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import os
import glob
import sys
import argparse
import logging
import json
import subprocess
import traceback
import librosa
import numpy as np
from scipy.io.wavfile import read
import torch
import logging
logging.getLogger("numba").setLevel(logging.ERROR)
logging.getLogger("matplotlib").setLevel(logging.ERROR)
MATPLOTLIB_FLAG = False
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging
def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if (
optimizer is not None
and not skip_optimizer
and checkpoint_dict["optimizer"] is not None
):
optimizer.load_state_dict(checkpoint_dict["optimizer"])
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:
# assert "quantizer" not in k
# print("load", k)
new_state_dict[k] = saved_state_dict[k]
assert saved_state_dict[k].shape == v.shape, (
saved_state_dict[k].shape,
v.shape,
)
except:
traceback.print_exc()
print(
"error, %s is not in the checkpoint" % k
) # shape不对也会,比如text_embedding当cleaner修改时
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)
print("load ")
logger.info(
"Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
)
return model, optimizer, learning_rate, iteration
from time import time as ttime
import shutil
def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
dir=os.path.dirname(path)
name=os.path.basename(path)
tmp_path="%s.pth"%(ttime())
torch.save(fea,tmp_path)
shutil.move(tmp_path,"%s/%s"%(dir,name))
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
"Saving model and optimizer state at iteration {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
# torch.save(
my_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]
print(x)
return x
def plot_spectrogram_to_numpy(spectrogram):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
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 plot_alignment_to_numpy(alignment, info=None):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.imshow(
alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
)
fig.colorbar(im, ax=ax)
xlabel = "Decoder timestep"
if info is not None:
xlabel += "\n\n" + info
plt.xlabel(xlabel)
plt.ylabel("Encoder timestep")
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):
data, sampling_rate = librosa.load(full_path, sr=None)
return torch.FloatTensor(data), 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(init=True, stage=1):
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config",
type=str,
default="./configs/s2.json",
help="JSON file for configuration",
)
parser.add_argument(
"-p", "--pretrain", type=str, required=False, default=None, help="pretrain dir"
)
parser.add_argument(
"-rs",
"--resume_step",
type=int,
required=False,
default=None,
help="resume step",
)
# parser.add_argument('-e', '--exp_dir', type=str, required=False,default=None,help='experiment directory')
# parser.add_argument('-g', '--pretrained_s2G', type=str, required=False,default=None,help='pretrained sovits gererator weights')
# parser.add_argument('-d', '--pretrained_s2D', type=str, required=False,default=None,help='pretrained sovits discriminator weights')
args = parser.parse_args()
config_path = args.config
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.pretrain = args.pretrain
hparams.resume_step = args.resume_step
# hparams.data.exp_dir = args.exp_dir
if stage == 1:
model_dir = hparams.s1_ckpt_dir
else:
model_dir = hparams.s2_ckpt_dir
config_save_path = os.path.join(model_dir, "config.json")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
with open(config_save_path, "w") as f:
f.write(data)
return hparams
def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
"""Freeing up space by deleting saved ckpts
Arguments:
path_to_models -- Path to the model directory
n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
sort_by_time -- True -> chronologically delete ckpts
False -> lexicographically delete ckpts
"""
import re
ckpts_files = [
f
for f in os.listdir(path_to_models)
if os.path.isfile(os.path.join(path_to_models, f))
]
name_key = lambda _f: int(re.compile("._(\d+)\.pth").match(_f).group(1))
time_key = lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))
sort_key = time_key if sort_by_time else name_key
x_sorted = lambda _x: sorted(
[f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
key=sort_key,
)
to_del = [
os.path.join(path_to_models, fn)
for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
]
del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
del_routine = lambda x: [os.remove(x), del_info(x)]
rs = [del_routine(fn) for fn in to_del]
def get_hparams_from_dir(model_dir):
config_save_path = os.path.join(model_dir, "config.json")
with open(config_save_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = model_dir
return hparams
def get_hparams_from_file(config_path):
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
return hparams
def check_git_hash(model_dir):
source_dir = os.path.dirname(os.path.realpath(__file__))
if not os.path.exists(os.path.join(source_dir, ".git")):
logger.warn(
"{} is not a git repository, therefore hash value comparison will be ignored.".format(
source_dir
)
)
return
cur_hash = subprocess.getoutput("git rev-parse HEAD")
path = os.path.join(model_dir, "githash")
if os.path.exists(path):
saved_hash = open(path).read()
if saved_hash != cur_hash:
logger.warn(
"git hash values are different. {}(saved) != {}(current)".format(
saved_hash[:8], cur_hash[:8]
)
)
else:
open(path, "w").write(cur_hash)
def get_logger(model_dir, filename="train.log"):
global logger
logger = logging.getLogger(os.path.basename(model_dir))
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
h = logging.FileHandler(os.path.join(model_dir, filename))
h.setLevel(logging.DEBUG)
h.setFormatter(formatter)
logger.addHandler(h)
return logger
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__()
if __name__ == "__main__":
print(
load_wav_to_torch(
"/home/fish/wenetspeech/dataset_vq/Y0000022499_wHFSeHEx9CM/S00261.flac"
)
)
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