mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-01-19 18:41:52 +00:00
train 1-2b
This commit is contained in:
Ftps
87
infer-web.py
87
infer-web.py
@@ -20,8 +20,13 @@ import faiss
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import gradio as gr
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from configs.config import Config
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import fairseq
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from i18n import I18nAuto
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from lib.train.process_ckpt import change_info, extract_small_model, merge, show_info
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from i18n.i18n import I18nAuto
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from infer.lib.train.process_ckpt import (
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change_info,
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extract_small_model,
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merge,
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show_info,
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)
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from sklearn.cluster import MiniBatchKMeans
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from dotenv import load_dotenv
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@@ -197,7 +202,7 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
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f.close()
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cmd = (
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config.python_cmd
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+ ' trainset_preprocess_pipeline_print.py "%s" %s %s "%s/logs/%s" '
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+ ' infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" '
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% (trainset_dir, sr, n_p, now_dir, exp_dir)
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+ str(config.noparallel)
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)
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@@ -232,11 +237,15 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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f.close()
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if if_f0:
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if f0method != "rmvpe_gpu":
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cmd = config.python_cmd + ' extract_f0_print.py "%s/logs/%s" %s %s' % (
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now_dir,
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exp_dir,
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n_p,
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f0method,
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cmd = (
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config.python_cmd
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+ ' infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
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% (
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now_dir,
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exp_dir,
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n_p,
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f0method,
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)
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)
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print(cmd)
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p = Popen(
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@@ -259,7 +268,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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for idx, n_g in enumerate(gpus_rmvpe):
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cmd = (
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config.python_cmd
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+ ' extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
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+ ' infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
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% (leng, idx, n_g, now_dir, exp_dir, config.is_half)
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)
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print(cmd)
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@@ -277,9 +286,13 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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),
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).start()
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else:
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cmd = config.python_cmd + ' extract_f0_rmvpe_dml.py "%s/logs/%s" ' % (
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now_dir,
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exp_dir,
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cmd = (
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config.python_cmd
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+ ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
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% (
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now_dir,
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exp_dir,
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)
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)
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print(cmd)
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p = Popen(
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@@ -312,7 +325,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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for idx, n_g in enumerate(gpus):
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cmd = (
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config.python_cmd
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+ ' extract_feature_print.py %s %s %s %s "%s/logs/%s" %s'
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+ ' infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s'
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% (
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config.device,
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leng,
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@@ -353,26 +366,26 @@ def change_sr2(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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if_pretrained_generator_exist = os.access(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if_pretrained_discriminator_exist = os.access(
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if not if_pretrained_generator_exist:
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print(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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)
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if not if_pretrained_discriminator_exist:
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print(
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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)
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return (
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_generator_exist
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else "",
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_discriminator_exist
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else "",
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)
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@@ -389,26 +402,26 @@ def change_version19(sr2, if_f0_3, version19):
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)
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f0_str = "f0" if if_f0_3 else ""
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if_pretrained_generator_exist = os.access(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if_pretrained_discriminator_exist = os.access(
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
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)
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if not if_pretrained_generator_exist:
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print(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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)
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if not if_pretrained_discriminator_exist:
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print(
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
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"not exist, will not use pretrained model",
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)
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return (
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_generator_exist
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else "",
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_discriminator_exist
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else "",
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to_return_sr2,
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@@ -418,37 +431,37 @@ def change_version19(sr2, if_f0_3, version19):
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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(
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"pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK
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"assets/pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK
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)
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if_pretrained_discriminator_exist = os.access(
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"pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK
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"assets/pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK
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)
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if not if_pretrained_generator_exist:
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print(
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"pretrained%s/f0G%s.pth" % (path_str, sr2),
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"assets/pretrained%s/f0G%s.pth" % (path_str, sr2),
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"not exist, will not use pretrained model",
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)
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if not if_pretrained_discriminator_exist:
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print(
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"pretrained%s/f0D%s.pth" % (path_str, sr2),
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"assets/pretrained%s/f0D%s.pth" % (path_str, sr2),
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"not exist, will not use pretrained model",
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)
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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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"assets/pretrained%s/f0G%s.pth" % (path_str, sr2)
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if if_pretrained_generator_exist
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else "",
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"pretrained%s/f0D%s.pth" % (path_str, sr2)
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"assets/pretrained%s/f0D%s.pth" % (path_str, sr2)
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if if_pretrained_discriminator_exist
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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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("assets/pretrained%s/G%s.pth" % (path_str, sr2))
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if if_pretrained_generator_exist
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else "",
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("pretrained%s/D%s.pth" % (path_str, sr2))
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("assets/pretrained%s/D%s.pth" % (path_str, sr2))
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if if_pretrained_discriminator_exist
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else "",
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)
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@@ -548,7 +561,7 @@ def click_train(
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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 %s %s -l %s -c %s -sw %s -v %s'
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+ ' infer/modules/train/train.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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@@ -568,7 +581,7 @@ 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 %s %s -l %s -c %s -sw %s -v %s'
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+ ' infer/modules/train/train.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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@@ -1482,12 +1495,12 @@ with gr.Blocks(title="RVC WebUI") as app:
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with gr.Row():
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pretrained_G14 = gr.Textbox(
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label=i18n("加载预训练底模G路径"),
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value="pretrained_v2/f0G40k.pth",
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value="assets/pretrained_v2/f0G40k.pth",
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interactive=True,
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)
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pretrained_D15 = gr.Textbox(
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label=i18n("加载预训练底模D路径"),
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value="pretrained_v2/f0D40k.pth",
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value="assets/pretrained_v2/f0D40k.pth",
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interactive=True,
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)
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sr2.change(
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