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Retrieval-based-Voice-Conversion-WebUI
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RVC-Boss 2023-07-26 19:50:50 +08:00 committed by GitHub
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5 changed files with 283 additions and 59 deletions
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9
extract_f0_print.py
View File

@ -74,6 +74,15 @@ class FeatureInput(object):
frame_period=1000 * self.hop / self.fs,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
elif f0_method == "rmvpe":
if hasattr(self, "model_rmvpe") == False:
from lib.rmvpe import RMVPE
print("loading rmvpe model")
self.model_rmvpe = RMVPE(
"rmvpe.pt", is_half=False, device="cpu"
)
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
return f0
def coarse_f0(self, f0):

135
extract_f0_rmvpe.py Normal file
View File

@ -0,0 +1,135 @@
import os, traceback, sys, parselmouth
now_dir = os.getcwd()
sys.path.append(now_dir)
from lib.audio import load_audio
import pyworld
import numpy as np, logging
logging.getLogger("numba").setLevel(logging.WARNING)
n_part = int(sys.argv[1])
i_part = int(sys.argv[2])
i_gpu = sys.argv[3]
os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
exp_dir = sys.argv[4]
is_half = sys.argv[5]
f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
def printt(strr):
print(strr)
f.write("%s\n" % strr)
f.flush()
class FeatureInput(object):
def __init__(self, samplerate=16000, hop_size=160):
self.fs = samplerate
self.hop = hop_size
self.f0_bin = 256
self.f0_max = 1100.0
self.f0_min = 50.0
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
def compute_f0(self, path, f0_method):
x = load_audio(path, self.fs)
p_len = x.shape[0] // self.hop
if(f0_method=="rmvpe"):
if hasattr(self, "model_rmvpe") == False:
from lib.rmvpe import RMVPE
print("loading rmvpe model")
self.model_rmvpe = RMVPE(
"rmvpe.pt", is_half=True, device="cuda"
)
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
return f0
def coarse_f0(self, f0):
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * (
self.f0_bin - 2
) / (self.f0_mel_max - self.f0_mel_min) + 1
# use 0 or 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1
f0_coarse = np.rint(f0_mel).astype(int)
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
f0_coarse.max(),
f0_coarse.min(),
)
return f0_coarse
def go(self, paths, f0_method):
if len(paths) == 0:
printt("no-f0-todo")
else:
printt("todo-f0-%s" % len(paths))
n = max(len(paths) // 5, 1) # 每个进程最多打印5条
for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths):
try:
if idx % n == 0:
printt("f0ing,now-%s,all-%s,-%s" % (idx, len(paths), inp_path))
if (
os.path.exists(opt_path1 + ".npy") == True
and os.path.exists(opt_path2 + ".npy") == True
):
continue
featur_pit = self.compute_f0(inp_path, f0_method)
np.save(
opt_path2,
featur_pit,
allow_pickle=False,
) # nsf
coarse_pit = self.coarse_f0(featur_pit)
np.save(
opt_path1,
coarse_pit,
allow_pickle=False,
) # ori
except:
printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc()))
if __name__ == "__main__":
# exp_dir=r"E:\codes\py39\dataset\mi-test"
# n_p=16
# f = open("%s/log_extract_f0.log"%exp_dir, "w")
printt(sys.argv)
featureInput = FeatureInput()
paths = []
inp_root = "%s/1_16k_wavs" % (exp_dir)
opt_root1 = "%s/2a_f0" % (exp_dir)
opt_root2 = "%s/2b-f0nsf" % (exp_dir)
os.makedirs(opt_root1, exist_ok=True)
os.makedirs(opt_root2, exist_ok=True)
for name in sorted(list(os.listdir(inp_root))):
inp_path = "%s/%s" % (inp_root, name)
if "spec" in inp_path:
continue
opt_path1 = "%s/%s" % (opt_root1, name)
opt_path2 = "%s/%s" % (opt_root2, name)
paths.append([inp_path, opt_path1, opt_path2])
try:
featureInput.go(paths[i_part::n_part],"rmvpe")
except:
printt("f0_all_fail-%s" % (traceback.format_exc()))
# ps = []
# for i in range(n_p):
# p = Process(
# target=featureInput.go,
# args=(
# paths[i::n_p],
# f0method,
# ),
# )
# ps.append(p)
# p.start()
# for i in range(n_p):
# ps[i].join()

195
infer-web.py
View File

@ -44,7 +44,7 @@ now_dir = os.getcwd()
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
shutil.rmtree(
"%s/runtime/Lib/site-packages/lib.infer_pack" % (now_dir), ignore_errors=True
"%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True
)
shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
@ -542,7 +542,7 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
f.close()
cmd = (
config.python_cmd
+ " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
+ ' trainset_preprocess_pipeline_print.py "%s" %s %s "%s/logs/%s" '
% (trainset_dir, sr, n_p, now_dir, exp_dir)
+ str(config.noparallel)
)
@ -570,41 +570,83 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19):
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19,gpus_rmvpe):
gpus = gpus.split("-")
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
f.close()
if if_f0:
cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
now_dir,
exp_dir,
n_p,
f0method,
)
print(cmd)
p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
done = [False]
threading.Thread(
target=if_done,
args=(
done,
p,
),
).start()
while 1:
with open(
"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
) as f:
yield (f.read())
sleep(1)
if done[0]:
break
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
print(log)
yield log
if(f0method!="rmvpe_gpu"):
cmd = config.python_cmd + ' extract_f0_print.py "%s/logs/%s" %s %s' % (
now_dir,
exp_dir,
n_p,
f0method,
)
print(cmd)
p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
done = [False]
threading.Thread(
target=if_done,
args=(
done,
p,
),
).start()
while 1:
with open(
"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
) as f:
yield (f.read())
sleep(1)
if done[0]:
break
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
print(log)
yield log
else:
gpus_rmvpe = gpus_rmvpe.split("-")
leng = len(gpus_rmvpe)
ps = []
for idx, n_g in enumerate(gpus_rmvpe):
cmd = (
config.python_cmd
+ ' extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
% (
leng,
idx,
n_g,
now_dir,
exp_dir,
config.is_half
)
)
print(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
ps.append(p)
###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
done = [False]
threading.Thread(
target=if_done_multi,
args=(
done,
ps,
),
).start()
while 1:
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
yield (f.read())
sleep(1)
if done[0]:
break
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
log = f.read()
print(log)
yield log
####对不同part分别开多进程
"""
n_part=int(sys.argv[1])
@ -618,7 +660,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19):
for idx, n_g in enumerate(gpus):
cmd = (
config.python_cmd
+ " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
+ ' extract_feature_print.py %s %s %s %s "%s/logs/%s" %s'
% (
config.device,
leng,
@ -854,7 +896,7 @@ def click_train(
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 %s %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,
@ -874,7 +916,7 @@ 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 %s %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,
@ -995,7 +1037,7 @@ def train1key(
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
version19,gpus_rmvpe
):
infos = []
@ -1018,7 +1060,7 @@ def train1key(
open(preprocess_log_path, "w").close()
cmd = (
config.python_cmd
+ " trainset_preprocess_pipeline_print.py %s %s %s %s "
+ ' trainset_preprocess_pipeline_print.py "%s" %s %s "%s" '
% (trainset_dir4, sr_dict[sr2], np7, model_log_dir)
+ str(config.noparallel)
)
@ -1032,14 +1074,38 @@ def train1key(
open(extract_f0_feature_log_path, "w")
if if_f0_3:
yield get_info_str("step2a:正在提取音高")
cmd = config.python_cmd + " extract_f0_print.py %s %s %s" % (
model_log_dir,
np7,
f0method8,
)
yield get_info_str(cmd)
p = Popen(cmd, shell=True, cwd=now_dir)
p.wait()
if(f0method8!="rmvpe_gpu"):
cmd = config.python_cmd + ' extract_f0_print.py "%s" %s %s' % (
model_log_dir,
np7,
f0method8,
)
yield get_info_str(cmd)
p = Popen(cmd, shell=True, cwd=now_dir)
p.wait()
else:
gpus_rmvpe = gpus_rmvpe.split("-")
leng = len(gpus_rmvpe)
ps = []
for idx, n_g in enumerate(gpus_rmvpe):
cmd = (
config.python_cmd
+ ' extract_f0_rmvpe.py %s %s %s "%s" %s '
% (
leng,
idx,
n_g,
model_log_dir,
config.is_half
)
)
yield get_info_str(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
ps.append(p)
for p in ps:
p.wait()
with open(extract_f0_feature_log_path, "r") as f:
print(f.read())
else:
@ -1050,7 +1116,7 @@ def train1key(
leng = len(gpus)
ps = []
for idx, n_g in enumerate(gpus):
cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % (
cmd = config.python_cmd + ' extract_feature_print.py %s %s %s %s "%s" %s' % (
config.device,
leng,
idx,
@ -1131,7 +1197,7 @@ def train1key(
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 %s %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,
@ -1151,7 +1217,7 @@ def train1key(
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 %s %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,
@ -1252,6 +1318,10 @@ def change_info_(ckpt_path):
traceback.print_exc()
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
def change_f0_method(f0method8):
if(f0method8=="rmvpe_gpu"):visible=True
else:visible=False
return {"visible": visible, "__type__": "update"}
def export_onnx(ModelPath, ExportedPath):
global cpt
@ -1340,7 +1410,7 @@ with gr.Blocks(title="RVC WebUI") as app:
)
f0method0 = gr.Radio(
label=i18n(
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
),
choices=["pm", "harvest", "crepe", "rmvpe"],
value="pm",
@ -1442,7 +1512,7 @@ with gr.Blocks(title="RVC WebUI") as app:
opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
f0method1 = gr.Radio(
label=i18n(
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
),
choices=["pm", "harvest", "crepe", "rmvpe"],
value="pm",
@ -1630,7 +1700,7 @@ with gr.Blocks(title="RVC WebUI") as app:
version19 = gr.Radio(
label=i18n("版本"),
choices=["v1", "v2"],
value="v1",
value="v2",
interactive=True,
visible=True,
)
@ -1680,15 +1750,26 @@ with gr.Blocks(title="RVC WebUI") as app:
label=i18n(
"选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢"
),
choices=["pm", "harvest", "dio"],
value="harvest",
choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
value="rmvpe_gpu",
interactive=True,
)
gpus_rmvpe = gr.Textbox(
label=i18n("rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程"),
value="%s-%s"%(gpus,gpus),
interactive=True,
visible=True
)
but2 = gr.Button(i18n("特征提取"), variant="primary")
info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
f0method8.change(
fn=change_f0_method,
inputs=[f0method8],
outputs=[gpus_rmvpe],
)
but2.click(
extract_f0_feature,
[gpus6, np7, f0method8, if_f0_3, exp_dir1, version19],
[gpus6, np7, f0method8, if_f0_3, exp_dir1, version19,gpus_rmvpe],
[info2],
)
with gr.Group():
@ -1741,12 +1822,12 @@ with gr.Blocks(title="RVC WebUI") as app:
with gr.Row():
pretrained_G14 = gr.Textbox(
label=i18n("加载预训练底模G路径"),
value="pretrained/f0G40k.pth",
value="pretrained_v2/f0G40k.pth",
interactive=True,
)
pretrained_D15 = gr.Textbox(
label=i18n("加载预训练底模D路径"),
value="pretrained/f0D40k.pth",
value="pretrained_v2/f0D40k.pth",
interactive=True,
)
sr2.change(
@ -1813,7 +1894,7 @@ with gr.Blocks(title="RVC WebUI") as app:
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
version19,gpus_rmvpe
],
info3,
)

1
train_nsf_sim_cache_sid_load_pretrain.py
View File

@ -3,7 +3,6 @@ now_dir = os.getcwd()
sys.path.append(os.path.join(now_dir))
sys.path.append(os.path.join(now_dir, "train"))
from lib.train import utils
from lib.train import utils
import datetime

2
trainset_preprocess_pipeline_print.py
View File

@ -3,7 +3,7 @@ from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(sys.argv)
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])