fix: code lint by optimizing train lib's importing

Author: fumiama

train/cmd.txt renamed to lib/train/cmd.txt

@@ -1,20 +1,16 @@
-import sys, os
+import os
 
-now_dir = os.getcwd()
-sys.path.append(os.path.join(now_dir))
-sys.path.append(os.path.join(now_dir, "train"))
-import utils
+from lib.train import utils
 import datetime
 
 hps = utils.get_hparams()
 os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
 n_gpus = len(hps.gpus.split("-"))
 from random import shuffle, randint
-import traceback, json, argparse, itertools, math, torch, pdb
 
+import torch
 torch.backends.cudnn.deterministic = False
 torch.backends.cudnn.benchmark = False
-from torch import nn, optim
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
@@ -25,7 +21,7 @@
 from lib.infer_pack import commons
 from time import sleep
 from time import time as ttime
-from data_utils import (
+from lib.train.data_utils import (
     TextAudioLoaderMultiNSFsid,
     TextAudioLoader,
     TextAudioCollateMultiNSFsid,
@@ -45,9 +41,9 @@
         SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
         MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
     )
-from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from process_ckpt import savee
+from lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
+from lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from lib.train.process_ckpt import savee
 
 global_step = 0