Use logger instead of print

bert_gen.py

@@ -7,9 +7,10 @@ from tqdm import tqdm
 
 import commons
 import utils
+from common.log import logger
+from common.stdout_wrapper import SAFE_STDOUT
 from config import config
 from text import cleaned_text_to_sequence, get_bert
-from common.stdout_wrapper import SAFE_STDOUT
 
 
 def process_line(x):
@@ -81,4 +82,4 @@ if __name__ == "__main__":
                 # 这里是缩进的代码块，表示循环体
                 pass  # 使用pass语句作为占位符
 
-    print(f"bert.pt is generated! total: {len(lines)} bert.pt files.")
+    logger.info(f"bert.pt is generated! total: {len(lines)} bert.pt files.")

config.py

@@ -208,10 +208,10 @@ class Config:
     def __init__(self, config_path: str, path_config: dict[str, str]):
         if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"):
             shutil.copy(src="default_config.yml", dst=config_path)
-            print(
+            logger.info(
                 f"A configuration file {config_path} has been generated based on the default configuration file default_config.yml."
             )
-            print(
+            logger.info(
                 "If you have no special needs, please do not modify default_config.yml."
             )
             # sys.exit(0)

data_utils.py

@@ -345,7 +345,11 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
             assert all(len(bucket) > 0 for bucket in buckets)
         # When one bucket is not traversed
         except Exception as e:
-            print("Bucket warning ", e)
+            logger.info("Bucket warning ", e)
+            logger.info(
+                "Empty buckets at indices: ",
+                [i for i, bucket in enumerate(buckets) if len(bucket) == 0],
+            )
             for i in range(len(buckets) - 1, -1, -1):
                 if len(buckets[i]) == 0:
                     buckets.pop(i)

preprocess_text.py

@@ -10,6 +10,7 @@ from tqdm import tqdm
 from config import config
 from text.cleaner import clean_text
 from common.stdout_wrapper import SAFE_STDOUT
+from common.log import logger
 
 preprocess_text_config = config.preprocess_text_config
 
@@ -70,11 +71,10 @@ def preprocess(
                                 )
                             )
                         except Exception as e:
-                            print(line)
+                            logger.error(
-                            print(
+                                f"An error occurred while generating the training set and validation set, at line:\n{line}\nDetails:\n{e}"
-                                f"An error occurred while generating the training set and validation set! Details:\n{e}"
                             )
-                            raise e
+                            raise
 
     transcription_path = cleaned_path
     spk_utt_map = defaultdict(list)
@@ -89,12 +89,12 @@ def preprocess(
             utt, spk, language, text, phones, tones, word2ph = line.strip().split("|")
             if utt in audioPaths:
                 # 过滤数据集错误：相同的音频匹配多个文本，导致后续bert出问题
-                print(f"Same audio matches multiple texts: {line}")
+                logger.warning(f"Same audio matches multiple texts: {line}")
                 countSame += 1
                 continue
             if not os.path.isfile(utt):
                 # 过滤数据集错误：不存在对应音频
-                print(f"Audio not found: {utt}")
+                logger.warning(f"Audio not found: {utt}")
                 countNotFound += 1
                 continue
             audioPaths.add(utt)
@@ -102,7 +102,7 @@ def preprocess(
             if spk not in spk_id_map.keys():
                 spk_id_map[spk] = current_sid
                 current_sid += 1
-        print(
+        logger.info(
             f"Total repeated audios: {countSame}, Total number of audio not found: {countNotFound}"
         )
 
@@ -140,7 +140,7 @@ def preprocess(
     )
     with open(config_path, "w", encoding="utf-8") as f:
         json.dump(json_config, f, indent=2, ensure_ascii=False)
-    print("Training set and validation set generation from texts is complete!")
+    logger.info("Training set and validation set generation from texts is complete!")
 
 
 if __name__ == "__main__":

resample.py

@@ -7,9 +7,9 @@ import pyloudnorm as pyln
 import soundfile
 from tqdm import tqdm
 
-from config import config
 from common.log import logger
 from common.stdout_wrapper import SAFE_STDOUT
+from config import config
 
 DEFAULT_BLOCK_SIZE: float = 0.400  # seconds
 
@@ -119,4 +119,4 @@ if __name__ == "__main__":
     pool.close()
     pool.join()
 
-    print("Resampling Done!")
+    logger.info("Resampling Done!")

style_gen.py

@@ -7,8 +7,9 @@ import torch
 from tqdm import tqdm
 
 import utils
-from config import config
+from common.log import logger
 from common.stdout_wrapper import SAFE_STDOUT
+from config import config
 
 warnings.filterwarnings("ignore", category=UserWarning)
 from pyannote.audio import Inference, Model
@@ -23,8 +24,7 @@ def save_style_vector(wav_path):
     try:
         style_vec = inference(wav_path)
     except Exception as e:
-        print(f"\nError occurred with file: {wav_path}")
+        logger.error(f"\nError occurred with file: {wav_path}, Details:\n{e}\n")
-        print(e)
         raise
     np.save(f"{wav_path}.npy", style_vec)  # `test.wav` -> `test.wav.npy`
     return style_vec
@@ -69,4 +69,4 @@ if __name__ == "__main__":
             )
         )
 
-    print(f"Finished generating style vectors! total: {len(wavnames)} npy files.")
+    logger.info(f"Finished generating style vectors! total: {len(wavnames)} npy files.")