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infer-web.py
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infer-web.py
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import os
import shutil
import traceback, pdb
import warnings
import numpy as np
import torch
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
import logging
import threading
from random import shuffle
from subprocess import Popen
from time import sleep
import faiss
import ffmpeg
import gradio as gr
import soundfile as sf
from config import Config
from fairseq import checkpoint_utils
from i18n import I18nAuto
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
from infer_uvr5 import _audio_pre_, _audio_pre_new
from lib.audio import load_audio
from lib.train.process_ckpt import change_info, extract_small_model, merge, show_info
from vc_infer_pipeline import VC
from sklearn.cluster import MiniBatchKMeans
logging.getLogger("numba").setLevel(logging.WARNING)
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
)
shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), 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, "weights"), exist_ok=True)
os.environ["TEMP"] = tmp
warnings.filterwarnings("ignore")
torch.manual_seed(114514)
config = Config()
i18n = I18nAuto()
i18n.print()
# 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",
]
):
# 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("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])
class ToolButton(gr.Button, gr.components.FormComponent):
"""Small button with single emoji as text, fits inside gradio forms"""
def __init__(self, **kwargs):
super().__init__(variant="tool", **kwargs)
def get_block_name(self):
return "button"
hubert_model = None
def load_hubert():
global hubert_model
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
["hubert_base.pt"],
suffix="",
)
hubert_model = models[0]
hubert_model = hubert_model.to(config.device)
if config.is_half:
hubert_model = hubert_model.half()
else:
hubert_model = hubert_model.float()
hubert_model.eval()
weight_root = "weights"
weight_uvr5_root = "uvr5_weights"
index_root = "logs"
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))
uvr5_names = []
for name in os.listdir(weight_uvr5_root):
if name.endswith(".pth") or "onnx" in name:
uvr5_names.append(name.replace(".pth", ""))
cpt = None
def vc_single(
sid,
input_audio_path,
f0_up_key,
f0_file,
f0_method,
file_index,
file_index2,
# file_big_npy,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
global tgt_sr, net_g, vc, hubert_model, version, cpt
if input_audio_path is None:
return "You need to upload an audio", None
f0_up_key = int(f0_up_key)
try:
audio = load_audio(input_audio_path, 16000)
audio_max = np.abs(audio).max() / 0.95
if audio_max > 1:
audio /= audio_max
times = [0, 0, 0]
if not hubert_model:
load_hubert()
if_f0 = cpt.get("f0", 1)
file_index = (
(
file_index.strip(" ")
.strip('"')
.strip("\n")
.strip('"')
.strip(" ")
.replace("trained", "added")
)
if file_index != ""
else file_index2
) # 防止小白写错，自动帮他替换掉
# file_big_npy = (
# file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
# )
audio_opt = vc.pipeline(
hubert_model,
net_g,
sid,
audio,
input_audio_path,
times,
f0_up_key,
f0_method,
file_index,
# file_big_npy,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
f0_file=f0_file,
)
if tgt_sr != resample_sr >= 16000:
tgt_sr = resample_sr
index_info = (
"Using index:%s." % file_index
if os.path.exists(file_index)
else "Index not used."
)
return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
index_info,
times[0],
times[1],
times[2],
), (tgt_sr, audio_opt)
except:
info = traceback.format_exc()
print(info)
return info, (None, None)
def vc_multi(
sid,
dir_path,
opt_root,
paths,
f0_up_key,
f0_method,
file_index,
file_index2,
# file_big_npy,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
format1,
):
try:
dir_path = (
dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
) # 防止小白拷路径头尾带了空格和"和回车
opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
os.makedirs(opt_root, exist_ok=True)
try:
if dir_path != "":
paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
else:
paths = [path.name for path in paths]
except:
traceback.print_exc()
paths = [path.name for path in paths]
infos = []
for path in paths:
info, opt = vc_single(
sid,
path,
f0_up_key,
None,
f0_method,
file_index,
file_index2,
# file_big_npy,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
)
if "Success" in info:
try:
tgt_sr, audio_opt = opt
if format1 in ["wav", "flac"]:
sf.write(
"%s/%s.%s" % (opt_root, os.path.basename(path), format1),
audio_opt,
tgt_sr,
)
else:
path = "%s/%s.wav" % (opt_root, os.path.basename(path))
sf.write(
path,
audio_opt,
tgt_sr,
)
if os.path.exists(path):
os.system(
"ffmpeg -i %s -vn %s -q:a 2 -y"
% (path, path[:-4] + ".%s" % format1)
)
except:
info += traceback.format_exc()
infos.append("%s->%s" % (os.path.basename(path), info))
yield "\n".join(infos)
yield "\n".join(infos)
except:
yield traceback.format_exc()
def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
infos = []
try:
inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
save_root_vocal = (
save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
)
save_root_ins = (
save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
)
if model_name == "onnx_dereverb_By_FoxJoy":
from MDXNet import MDXNetDereverb
pre_fun = MDXNetDereverb(15)
else:
func = _audio_pre_ if "DeEcho" not in model_name else _audio_pre_new
pre_fun = func(
agg=int(agg),
model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
device=config.device,
is_half=config.is_half,
)
if inp_root != "":
paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
else:
paths = [path.name for path in paths]
for path in paths:
inp_path = os.path.join(inp_root, path)
need_reformat = 1
done = 0
try:
info = ffmpeg.probe(inp_path, cmd="ffprobe")
if (
info["streams"][0]["channels"] == 2
and info["streams"][0]["sample_rate"] == "44100"
):
need_reformat = 0
pre_fun._path_audio_(
inp_path, save_root_ins, save_root_vocal, format0
)
done = 1
except:
need_reformat = 1
traceback.print_exc()
if need_reformat == 1:
tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path))
os.system(
"ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
% (inp_path, tmp_path)
)
inp_path = tmp_path
try:
if done == 0:
pre_fun._path_audio_(
inp_path, save_root_ins, save_root_vocal, format0
)
infos.append("%s->Success" % (os.path.basename(inp_path)))
yield "\n".join(infos)
except:
infos.append(
"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
)
yield "\n".join(infos)
except:
infos.append(traceback.format_exc())
yield "\n".join(infos)
finally:
try:
if model_name == "onnx_dereverb_By_FoxJoy":
del pre_fun.pred.model
del pre_fun.pred.model_
else:
del pre_fun.model
del pre_fun
except:
traceback.print_exc()
print("clean_empty_cache")
if torch.cuda.is_available():
torch.cuda.empty_cache()
yield "\n".join(infos)
# 一个选项卡全局只能有一个音色
def get_vc(sid, to_return_protect0, to_return_protect1):
global n_spk, tgt_sr, net_g, vc, cpt, version
if sid == "" or sid == []:
global hubert_model
if hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
print("clean_empty_cache")
del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt
hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
if torch.cuda.is_available():
torch.cuda.empty_cache()
###楼下不这么折腾清理不干净
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(
*cpt["config"], is_half=config.is_half
)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(
*cpt["config"], is_half=config.is_half
)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g, cpt
if torch.cuda.is_available():
torch.cuda.empty_cache()
return {"visible": False, "__type__": "update"}
person = "%s/%s" % (weight_root, sid)
print("loading %s" % person)
cpt = torch.load(person, map_location="cpu")
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
if_f0 = cpt.get("f0", 1)
if if_f0 == 0:
to_return_protect0 = to_return_protect1 = {
"visible": False,
"value": 0.5,
"__type__": "update",
}
else:
to_return_protect0 = {
"visible": True,
"value": to_return_protect0,
"__type__": "update",
}
to_return_protect1 = {
"visible": True,
"value": to_return_protect1,
"__type__": "update",
}
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g.enc_q
print(net_g.load_state_dict(cpt["weight"], strict=False))
net_g.eval().to(config.device)
if config.is_half:
net_g = net_g.half()
else:
net_g = net_g.float()
vc = VC(tgt_sr, config)
n_spk = cpt["config"][-3]
return (
{"visible": True, "maximum": n_spk, "__type__": "update"},
to_return_protect0,
to_return_protect1,
)
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"}
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 = (
config.python_cmd
+ " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
% (trainset_dir, sr, n_p, now_dir, exp_dir)
+ str(config.noparallel)
)
print(cmd)
p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
###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()
print(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 = 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
####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 = (
config.python_cmd
+ " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
% (
config.device,
leng,
idx,
n_g,
now_dir,
exp_dir,
version19,
)
)
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
def change_sr2(sr2, if_f0_3, version19):
path_str = "" if version19 == "v1" else "_v2"
f0_str = "f0" if if_f0_3 else ""
if_pretrained_generator_exist = os.access(
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
)
if_pretrained_discriminator_exist = os.access(
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
)
if not if_pretrained_generator_exist:
print(
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
"not exist, will not use pretrained model",
)
if not if_pretrained_discriminator_exist:
print(
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
"not exist, will not use pretrained model",
)
return (
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_generator_exist
else "",
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_discriminator_exist
else "",
)
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 ""
if_pretrained_generator_exist = os.access(
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
)
if_pretrained_discriminator_exist = os.access(
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
)
if not if_pretrained_generator_exist:
print(
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
"not exist, will not use pretrained model",
)
if not if_pretrained_discriminator_exist:
print(
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
"not exist, will not use pretrained model",
)
return (
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_generator_exist
else "",
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_discriminator_exist
else "",
to_return_sr2,
)
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
path_str = "" if version19 == "v1" else "_v2"
if_pretrained_generator_exist = os.access(
"pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK
)
if_pretrained_discriminator_exist = os.access(
"pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK
)
if not if_pretrained_generator_exist:
print(
"pretrained%s/f0G%s.pth" % (path_str, sr2),
"not exist, will not use pretrained model",
)
if not if_pretrained_discriminator_exist:
print(
"pretrained%s/f0D%s.pth" % (path_str, sr2),
"not exist, will not use pretrained model",
)
if if_f0_3:
return (
{"visible": True, "__type__": "update"},
"pretrained%s/f0G%s.pth" % (path_str, sr2)
if if_pretrained_generator_exist
else "",
"pretrained%s/f0D%s.pth" % (path_str, sr2)
if if_pretrained_discriminator_exist
else "",
)
return (
{"visible": False, "__type__": "update"},
("pretrained%s/G%s.pth" % (path_str, sr2))
if if_pretrained_generator_exist
else "",
("pretrained%s/D%s.pth" % (path_str, sr2))
if if_pretrained_discriminator_exist
else "",
)
# 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,
):
# 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))
print("write filelist done")
# 生成config#无需生成config
# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
print("use gpus:", gpus16)
if pretrained_G14 == "":
print("no pretrained Generator")
if pretrained_D15 == "":
print("no pretrained Discriminator")
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"
% (
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
gpus16,
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("是") else 0,
1 if if_cache_gpu17 == i18n("是") else 0,
1 if if_save_every_weights18 == i18n("是") else 0,
version19,
)
)
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"
% (
exp_dir1,
sr2,
1 if if_f0_3 else 0,
batch_size12,
total_epoch11,
save_epoch10,
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "\b",
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "\b",
1 if if_save_latest13 == i18n("是") else 0,
1 if if_cache_gpu17 == i18n("是") else 0,
1 if if_save_every_weights18 == i18n("是") else 0,
version19,
)
)
print(cmd)
p = Popen(cmd, shell=True, cwd=now_dir)
p.wait()
return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"
# but4.click(train_index, [exp_dir1], info3)
def train_index(exp_dir1, version19):
exp_dir = "%s/logs/%s" % (now_dir, 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:
# if(1):
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()
print(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),
)
# faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,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])
faiss.write_index(
index,
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
)
infos.append(
"成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
)
# 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,
):
infos = []
def get_info_str(strr):
infos.append(strr)
return "\n".join(infos)