Upload myinfer.py

myinfer.py

@@ -1,12 +1,102 @@
+'''
+runtime\python.exe myinfer.py 0 "E:\codes\py39\RVC-beta\todo-songs\1111.wav" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "test.wav" "weights/mi-test.pth" 0.6 cuda:0 True
+'''
 import os,sys,pdb,torch
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+import argparse
+import glob
+import sys
+import torch
+from multiprocessing import cpu_count
+class Config:
+    def __init__(self,device,is_half):
+        self.device = device
+        self.is_half = is_half
+        self.n_cpu = 0
+        self.gpu_name = None
+        self.gpu_mem = None
+        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+
+    def device_config(self) -> tuple:
+        if torch.cuda.is_available():
+            i_device = int(self.device.split(":")[-1])
+            self.gpu_name = torch.cuda.get_device_name(i_device)
+            if (
+                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
+                or "P40" in self.gpu_name.upper()
+                or "1060" in self.gpu_name
+                or "1070" in self.gpu_name
+                or "1080" in self.gpu_name
+            ):
+                print("16系/10系显卡和P40强制单精度")
+                self.is_half = False
+                for config_file in ["32k.json", "40k.json", "48k.json"]:
+                    with open(f"configs/{config_file}", "r") as f:
+                        strr = f.read().replace("true", "false")
+                    with open(f"configs/{config_file}", "w") as f:
+                        f.write(strr)
+                with open("trainset_preprocess_pipeline_print.py", "r") as f:
+                    strr = f.read().replace("3.7", "3.0")
+                with open("trainset_preprocess_pipeline_print.py", "w") as f:
+                    f.write(strr)
+            else:
+                self.gpu_name = None
+            self.gpu_mem = int(
+                torch.cuda.get_device_properties(i_device).total_memory
+                / 1024
+                / 1024
+                / 1024
+                + 0.4
+            )
+            if self.gpu_mem <= 4:
+                with open("trainset_preprocess_pipeline_print.py", "r") as f:
+                    strr = f.read().replace("3.7", "3.0")
+                with open("trainset_preprocess_pipeline_print.py", "w") as f:
+                    f.write(strr)
+        elif torch.backends.mps.is_available():
+            print("没有发现支持的N卡, 使用MPS进行推理")
+            self.device = "mps"
+        else:
+            print("没有发现支持的N卡, 使用CPU进行推理")
+            self.device = "cpu"
+            self.is_half = True
+
+        if self.n_cpu == 0:
+            self.n_cpu = cpu_count()
+
+        if self.is_half:
+            # 6G显存配置
+            x_pad = 3
+            x_query = 10
+            x_center = 60
+            x_max = 65
+        else:
+            # 5G显存配置
+            x_pad = 1
+            x_query = 6
+            x_center = 38
+            x_max = 41
+
+        if self.gpu_mem != None and self.gpu_mem <= 4:
+            x_pad = 1
+            x_query = 5
+            x_center = 30
+            x_max = 32
+
+        return x_pad, x_query, x_center, x_max
+
 f0up_key=sys.argv[1]
 input_path=sys.argv[2]
 index_path=sys.argv[3]
-npy_path=sys.argv[4]
+f0method=sys.argv[4]#harvest or pm
 opt_path=sys.argv[5]
 model_path=sys.argv[6]
+index_rate=float(sys.argv[7])
+device=sys.argv[8]
+is_half=bool(sys.argv[9])
 print(sys.argv)
-sys.argv=['myinfer.py']
+config=Config(device,is_half)
 now_dir=os.getcwd()
 sys.path.append(now_dir)
 from vc_infer_pipeline import VC
@@ -15,19 +105,7 @@ from my_utils import load_audio
 from fairseq import checkpoint_utils
 from scipy.io import wavfile
 
-
-# f0up_key=0
-# input_path=r"E:\codes\py39\RVC-beta\todo-songs\1111.wav"
-# index_path=r"E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index"
-# npy_path  =r"E:\codes\py39\logs\mi-test\total_fea.npy"
-# opt_path  ="test.wav"
-# model_path="mi-test.pth"
-
-
-
 hubert_model=None
-is_half=False
-device="cuda"
 def load_hubert():
     global hubert_model
     models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
@@ -37,8 +115,7 @@ def load_hubert():
     else:hubert_model = hubert_model.float()
     hubert_model.eval()
 
-
-def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_npy,index_rate):#spk_item, input_audio0, vc_transform0,f0_file,f0method0
+def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,index_rate):
     global tgt_sr,net_g,vc,hubert_model
     if input_audio is None:return "You need to upload an audio", None
     f0_up_key = int(f0_up_key)
@@ -46,16 +123,16 @@ def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_np
     times = [0, 0, 0]
     if(hubert_model==None):load_hubert()
     if_f0 = cpt.get("f0", 1)
-    audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
+    # audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
+    audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,index_rate,if_f0,f0_file=f0_file)
     print(times)
     return audio_opt
 
 
-def get_vc(sid):
-    global n_spk,tgt_sr,net_g,vc,cpt
-    person = "weights/%s" % (sid)
-    print("loading %s"%person)
-    cpt = torch.load(person, map_location="cpu")
+def get_vc(model_path):
+    global n_spk,tgt_sr,net_g,vc,cpt,device,is_half
+    print("loading pth %s"%model_path)
+    cpt = torch.load(model_path, map_location="cpu")
     tgt_sr = cpt["config"][-1]
     cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0]#n_spk
     if_f0=cpt.get("f0",1)
@@ -68,12 +145,12 @@ def get_vc(sid):
     net_g.eval().to(device)
     if (is_half):net_g = net_g.half()
     else:net_g = net_g.float()
-    vc = VC(tgt_sr, device, is_half)
+    vc = VC(tgt_sr, config)
     n_spk=cpt["config"][-3]
     # return {"visible": True,"maximum": n_spk, "__type__": "update"}
 
 
 get_vc(model_path)
-wav_opt=vc_single(0,input_path,f0up_key,None,"harvest",index_path,npy_path,0.6)
+wav_opt=vc_single(0,input_path,f0up_key,None,f0method,index_path,index_rate)
 wavfile.write(opt_path, tgt_sr, wav_opt)