fix: 卸载音色省显存

顺便将所有print换成了统一的logger

infer/lib/infer_pack/models.py

@@ -1,7 +1,6 @@
 import math
-import os
-import pdb
-from time import time as ttime
+import logging
+logger = logging.getLogger(__name__)
 
 import numpy as np
 import torch
@@ -616,7 +615,7 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()
@@ -732,7 +731,7 @@ class SynthesizerTrnMs768NSFsid(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()
@@ -845,7 +844,7 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()
@@ -951,7 +950,7 @@ class SynthesizerTrnMs768NSFsid_nono(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()

infer/lib/infer_pack/models_onnx.py

@@ -1,7 +1,6 @@
 import math
-import os
-import pdb
-from time import time as ttime
+import logging
+logger = logging.getLogger(__name__)
 
 import numpy as np
 import torch
@@ -620,7 +619,7 @@ class SynthesizerTrnMsNSFsidM(nn.Module):
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
         self.speaker_map = None
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()

infer/lib/infer_pack/onnx_inference.py

@@ -3,10 +3,13 @@ import numpy as np
 import onnxruntime
 import soundfile
 
+import logging
+logger = logging.getLogger(__name__)
+
 
 class ContentVec:
     def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("Load model(s) from {}".format(vec_path))
+        logger.info("Load model(s) from {}".format(vec_path))
         if device == "cpu" or device is None:
             providers = ["CPUExecutionProvider"]
         elif device == "cuda":

infer/lib/rmvpe.py

@@ -7,6 +7,10 @@ import torch.nn.functional as F
 from librosa.util import normalize, pad_center, tiny
 from scipy.signal import get_window
 
+import logging
+
+logger = logging.getLogger(__name__)
+
 
 ###stft codes from https://github.com/pseeth/torch-stft/blob/master/torch_stft/util.py
 def window_sumsquare(
@@ -691,4 +695,4 @@ if __name__ == "__main__":
     # f0 = rmvpe.infer_from_audio(audio, thred=thred)
     # f0 = rmvpe.infer_from_audio(audio, thred=thred)
     t1 = ttime()
-    print(f0.shape, t1 - t0)
+    logger.info(f0.shape, t1 - t0)

infer/lib/train/data_utils.py

@@ -1,5 +1,7 @@
 import os
 import traceback
+import logging
+logger = logging.getLogger(__name__)
 
 import numpy as np
 import torch
@@ -110,7 +112,7 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
             try:
                 spec = torch.load(spec_filename)
             except:
-                print(spec_filename, traceback.format_exc())
+                logger.warn(spec_filename, traceback.format_exc())
                 spec = spectrogram_torch(
                     audio_norm,
                     self.filter_length,
@@ -302,7 +304,7 @@ class TextAudioLoader(torch.utils.data.Dataset):
             try:
                 spec = torch.load(spec_filename)
             except:
-                print(spec_filename, traceback.format_exc())
+                logger.warn(spec_filename, traceback.format_exc())
                 spec = spectrogram_torch(
                     audio_norm,
                     self.filter_length,

infer/lib/train/mel_processing.py

@@ -1,6 +1,8 @@
 import torch
 import torch.utils.data
 from librosa.filters import mel as librosa_mel_fn
+import logging
+logger = logging.getLogger(__name__)
 
 MAX_WAV_VALUE = 32768.0
 
@@ -51,9 +53,9 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     """
     # Validation
     if torch.min(y) < -1.07:
-        print("spectrogram_torch min value is ", torch.min(y))
+        logger.debug("min value is ", torch.min(y))
     if torch.max(y) > 1.07:
-        print("spectrogram_torch max value is ", torch.max(y))
+        logger.debug("max value is ", torch.max(y))
 
     # Window - Cache if needed
     global hann_window

infer/lib/train/utils.py

@@ -33,7 +33,7 @@ def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
             try:
                 new_state_dict[k] = saved_state_dict[k]
                 if saved_state_dict[k].shape != state_dict[k].shape:
-                    print(
+                    logger.warn(
                         "shape-%s-mismatch. need: %s, get: %s"
                         % (k, state_dict[k].shape, saved_state_dict[k].shape)
                     )  #
@@ -109,7 +109,7 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
         try:
             new_state_dict[k] = saved_state_dict[k]
             if saved_state_dict[k].shape != state_dict[k].shape:
-                print(
+                logger.warn(
                     "shape-%s-mismatch|need-%s|get-%s"
                     % (k, state_dict[k].shape, saved_state_dict[k].shape)
                 )  #
@@ -207,7 +207,7 @@ def latest_checkpoint_path(dir_path, regex="G_*.pth"):
     f_list = glob.glob(os.path.join(dir_path, regex))
     f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
     x = f_list[-1]
-    print(x)
+    logger.debug(x)
     return x