fix: 卸载音色省显存

顺便将所有print换成了统一的logger

infer/modules/train/train.py

@@ -1,5 +1,7 @@
 import os
 import sys
+import logging
+logger = logging.getLogger(__name__)
 
 now_dir = os.getcwd()
 sys.path.append(os.path.join(now_dir))
@@ -82,7 +84,7 @@ def main():
         n_gpus = 1
     if n_gpus < 1:
         # patch to unblock people without gpus. there is probably a better way.
-        print("NO GPU DETECTED: falling back to CPU - this may take a while")
+        logger.warn("NO GPU DETECTED: falling back to CPU - this may take a while")
         n_gpus = 1
     os.environ["MASTER_ADDR"] = "localhost"
     os.environ["MASTER_PORT"] = str(randint(20000, 55555))
@@ -209,7 +211,7 @@ def run(rank, n_gpus, hps):
         if hps.pretrainG != "":
             if rank == 0:
                 logger.info("loaded pretrained %s" % (hps.pretrainG))
-            print(
+            logger.info(
                 net_g.module.load_state_dict(
                     torch.load(hps.pretrainG, map_location="cpu")["model"]
                 )
@@ -217,7 +219,7 @@ def run(rank, n_gpus, hps):
         if hps.pretrainD != "":
             if rank == 0:
                 logger.info("loaded pretrained %s" % (hps.pretrainD))
-            print(
+            logger.info(
                 net_d.module.load_state_dict(
                     torch.load(hps.pretrainD, map_location="cpu")["model"]
                 )

infer/modules/uvr5/mdxnet.py

@@ -1,4 +1,6 @@
 import os
+import logging
+logger = logging.getLogger(__name__)
 
 import librosa
 import numpy as np
@@ -88,7 +90,7 @@ class Predictor:
     def __init__(self, args):
         import onnxruntime as ort
 
-        print(ort.get_available_providers())
+        logger.info(ort.get_available_providers())
         self.args = args
         self.model_ = get_models(
             device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
@@ -101,7 +103,7 @@ class Predictor:
                 "CPUExecutionProvider",
             ],
         )
-        print("ONNX load done")
+        logger.info("ONNX load done")
 
     def demix(self, mix):
         samples = mix.shape[-1]

infer/modules/uvr5/modules.py

@@ -1,5 +1,7 @@
 import os
 import traceback
+import logging
+logger = logging.getLogger(__name__)
 
 import ffmpeg
 import torch
@@ -92,5 +94,5 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
             traceback.print_exc()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
-            print("Executed torch.cuda.empty_cache()")
+            logger.info("Executed torch.cuda.empty_cache()")
     yield "\n".join(infos)

infer/modules/uvr5/preprocess.py

@@ -1,4 +1,6 @@
 import os
+import logging
+logger = logging.getLogger(__name__)
 
 import librosa
 import numpy as np
@@ -116,7 +118,7 @@ class AudioPre:
                 )
             else:
                 wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
-            print("%s instruments done" % name)
+            logger.info("%s instruments done" % name)
             if format in ["wav", "flac"]:
                 sf.write(
                     os.path.join(
@@ -150,7 +152,7 @@ class AudioPre:
                 )
             else:
                 wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
-            print("%s vocals done" % name)
+            logger.info("%s vocals done" % name)
             if format in ["wav", "flac"]:
                 sf.write(
                     os.path.join(
@@ -283,7 +285,7 @@ class AudioPreDeEcho:
                 )
             else:
                 wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
-            print("%s instruments done" % name)
+            logger.info("%s instruments done" % name)
             if format in ["wav", "flac"]:
                 sf.write(
                     os.path.join(
@@ -317,7 +319,7 @@ class AudioPreDeEcho:
                 )
             else:
                 wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
-            print("%s vocals done" % name)
+            logger.info("%s vocals done" % name)
             if format in ["wav", "flac"]:
                 sf.write(
                     os.path.join(

infer/modules/vc/modules.py

@@ -1,4 +1,6 @@
 import traceback
+import logging
+logger = logging.getLogger(__name__)
 
 import numpy as np
 import soundfile as sf
@@ -30,14 +32,7 @@ class VC:
         self.config = config
 
     def get_vc(self, sid, *to_return_protect):
-        person = f'{os.getenv("weight_root")}/{sid}'
-        print(f"Loading: {person}")
-
-        self.cpt = torch.load(person, map_location="cpu")
-        self.tgt_sr = self.cpt["config"][-1]
-        self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-        self.if_f0 = self.cpt.get("f0", 1)
-        self.version = self.cpt.get("version", "v1")
+        logger.info("Get sid: " + sid)
 
         to_return_protect0 = {
             "visible": self.if_f0 != 0,
@@ -54,6 +49,57 @@ class VC:
             "__type__": "update",
         }
 
+        if not sid:
+            if self.hubert_model is not None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+                logger.info("Clean model cache")
+                del self.net_g, self.n_spk, self.vc, self.hubert_model, self.tgt_sr  # ,cpt
+                self.hubert_model = self.net_g = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = None
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
+                ###楼下不这么折腾清理不干净
+                self.if_f0 = self.cpt.get("f0", 1)
+                self.version = self.cpt.get("version", "v1")
+                if self.version == "v1":
+                    if self.if_f0 == 1:
+                        self.net_g = SynthesizerTrnMs256NSFsid(
+                            *self.cpt["config"], is_half=self.config.is_half
+                        )
+                    else:
+                        self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"])
+                elif self.version == "v2":
+                    if self.if_f0 == 1:
+                        self.net_g = SynthesizerTrnMs768NSFsid(
+                            *self.cpt["config"], is_half=self.config.is_half
+                        )
+                    else:
+                        self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"])
+                del self.net_g, self.cpt
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
+            return (
+                {"visible": False, "__type__": "update"},
+                {
+                    "visible": True,
+                    "value": to_return_protect0,
+                    "__type__": "update",
+                },
+                {
+                    "visible": True,
+                    "value": to_return_protect1,
+                    "__type__": "update",
+                },
+                "",
+                "",
+            )
+        person = f'{os.getenv("weight_root")}/{sid}'
+        logger.info(f"Loading: {person}")
+
+        self.cpt = torch.load(person, map_location="cpu")
+        self.tgt_sr = self.cpt["config"][-1]
+        self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+        self.if_f0 = self.cpt.get("f0", 1)
+        self.version = self.cpt.get("version", "v1")
+
         synthesizer_class = {
             ("v1", 1): SynthesizerTrnMs256NSFsid,
             ("v1", 0): SynthesizerTrnMs256NSFsid_nono,
@@ -77,7 +123,7 @@ class VC:
         self.pipeline = Pipeline(self.tgt_sr, self.config)
         n_spk = self.cpt["config"][-3]
         index = {"value": get_index_path_from_model(sid), "__type__": "update"}
-        print("Select index:", index["value"])
+        logger.info("Select index: " + index["value"])
 
         return (
             (
@@ -165,7 +211,7 @@ class VC:
             )
         except:
             info = traceback.format_exc()
-            print(info)
+            logger.warn(info)
             return info, (None, None)
 
     def vc_multi(

infer/modules/vc/pipeline.py

@@ -1,6 +1,9 @@
 import os
 import sys
 import traceback
+import logging
+logger = logging.getLogger(__name__)
+
 from functools import lru_cache
 from time import time as ttime
 
@@ -139,7 +142,7 @@ class Pipeline(object):
             if not hasattr(self, "model_rmvpe"):
                 from infer.lib.rmvpe import RMVPE
 
-                print(
+                logger.info(
                     "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"]
                 )
                 self.model_rmvpe = RMVPE(
@@ -152,7 +155,7 @@ class Pipeline(object):
         if "privateuseone" in str(self.device):  # clean ortruntime memory
             del self.model_rmvpe.model
             del self.model_rmvpe
-            print("Cleaning ortruntime memory")
+            logger.info("Cleaning ortruntime memory")
 
         f0 *= pow(2, f0_up_key / 12)
         # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))