Add files via upload
This commit is contained in:
RVC-Boss
GitHub
parent
0bc1ea782e
commit
44449efc2e
|
|
@ -18,6 +18,10 @@ 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"
|
||||
|
||||
f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
|
||||
|
||||
|
||||
|
|
@ -60,7 +64,7 @@ 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 != "cpu":
|
||||
if device not in ["mps","cpu"]:
|
||||
model = model.half()
|
||||
model.eval()
|
||||
|
||||
|
|
@ -83,7 +87,7 @@ else:
|
|||
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
|
||||
inputs = {
|
||||
"source": feats.half().to(device)
|
||||
if device != "cpu"
|
||||
if device not in ["mps", "cpu"]
|
||||
else feats.to(device),
|
||||
"padding_mask": padding_mask.to(device),
|
||||
"output_layer": 9, # layer 9
|
||||
|
|
|
|||
169
infer-web.py
169
infer-web.py
|
|
@ -11,6 +11,8 @@ 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)
|
||||
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)
|
||||
|
|
@ -114,10 +116,16 @@ def load_hubert():
|
|||
|
||||
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"):
|
||||
|
|
@ -126,20 +134,23 @@ for name in os.listdir(weight_uvr5_root):
|
|||
|
||||
def vc_single(
|
||||
sid,
|
||||
input_audio,
|
||||
input_audio_path,
|
||||
f0_up_key,
|
||||
f0_file,
|
||||
f0_method,
|
||||
file_index,
|
||||
file_index2,
|
||||
# file_big_npy,
|
||||
index_rate,
|
||||
filter_radius,
|
||||
resample_sr,
|
||||
): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
|
||||
global tgt_sr, net_g, vc, hubert_model
|
||||
if input_audio is None:
|
||||
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, 16000)
|
||||
audio = load_audio(input_audio_path, 16000)
|
||||
times = [0, 0, 0]
|
||||
if hubert_model == None:
|
||||
load_hubert()
|
||||
|
|
@ -151,7 +162,7 @@ def vc_single(
|
|||
.strip('"')
|
||||
.strip(" ")
|
||||
.replace("trained", "added")
|
||||
) # 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(" ")
|
||||
# )
|
||||
|
|
@ -160,6 +171,7 @@ def vc_single(
|
|||
net_g,
|
||||
sid,
|
||||
audio,
|
||||
input_audio_path,
|
||||
times,
|
||||
f0_up_key,
|
||||
f0_method,
|
||||
|
|
@ -167,12 +179,15 @@ def vc_single(
|
|||
# file_big_npy,
|
||||
index_rate,
|
||||
if_f0,
|
||||
filter_radius,
|
||||
tgt_sr,
|
||||
resample_sr,
|
||||
f0_file=f0_file,
|
||||
)
|
||||
print(
|
||||
"npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep=""
|
||||
)
|
||||
return "Success", (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)
|
||||
|
|
@ -187,8 +202,11 @@ def vc_multi(
|
|||
f0_up_key,
|
||||
f0_method,
|
||||
file_index,
|
||||
file_index2,
|
||||
# file_big_npy,
|
||||
index_rate,
|
||||
filter_radius,
|
||||
resample_sr,
|
||||
):
|
||||
try:
|
||||
dir_path = (
|
||||
|
|
@ -205,14 +223,6 @@ def vc_multi(
|
|||
traceback.print_exc()
|
||||
paths = [path.name for path in paths]
|
||||
infos = []
|
||||
file_index = (
|
||||
file_index.strip(" ")
|
||||
.strip('"')
|
||||
.strip("\n")
|
||||
.strip('"')
|
||||
.strip(" ")
|
||||
.replace("trained", "added")
|
||||
) # 防止小白写错，自动帮他替换掉
|
||||
for path in paths:
|
||||
info, opt = vc_single(
|
||||
sid,
|
||||
|
|
@ -221,17 +231,20 @@ def vc_multi(
|
|||
None,
|
||||
f0_method,
|
||||
file_index,
|
||||
file_index2,
|
||||
# file_big_npy,
|
||||
index_rate,
|
||||
filter_radius,
|
||||
resample_sr,
|
||||
)
|
||||
if info == "Success":
|
||||
if "Success"in info:
|
||||
try:
|
||||
tgt_sr, audio_opt = opt
|
||||
wavfile.write(
|
||||
"%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt
|
||||
)
|
||||
except:
|
||||
info = traceback.format_exc()
|
||||
info += traceback.format_exc()
|
||||
infos.append("%s->%s" % (os.path.basename(path), info))
|
||||
yield "\n".join(infos)
|
||||
yield "\n".join(infos)
|
||||
|
|
@ -310,7 +323,7 @@ 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
|
||||
if sid == "":
|
||||
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")
|
||||
|
|
@ -358,7 +371,12 @@ def change_choices():
|
|||
for name in os.listdir(weight_root):
|
||||
if name.endswith(".pth"):
|
||||
names.append(name)
|
||||
return {"choices": sorted(names), "__type__": "update"}
|
||||
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():
|
||||
|
|
@ -412,7 +430,7 @@ def if_done_multi(done, ps):
|
|||
done[0] = True
|
||||
|
||||
|
||||
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p=ncpu):
|
||||
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")
|
||||
|
|
@ -684,7 +702,6 @@ def train_index(exp_dir1):
|
|||
infos.append("training")
|
||||
yield "\n".join(infos)
|
||||
index_ivf = faiss.extract_index_ivf(index) #
|
||||
# index_ivf.nprobe = int(np.power(n_ivf,0.3))
|
||||
index_ivf.nprobe = 1
|
||||
index.train(big_npy)
|
||||
faiss.write_index(
|
||||
|
|
@ -743,7 +760,7 @@ def train1key(
|
|||
cmd = (
|
||||
config.python_cmd
|
||||
+ " trainset_preprocess_pipeline_print.py %s %s %s %s "
|
||||
% (trainset_dir4, sr_dict[sr2], ncpu, model_log_dir)
|
||||
% (trainset_dir4, sr_dict[sr2], np7, model_log_dir)
|
||||
+ str(config.noparallel)
|
||||
)
|
||||
yield get_info_str(i18n("step1:正在处理数据"))
|
||||
|
|
@ -908,7 +925,6 @@ def train1key(
|
|||
index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
|
||||
yield get_info_str("training index")
|
||||
index_ivf = faiss.extract_index_ivf(index) #
|
||||
# index_ivf.nprobe = int(np.power(n_ivf,0.3))
|
||||
index_ivf.nprobe = 1
|
||||
index.train(big_npy)
|
||||
faiss.write_index(
|
||||
|
|
@ -1044,8 +1060,7 @@ with gr.Blocks() as app:
|
|||
with gr.TabItem(i18n("模型推理")):
|
||||
with gr.Row():
|
||||
sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
|
||||
refresh_button = gr.Button(i18n("刷新音色列表"), variant="primary")
|
||||
refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0])
|
||||
refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), variant="primary")
|
||||
clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
|
||||
spk_item = gr.Slider(
|
||||
minimum=0,
|
||||
|
|
@ -1073,7 +1088,7 @@ with gr.Blocks() as app:
|
|||
)
|
||||
input_audio0 = gr.Textbox(
|
||||
label=i18n("输入待处理音频文件路径(默认是正确格式示例)"),
|
||||
value="E:\\codes\\py39\\vits_vc_gpu_train\\todo-songs\\冬之花clip1.wav",
|
||||
value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav",
|
||||
)
|
||||
f0method0 = gr.Radio(
|
||||
label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"),
|
||||
|
|
@ -1081,12 +1096,26 @@ with gr.Blocks() as app:
|
|||
value="pm",
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Column():
|
||||
file_index1 = gr.Textbox(
|
||||
label=i18n("特征检索库文件路径"),
|
||||
value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\added_IVF677_Flat_nprobe_7.index",
|
||||
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"),
|
||||
value=3,
|
||||
step=1,
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Column():
|
||||
file_index1 = gr.Textbox(
|
||||
label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
|
||||
value="",
|
||||
interactive=True,
|
||||
)
|
||||
file_index2 = gr.Dropdown(
|
||||
label=i18n("自动检测index路径,下拉式选择(dropdown)"),
|
||||
choices=sorted(index_paths),
|
||||
interactive=True,
|
||||
)
|
||||
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",
|
||||
|
|
@ -1099,6 +1128,14 @@ with gr.Blocks() as app:
|
|||
value=0.76,
|
||||
interactive=True,
|
||||
)
|
||||
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"),
|
||||
value=0,
|
||||
step=1,
|
||||
interactive=True,
|
||||
)
|
||||
f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
|
||||
but0 = gr.Button(i18n("转换"), variant="primary")
|
||||
with gr.Column():
|
||||
|
|
@ -1113,8 +1150,11 @@ with gr.Blocks() as app:
|
|||
f0_file,
|
||||
f0method0,
|
||||
file_index1,
|
||||
file_index2,
|
||||
# file_big_npy1,
|
||||
index_rate1,
|
||||
filter_radius0,
|
||||
resample_sr0
|
||||
],
|
||||
[vc_output1, vc_output2],
|
||||
)
|
||||
|
|
@ -1134,10 +1174,23 @@ with gr.Blocks() as app:
|
|||
value="pm",
|
||||
interactive=True,
|
||||
)
|
||||
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"),
|
||||
value=3,
|
||||
step=1,
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Column():
|
||||
file_index2 = gr.Textbox(
|
||||
label=i18n("特征检索库文件路径"),
|
||||
value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\added_IVF677_Flat_nprobe_7.index",
|
||||
file_index3 = gr.Textbox(
|
||||
label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
|
||||
value="",
|
||||
interactive=True,
|
||||
)
|
||||
file_index4 = gr.Dropdown(
|
||||
label=i18n("自动检测index路径,下拉式选择(dropdown)"),
|
||||
choices=sorted(index_paths),
|
||||
interactive=True,
|
||||
)
|
||||
# file_big_npy2 = gr.Textbox(
|
||||
|
|
@ -1152,10 +1205,18 @@ with gr.Blocks() as app:
|
|||
value=1,
|
||||
interactive=True,
|
||||
)
|
||||
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"),
|
||||
value=0,
|
||||
step=1,
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Column():
|
||||
dir_input = gr.Textbox(
|
||||
label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
|
||||
value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs",
|
||||
value="E:\codes\py39\\test-20230416b\\todo-songs",
|
||||
)
|
||||
inputs = gr.File(
|
||||
file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
|
||||
|
|
@ -1171,9 +1232,12 @@ with gr.Blocks() as app:
|
|||
inputs,
|
||||
vc_transform1,
|
||||
f0method1,
|
||||
file_index2,
|
||||
file_index3,
|
||||
file_index4,
|
||||
# file_big_npy2,
|
||||
index_rate2,
|
||||
filter_radius1,
|
||||
resample_sr1
|
||||
],
|
||||
[vc_output3],
|
||||
)
|
||||
|
|
@ -1188,7 +1252,7 @@ with gr.Blocks() as app:
|
|||
with gr.Column():
|
||||
dir_wav_input = gr.Textbox(
|
||||
label=i18n("输入待处理音频文件夹路径"),
|
||||
value="E:\\codes\\py39\\vits_vc_gpu_train\\todo-songs",
|
||||
value="E:\\codes\\py39\\test-20230416b\\todo-songs\\todo-songs",
|
||||
)
|
||||
wav_inputs = gr.File(
|
||||
file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
|
||||
|
|
@ -1242,6 +1306,14 @@ with gr.Blocks() as app:
|
|||
value=True,
|
||||
interactive=True,
|
||||
)
|
||||
np7 = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=ncpu,
|
||||
step=1,
|
||||
label=i18n("提取音高和处理数据使用的CPU进程数"),
|
||||
value=ncpu,
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
|
||||
gr.Markdown(
|
||||
value=i18n(
|
||||
|
|
@ -1263,7 +1335,7 @@ 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], [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)"))
|
||||
|
|
@ -1276,14 +1348,6 @@ with gr.Blocks() as app:
|
|||
)
|
||||
gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
|
||||
with gr.Column():
|
||||
np7 = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=ncpu,
|
||||
step=1,
|
||||
label=i18n("提取音高使用的CPU进程数"),
|
||||
value=ncpu,
|
||||
interactive=True,
|
||||
)
|
||||
f0method8 = gr.Radio(
|
||||
label=i18n(
|
||||
"选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢"
|
||||
|
|
@ -1533,6 +1597,19 @@ 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")
|
||||
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()
|
||||
else:
|
||||
with open("docs/faq_en.md", "r")as f:info = f.read()
|
||||
gr.Markdown(
|
||||
value=info
|
||||
)
|
||||
except:
|
||||
gr.Markdown(traceback.format_exc())
|
||||
|
||||
# with gr.TabItem(i18n("招募音高曲线前端编辑器")):
|
||||
# gr.Markdown(value=i18n("加开发群联系我xxxxx"))
|
||||
# with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import sys, os
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(os.path.join(now_dir))
|
||||
sys.path.append(os.path.join(now_dir, "train"))
|
||||
import utils
|
||||
|
||||
|
|
|
|||
|
|
@ -2,11 +2,25 @@ 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
|
||||
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={}
|
||||
@lru_cache
|
||||
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,
|
||||
f0_ceil=f0max,
|
||||
f0_floor=f0min,
|
||||
frame_period=frame_period,
|
||||
)
|
||||
f0 = pyworld.stonemask(audio, f0, t, fs)
|
||||
return f0
|
||||
|
||||
class VC(object):
|
||||
def __init__(self, tgt_sr, config):
|
||||
|
|
@ -27,7 +41,8 @@ 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, x, p_len, f0_up_key, f0_method, 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
|
||||
f0_max = 1100
|
||||
|
|
@ -49,16 +64,11 @@ class VC(object):
|
|||
f0 = np.pad(
|
||||
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
|
||||
)
|
||||
else:
|
||||
f0, t = pyworld.harvest(
|
||||
x.astype(np.double),
|
||||
fs=self.sr,
|
||||
f0_ceil=f0_max,
|
||||
f0_floor=f0_min,
|
||||
frame_period=10,
|
||||
)
|
||||
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
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):
|
||||
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()]))
|
||||
tf0 = self.sr // self.window # 每秒f0点数
|
||||
|
|
@ -158,7 +168,6 @@ class VC(object):
|
|||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
else:
|
||||
audio1 = (
|
||||
|
|
@ -166,7 +175,6 @@ class VC(object):
|
|||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
del feats, p_len, padding_mask
|
||||
if torch.cuda.is_available():
|
||||
|
|
@ -182,6 +190,7 @@ class VC(object):
|
|||
net_g,
|
||||
sid,
|
||||
audio,
|
||||
input_audio_path,
|
||||
times,
|
||||
f0_up_key,
|
||||
f0_method,
|
||||
|
|
@ -189,6 +198,9 @@ class VC(object):
|
|||
# file_big_npy,
|
||||
index_rate,
|
||||
if_f0,
|
||||
filter_radius,
|
||||
tgt_sr,
|
||||
resample_sr,
|
||||
f0_file=None,
|
||||
):
|
||||
if (
|
||||
|
|
@ -243,7 +255,7 @@ 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(audio_pad, p_len, f0_up_key, f0_method, 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":
|
||||
|
|
@ -316,6 +328,11 @@ class VC(object):
|
|||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
if(resample_sr>=16000 and tgt_sr!=resample_sr):
|
||||
audio_opt = librosa.resample(
|
||||
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
|
||||
)
|
||||
audio_opt=audio_opt.astype(np.int16)
|
||||
del pitch, pitchf, sid
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
|
|
|
|||
Loading…
Reference in New Issue