f:
filepaths_and_text = [line.strip().split(split) for line in f]
return filepaths_and_text
def get_hparams():
parser = argparse.ArgumentParser()
parser.add_argument(
"-se",
"--save_every_epoch",
type=int,
required=True,
help="checkpoint save frequency (epoch)",
)
parser.add_argument(
"-te", "--total_epoch", type=int, required=True, help="total_epoch"
)
parser.add_argument(
"-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
)
parser.add_argument(
"-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
)
parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
parser.add_argument(
"-bs", "--batch_size", type=int, required=True, help="batch size"
)
parser.add_argument(
"-e", "--experiment_dir", type=str, required=True, help="experiment dir"
)
parser.add_argument(
"-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
)
parser.add_argument(
"-sw",
"--save_every_weights",
type=str,
default="0",
help="save the extracted model in weights directory when saving checkpoints",
)
parser.add_argument(
"-v", "--version", type=str, required=True, help="model version"
)
parser.add_argument(
"-f0",
"--if_f0",
type=int,
required=True,
help="use f0 as one of the inputs of the model, 1 or 0",
)
parser.add_argument(
"-l",
"--if_latest",
type=int,
required=True,
help="if only save the latest G/D pth file, 1 or 0",
)
parser.add_argument(
"-c",
"--if_cache_data_in_gpu",
type=int,
required=True,
help="if caching the dataset in GPU memory, 1 or 0",
)
args = parser.parse_args()
name = args.experiment_dir
experiment_dir = os.path.join("./logs", args.experiment_dir)
config_save_path = os.path.join(experiment_dir, "config.json")
with open(config_save_path, "r") as f:
config = json.load(f)
hparams = HParams(**config)
hparams.model_dir = hparams.experiment_dir = experiment_dir
hparams.save_every_epoch = args.save_every_epoch
hparams.name = name
hparams.total_epoch = args.total_epoch
hparams.pretrainG = args.pretrainG
hparams.pretrainD = args.pretrainD
hparams.version = args.version
hparams.gpus = args.gpus
hparams.train.batch_size = args.batch_size
hparams.sample_rate = args.sample_rate
hparams.if_f0 = args.if_f0
hparams.if_latest = args.if_latest
hparams.save_every_weights = args.save_every_weights
hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
hparams.data.training_files = f"{experiment_dir}/filelist.txt"
return hparams
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__()
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