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Retrieval-based-Voice-Conversion-WebUI
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support 16xx GPU and 4G GPU inference 

support 16xx GPU and 4G GPU inference
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RVC-Boss 2023-04-27 01:40:04 +08:00 committed by GitHub
parent 2ac8d553ab
commit a6cb4d3625
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 57 additions and 30 deletions
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26
config.py
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@ -64,12 +64,25 @@ if not torch.cuda.is_available():
device = "cpu"
is_half = False
gpu_mem=None
if device not in ["cpu", "mps"]:
gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1]))
if "16" in gpu_name or "MX" in gpu_name:
print("16系显卡/MX系显卡强制单精度")
i_device=int(device.split(":")[-1])
gpu_name = torch.cuda.get_device_name(i_device)
if "16" in gpu_name or "P40"in gpu_name.upper() or "1070"in gpu_name or "1080"in gpu_name:
print("16系显卡强制单精度")
is_half = False
with open("configs/32k.json","r")as f:strr=f.read().replace("true","false")
with open("configs/32k.json","w")as f:f.write(strr)
with open("configs/40k.json","r")as f:strr=f.read().replace("true","false")
with open("configs/40k.json","w")as f:f.write(strr)
with open("configs/48k.json","r")as f:strr=f.read().replace("true","false")
with open("configs/48k.json","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)
gpu_mem=int(torch.cuda.get_device_properties(i_device).total_memory/1024/1024/1024+0.4)
if(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)
from multiprocessing import cpu_count
if n_cpu == 0:
@ -86,3 +99,8 @@ else:
x_query = 6
x_center = 38
x_max = 41
if(gpu_mem!=None and gpu_mem<=4):
x_pad = 1
x_query = 5
x_center = 30
x_max = 32

61
infer-web.py
View File

@ -5,7 +5,7 @@ from subprocess import Popen
from time import sleep
import torch, os, traceback, sys, warnings, shutil, numpy as np
import faiss
from random import shuffle
now_dir = os.getcwd()
sys.path.append(now_dir)
tmp = os.path.join(now_dir, "TEMP")
@ -23,6 +23,7 @@ i18n = I18nAuto()
ncpu = cpu_count()
ngpu = torch.cuda.device_count()
gpu_infos = []
mem=[]
if (not torch.cuda.is_available()) or ngpu == 0:
if_gpu_ok = False
else:
@ -48,11 +49,13 @@ else:
): # A10#A100#V100#A40#P40#M40#K80#A4500
if_gpu_ok = True # 至少有一张能用的N卡
gpu_infos.append("%s\t%s" % (i, gpu_name))
gpu_info = (
"\n".join(gpu_infos)
if if_gpu_ok == True and len(gpu_infos) > 0
else "很遗憾您这没有能用的显卡来支持您训练"
)
mem.append(int(torch.cuda.get_device_properties(i).total_memory/1024/1024/1024+0.4))
if if_gpu_ok == True and len(gpu_infos) > 0:
gpu_info ="\n".join(gpu_infos)
default_batch_size=min(mem)//2
else:
gpu_info = "很遗憾您这没有能用的显卡来支持您训练"
default_batch_size=1
gpus = "-".join([i[0] for i in gpu_infos])
from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
from scipy.io import wavfile
@ -564,15 +567,18 @@ def click_train(
)
)
if if_f0_3 == "是":
opt.append(
"%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"
% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
)
for _ in range(2):
opt.append(
"%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"
% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
)
else:
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
% (now_dir, sr2, now_dir, spk_id5)
)
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
% (now_dir, sr2, now_dir, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % exp_dir, "w") as f:
f.write("\n".join(opt))
print("write filelist done")
@ -789,15 +795,18 @@ def train1key(
)
)
if if_f0_3 == "是":
opt.append(
"%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"
% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
)
for _ in range(2):
opt.append(
"%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"
% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
)
else:
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
% (now_dir, sr2, now_dir, spk_id5)
)
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
% (now_dir, sr2, now_dir, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % exp_dir, "w") as f:
f.write("\n".join(opt))
yield get_info_str("write filelist done")
@ -1039,7 +1048,7 @@ with gr.Blocks() as app:
minimum=0,
maximum=1,
label="检索特征占比",
value=0.65,
value=0.76,
interactive=True,
)
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"))
@ -1253,10 +1262,10 @@ with gr.Blocks() as app:
)
batch_size12 = gr.Slider(
minimum=0,
maximum=32,
maximum=40,
step=1,
label="每张显卡的batch_size",
value=4,
value=default_batch_size,
interactive=True,
)
if_save_latest13 = gr.Radio(
@ -1270,7 +1279,7 @@ with gr.Blocks() as app:
"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"
),
choices=["是", "否"],
value="是",
value="否",
interactive=True,
)
with gr.Row():