Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab

GPT_SoVITS/s1_train.py

@@ -1,31 +1,28 @@
 # modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/train_t2s.py
 import os
-import pdb
 
 if "_CUDA_VISIBLE_DEVICES" in os.environ:
     os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
 import argparse
 import logging
+import platform
 from pathlib import Path
 
-import torch, platform
-from pytorch_lightning import seed_everything
-from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.loggers import TensorBoardLogger  # WandbLogger
-from pytorch_lightning.strategies import DDPStrategy
+import torch
 from AR.data.data_module import Text2SemanticDataModule
 from AR.models.t2s_lightning_module import Text2SemanticLightningModule
 from AR.utils.io import load_yaml_config
+from pytorch_lightning import Trainer, seed_everything
+from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.loggers import TensorBoardLogger  # WandbLogger
+from pytorch_lightning.strategies import DDPStrategy
 
 logging.getLogger("numba").setLevel(logging.WARNING)
 logging.getLogger("matplotlib").setLevel(logging.WARNING)
 torch.set_float32_matmul_precision("high")
-from AR.utils import get_newest_ckpt
-
 from collections import OrderedDict
-from time import time as ttime
-import shutil
+
+from AR.utils import get_newest_ckpt
 from process_ckpt import my_save
 
 
@@ -37,7 +34,7 @@ class my_model_ckpt(ModelCheckpoint):
         if_save_every_weights,
         half_weights_save_dir,
         exp_name,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(**kwargs)
         self.if_save_latest = if_save_latest
@@ -50,10 +47,7 @@ class my_model_ckpt(ModelCheckpoint):
         # if not self._should_skip_saving_checkpoint(trainer) and self._should_save_on_train_epoch_end(trainer):
         if self._should_save_on_train_epoch_end(trainer):
             monitor_candidates = self._monitor_candidates(trainer)
-            if (
-                self._every_n_epochs >= 1
-                and (trainer.current_epoch + 1) % self._every_n_epochs == 0
-            ):
+            if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0:
                 if (
                     self.if_save_latest == True
                 ):  ####如果设置只保存最后一个ckpt，在保存下一个ckpt后要清理掉之前的所有ckpt
@@ -75,7 +69,7 @@ class my_model_ckpt(ModelCheckpoint):
                     to_save_od["info"] = "GPT-e%s" % (trainer.current_epoch + 1)
                     # torch.save(
                     # print(os.environ)
-                    if(os.environ.get("LOCAL_RANK","0")=="0"):
+                    if os.environ.get("LOCAL_RANK", "0") == "0":
                         my_save(
                             to_save_od,
                             "%s/%s-e%s.ckpt"
@@ -112,7 +106,7 @@ def main(args):
         dirpath=ckpt_dir,
     )
     logger = TensorBoardLogger(name=output_dir.stem, save_dir=output_dir)
-    os.environ["MASTER_ADDR"]="localhost"
+    os.environ["MASTER_ADDR"] = "localhost"
     os.environ["USE_LIBUV"] = "0"
     trainer: Trainer = Trainer(
         max_epochs=config["train"]["epochs"],
@@ -123,9 +117,9 @@ def main(args):
         devices=-1 if torch.cuda.is_available() else 1,
         benchmark=False,
         fast_dev_run=False,
-        strategy = DDPStrategy(
-            process_group_backend="nccl" if platform.system() != "Windows" else "gloo"
-        ) if torch.cuda.is_available() else "auto",
+        strategy=DDPStrategy(process_group_backend="nccl" if platform.system() != "Windows" else "gloo")
+        if torch.cuda.is_available()
+        else "auto",
         precision=config["train"]["precision"],
         logger=logger,
         num_sanity_val_steps=0,
@@ -133,9 +127,7 @@ def main(args):
         use_distributed_sampler=False,  # 非常简单的修改，但解决了采用自定义的 bucket_sampler 下训练步数不一致的问题！
     )
 
-    model: Text2SemanticLightningModule = Text2SemanticLightningModule(
-        config, output_dir
-    )
+    model: Text2SemanticLightningModule = Text2SemanticLightningModule(config, output_dir)
 
     data_module: Text2SemanticDataModule = Text2SemanticDataModule(
         config,