Add support for train without specify pretrained model, add support for selecting v2 48k as training setting, and add support for auto remove pretrained model when the user do not have pretrained model in designate folder. (#528)

* support detection of pretrained model, support train without pretrained model path in web ui

* support detection of pretrained model, support train without pretrained model path in web ui

* support detection of pretrained model, support train without pretrained model path in web ui

Author: JunityZhan

infer-web.py

@@ -614,43 +614,53 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19):
 
 
 def change_sr2(sr2, if_f0_3, version19):
-    vis_v = True if sr2 == "40k" else False
-    if sr2 != "40k":
-        version19 = "v1"
     path_str = "" if version19 == "v1" else "_v2"
-    version_state = {"visible": vis_v, "__type__": "update"}
-    if vis_v == False:
-        version_state["value"] = "v1"
     f0_str = "f0" if if_f0_3 else ""
+    if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
     return (
-        "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
-        "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
-        version_state,
+        ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
+        {"visible": True, "__type__": "update"}
     )
 
-
 def change_version19(sr2, if_f0_3, version19):
     path_str = "" if version19 == "v1" else "_v2"
     f0_str = "f0" if if_f0_3 else ""
-    return "pretrained%s/%sG%s.pth" % (
-        path_str,
-        f0_str,
-        sr2,
-    ), "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
+    if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+    return (
+        ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
+    )
 
 
 def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
     path_str = "" if version19 == "v1" else "_v2"
+    if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK)
+    if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK)
+    if (if_pretrained_generator_exist == False):
+        print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
+    if (if_pretrained_discriminator_exist == False):
+        print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
     if if_f0_3:
         return (
             {"visible": True, "__type__": "update"},
-            "pretrained%s/f0G%s.pth" % (path_str, sr2),
-            "pretrained%s/f0D%s.pth" % (path_str, sr2),
+            "pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "",
+            "pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "",
         )
     return (
         {"visible": False, "__type__": "update"},
-        "pretrained%s/G%s.pth" % (path_str, sr2),
-        "pretrained%s/D%s.pth" % (path_str, sr2),
+        ("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "",
+        ("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "",
     )
 
 
@@ -741,10 +751,14 @@ def click_train(
     # 生成config#无需生成config
     # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
     print("use gpus:", gpus16)
+    if pretrained_G14 == "":
+        print("no pretrained Generator")
+    if pretrained_D15 == "":
+        print("no pretrained Discriminator")
     if gpus16:
         cmd = (
             config.python_cmd
-            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
             % (
                 exp_dir1,
                 sr2,
@@ -753,8 +767,8 @@ def click_train(
                 gpus16,
                 total_epoch11,
                 save_epoch10,
-                pretrained_G14,
-                pretrained_D15,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
                 1 if if_save_latest13 == i18n("是") else 0,
                 1 if if_cache_gpu17 == i18n("是") else 0,
                 1 if if_save_every_weights18 == i18n("是") else 0,
@@ -764,16 +778,16 @@ def click_train(
     else:
         cmd = (
             config.python_cmd
-            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
             % (
                 exp_dir1,
                 sr2,
                 1 if if_f0_3 else 0,
                 batch_size12,
                 total_epoch11,
                 save_epoch10,
-                pretrained_G14,
-                pretrained_D15,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b",
                 1 if if_save_latest13 == i18n("是") else 0,
                 1 if if_cache_gpu17 == i18n("是") else 0,
                 1 if if_save_every_weights18 == i18n("是") else 0,
@@ -1000,7 +1014,7 @@ def get_info_str(strr):
     if gpus16:
         cmd = (
             config.python_cmd
-            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
+            +" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
             % (
                 exp_dir1,
                 sr2,
@@ -1009,8 +1023,8 @@ def get_info_str(strr):
                 gpus16,
                 total_epoch11,
                 save_epoch10,
-                pretrained_G14,
-                pretrained_D15,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
                 1 if if_save_latest13 == i18n("是") else 0,
                 1 if if_cache_gpu17 == i18n("是") else 0,
                 1 if if_save_every_weights18 == i18n("是") else 0,
@@ -1020,16 +1034,16 @@ def get_info_str(strr):
     else:
         cmd = (
             config.python_cmd
-            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
             % (
                 exp_dir1,
                 sr2,
                 1 if if_f0_3 else 0,
                 batch_size12,
                 total_epoch11,
                 save_epoch10,
-                pretrained_G14,
-                pretrained_D15,
+                ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+                ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
                 1 if if_save_latest13 == i18n("是") else 0,
                 1 if if_cache_gpu17 == i18n("是") else 0,
                 1 if if_save_every_weights18 == i18n("是") else 0,

train_nsf_sim_cache_sid_load_pretrain.py

@@ -191,18 +191,24 @@ def run(rank, n_gpus, hps):
         # traceback.print_exc()
         epoch_str = 1
         global_step = 0
-        if rank == 0:
-            logger.info("loaded pretrained %s %s" % (hps.pretrainG, hps.pretrainD))
-        print(
-            net_g.module.load_state_dict(
-                torch.load(hps.pretrainG, map_location="cpu")["model"]
-            )
-        )  ##测试不加载优化器
-        print(
-            net_d.module.load_state_dict(
-                torch.load(hps.pretrainD, map_location="cpu")["model"]
+        if hps.pretrainG != "":
+
+            if rank == 0:
+                logger.info("loaded pretrained %s" % (hps.pretrainG))
+            print(
+                net_g.module.load_state_dict(
+                    torch.load(hps.pretrainG, map_location="cpu")["model"]
+                )
+            )  ##测试不加载优化器
+        if hps.pretrainD != "":
+
+            if rank == 0:
+                logger.info("loaded pretrained %s" % (hps.pretrainD))
+            print(
+                net_d.module.load_state_dict(
+                    torch.load(hps.pretrainD, map_location="cpu")["model"]
+                )
             )
-        )
 
     scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
         optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2