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import os
import sys
from dotenv import load_dotenv
from apllio import *
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"
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 = []
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_paths = []
def lookup_indices(index_root):
global index_paths
for root, dirs, files in os.walk(index_root, topdown=False):
for name in files:
if name.endswith(".index") and "trained" not in name:
index_paths.append("%s/%s" % (root, name))
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():
names = []
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_paths = []
for root, dirs, files in os.walk(index_root, topdown=False):
for name in files:
if name.endswith(".index") and "trained" not in name:
index_paths.append("%s/%s" % (root, name))
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]
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
f.close()
cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
config.python_cmd,
trainset_dir,
sr,
n_p,
now_dir,
exp_dir,
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("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
yield (f.read())
sleep(1)
if done[0]:
break
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "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(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:
if f0method != "rmvpe_gpu":
cmd = (
'"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s'
% (
config.python_cmd,
now_dir,
exp_dir,
n_p,
f0method,
)
)
logger.info("Execute: " + 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()
else:
if gpus_rmvpe != "-":
gpus_rmvpe = gpus_rmvpe.split("-")
leng = len(gpus_rmvpe)
ps = []
for idx, n_g in enumerate(gpus_rmvpe):
cmd = (
'"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
% (
config.python_cmd,
leng,
idx,
n_g,
now_dir,
exp_dir,
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()
else:
cmd = (
config.python_cmd
+ ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" '
% (
now_dir,
exp_dir,
)
)
logger.info("Execute: " + cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
p.wait()
done = [True]
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": ["40k", "48k", "32k"], "__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"},
{"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":
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,
gpus_rmvpe,
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(
gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe
)
]
# 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"}
F0GPUVisible = config.dml == False
def change_f0_method(f0method8):
if f0method8 == "rmvpe_gpu":
visible = F0GPUVisible
else:
visible = False
return {"visible": visible, "__type__": "update"}
with gr.Blocks(theme=applio, title="RVC UI") as app:
gr.Markdown("<center><h1> RVC UI 🗣️")
gr.Markdown("<h1>this ui not done yet!")
with gr.Tabs():
with gr.TabItem("Single inference"):
models = gr.Dropdown(label="voice model", choices=sorted(names), interactive=True)
with gr.Row():
clean_button = gr.Button("Refresh model", variant="primary")
clean_button.click(fn=clean, inputs=[], outputs=[models])
with gr.Row():
with gr.Row():
pith_voice = gr.Number(label="Transpose 12 for femal, -12 fo male)", value=0)
but0 = gr.Button("Convert", variant="primary")
spk_item = gr.Slider(minimum=0, maximum=2333, step=1, label="Select Speaker/Singer ID", value=0, visible=False, interactive=False)
modelinfo = gr.Textbox(label="Model info", max_lines=8, visible=False)
input_audio0 = gr.Audio(label="The audio file to be processed", type="filepath")
file_index1 = gr.File(label="Path to the feature index file. Leave blank to use the selected result from the dropdown")
with gr.Column():
f0method0 = gr.Radio(label="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="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="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="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="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)
f0_file = gr.File(label="F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation", visible=False)
vc_output1 = gr.Textbox(label="Output information", interactive=False)
vc_output2 = gr.Audio(label="Export audio (click on the three dots in the lower right corner to download)", type="filepath", interactive=False)
refresh_button.click(fn=change_choices, inputs=[], outputs=[models, file_index2], api_name="infer_refresh")
with gr.TabItem("Batch inference"):
gr.Markdown("<center>Batch conversion\n. 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').")
vc_transform1 = gr.Number(label="Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12)", value=0)
dir_input = gr.Textbox(label="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="Multiple audio files can also be imported. If a folder path exists, this input is ignored.")
opt_input = gr.Textbox(label="Specify output folder", value="opt")
file_index4 = gr.Dropdown(label="Auto-detect index path and select from the dropdown", choices=sorted(index_paths), interactive=True)
file_index3 = gr.File(label="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")
f0method1 = gr.Radio(label="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", "harvest", "crepe", "rmvpe", "fcpe"], value="rmvpe", interactive=True)
resample_sr1 = gr.Slider(minimum=0, maximum=48000, label="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="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="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_radius1 = gr.Slider(minimum=0, maximum=7, label="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_rate2 = gr.Slider(minimum=0, maximum=1, label="Feature searching ratio", value=1, interactive=True)
format1 = gr.Radio(label="Export file format", choices=["wav", "flac", "mp3", "m4a"], value="wav", interactive=True)
but1 = gr.Button("Convert", variant="primary")
vc_output3 = gr.Textbox(label="Output information", interactive=False)
with gr.TabItem("Train"):
gr.Markdown("### Step 1. Fill in the experimental configuration.\nExperimental data is stored in the 'logs' folder, with each experiment having a separate folder. Manually enter the experiment name path, which contains the experimental configuration, logs, and trained model files.")
exp_dir1 = gr.Textbox(label="Enter the experiment name", value="my voice")
author = gr.Textbox(label="Model Author (Nullable)")
np7 = gr.Slider(minimum=0, maximum=config.n_cpu, step=1, label="Number of CPU processes used for pitch extraction and data processing", value=int(np.ceil(config.n_cpu / 1.5)), interactive=True)
sr2 = gr.Radio(label="Target sample rate", choices=["40k", "48k"], value="40k", interactive=True)
if_f0_3 = gr.Radio(label="Whether the model has pitch guidance (required for singing, optional for speech)", choices=["Yes", "No"], value="Yes", interactive=True)
version19 = gr.Radio(label="Version", choices=["v1", "v2"], value="v2", interactive=True, visible=True)
gr.Markdown("### Step 2. Audio processing. \n#### 1. Slicing.\nAutomatically traverse all files in the training folder that can be decoded into audio and perform slice normalization. Generates 2 wav folders in the experiment directory. Currently, only single-singer/speaker training is supported.")
trainset_dir4 = gr.Textbox(label="Enter the path of the training folder")
spk_id5 = gr.Slider(minimum=0, maximum=4, step=1, label="Please specify the speaker/singer ID", value=0, interactive=True)
but1 = gr.Button("Process data", variant="primary")
info1 = gr.Textbox(label="Output information", value="")
but1.click(preprocess_dataset,[trainset_dir4, exp_dir1, sr2, np7],[info1],api_name="train_preprocess")
gr.Markdown("#### 2. Feature extraction.\nUse CPU to extract pitch (if the model has pitch), use GPU to extract features (select GPU index).")
gpu_info9 = gr.Textbox(label="GPU Information",value=gpu_info,visible=F0GPUVisible)
gpus6 = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2",value=gpus,interactive=True,visible=F0GPUVisible)
gpus_rmvpe = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-0-1 to use 2 processes in GPU0 and 1 process in GPU1",value="%s-%s" % (gpus, gpus),interactive=True,visible=F0GPUVisible)
f0method8 = gr.Radio(label="Select the pitch extraction algorithm: when extracting singing, you can use 'pm' to speed up. For high-quality speech with fast performance, but worse CPU usage, you can use 'dio'. 'harvest' results in better quality but is slower. 'rmvpe' has the best results and consumes less CPU/GPU", choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], value="rmvpe_gpu", interactive=True)
but2 = gr.Button("Feature extraction", variant="primary")
info2 = gr.Textbox(label="Output information", value="")
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,gpus_rmvpe,],[info2],api_name="train_extract_f0_feature")
gr.Markdown("### Step 3. Start training.\nFill in the training settings and start training the model and index.")
save_epoch10 = gr.Slider(minimum=1, maximum=50, step=1, label="Save frequency (save_every_epoch)", value=5, interactive=True)
total_epoch11 = gr.Slider(minimum=2, maximum=1000, step=1, label="Total training epochs (total_epoch)", value=20, interactive=True)
batch_size12 = gr.Slider(minimum=1, maximum=40, step=1, label="Batch size per GPU", value=20, interactive=True)
if_save_latest13 = gr.Radio(label="Save only the latest '.ckpt' file to save disk space", choices=["Yes", "No"], value="No", interactive=True)
if_cache_gpu17 = gr.Radio(label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement", choices=["Yes", "No"], value="No", interactive=True)
if_save_every_weights18 = gr.Radio(label="Save a small final model to the 'weights' folder at each save point", choices=["Yes","No"], value="No", interactive=True)
pretrained_G14 = gr.Textbox(label="Load pre-trained base model G path", value="assets/pretrained_v2/f0G40k.pth", interactive=True)
pretrained_D15 = gr.Textbox(label="Load pre-trained base model D path", value="assets/pretrained_v2/f0D40k.pth", interactive=True)
gpus16 = gr.Textbox(label="Enter the GPU index(es) separated by '-', e.g., 0-1-2 to use GPU 0, 1, and 2", value="0", interactive=True)
sr2.change(change_sr2,[sr2, if_f0_3, version19],[pretrained_G14, pretrained_D15])
version19.change(change_version19,[sr2, if_f0_3, version19],[pretrained_G14, pretrained_D15, sr2])
if_f0_3.change(fn=lambda: None, inputs=[if_f0_3, sr2, version19], outputs=[f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15])
but3 = gr.Button("Train model", variant="primary")
but4 = gr.Button("Train feature index", variant="primary")
but5 = gr.Button("One-click training", variant="primary")
info3 = gr.Textbox(label=i18n("Output information"), value="")
but3.click(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,],info3,api_name="train_start")
but4.click(train_index, [exp_dir1, version19], info3)
but5.click(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,gpus_rmvpe,author],info3,api_name="train_start_all")
with gr.TabItem('Credits'):
gr.Markdown(
f"""
This UI's Made by Blane187
"""
)
app.launch() |