Add preprocess log and adjust log files

common/subprocess_utils.py

@@ -16,12 +16,10 @@ def run_script_with_log(cmd: list[str], ignore_warning=False) -> tuple[bool, str
         text=True,
     )
     if result.returncode != 0:
-        logger.error(f"Error: {' '.join(cmd)}")
-        print(result.stderr)
+        logger.error(f"Error: {' '.join(cmd)}\n{result.stderr}")
         return False, result.stderr
     elif result.stderr and not ignore_warning:
-        logger.warning(f"Warning: {' '.join(cmd)}")
-        print(result.stderr)
+        logger.warning(f"Warning: {' '.join(cmd)}\n{result.stderr}")
         return True, result.stderr
     logger.success(f"Success: {' '.join(cmd)}")
     return True, ""

train_ms.py

@@ -3,8 +3,6 @@ import datetime
 import gc
 import os
 import platform
-import shutil
-import sys
 
 import torch
 import torch.distributed as dist
@@ -49,14 +47,53 @@ global_step = 0
 
 
 def run():
-    # 环境变量解析
+    # Command line configuration is not recommended unless necessary, use config.yml
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--config",
+        type=str,
+        default=config.train_ms_config.config_path,
+        help="JSON file for configuration",
+    )
+    parser.add_argument(
+        "-m",
+        "--model",
+        type=str,
+        help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
+        default=config.dataset_path,
+    )
+    parser.add_argument(
+        "--assets_root",
+        type=str,
+        help="Root directory of model assets needed for inference.",
+        default=config.assets_root,
+    )
+    parser.add_argument(
+        "--skip_default_style",
+        action="store_true",
+        help="Skip saving default style config and mean vector.",
+    )
+    parser.add_argument(
+        "--no_progress_bar",
+        action="store_true",
+        help="Do not show the progress bar while training.",
+    )
+    args = parser.parse_args()
+
+    # Set log file
+    model_dir = os.path.join(args.model, config.train_ms_config.model_dir)
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    logger.add(os.path.join(args.model, f"train_{timestamp}.log"))
+
+    # Parsing environment variables
     envs = config.train_ms_config.env
     for env_name, env_value in envs.items():
         if env_name not in os.environ.keys():
-            logger.info("加载config中的配置{}".format(str(env_value)))
+            logger.info("Loading configuration from config {}".format(str(env_value)))
             os.environ[env_name] = str(env_value)
     logger.info(
-        "加载环境变量 \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
+        "Loading environment variables \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
             os.environ["MASTER_ADDR"],
             os.environ["MASTER_PORT"],
             os.environ["WORLD_SIZE"],
@@ -77,47 +114,7 @@ def run():
     local_rank = int(os.environ["LOCAL_RANK"])
     n_gpus = dist.get_world_size()
 
-    # 命令行/config.yml配置解析
-    # hps = utils.get_hparams()
-    parser = argparse.ArgumentParser()
-    # Command line configuration is not recommended unless necessary, use config.yml
-    parser.add_argument(
-        "-c",
-        "--config",
-        type=str,
-        default=config.train_ms_config.config_path,
-        help="JSON file for configuration",
-    )
-
-    parser.add_argument(
-        "-m",
-        "--model",
-        type=str,
-        help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
-        default=config.dataset_path,
-    )
-    parser.add_argument(
-        "--assets_root",
-        type=str,
-        help="Root directory of model assets needed for inference.",
-        default=config.assets_root,
-    )
-    parser.add_argument(
-        "--skip_default_style",
-        action="store_true",
-        help="Skip saving default style config and mean vector.",
-    )
-    parser.add_argument(
-        '--no_progress_bar',
-        action='store_true',
-        help='Do not show the progress bar while training.'
-    )
-    args = parser.parse_args()
-    model_dir = os.path.join(args.model, config.train_ms_config.model_dir)
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
     hps = utils.get_hparams_from_file(args.config)
-
     # This is needed because we have to pass `model_dir` to `train_and_evaluate()`
     hps.model_dir = model_dir
 
@@ -167,7 +164,6 @@ def run():
     if rank == 0:
         # logger = utils.get_logger(hps.model_dir)
         # logger.info(hps)
-        logger.add(os.path.join(model_dir, "train.log"))
         utils.check_git_hash(model_dir)
         writer = SummaryWriter(log_dir=model_dir)
         writer_eval = SummaryWriter(log_dir=os.path.join(model_dir, "eval"))
@@ -371,10 +367,17 @@ def run():
     scaler = GradScaler(enabled=hps.train.bf16_run)
     logger.info("Start training.")
 
-    diff = abs(epoch_str * len(train_loader) - (hps.train.epochs + 1) * len(train_loader))
+    diff = abs(
+        epoch_str * len(train_loader) - (hps.train.epochs + 1) * len(train_loader)
+    )
     pbar = None
     if not args.no_progress_bar:
-        pbar = tqdm(total=global_step + diff, initial=global_step, smoothing=0.05, file=SAFE_STDOUT)
+        pbar = tqdm(
+            total=global_step + diff,
+            initial=global_step,
+            smoothing=0.05,
+            file=SAFE_STDOUT,
+        )
     initial_step = global_step
 
     for epoch in range(epoch_str, hps.train.epochs + 1):
@@ -466,7 +469,7 @@ def train_and_evaluate(
     logger,
     writers,
     pbar: tqdm,
-    initial_step: int
+    initial_step: int,
 ):
     net_g, net_d, net_dur_disc = nets
     optim_g, optim_d, optim_dur_disc = optims
@@ -684,7 +687,11 @@ def train_and_evaluate(
                     scalars=scalar_dict,
                 )
 
-            if global_step % hps.train.eval_interval == 0 and global_step != 0 and initial_step != global_step:
+            if (
+                global_step % hps.train.eval_interval == 0
+                and global_step != 0
+                and initial_step != global_step
+            ):
                 evaluate(hps, net_g, eval_loader, writer_eval)
                 utils.save_checkpoint(
                     net_g,
@@ -728,7 +735,11 @@ def train_and_evaluate(
 
         global_step += 1
         if pbar is not None:
-            pbar.set_description("Epoch {}({:.0f}%)/{}".format(epoch, 100.0 * batch_idx / len(train_loader), hps.train.epochs))
+            pbar.set_description(
+                "Epoch {}({:.0f}%)/{}".format(
+                    epoch, 100.0 * batch_idx / len(train_loader), hps.train.epochs
+                )
+            )
             pbar.update()
     # 本家ではこれをスピードアップのために消すと書かれていたので、一応消してみる
     # gc.collect()
@@ -736,6 +747,7 @@ def train_and_evaluate(
     if pbar is None and rank == 0:
         logger.info(f"====> Epoch: {epoch}, step: {global_step}")
 
+
 def evaluate(hps, generator, eval_loader, writer_eval):
     generator.eval()
     image_dict = {}

webui_train.py

@@ -8,6 +8,10 @@ import yaml
 
 from common.log import logger
 from common.subprocess_utils import run_script_with_log, second_elem_of
+from datetime import datetime
+
+
+logger_handler = None
 
 # Get path settings
 with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
@@ -27,8 +31,19 @@ def get_path(model_name):
 
 
 def initialize(model_name, batch_size, epochs, save_every_steps, bf16_run):
-    logger.info("Step 1: start initialization...")
+    global logger_handler
     dataset_path, _, train_path, val_path, config_path = get_path(model_name)
+
+    # 前処理のログをファイルに保存する
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    file_name = f"preprocess_{timestamp}.log"
+    if logger_handler is not None:
+        logger.remove(logger_handler)
+    logger_handler = logger.add(os.path.join(dataset_path, file_name))
+
+    logger.info(
+        f"Step 1: start initialization...\nmodel_name: {model_name}, batch_size: {batch_size}, epochs: {epochs}, save_every_steps: {save_every_steps}, bf16_run: {bf16_run}"
+    )
     if os.path.isfile(config_path):
         with open(config_path, "r", encoding="utf-8") as f:
             config = json.load(f)
@@ -230,13 +245,13 @@ def train(model_name):
         ["train_ms.py", "--config", config_path, "--model", dataset_path]
     )
     if not success:
-        logger.error(f"Final Step: train failed.")
-        return False, f"Final Step, Error: 学習に失敗しました:\n{message}"
+        logger.error(f"Train failed.")
+        return False, f"Error: 学習に失敗しました:\n{message}"
     elif message:
-        logger.warning(f"Final Step: train finished with stderr.")
-        return True, f"Final Step, Success: 学習が完了しました:\n{message}"
-    logger.success("Final Step: train finished.")
-    return True, "Final Step, Success: 学習が完了しました"
+        logger.warning(f"Train finished with stderr.")
+        return True, f"Success: 学習が完了しました:\n{message}"
+    logger.success("Train finished.")
+    return True, "Success: 学習が完了しました"
 
 
 initial_md = """
@@ -299,7 +314,6 @@ if __name__ == "__main__":
             with gr.Column():
                 batch_size = gr.Slider(
                     label="バッチサイズ",
-                    info="VRAM 12GBで4くらい",
                     value=4,
                     minimum=1,
                     maximum=64,
@@ -335,7 +349,7 @@ if __name__ == "__main__":
                     step=1,
                 )
                 normalize = gr.Checkbox(
-                    label="音声の音量を正規化する",
+                    label="音声の音量を正規化する(音量の大小が揃っていない場合など)",
                     value=False,
                 )
                 trim = gr.Checkbox(