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web.py
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import os
import sys
from dotenv import load_dotenv
now_dir = os.getcwd()
sys.path.append(now_dir)
load_dotenv()
load_dotenv("sha256.env")
if sys.platform == "darwin":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
os.environ["OMP_NUM_THREADS"] = "1"
from infer.modules.vc import VC, show_info, hash_similarity
from infer.modules.uvr5.modules import uvr
from infer.lib.train.process_ckpt import (
change_info,
extract_small_model,
merge,
)
from i18n.i18n import I18nAuto
from configs import Config
from sklearn.cluster import MiniBatchKMeans
import torch, platform
import numpy as np
import gradio as gr
import faiss
import pathlib
import json
from time import sleep
from subprocess import Popen
from random import shuffle
import warnings
import traceback
import threading
import shutil
import logging
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("httpx").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
os.makedirs(os.path.join(now_dir, "assets", "weights"), exist_ok=True)
os.environ["TEMP"] = tmp
warnings.filterwarnings("ignore")
torch.manual_seed(114514)
config = Config()
vc = VC(config)
if not config.nocheck:
from infer.lib.rvcmd import check_all_assets, download_all_assets
if not check_all_assets(update=config.update):
if config.update:
download_all_assets(tmpdir=tmp)
if not check_all_assets(update=config.update):
logging.error("counld not satisfy all assets needed.")
exit(1)
if config.dml == True:
def forward_dml(ctx, x, scale):
ctx.scale = scale
res = x.clone().detach()
return res
import fairseq
fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
i18n = I18nAuto()
logger.info(i18n)
# 判断是否有能用来训练和加速推理的N卡
ngpu = torch.cuda.device_count()
gpu_infos = []
mem = []
if_gpu_ok = False
if torch.cuda.is_available() or ngpu != 0:
for i in range(ngpu):
gpu_name = torch.cuda.get_device_name(i)
if any(
value in gpu_name.upper()
for value in [
"10",
"16",
"20",
"30",
"40",
"A2",
"A3",
"A4",
"P4",
"A50",
"500",
"A60",
"70",
"80",
"90",
"M4",
"T4",
"TITAN",
"4060",
"L",
"6000",
]
):
# 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))
mem.append(
int(
torch.cuda.get_device_properties(i).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4
)
)
if if_gpu_ok and len(gpu_infos) > 0:
gpu_info = "\n".join(gpu_infos)
default_batch_size = min(mem) // 2
else:
gpu_info = i18n(
"Unfortunately, there is no compatible GPU available to support your training."
)
default_batch_size = 1
gpus = "-".join([i[0] for i in gpu_infos])
weight_root = os.getenv("weight_root")
weight_uvr5_root = os.getenv("weight_uvr5_root")
index_root = os.getenv("index_root")
outside_index_root = os.getenv("outside_index_root")
names = [""]
index_paths = [""]
def lookup_names(weight_root):
global names
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
def lookup_indices(index_root):
global index_paths
for root, _, files in os.walk(index_root, topdown=False):
for name in files:
if name.endswith(".index") and "trained" not in name:
index_paths.append(str(pathlib.Path(root, name)))
lookup_names(weight_root)
lookup_indices(index_root)
lookup_indices(outside_index_root)
uvr5_names = []
for name in os.listdir(weight_uvr5_root):
if name.endswith(".pth") or "onnx" in name:
uvr5_names.append(name.replace(".pth", ""))
def change_choices():
global index_paths, names
names = [""]
lookup_names(weight_root)
index_paths = [""]
lookup_indices(index_root)
lookup_indices(outside_index_root)
return {"choices": sorted(names), "__type__": "update"}, {
"choices": sorted(index_paths),
"__type__": "update",
}
def clean():
return {"value": "", "__type__": "update"}
def export_onnx(ModelPath, ExportedPath):
from rvc.onnx import export_onnx as eo
eo(ModelPath, ExportedPath)
sr_dict = {
"32k": 32000,
"40k": 40000,
"48k": 48000,
}
def if_done(done, p):
while 1:
if p.poll() is None:
sleep(0.5)
else:
break
done[0] = True
def if_done_multi(done, ps):
while 1:
# poll==None代表进程未结束
# 只要有一个进程未结束都不停
flag = 1
for p in ps:
if p.poll() is None:
flag = 0
sleep(0.5)
break
if flag == 1:
break
done[0] = True
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
sr = sr_dict[sr]
exp_path = pathlib.Path(now_dir, "logs", exp_dir)
os.makedirs(exp_path, exist_ok=True)
log_file_path = exp_path / "preprocess.log"
f = open(log_file_path, "w")
f.close()
cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s" %s %.1f' % (
config.python_cmd,
trainset_dir,
sr,
n_p,
str(exp_path),
config.noparallel,
config.preprocess_per,
)
logger.info("Execute: " + cmd)
# , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
p = Popen(cmd, shell=True)
# 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(log_file_path, "r") as f:
yield (f.read())
sleep(1)
if done[0]:
break
with open(log_file_path, "r") as f:
log = f.read()
logger.info(log)
yield log
# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
def extract_f0_feature(n_p, f0method, if_f0, exp_dir, version19):
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:
if f0method != "rmvpe_gpu":
cmd = (
'"%s" infer/modules/train/extract_f0_print.py "%s/logs/%s" %s %s "%s" %s'
% (
config.python_cmd,
now_dir,
exp_dir,
n_p,
f0method,
config.device,
str(config.is_half),
)
)
logger.info("Execute: " + cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
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()
logger.info(log)
yield log
# 对不同part分别开多进程
"""
n_part=int(sys.argv[1])
i_part=int(sys.argv[2])
i_gpu=sys.argv[3]
exp_dir=sys.argv[4]
os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
"""
leng = len(gpus)
ps = []
for idx, n_g in enumerate(gpus):
cmd = (
'"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s %s'
% (
config.python_cmd,
config.device,
leng,
idx,
n_g,
now_dir,
exp_dir,
version19,
config.is_half,
)
)
logger.info("Execute: " + 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()
logger.info(log)
yield log
def get_pretrained_models(path_str, f0_str, sr2):
if_pretrained_generator_exist = os.access(
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
)
if_pretrained_discriminator_exist = os.access(
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
)
if not if_pretrained_generator_exist:
logger.warning(
"assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
path_str,
f0_str,
sr2,
)
if not if_pretrained_discriminator_exist:
logger.warning(
"assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
path_str,
f0_str,
sr2,
)
return (
(
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_generator_exist
else ""
),
(
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_discriminator_exist
else ""
),
)
def change_sr2(sr2, if_f0_3, version19):
path_str = "" if version19 == "v1" else "_v2"
f0_str = "f0" if if_f0_3 else ""
return get_pretrained_models(path_str, f0_str, sr2)
def change_version19(sr2, if_f0_3, version19):
path_str = "" if version19 == "v1" else "_v2"
if sr2 == "32k" and version19 == "v1":
sr2 = "40k"
to_return_sr2 = (
{"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
if version19 == "v1"
else {"choices": ["32k", "40k", "48k"], "__type__": "update", "value": sr2}
)
f0_str = "f0" if if_f0_3 else ""
return (
*get_pretrained_models(path_str, f0_str, sr2),
to_return_sr2,
)
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
path_str = "" if version19 == "v1" else "_v2"
return (
{"visible": if_f0_3, "__type__": "update"},
*get_pretrained_models(path_str, "f0" if if_f0_3 == True else "", sr2),
)
# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
def click_train(
exp_dir1,
sr2,
if_f0_3,
spk_id5,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
author,
):
# 生成filelist
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
os.makedirs(exp_dir, exist_ok=True)
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
feature_dir = (
"%s/3_feature256" % (exp_dir)
if version19 == "v1"
else "%s/3_feature768" % (exp_dir)
)
if if_f0_3:
f0_dir = "%s/2a_f0" % (exp_dir)
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
names = (
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
)
else:
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
[name.split(".")[0] for name in os.listdir(feature_dir)]
)
opt = []
for name in names:
if if_f0_3:
opt.append(
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
f0_dir.replace("\\", "\\\\"),
name,
f0nsf_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
else:
opt.append(
"%s/%s.wav|%s/%s.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
fea_dim = 256 if version19 == "v1" else 768
if if_f0_3:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
)
else:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % exp_dir, "w") as f:
f.write("\n".join(opt))
logger.debug("Write filelist done")
logger.info("Use gpus: %s", str(gpus16))
if pretrained_G14 == "":
logger.info("No pretrained Generator")
if pretrained_D15 == "":
logger.info("No pretrained Discriminator")
if version19 == "v1" or sr2 == "40k": # v2 40k falls back to v1
config_path = "v1/%s.json" % sr2
else:
config_path = "v2/%s.json" % sr2
config_save_path = os.path.join(exp_dir, "config.json")
if not pathlib.Path(config_save_path).exists():
with open(config_save_path, "w", encoding="utf-8") as f:
json.dump(
config.json_config[config_path],
f,
ensure_ascii=False,
indent=4,
sort_keys=True,
)
f.write("\n")
cmd = (
'"%s" 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 -a "%s"'
% (
config.python_cmd,
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
total_epoch11,
save_epoch10,
'-pg "%s"' % pretrained_G14 if pretrained_G14 != "" else "",
'-pd "%s"' % pretrained_D15 if pretrained_D15 != "" else "",
1 if if_save_latest13 == i18n("Yes") else 0,
1 if if_cache_gpu17 == i18n("Yes") else 0,
1 if if_save_every_weights18 == i18n("Yes") else 0,
version19,
author,
)
)
if gpus16:
cmd += ' -g "%s"' % (gpus16)
logger.info("Execute: " + cmd)
p = Popen(cmd, shell=True, cwd=now_dir)
p.wait()
return "Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder."
# but4.click(train_index, [exp_dir1], info3)
def train_index(exp_dir1, version19):
# exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
exp_dir = "logs/%s" % (exp_dir1)
os.makedirs(exp_dir, exist_ok=True)
feature_dir = (
"%s/3_feature256" % (exp_dir)
if version19 == "v1"
else "%s/3_feature768" % (exp_dir)
)
if not os.path.exists(feature_dir):
return "请先进行特征提取!"
listdir_res = list(os.listdir(feature_dir))
if len(listdir_res) == 0:
return "请先进行特征提取！"
infos = []
npys = []
for name in sorted(listdir_res):
phone = np.load("%s/%s" % (feature_dir, name))
npys.append(phone)
big_npy = np.concatenate(npys, 0)
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
if big_npy.shape[0] > 2e5:
infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
yield "\n".join(infos)
try:
big_npy = (
MiniBatchKMeans(
n_clusters=10000,
verbose=True,
batch_size=256 * config.n_cpu,
compute_labels=False,
init="random",
)
.fit(big_npy)
.cluster_centers_
)
except:
info = traceback.format_exc()
logger.info(info)
infos.append(info)
yield "\n".join(infos)
np.save("%s/total_fea.npy" % exp_dir, big_npy)
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
infos.append("%s,%s" % (big_npy.shape, n_ivf))
yield "\n".join(infos)
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
# index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
infos.append("training")
yield "\n".join(infos)
index_ivf = faiss.extract_index_ivf(index) #
index_ivf.nprobe = 1
index.train(big_npy)
faiss.write_index(
index,
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
)
infos.append("adding")
yield "\n".join(infos)
batch_size_add = 8192
for i in range(0, big_npy.shape[0], batch_size_add):
index.add(big_npy[i : i + batch_size_add])
index_save_path = "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" % (
exp_dir,
n_ivf,
index_ivf.nprobe,
exp_dir1,
version19,
)
faiss.write_index(index, index_save_path)
infos.append(i18n("Successfully built index into") + " " + index_save_path)
link_target = "%s/%s_IVF%s_Flat_nprobe_%s_%s_%s.index" % (
outside_index_root,
exp_dir1,
n_ivf,
index_ivf.nprobe,
exp_dir1,
version19,
)
try:
link = os.link if platform.system() == "Windows" else os.symlink
link(index_save_path, link_target)
infos.append(i18n("Link index to outside folder") + " " + link_target)
except:
infos.append(
i18n("Link index to outside folder")
+ " "
+ link_target
+ " "
+ i18n("Fail")
)
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
# infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
yield "\n".join(infos)
# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
def train1key(
exp_dir1,
sr2,
if_f0_3,
trainset_dir4,
spk_id5,
np7,
f0method8,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
author,
):
infos = []
def get_info_str(strr):
infos.append(strr)
return "\n".join(infos)
# step1:Process data
yield get_info_str(i18n("Step 1: Processing data"))
[get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)]
# step2a:提取音高
yield get_info_str(i18n("step2:Pitch extraction & feature extraction"))
[
get_info_str(_)
for _ in extract_f0_feature(
np7,
f0method8,
if_f0_3,
exp_dir1,
version19,
)
]
# step3a:Train model
yield get_info_str(i18n("Step 3a: Model training started"))
click_train(
exp_dir1,
sr2,
if_f0_3,
spk_id5,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
author,
)
yield get_info_str(
i18n(
"Training complete. You can check the training logs in the console or the 'train.log' file under the experiment folder."
)
)
# step3b:训练索引
[get_info_str(_) for _ in train_index(exp_dir1, version19)]
yield get_info_str(i18n("All processes have been completed!"))
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
def change_info_(ckpt_path):
if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
try:
with open(
ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
) as f:
info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
sr, f0 = info["sample_rate"], info["if_f0"]
version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
return sr, str(f0), version
except:
traceback.print_exc()
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
with gr.Blocks(title="RVC WebUI") as app:
gr.Markdown("## RVC WebUI")
gr.Markdown(
value=i18n(
"This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible. <br>If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory <b>Agreement-LICENSE.txt</b> for details."
)
)
with gr.Tabs():
with gr.TabItem(i18n("Model Inference")):
with gr.Row():
sid0 = gr.Dropdown(
label=i18n("Inferencing voice"), choices=sorted(names)
)
with gr.Column():
refresh_button = gr.Button(
i18n("Refresh voice list and index path"), variant="primary"
)
clean_button = gr.Button(
i18n("Unload model to save GPU memory"), variant="primary"
)
spk_item = gr.Slider(
minimum=0,
maximum=2333,
step=1,
label=i18n("Select Speaker/Singer ID"),
value=0,
visible=False,
interactive=True,
)
clean_button.click(
fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
)
modelinfo = gr.Textbox(label=i18n("Model info"), max_lines=8)
with gr.TabItem(i18n("Single inference")):
with gr.Row():
with gr.Column():
vc_transform0 = gr.Number(
label=i18n(
"Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)"
),
value=0,
)
input_audio0 = gr.Audio(
label=i18n("The audio file to be processed"),
type="filepath",
)
file_index2 = gr.Dropdown(
label=i18n(
"Auto-detect index path and select from the dropdown"
),
choices=sorted(index_paths),
interactive=True,
)
file_index1 = gr.File(
label=i18n(
"Path to the feature index file. Leave blank to use the selected result from the dropdown"
),
)
with gr.Column():
f0method0 = gr.Radio(
label=i18n(
"Select the pitch extraction algorithm ('pm': faster extraction but lower-quality speech; 'harvest': better bass but extremely slow; 'crepe': better quality but GPU intensive), 'rmvpe': best quality, and little GPU requirement"
),
choices=(
["pm", "dio", "harvest", "crepe", "rmvpe", "fcpe"]
),
value="rmvpe",
interactive=True,
)
resample_sr0 = gr.Slider(
minimum=0,
maximum=48000,
label=i18n(
"Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling"
),
value=0,
step=1,
interactive=True,
)
rms_mix_rate0 = gr.Slider(
minimum=0,
maximum=1,
label=i18n(
"Adjust the volume envelope scaling. Closer to 0, the more it mimicks the volume of the original vocals. Can help mask noise and make volume sound more natural when set relatively low. Closer to 1 will be more of a consistently loud volume"
),
value=0.25,
interactive=True,
)
protect0 = gr.Slider(
minimum=0,
maximum=0.5,
label=i18n(
"Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy"
),
value=0.33,
step=0.01,
interactive=True,
)
filter_radius0 = gr.Slider(
minimum=0,
maximum=7,
label=i18n(
"If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."
),
value=3,
step=1,
interactive=True,
)
index_rate1 = gr.Slider(
minimum=0,
maximum=1,
label=i18n("Feature searching ratio"),
value=0.75,
interactive=True,
)
f0_file = gr.File(
label=i18n(
"F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation"
),
visible=False,
)
but0 = gr.Button(i18n("Convert"), variant="primary")
vc_output2 = gr.Audio(
label=i18n(
"Export audio (click on the three dots in the lower right corner to download)"
)
)
refresh_button.click(
fn=change_choices,
inputs=[],
outputs=[sid0, file_index2],
api_name="infer_refresh",
)
vc_output1 = gr.Textbox(label=i18n("Output information"))
but0.click(
vc.vc_single,
[
spk_item,
input_audio0,
vc_transform0,
f0_file,
f0method0,
file_index1,
file_index2,
# file_big_npy1,
index_rate1,
filter_radius0,
resample_sr0,
rms_mix_rate0,
protect0,
],
[vc_output1, vc_output2],
api_name="infer_convert",
)
with gr.TabItem(i18n("Batch inference")):
gr.Markdown(
value=i18n(
"Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt')."
)
)
with gr.Row():
with gr.Column():
vc_transform1 = gr.Number(
label=i18n(
"Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)"
),
value=0,
)
dir_input = gr.Textbox(
label=i18n(
"Enter the path of the audio folder to be processed (copy it from the address bar of the file manager)"
),
placeholder="C:\\Users\\Desktop\\input_vocal_dir",
)
inputs = gr.File(
file_count="multiple",
label=i18n(
"Multiple audio files can also be imported. If a folder path exists, this input is ignored."
),
)
opt_input = gr.Textbox(
label=i18n("Specify output folder"), value="opt"
)
file_index4 = gr.Dropdown(
label=i18n(
"Auto-detect index path and select from the dropdown"
),
choices=sorted(index_paths),
interactive=True,
)
file_index3 = gr.File(
label=i18n(
"Path to the feature index file. Leave blank to use the selected result from the dropdown"
),
)
refresh_button.click(
fn=lambda: change_choices()[1],
inputs=[],
outputs=file_index4,
api_name="infer_refresh_batch",
)
# file_big_npy2 = gr.Textbox(
# label=i18n("特征文件路径"),
# value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
# interactive=True,
# )
with gr.Column():
f0method1 = gr.Radio(
label=i18n(
"Select the pitch extraction algorithm ('pm': faster extraction but lower-quality speech; 'harvest': better bass but extremely slow; 'crepe': better quality but GPU intensive), 'rmvpe': best quality, and little GPU requirement"
),
choices=(
["pm", "dio", "harvest", "crepe", "rmvpe", "fcpe"]
),
value="rmvpe",
interactive=True,
)
resample_sr1 = gr.Slider(
minimum=0,
maximum=48000,
label=i18n(
"Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling"
),
value=0,
step=1,
interactive=True,
)
rms_mix_rate1 = gr.Slider(
minimum=0,
maximum=1,
label=i18n(
"Adjust the volume envelope scaling. Closer to 0, the more it mimicks the volume of the original vocals. Can help mask noise and make volume sound more natural when set relatively low. Closer to 1 will be more of a consistently loud volume"
),
value=1,
interactive=True,
)
protect1 = gr.Slider(
minimum=0,
maximum=0.5,
label=i18n(
"Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy"
),
value=0.33,
step=0.01,
interactive=True,
)