mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 19:15:04 +08:00
support 16xx GPU and 4G GPU inference
support 16xx GPU and 4G GPU inference
This commit is contained in:
RVC-Boss
GitHub
parent
2ac8d553ab
commit
a6cb4d3625
26
config.py
26
config.py
@ -64,12 +64,25 @@ if not torch.cuda.is_available():
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device = "cpu"
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device = "cpu"
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is_half = False
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is_half = False
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gpu_mem=None
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if device not in ["cpu", "mps"]:
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if device not in ["cpu", "mps"]:
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gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1]))
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i_device=int(device.split(":")[-1])
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if "16" in gpu_name or "MX" in gpu_name:
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gpu_name = torch.cuda.get_device_name(i_device)
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print("16系显卡/MX系显卡强制单精度")
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if "16" in gpu_name or "P40"in gpu_name.upper() or "1070"in gpu_name or "1080"in gpu_name:
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print("16系显卡强制单精度")
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is_half = False
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is_half = False
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with open("configs/32k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/32k.json","w")as f:f.write(strr)
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with open("configs/40k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/40k.json","w")as f:f.write(strr)
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with open("configs/48k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/48k.json","w")as f:f.write(strr)
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with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0")
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with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr)
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gpu_mem=int(torch.cuda.get_device_properties(i_device).total_memory/1024/1024/1024+0.4)
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if(gpu_mem<=4):
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with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0")
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with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr)
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from multiprocessing import cpu_count
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from multiprocessing import cpu_count
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if n_cpu == 0:
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if n_cpu == 0:
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@ -86,3 +99,8 @@ else:
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x_query = 6
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x_query = 6
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x_center = 38
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x_center = 38
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x_max = 41
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x_max = 41
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if(gpu_mem!=None and gpu_mem<=4):
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x_pad = 1
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x_query = 5
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x_center = 30
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x_max = 32
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61
infer-web.py
61
infer-web.py
@ -5,7 +5,7 @@ from subprocess import Popen
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from time import sleep
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from time import sleep
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import torch, os, traceback, sys, warnings, shutil, numpy as np
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import torch, os, traceback, sys, warnings, shutil, numpy as np
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import faiss
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import faiss
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from random import shuffle
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now_dir = os.getcwd()
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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sys.path.append(now_dir)
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tmp = os.path.join(now_dir, "TEMP")
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tmp = os.path.join(now_dir, "TEMP")
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@ -23,6 +23,7 @@ i18n = I18nAuto()
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ncpu = cpu_count()
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ncpu = cpu_count()
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ngpu = torch.cuda.device_count()
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ngpu = torch.cuda.device_count()
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gpu_infos = []
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gpu_infos = []
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mem=[]
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if (not torch.cuda.is_available()) or ngpu == 0:
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if (not torch.cuda.is_available()) or ngpu == 0:
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if_gpu_ok = False
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if_gpu_ok = False
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else:
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else:
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@ -48,11 +49,13 @@ else:
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): # A10#A100#V100#A40#P40#M40#K80#A4500
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): # A10#A100#V100#A40#P40#M40#K80#A4500
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if_gpu_ok = True # 至少有一张能用的N卡
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if_gpu_ok = True # 至少有一张能用的N卡
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gpu_infos.append("%s\t%s" % (i, gpu_name))
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gpu_infos.append("%s\t%s" % (i, gpu_name))
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gpu_info = (
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mem.append(int(torch.cuda.get_device_properties(i).total_memory/1024/1024/1024+0.4))
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"\n".join(gpu_infos)
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if if_gpu_ok == True and len(gpu_infos) > 0:
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if if_gpu_ok == True and len(gpu_infos) > 0
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gpu_info ="\n".join(gpu_infos)
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else "很遗憾您这没有能用的显卡来支持您训练"
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default_batch_size=min(mem)//2
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)
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else:
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gpu_info = "很遗憾您这没有能用的显卡来支持您训练"
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default_batch_size=1
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gpus = "-".join([i[0] for i in gpu_infos])
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gpus = "-".join([i[0] for i in gpu_infos])
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from scipy.io import wavfile
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from scipy.io import wavfile
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@ -564,15 +567,18 @@ def click_train(
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)
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)
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)
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)
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if if_f0_3 == "是":
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if if_f0_3 == "是":
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opt.append(
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for _ in range(2):
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
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opt.append(
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% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
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)
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% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
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)
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else:
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else:
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opt.append(
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for _ in range(2):
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
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opt.append(
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% (now_dir, sr2, now_dir, spk_id5)
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
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)
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% (now_dir, sr2, now_dir, spk_id5)
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)
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shuffle(opt)
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with open("%s/filelist.txt" % exp_dir, "w") as f:
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with open("%s/filelist.txt" % exp_dir, "w") as f:
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f.write("\n".join(opt))
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f.write("\n".join(opt))
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print("write filelist done")
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print("write filelist done")
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@ -789,15 +795,18 @@ def train1key(
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)
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)
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)
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)
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if if_f0_3 == "是":
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if if_f0_3 == "是":
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opt.append(
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for _ in range(2):
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
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opt.append(
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% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
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)
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% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
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)
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else:
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else:
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opt.append(
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for _ in range(2):
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
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opt.append(
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% (now_dir, sr2, now_dir, spk_id5)
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"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
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)
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% (now_dir, sr2, now_dir, spk_id5)
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)
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shuffle(opt)
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with open("%s/filelist.txt" % exp_dir, "w") as f:
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with open("%s/filelist.txt" % exp_dir, "w") as f:
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f.write("\n".join(opt))
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f.write("\n".join(opt))
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yield get_info_str("write filelist done")
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yield get_info_str("write filelist done")
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@ -1039,7 +1048,7 @@ with gr.Blocks() as app:
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minimum=0,
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minimum=0,
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maximum=1,
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maximum=1,
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label="检索特征占比",
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label="检索特征占比",
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value=0.65,
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value=0.76,
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interactive=True,
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interactive=True,
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)
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)
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f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
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f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
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@ -1253,10 +1262,10 @@ with gr.Blocks() as app:
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)
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)
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batch_size12 = gr.Slider(
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batch_size12 = gr.Slider(
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minimum=0,
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minimum=0,
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maximum=32,
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maximum=40,
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step=1,
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step=1,
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label="每张显卡的batch_size",
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label="每张显卡的batch_size",
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value=4,
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value=default_batch_size,
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interactive=True,
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interactive=True,
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)
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)
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if_save_latest13 = gr.Radio(
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if_save_latest13 = gr.Radio(
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@ -1270,7 +1279,7 @@ with gr.Blocks() as app:
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"是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速"
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"是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速"
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),
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),
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choices=["是", "否"],
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choices=["是", "否"],
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value="是",
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value="否",
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interactive=True,
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interactive=True,
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)
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)
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with gr.Row():
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with gr.Row():
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