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
This commit is contained in:
LINKANG ZHAN
GitHub
parent
eb1a88cf7e
commit
f349adc9df
76
infer-web.py
76
infer-web.py
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@ -614,43 +614,53 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19):
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def change_sr2(sr2, if_f0_3, version19):
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vis_v = True if sr2 == "40k" else False
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if sr2 != "40k":
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version19 = "v1"
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path_str = "" if version19 == "v1" else "_v2"
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version_state = {"visible": vis_v, "__type__": "update"}
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if vis_v == False:
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version_state["value"] = "v1"
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f0_str = "f0" if if_f0_3 else ""
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if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
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if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
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if (if_pretrained_generator_exist == False):
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print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
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if (if_pretrained_discriminator_exist == False):
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print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
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return (
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
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version_state,
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("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
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("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
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{"visible": True, "__type__": "update"}
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)
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def change_version19(sr2, if_f0_3, version19):
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path_str = "" if version19 == "v1" else "_v2"
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f0_str = "f0" if if_f0_3 else ""
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return "pretrained%s/%sG%s.pth" % (
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path_str,
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f0_str,
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sr2,
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), "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
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if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
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if (if_pretrained_generator_exist == False):
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print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
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if (if_pretrained_discriminator_exist == False):
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print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
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return (
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("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
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("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
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)
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def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
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path_str = "" if version19 == "v1" else "_v2"
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if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK)
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if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK)
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if (if_pretrained_generator_exist == False):
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print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
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if (if_pretrained_discriminator_exist == False):
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print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
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if if_f0_3:
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return (
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{"visible": True, "__type__": "update"},
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"pretrained%s/f0G%s.pth" % (path_str, sr2),
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"pretrained%s/f0D%s.pth" % (path_str, sr2),
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"pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "",
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"pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "",
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)
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return (
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{"visible": False, "__type__": "update"},
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"pretrained%s/G%s.pth" % (path_str, sr2),
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"pretrained%s/D%s.pth" % (path_str, sr2),
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("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "",
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("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "",
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)
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@ -741,10 +751,14 @@ def click_train(
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# 生成config#无需生成config
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# 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"
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print("use gpus:", gpus16)
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if pretrained_G14 == "":
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print("no pretrained Generator")
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if pretrained_D15 == "":
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print("no pretrained Discriminator")
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if gpus16:
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cmd = (
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config.python_cmd
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+ " 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"
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+ " 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"
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% (
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exp_dir1,
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sr2,
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@ -753,8 +767,8 @@ def click_train(
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gpus16,
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total_epoch11,
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save_epoch10,
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pretrained_G14,
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pretrained_D15,
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("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
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("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
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1 if if_save_latest13 == i18n("是") else 0,
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1 if if_cache_gpu17 == i18n("是") else 0,
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1 if if_save_every_weights18 == i18n("是") else 0,
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@ -764,7 +778,7 @@ def click_train(
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else:
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cmd = (
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config.python_cmd
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+ " 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"
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+ " 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"
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% (
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exp_dir1,
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sr2,
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@ -772,8 +786,8 @@ def click_train(
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batch_size12,
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total_epoch11,
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save_epoch10,
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pretrained_G14,
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pretrained_D15,
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("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b",
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("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b",
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1 if if_save_latest13 == i18n("是") else 0,
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1 if if_cache_gpu17 == i18n("是") else 0,
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1 if if_save_every_weights18 == i18n("是") else 0,
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@ -1000,7 +1014,7 @@ def train1key(
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if gpus16:
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cmd = (
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config.python_cmd
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+ " 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"
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+" 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"
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% (
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exp_dir1,
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sr2,
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@ -1009,8 +1023,8 @@ def train1key(
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gpus16,
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total_epoch11,
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save_epoch10,
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pretrained_G14,
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pretrained_D15,
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("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
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("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
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1 if if_save_latest13 == i18n("是") else 0,
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1 if if_cache_gpu17 == i18n("是") else 0,
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1 if if_save_every_weights18 == i18n("是") else 0,
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@ -1020,7 +1034,7 @@ def train1key(
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else:
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cmd = (
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config.python_cmd
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+ " 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"
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+ " 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"
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% (
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exp_dir1,
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sr2,
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@ -1028,8 +1042,8 @@ def train1key(
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batch_size12,
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total_epoch11,
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save_epoch10,
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pretrained_G14,
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pretrained_D15,
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("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
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("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
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1 if if_save_latest13 == i18n("是") else 0,
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1 if if_cache_gpu17 == i18n("是") else 0,
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1 if if_save_every_weights18 == i18n("是") else 0,
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@ -191,18 +191,24 @@ def run(rank, n_gpus, hps):
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# traceback.print_exc()
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epoch_str = 1
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global_step = 0
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if rank == 0:
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logger.info("loaded pretrained %s %s" % (hps.pretrainG, hps.pretrainD))
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print(
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net_g.module.load_state_dict(
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torch.load(hps.pretrainG, map_location="cpu")["model"]
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if hps.pretrainG != "":
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if rank == 0:
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logger.info("loaded pretrained %s" % (hps.pretrainG))
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print(
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net_g.module.load_state_dict(
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torch.load(hps.pretrainG, map_location="cpu")["model"]
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)
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) ##测试不加载优化器
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if hps.pretrainD != "":
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if rank == 0:
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logger.info("loaded pretrained %s" % (hps.pretrainD))
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print(
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net_d.module.load_state_dict(
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torch.load(hps.pretrainD, map_location="cpu")["model"]
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)
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)
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) ##测试不加载优化器
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print(
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net_d.module.load_state_dict(
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torch.load(hps.pretrainD, map_location="cpu")["model"]
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)
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)
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scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
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optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
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