initial upload attempt
initial upload attempt
This commit is contained in:
erew123 committed
1 parent
84c19ed5a9
commit
9fd928f6e7
527 files changed
+37359
No files matched your search
@@ -0,0 +1,311 @@
|
||||
import os
|
||||
import glob
|
||||
import json
|
||||
import torch
|
||||
import argparse
|
||||
import numpy as np
|
||||
from scipy.io.wavfile import read
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
def replace_keys_in_dict(d, old_key_part, new_key_part):
|
||||
if isinstance(d, OrderedDict):
|
||||
updated_dict = OrderedDict()
|
||||
else:
|
||||
updated_dict = {}
|
||||
for key, value in d.items():
|
||||
if isinstance(key, str):
|
||||
new_key = key.replace(old_key_part, new_key_part)
|
||||
else:
|
||||
new_key = key
|
||||
if isinstance(value, dict):
|
||||
value = replace_keys_in_dict(value, old_key_part, new_key_part)
|
||||
updated_dict[new_key] = value
|
||||
return updated_dict
|
||||
|
||||
|
||||
def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
|
||||
assert os.path.isfile(checkpoint_path)
|
||||
checkpoint_old_dict = torch.load(checkpoint_path, map_location="cpu")
|
||||
checkpoint_new_version_path = os.path.join(
|
||||
os.path.dirname(checkpoint_path),
|
||||
f"{os.path.splitext(os.path.basename(checkpoint_path))[0]}_new_version.pth",
|
||||
)
|
||||
|
||||
torch.save(
|
||||
replace_keys_in_dict(
|
||||
replace_keys_in_dict(
|
||||
checkpoint_old_dict, ".weight_v", ".parametrizations.weight.original1"
|
||||
),
|
||||
".weight_g",
|
||||
".parametrizations.weight.original0",
|
||||
),
|
||||
checkpoint_new_version_path,
|
||||
)
|
||||
|
||||
os.remove(checkpoint_path)
|
||||
os.rename(checkpoint_new_version_path, 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})")
|
||||
checkpoint_old_version_path = os.path.join(
|
||||
os.path.dirname(checkpoint_path),
|
||||
f"{os.path.splitext(os.path.basename(checkpoint_path))[0]}_old_version.pth",
|
||||
)
|
||||
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,
|
||||
)
|
||||
checkpoint = torch.load(checkpoint_path, map_location=torch.device("cpu"))
|
||||
torch.save(
|
||||
replace_keys_in_dict(
|
||||
replace_keys_in_dict(
|
||||
checkpoint, ".parametrizations.weight.original1", ".weight_v"
|
||||
),
|
||||
".parametrizations.weight.original0",
|
||||
".weight_g",
|
||||
),
|
||||
checkpoint_old_version_path,
|
||||
)
|
||||
os.remove(checkpoint_path)
|
||||
os.rename(checkpoint_old_version_path, 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",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"-od",
|
||||
"--overtraining_detector",
|
||||
type=int,
|
||||
required=True,
|
||||
help="Detect overtraining or not, 1 or 0",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-ot",
|
||||
"--overtraining_threshold",
|
||||
type=int,
|
||||
default=50,
|
||||
help="overtraining_threshold",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-sg",
|
||||
"--sync-graph",
|
||||
type=int,
|
||||
required=True,
|
||||
help="Sync graph or not, 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"
|
||||
hparams.overtraining_detector = args.overtraining_detector
|
||||
hparams.overtraining_threshold = args.overtraining_threshold
|
||||
hparams.sync_graph = args.sync_graph
|
||||
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__()
|
||||
Reference in new issue
Block a user