Format code (#275)

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github-actions[bot] 2023-05-14 07:52:36 +00:00 committed by GitHub
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8 changed files with 295 additions and 158 deletions

View File

@ -1,4 +1,5 @@
import os, traceback, sys, parselmouth
now_dir = os.getcwd()
sys.path.append(now_dir)
from my_utils import load_audio
@ -35,7 +36,7 @@ class FeatureInput(object):
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)
x = load_audio(path, self.fs)
p_len = x.shape[0] // self.hop
if f0_method == "pm":
time_step = 160 / 16000 * 1000

View File

@ -18,9 +18,12 @@ from fairseq import checkpoint_utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():device="cuda"
elif torch.backends.mps.is_available():device="mps"
else:device="cpu"
if torch.cuda.is_available():
device = "cuda"
elif torch.backends.mps.is_available():
device = "mps"
else:
device = "cpu"
f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
@ -36,7 +39,9 @@ model_path = "hubert_base.pt"
printt(exp_dir)
wavPath = "%s/1_16k_wavs" % exp_dir
outPath = "%s/3_feature256" % exp_dir if version=="v1"else "%s/3_feature768" % exp_dir
outPath = (
"%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir
)
os.makedirs(outPath, exist_ok=True)
@ -64,7 +69,7 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
model = models[0]
model = model.to(device)
printt("move model to %s" % device)
if device not in ["mps","cpu"]:
if device not in ["mps", "cpu"]:
model = model.half()
model.eval()
@ -90,11 +95,13 @@ else:
if device not in ["mps", "cpu"]
else feats.to(device),
"padding_mask": padding_mask.to(device),
"output_layer": 9 if version=="v1"else 12, # layer 9
"output_layer": 9 if version == "v1" else 12, # layer 9
}
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = model.final_proj(logits[0])if version=="v1"else logits[0]
feats = (
model.final_proj(logits[0]) if version == "v1" else logits[0]
)
feats = feats.squeeze(0).float().cpu().numpy()
if np.isnan(feats).sum() == 0:

View File

@ -1,5 +1,6 @@
import torch, os, traceback, sys, warnings, shutil, numpy as np
os.environ["no_proxy"]="localhost, 127.0.0.1, ::1"
os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
from multiprocessing import cpu_count
import threading
from time import sleep
@ -11,8 +12,8 @@ now_dir = os.getcwd()
sys.path.append(now_dir)
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
shutil.rmtree("%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)
shutil.rmtree("%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)
os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
@ -70,7 +71,12 @@ 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])
from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono,SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
from infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from scipy.io import wavfile
from fairseq import checkpoint_utils
import gradio as gr
@ -121,11 +127,11 @@ names = []
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_paths=[]
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))
index_paths.append("%s/%s" % (root, name))
uvr5_names = []
for name in os.listdir(weight_uvr5_root):
if name.endswith(".pth"):
@ -144,29 +150,33 @@ def vc_single(
index_rate,
filter_radius,
resample_sr,
rms_mix_rate
rms_mix_rate,
): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
global tgt_sr, net_g, vc, hubert_model,version
global tgt_sr, net_g, vc, hubert_model, version
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
audio_max = np.abs(audio).max() / 0.95
if audio_max > 1:
audio /= audio_max
times = [0, 0, 0]
if hubert_model == None:
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
)
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(" ")
# )
@ -190,10 +200,19 @@ def vc_single(
version,
f0_file=f0_file,
)
if(resample_sr>=16000 and tgt_sr!=resample_sr):
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)
if resample_sr >= 16000 and tgt_sr != resample_sr:
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)
@ -213,7 +232,7 @@ def vc_multi(
index_rate,
filter_radius,
resample_sr,
rms_mix_rate
rms_mix_rate,
):
try:
dir_path = (
@ -243,9 +262,9 @@ def vc_multi(
index_rate,
filter_radius,
resample_sr,
rms_mix_rate
rms_mix_rate,
)
if "Success"in info:
if "Success" in info:
try:
tgt_sr, audio_opt = opt
wavfile.write(
@ -330,8 +349,8 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
# 一个选项卡全局只能有一个音色
def get_vc(sid):
global n_spk, tgt_sr, net_g, vc, cpt,version
if sid == ""or sid==[]:
global n_spk, tgt_sr, net_g, vc, cpt, version
if sid == "" or sid == []:
global hubert_model
if hubert_model != 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")
@ -342,14 +361,18 @@ def get_vc(sid):
###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 version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
net_g = SynthesizerTrnMs256NSFsid(
*cpt["config"], is_half=config.is_half
)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif (version == "v2"):
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
net_g = SynthesizerTrnMs768NSFsid(
*cpt["config"], is_half=config.is_half
)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g, cpt
@ -364,12 +387,12 @@ def get_vc(sid):
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if(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"):
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
else:
@ -391,17 +414,21 @@ def change_choices():
for name in os.listdir(weight_root):
if name.endswith(".pth"):
names.append(name)
index_paths=[]
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"}
return {"choices": sorted(names), "__type__": "update"}, {
"choices": sorted(index_paths),
"__type__": "update",
}
def clean():
return {"value": "", "__type__": "update"}
sr_dict = {
"32k": 32000,
"40k": 40000,
@ -468,7 +495,7 @@ 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 = 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")
@ -514,7 +541,10 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
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" % (
cmd = (
config.python_cmd
+ " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
% (
config.device,
leng,
idx,
@ -523,6 +553,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
exp_dir,
version19,
)
)
print(cmd)
p = Popen(
cmd, shell=True, cwd=now_dir
@ -549,34 +580,47 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
yield log
def change_sr2(sr2, if_f0_3,version19):
vis_v=True if sr2=="40k"else False
if(sr2!="40k"):version19="v1"
path_str=""if version19=="v1"else "_v2"
version_state={"visible": vis_v, "__type__": "update"}
if(vis_v==False):version_state["value"]="v1"
f0_str="f0"if if_f0_3 else""
return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2),version_state
def change_sr2(sr2, if_f0_3, version19):
vis_v = True if sr2 == "40k" else False
if sr2 != "40k":
version19 = "v1"
path_str = "" if version19 == "v1" else "_v2"
version_state = {"visible": vis_v, "__type__": "update"}
if vis_v == False:
version_state["value"] = "v1"
f0_str = "f0" if if_f0_3 else ""
return (
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
version_state,
)
def change_version19(sr2,if_f0_3,version19):
path_str=""if version19=="v1"else "_v2"
f0_str="f0"if if_f0_3 else""
return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2)
def change_f0(if_f0_3, sr2,version19): # f0method8,pretrained_G14,pretrained_D15
path_str=""if version19=="v1"else "_v2"
def change_version19(sr2, if_f0_3, version19):
path_str = "" if version19 == "v1" else "_v2"
f0_str = "f0" if if_f0_3 else ""
return "pretrained%s/%sG%s.pth" % (
path_str,
f0_str,
sr2,
), "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
path_str = "" if version19 == "v1" else "_v2"
if if_f0_3:
return (
{"visible": True, "__type__": "update"},
"pretrained%s/f0G%s.pth" % (path_str,sr2),
"pretrained%s/f0D%s.pth" % (path_str,sr2),
"pretrained%s/f0G%s.pth" % (path_str, sr2),
"pretrained%s/f0D%s.pth" % (path_str, sr2),
)
return (
{"visible": False, "__type__": "update"},
"pretrained%s/G%s.pth" % (path_str,sr2),
"pretrained%s/D%s.pth" % (path_str,sr2),
"pretrained%s/G%s.pth" % (path_str, sr2),
"pretrained%s/D%s.pth" % (path_str, 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,
@ -598,7 +642,11 @@ def click_train(
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)
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)
@ -640,18 +688,18 @@ def click_train(
spk_id5,
)
)
fea_dim = 256 if version19 == "v1"else 768
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)
% (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)
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % exp_dir, "w") as f:
@ -706,10 +754,14 @@ def click_train(
# but4.click(train_index, [exp_dir1], info3)
def train_index(exp_dir1,version19):
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)
feature_dir = (
"%s/3_feature256" % (exp_dir)
if version19 == "v1"
else "%s/3_feature768" % (exp_dir)
)
if os.path.exists(feature_dir) == False:
return "请先进行特征提取!"
listdir_res = list(os.listdir(feature_dir))
@ -729,7 +781,7 @@ def train_index(exp_dir1,version19):
infos = []
infos.append("%s,%s" % (big_npy.shape, n_ivf))
yield "\n".join(infos)
index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,Flat" % n_ivf)
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)
@ -738,7 +790,8 @@ def train_index(exp_dir1,version19):
index.train(big_npy)
faiss.write_index(
index,
"%s/trained_IVF%s_Flat_nprobe_%s_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe,version19),
"%s/trained_IVF%s_Flat_nprobe_%s_%s.index"
% (exp_dir, n_ivf, index_ivf.nprobe, version19),
)
# faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
infos.append("adding")
@ -750,7 +803,10 @@ def train_index(exp_dir1,version19):
index,
"%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
)
infos.append("成功构建索引added_IVF%s_Flat_nprobe_%s_%s.index" % (n_ivf, index_ivf.nprobe,version19))
infos.append(
"成功构建索引added_IVF%s_Flat_nprobe_%s_%s.index"
% (n_ivf, index_ivf.nprobe, 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)
@ -786,7 +842,11 @@ def train1key(
preprocess_log_path = "%s/preprocess.log" % model_log_dir
extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
feature_dir = "%s/3_feature256" % model_log_dir if version19=="v1"else "%s/3_feature768" % model_log_dir
feature_dir = (
"%s/3_feature256" % model_log_dir
if version19 == "v1"
else "%s/3_feature768" % model_log_dir
)
os.makedirs(model_log_dir, exist_ok=True)
#########step1:处理数据
@ -830,7 +890,8 @@ def train1key(
leng,
idx,
n_g,
model_log_dir,version19,
model_log_dir,
version19,
)
yield get_info_str(cmd)
p = Popen(
@ -885,18 +946,18 @@ def train1key(
spk_id5,
)
)
fea_dim=256 if version19=="v1"else 768
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)
% (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)
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % model_log_dir, "w") as f:
@ -961,7 +1022,7 @@ def train1key(
# n_ivf = big_npy.shape[0] // 39
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
index = faiss.index_factory(256 if version19=="v1"else 768, "IVF%s,Flat" % n_ivf)
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
yield get_info_str("training index")
index_ivf = faiss.extract_index_ivf(index) #
index_ivf.nprobe = 1
@ -969,7 +1030,7 @@ def train1key(
faiss.write_index(
index,
"%s/trained_IVF%s_Flat_nprobe_%s_%s.index"
% (model_log_dir, n_ivf, index_ivf.nprobe,version19),
% (model_log_dir, n_ivf, index_ivf.nprobe, version19),
)
yield get_info_str("adding index")
batch_size_add = 8192
@ -978,10 +1039,11 @@ def train1key(
faiss.write_index(
index,
"%s/added_IVF%s_Flat_nprobe_%s_%s.index"
% (model_log_dir, n_ivf, index_ivf.nprobe,version19),
% (model_log_dir, n_ivf, index_ivf.nprobe, version19),
)
yield get_info_str(
"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index" % (n_ivf, index_ivf.nprobe,version19)
"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index"
% (n_ivf, index_ivf.nprobe, version19)
)
yield get_info_str(i18n("全流程结束!"))
@ -999,8 +1061,8 @@ def change_info_(ckpt_path):
) 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
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"}
@ -1136,7 +1198,7 @@ with gr.Blocks() as app:
value="pm",
interactive=True,
)
filter_radius0=gr.Slider(
filter_radius0 = gr.Slider(
minimum=0,
maximum=7,
label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波数值为滤波半径使用可以削弱哑音"),
@ -1155,7 +1217,9 @@ with gr.Blocks() as app:
choices=sorted(index_paths),
interactive=True,
)
refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0, file_index2])
refresh_button.click(
fn=change_choices, inputs=[], outputs=[sid0, file_index2]
)
# file_big_npy1 = 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",
@ -1168,7 +1232,7 @@ with gr.Blocks() as app:
value=0.76,
interactive=True,
)
resample_sr0=gr.Slider(
resample_sr0 = gr.Slider(
minimum=0,
maximum=48000,
label=i18n("后处理重采样至最终采样率0为不进行重采样"),
@ -1202,7 +1266,7 @@ with gr.Blocks() as app:
index_rate1,
filter_radius0,
resample_sr0,
rms_mix_rate0
rms_mix_rate0,
],
[vc_output1, vc_output2],
)
@ -1222,7 +1286,7 @@ with gr.Blocks() as app:
value="pm",
interactive=True,
)
filter_radius1=gr.Slider(
filter_radius1 = gr.Slider(
minimum=0,
maximum=7,
label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波数值为滤波半径使用可以削弱哑音"),
@ -1253,7 +1317,7 @@ with gr.Blocks() as app:
value=1,
interactive=True,
)
resample_sr1=gr.Slider(
resample_sr1 = gr.Slider(
minimum=0,
maximum=48000,
label=i18n("后处理重采样至最终采样率0为不进行重采样"),
@ -1293,7 +1357,7 @@ with gr.Blocks() as app:
index_rate2,
filter_radius1,
resample_sr1,
rms_mix_rate1
rms_mix_rate1,
],
[vc_output3],
)
@ -1398,7 +1462,7 @@ with gr.Blocks() as app:
but1 = gr.Button(i18n("处理数据"), variant="primary")
info1 = gr.Textbox(label=i18n("输出信息"), value="")
but1.click(
preprocess_dataset, [trainset_dir4, exp_dir1, sr2,np7], [info1]
preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
)
with gr.Group():
gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)"))
@ -1423,7 +1487,7 @@ with gr.Blocks() as app:
info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
but2.click(
extract_f0_feature,
[gpus6, np7, f0method8, if_f0_3, exp_dir1,version19],
[gpus6, np7, f0method8, if_f0_3, exp_dir1, version19],
[info2],
)
with gr.Group():
@ -1468,9 +1532,7 @@ with gr.Blocks() as app:
interactive=True,
)
if_save_every_weights18 = gr.Radio(
label=i18n(
"是否在每次保存时间点将最终小模型保存至weights文件夹"
),
label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
choices=[i18n(""), i18n("")],
value=i18n(""),
interactive=True,
@ -1487,14 +1549,18 @@ with gr.Blocks() as app:
interactive=True,
)
sr2.change(
change_sr2, [sr2, if_f0_3,version19], [pretrained_G14, pretrained_D15,version19]
change_sr2,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15, version19],
)
version19.change(
change_version19, [sr2, if_f0_3,version19], [pretrained_G14, pretrained_D15]
change_version19,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15],
)
if_f0_3.change(
change_f0,
[if_f0_3, sr2,version19],
[if_f0_3, sr2, version19],
[f0method8, pretrained_G14, pretrained_D15],
)
gpus16 = gr.Textbox(
@ -1526,7 +1592,7 @@ with gr.Blocks() as app:
],
info3,
)
but4.click(train_index, [exp_dir1,version19], info3)
but4.click(train_index, [exp_dir1, version19], info3)
but5.click(
train1key,
[
@ -1586,7 +1652,7 @@ with gr.Blocks() as app:
max_lines=1,
interactive=True,
)
version_2=gr.Radio(
version_2 = gr.Radio(
label=i18n("模型版本型号"),
choices=["v1", "v2"],
value="v1",
@ -1597,7 +1663,16 @@ with gr.Blocks() as app:
info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
but6.click(
merge,
[ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0,version_2],
[
ckpt_a,
ckpt_b,
alpha_a,
sr_,
if_f0_,
info__,
name_to_save0,
version_2,
],
info4,
) # def merge(path1,path2,alpha1,sr,f0,info):
with gr.Group():
@ -1655,7 +1730,7 @@ with gr.Blocks() as app:
value="1",
interactive=True,
)
version_1=gr.Radio(
version_1 = gr.Radio(
label=i18n("模型版本型号"),
choices=["v1", "v2"],
value="v1",
@ -1666,10 +1741,12 @@ with gr.Blocks() as app:
)
but9 = gr.Button(i18n("提取"), variant="primary")
info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
ckpt_path2.change(change_info_, [ckpt_path2], [sr__, if_f0__,version_1])
ckpt_path2.change(
change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
)
but9.click(
extract_small_model,
[ckpt_path2, save_name, sr__, if_f0__, info___,version_1],
[ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
info7,
)
@ -1687,16 +1764,16 @@ with gr.Blocks() as app:
butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx)
tab_faq=i18n("常见问题解答")
tab_faq = i18n("常见问题解答")
with gr.TabItem(tab_faq):
try:
if(tab_faq=="常见问题解答"):
with open("docs/faq.md","r",encoding="utf8")as f:info=f.read()
if tab_faq == "常见问题解答":
with open("docs/faq.md", "r", encoding="utf8") as f:
info = f.read()
else:
with open("docs/faq_en.md", "r")as f:info = f.read()
gr.Markdown(
value=info
)
with open("docs/faq_en.md", "r") as f:
info = f.read()
gr.Markdown(value=info)
except:
gr.Markdown(traceback.format_exc())

View File

@ -59,6 +59,8 @@ class TextEncoder256(nn.Module):
m, logs = torch.split(stats, self.out_channels, dim=1)
return m, logs, x_mask
class TextEncoder768(nn.Module):
def __init__(
self,
@ -105,6 +107,7 @@ class TextEncoder768(nn.Module):
m, logs = torch.split(stats, self.out_channels, dim=1)
return m, logs, x_mask
class ResidualCouplingBlock(nn.Module):
def __init__(
self,
@ -635,6 +638,8 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
class SynthesizerTrnMs768NSFsid(nn.Module):
def __init__(
self,
@ -846,6 +851,8 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec((z * x_mask)[:, :, :max_len], g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
class SynthesizerTrnMs768NSFsid_nono(nn.Module):
def __init__(
self,
@ -977,11 +984,12 @@ class MultiPeriodDiscriminator(torch.nn.Module):
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class MultiPeriodDiscriminatorV2(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminatorV2, self).__init__()
# periods = [2, 3, 5, 7, 11, 17]
periods = [2,3, 5, 7, 11, 17, 23, 37]
periods = [2, 3, 5, 7, 11, 17, 23, 37]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
discs = discs + [

View File

@ -2,7 +2,7 @@ import torch, traceback, os, pdb
from collections import OrderedDict
def savee(ckpt, sr, if_f0, name, epoch,version):
def savee(ckpt, sr, if_f0, name, epoch, version):
try:
opt = OrderedDict()
opt["weight"] = {}
@ -96,7 +96,7 @@ def show_info(path):
return traceback.format_exc()
def extract_small_model(path, name, sr, if_f0, info,version):
def extract_small_model(path, name, sr, if_f0, info, version):
try:
ckpt = torch.load(path, map_location="cpu")
if "model" in ckpt:
@ -194,7 +194,7 @@ def change_info(path, info, name):
return traceback.format_exc()
def merge(path1, path2, alpha1, sr, f0, info, name,version):
def merge(path1, path2, alpha1, sr, f0, info, name, version):
try:
def extract(ckpt):

View File

@ -322,7 +322,11 @@ def get_hparams(init=True):
"-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
)
parser.add_argument(
"-sw", "--save_every_weights", type=str, default="0", help="save the extracted model in weights directory when saving checkpoints"
"-sw",
"--save_every_weights",
type=str,
default="0",
help="save the extracted model in weights directory when saving checkpoints",
)
parser.add_argument(
"-v", "--version", type=str, required=True, help="model version"

View File

@ -31,7 +31,8 @@ from data_utils import (
TextAudioCollate,
DistributedBucketSampler,
)
if(hps.version=="v1"):
if hps.version == "v1":
from infer_pack.models import (
SynthesizerTrnMs256NSFsid as RVC_Model_f0,
SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
@ -519,14 +520,25 @@ def train_and_evaluate(
epoch,
os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
)
if(rank==0 and hps.save_every_weights=="1"):
if rank == 0 and hps.save_every_weights == "1":
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
logger.info(
"saving ckpt %s_e%s:%s"
% (hps.name,epoch,savee(ckpt, hps.sample_rate, hps.if_f0, hps.name+"_e%s"%epoch, epoch,hps.version))
% (
hps.name,
epoch,
savee(
ckpt,
hps.sample_rate,
hps.if_f0,
hps.name + "_e%s" % epoch,
epoch,
hps.version,
),
)
)
if rank == 0:
@ -540,7 +552,7 @@ def train_and_evaluate(
ckpt = net_g.state_dict()
logger.info(
"saving final ckpt:%s"
% (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch,hps.version))
% (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version))
)
sleep(1)
os._exit(2333333)

View File

@ -2,16 +2,18 @@ import numpy as np, parselmouth, torch, pdb
from time import time as ttime
import torch.nn.functional as F
import scipy.signal as signal
import pyworld, os, traceback, faiss,librosa
import pyworld, os, traceback, faiss, librosa
from scipy import signal
from functools import lru_cache
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
input_audio_path2wav={}
input_audio_path2wav = {}
@lru_cache
def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period):
audio=input_audio_path2wav[input_audio_path]
def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
audio = input_audio_path2wav[input_audio_path]
f0, t = pyworld.harvest(
audio,
fs=fs,
@ -22,18 +24,29 @@ def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period):
f0 = pyworld.stonemask(audio, f0, t, fs)
return f0
def change_rms(data1,sr1,data2,sr2,rate):#1是输入音频2是输出音频,rate是2的占比
def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频2是输出音频,rate是2的占比
# print(data1.max(),data2.max())
rms1 = librosa.feature.rms(y=data1, frame_length=sr1//2*2, hop_length=sr1//2)#每半秒一个点
rms2 = librosa.feature.rms(y=data2, frame_length=sr2//2*2, hop_length=sr2//2)
rms1=torch.from_numpy(rms1)
rms1=F.interpolate(rms1.unsqueeze(0), size=data2.shape[0],mode='linear').squeeze()
rms2=torch.from_numpy(rms2)
rms2=F.interpolate(rms2.unsqueeze(0), size=data2.shape[0],mode='linear').squeeze()
rms2=torch.max(rms2,torch.zeros_like(rms2)+1e-6)
data2*=(torch.pow(rms1,torch.tensor(1-rate))*torch.pow(rms2,torch.tensor(rate-1))).numpy()
rms1 = librosa.feature.rms(
y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
) # 每半秒一个点
rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
rms1 = torch.from_numpy(rms1)
rms1 = F.interpolate(
rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
).squeeze()
rms2 = torch.from_numpy(rms2)
rms2 = F.interpolate(
rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
).squeeze()
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
data2 *= (
torch.pow(rms1, torch.tensor(1 - rate))
* torch.pow(rms2, torch.tensor(rate - 1))
).numpy()
return data2
class VC(object):
def __init__(self, tgt_sr, config):
self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
@ -53,7 +66,16 @@ class VC(object):
self.t_max = self.sr * self.x_max # 免查询时长阈值
self.device = config.device
def get_f0(self, input_audio_path,x, p_len, f0_up_key, f0_method,filter_radius, inp_f0=None):
def get_f0(
self,
input_audio_path,
x,
p_len,
f0_up_key,
f0_method,
filter_radius,
inp_f0=None,
):
global input_audio_path2wav
time_step = self.window / self.sr * 1000
f0_min = 50
@ -77,9 +99,9 @@ class VC(object):
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
)
elif f0_method == "harvest":
input_audio_path2wav[input_audio_path]=x.astype(np.double)
f0=cache_harvest_f0(input_audio_path,self.sr,f0_max,f0_min,10)
if(filter_radius>2):
input_audio_path2wav[input_audio_path] = x.astype(np.double)
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
if filter_radius > 2:
f0 = signal.medfilt(f0, 3)
f0 *= pow(2, f0_up_key / 12)
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
@ -134,12 +156,12 @@ class VC(object):
inputs = {
"source": feats.to(self.device),
"padding_mask": padding_mask,
"output_layer": 9if version=="v1"else 12,
"output_layer": 9 if version == "v1" else 12,
}
t0 = ttime()
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = model.final_proj(logits[0])if version=="v1"else logits[0]
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
if (
isinstance(index, type(None)) == False
@ -184,10 +206,7 @@ class VC(object):
)
else:
audio1 = (
(net_g.infer(feats, p_len, sid)[0][0, 0])
.data.cpu()
.float()
.numpy()
(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
)
del feats, p_len, padding_mask
if torch.cuda.is_available():
@ -270,7 +289,15 @@ class VC(object):
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
pitch, pitchf = None, None
if if_f0 == 1:
pitch, pitchf = self.get_f0(input_audio_path,audio_pad, p_len, f0_up_key, f0_method,filter_radius, inp_f0)
pitch, pitchf = self.get_f0(
input_audio_path,
audio_pad,
p_len,
f0_up_key,
f0_method,
filter_radius,
inp_f0,
)
pitch = pitch[:p_len]
pitchf = pitchf[:p_len]
if self.device == "mps":
@ -347,16 +374,17 @@ class VC(object):
)[self.t_pad_tgt : -self.t_pad_tgt]
)
audio_opt = np.concatenate(audio_opt)
if(rms_mix_rate!=1):
audio_opt=change_rms(audio,16000,audio_opt,tgt_sr,rms_mix_rate)
if(resample_sr>=16000 and tgt_sr!=resample_sr):
if rms_mix_rate != 1:
audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
if resample_sr >= 16000 and tgt_sr != resample_sr:
audio_opt = librosa.resample(
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
)
audio_max=np.abs(audio_opt).max()/0.99
max_int16=32768
if(audio_max>1):max_int16/=audio_max
audio_opt=(audio_opt * max_int16).astype(np.int16)
audio_max = np.abs(audio_opt).max() / 0.99
max_int16 = 32768
if audio_max > 1:
max_int16 /= audio_max
audio_opt = (audio_opt * max_int16).astype(np.int16)
del pitch, pitchf, sid
if torch.cuda.is_available():
torch.cuda.empty_cache()