tqdm progress bar

GPT_SoVITS/prepare_datasets/1-get-text.py

@@ -84,11 +84,10 @@ if os.path.exists(txt_path) == False:
         return phone_level_feature.T
 
     def process(data, res):
-        for name, text, lan in data:
+        for name, text, lan in tqdm(data,position=int(i_part),delay=0.5):
             try:
                 name=clean_path(name)
                 name = os.path.basename(name)
-                print(name)
                 phones, word2ph, norm_text = clean_text(
                     text.replace("%", "-").replace("￥", ","), lan, version
                 )
@@ -102,7 +101,7 @@ if os.path.exists(txt_path) == False:
                 # res.append([name,phones])
                 res.append([name, phones, word2ph, norm_text])
             except:
-                print(name, text, traceback.format_exc())
+                tqdm.write(name, text, traceback.format_exc())
 
     todo = []
     res = []
@@ -135,9 +134,9 @@ if os.path.exists(txt_path) == False:
                     [wav_name, text, language_v1_to_language_v2.get(language, language)]
                 )
             else:
-                print(f"\033[33m[Waring] The {language = } of {wav_name} is not supported for training.\033[0m")
+                tqdm.write(f"\033[33m[Waring] The {language = } of {wav_name} is not supported for training.\033[0m")
         except:
-            print(line, traceback.format_exc())
+            tqdm.write(line, traceback.format_exc())
 
     process(todo, res)
     opt = []

GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py

@@ -20,6 +20,7 @@ import librosa
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 from tools.my_utils import load_audio,clean_path
+from tqdm import tqdm
 
 # from config import cnhubert_base_path
 # cnhubert.cnhubert_base_path=cnhubert_base_path
@@ -70,7 +71,7 @@ def name2go(wav_name,wav_path):
     tmp_audio = load_audio(wav_path, 32000)
     tmp_max = np.abs(tmp_audio).max()
     if tmp_max > 2.2:
-        print("%s-filtered,%s" % (wav_name, tmp_max))
+        tqdm.write("%s-filtered,%s" % (wav_name, tmp_max))
         return
     tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * tmp_audio
     tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha*1145.14)) + ((1 - alpha)*1145.14) * tmp_audio
@@ -85,7 +86,7 @@ def name2go(wav_name,wav_path):
     ssl=model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1,2).cpu()#torch.Size([1, 768, 215])
     if np.isnan(ssl.detach().numpy()).sum()!= 0:
         nan_fails.append((wav_name,wav_path))
-        print("nan filtered:%s"%wav_name)
+        tqdm.write("nan filtered:%s"%wav_name)
         return
     wavfile.write(
         "%s/%s"%(wav32dir,wav_name),
@@ -97,7 +98,7 @@ def name2go(wav_name,wav_path):
 with open(inp_text,"r",encoding="utf8")as f:
     lines=f.read().strip("\n").split("\n")
 
-for line in lines[int(i_part)::int(all_parts)]:
+for line in tqdm(lines[int(i_part)::int(all_parts)],position=int(i_part)):
     try:
         # wav_name,text=line.split("\t")
         wav_name, spk_name, language, text = line.split("|")
@@ -111,13 +112,13 @@ for line in lines[int(i_part)::int(all_parts)]:
             wav_name = os.path.basename(wav_name)
         name2go(wav_name,wav_path)
     except:
-        print(line,traceback.format_exc())
+        tqdm.write(line,traceback.format_exc())
 
 if(len(nan_fails)>0 and is_half==True):
     is_half=False
     model=model.float()
-    for wav in nan_fails:
+    for wav in tqdm(nan_fails,position=int(i_part)):
         try:
             name2go(wav[0],wav[1])
         except:
-            print(wav_name,traceback.format_exc())
+            tqdm.write(wav_name,traceback.format_exc())

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -25,6 +25,7 @@ from tqdm import tqdm
 import logging, librosa, utils
 from module.models import SynthesizerTrn
 from tools.my_utils import clean_path
+from tqdm import tqdm
 logging.getLogger("numba").setLevel(logging.WARNING)
 # from config import pretrained_s2G
 
@@ -87,8 +88,8 @@ if os.path.exists(semantic_path) == False:
         lines = f.read().strip("\n").split("\n")
 
     lines1 = []
-    for line in lines[int(i_part) :: int(all_parts)]:
-        # print(line)
+    for line in tqdm(lines[int(i_part) :: int(all_parts)],position=int(i_part)):
+        # tqdm.write(line)
         try:
             # wav_name,text=line.split("\t")
             wav_name, spk_name, language, text = line.split("|")
@@ -97,6 +98,6 @@ if os.path.exists(semantic_path) == False:
             # name2go(name,lines1)
             name2go(wav_name, lines1)
         except:
-            print(line, traceback.format_exc())
+            tqdm.write(line, traceback.format_exc())
     with open(semantic_path, "w", encoding="utf8") as f:
         f.write("\n".join(lines1))

GPT_SoVITS/s2_train.py

@@ -306,7 +306,7 @@ def train_and_evaluate(
         y_lengths,
         text,
         text_lengths,
-    ) in enumerate(tqdm(train_loader)):
+    ) in enumerate(tqdm(train_loader,position=rank+1,leave=(epoch==hps.train.epochs),postfix=f'epoch:{epoch}')):
         if torch.cuda.is_available():
             spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(
                 rank, non_blocking=True