Merge pull request #333 from svc-develop-team/add-fancy-logger

Add new logger/添加一个花里胡哨的牛逼logger

preprocess_flist_config.py

@@ -5,6 +5,7 @@ import re
 import wave
 from random import shuffle
 
+from loguru import logger
 from tqdm import tqdm
 
 import diffusion.logger.utils as du
@@ -46,9 +47,9 @@ if __name__ == "__main__":
             if not file.endswith("wav"):
                 continue
             if not pattern.match(file):
-                print(f"warning：文件名{file}中包含非字母数字下划线，可能会导致错误。（也可能不会）")
+                logger.warning(f"文件名{file}中包含非字母数字下划线，可能会导致错误。（也可能不会）")
             if get_wav_duration(file) < 0.3:
-                print("skip too short audio:", file)
+                logger.info("Skip too short audio:" + file)
                 continue
             new_wavs.append(file)
         wavs = new_wavs
@@ -59,13 +60,13 @@ if __name__ == "__main__":
     shuffle(train)
     shuffle(val)
             
-    print("Writing", args.train_list)
+    logger.info("Writing" + args.train_list)
     with open(args.train_list, "w") as f:
         for fname in tqdm(train):
             wavpath = fname
             f.write(wavpath + "\n")
         
-    print("Writing", args.val_list)
+    logger.info("Writing" + args.val_list)
     with open(args.val_list, "w") as f:
         for fname in tqdm(val):
             wavpath = fname
@@ -97,8 +98,8 @@ if __name__ == "__main__":
     if args.vol_aug:
         config_template["train"]["vol_aug"] = config_template["model"]["vol_embedding"] = True
 
-    print("Writing configs/config.json")
+    logger.info("Writing to configs/config.json")
     with open("configs/config.json", "w") as f:
         json.dump(config_template, f, indent=2)
-    print("Writing configs/diffusion.yaml")
+    logger.info("Writing to configs/diffusion.yaml")
     du.save_config("configs/diffusion.yaml",d_config_template)

preprocess_hubert_f0.py

@@ -5,6 +5,7 @@ import random
 from concurrent.futures import ProcessPoolExecutor
 from glob import glob
 from random import shuffle
+from loguru import logger
 
 import librosa
 import numpy as np
@@ -28,7 +29,6 @@ speech_encoder = hps["model"]["speech_encoder"]
 
 
 def process_one(filename, hmodel,f0p,rank,diff=False,mel_extractor=None):
-    # print(filename)
     wav, sr = librosa.load(filename, sr=sampling_rate)
     audio_norm = torch.FloatTensor(wav)
     audio_norm = audio_norm.unsqueeze(0)
@@ -104,15 +104,15 @@ def process_one(filename, hmodel,f0p,rank,diff=False,mel_extractor=None):
             np.save(aug_vol_path,aug_vol.to('cpu').numpy())
 
 def process_batch(file_chunk, f0p, diff=False, mel_extractor=None):
-    print("Loading speech encoder for content...")
+    logger.info("Loading speech encoder for content...")
     rank = mp.current_process()._identity
     rank = rank[0] if len(rank) > 0 else 0
     if torch.cuda.is_available():
         gpu_id = rank % torch.cuda.device_count()
         device = torch.device(f"cuda:{gpu_id}")
-    print(f"Rank {rank} uses device {device}")
+    logger.info(f"Rank {rank} uses device {device}")
     hmodel = utils.get_speech_encoder(speech_encoder, device=device)
-    print("Loaded speech encoder.")
+    logger.info(f"Loaded speech encoder for rank {rank}")
     for filename in tqdm(file_chunk):
         process_one(filename, hmodel, f0p, rank, diff, mel_extractor)
 
@@ -144,7 +144,9 @@ if __name__ == "__main__":
     args = parser.parse_args()
     f0p = args.f0_predictor
     print(speech_encoder)
-    print(f0p)
+    logger.info("Using " + speech_encoder + " SpeechEncoder")
+    logger.info("Using " + f0p + "f0 extractor")
+    logger.info("Using diff Mode:")
     print(args.use_diff)
     if args.use_diff:
         print("use_diff")

requirements.txt

@@ -25,3 +25,4 @@ langdetect
 pyyaml
 pynvml
 faiss-cpu
+loguru

requirements_win.txt

@@ -29,3 +29,4 @@ langdetect
 pyyaml
 pynvml
 faiss-cpu
+loguru

train_diff.py

@@ -8,7 +8,7 @@ from diffusion.logger import utils
 from diffusion.solver import train
 from diffusion.unit2mel import Unit2Mel
 from diffusion.vocoder import Vocoder
-
+from loguru import logger
 
 def parse_args(args=None, namespace=None):
     """Parse command-line arguments."""
@@ -28,8 +28,8 @@ if __name__ == '__main__':
     
     # load config
     args = utils.load_config(cmd.config)
-    print(' > config:', cmd.config)
-    print(' >    exp:', args.env.expdir)
+    logger.info(' > config:'+ cmd.config)
+    logger.info(' > exp:'+ args.env.expdir)
     
     # load vocoder
     vocoder = Vocoder(args.vocoder.type, args.vocoder.ckpt, device=args.device)
@@ -47,7 +47,7 @@ if __name__ == '__main__':
                 args.model.k_step_max
                 )
     
-    print(f' > INFO: now model timesteps is {model.timesteps}, and k_step_max is {model.k_step_max}')
+    logger.info(f' > Now model timesteps is {model.timesteps}, and k_step_max is {model.k_step_max}')
     
     # load parameters
     optimizer = torch.optim.AdamW(model.parameters())