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import torch, pdb, os,traceback,sys,warnings,shutil
now_dir=os.getcwd()
sys.path.append(now_dir)
tmp=os.path.join(now_dir,"TEMP")
shutil.rmtree(tmp,ignore_errors=True)
os.makedirs(tmp,exist_ok=True)
os.environ["TEMP"]=tmp
warnings.filterwarnings("ignore")
torch.manual_seed(114514)
from infer_pack.models import SynthesizerTrnMs256NSF as SynthesizerTrn256
from scipy.io import wavfile
from fairseq import checkpoint_utils
import gradio as gr
import librosa
import logging
from vc_infer_pipeline import VC
import soundfile as sf
from config import is_half,device,is_half
from infer_uvr5 import _audio_pre_
logging.getLogger('numba').setLevel(logging.WARNING)
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
hubert_model = models[0]
hubert_model = hubert_model.to(device)
if(is_half):hubert_model = hubert_model.half()
else:hubert_model = hubert_model.float()
hubert_model.eval()
weight_root="weights"
weight_uvr5_root="uvr5_weights"
names=[]
for name in os.listdir(weight_root):names.append(name.replace(".pt",""))
uvr5_names=[]
for name in os.listdir(weight_uvr5_root):uvr5_names.append(name.replace(".pth",""))
def get_vc(sid):
person = "%s/%s.pt" % (weight_root, sid)
cpt = torch.load(person, map_location="cpu")
dv = cpt["dv"]
tgt_sr = cpt["config"][-1]
net_g = SynthesizerTrn256(*cpt["config"], is_half=is_half)
net_g.load_state_dict(cpt["weight"], strict=True)
net_g.eval().to(device)
if (is_half):net_g = net_g.half()
else:net_g = net_g.float()
vc = VC(tgt_sr, device, is_half)
return dv,tgt_sr,net_g,vc
def vc_single(sid,input_audio,f0_up_key,f0_file):
if input_audio is None:return "You need to upload an audio", None
f0_up_key = int(f0_up_key)
try:
if(type(input_audio)==str):
print("processing %s" % input_audio)
audio, sampling_rate = sf.read(input_audio)
else:
sampling_rate, audio = input_audio
audio = audio.astype("float32") / 32768
if(type(sid)==str):dv, tgt_sr, net_g, vc=get_vc(sid)
else:dv,tgt_sr,net_g,vc=sid
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
if sampling_rate != 16000:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
times = [0, 0, 0]
audio_opt=vc.pipeline(hubert_model,net_g,dv,audio,times,f0_up_key,f0_file=f0_file)
print(times)
return "Success", (tgt_sr, audio_opt)
except:
info=traceback.format_exc()
print(info)
return info,(None,None)
finally:
print("clean_empty_cache")
del net_g,dv,vc
torch.cuda.empty_cache()
def vc_multi(sid,dir_path,opt_root,paths,f0_up_key):
try:
dir_path=dir_path.strip(" ")#防止小白拷路径头尾带了空格
opt_root=opt_root.strip(" ")
os.makedirs(opt_root, exist_ok=True)
dv, tgt_sr, net_g, vc = get_vc(sid)
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([dv,tgt_sr,net_g,vc],path,f0_up_key,f0_file=None)
if(info=="Success"):
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()
infos.append("%s->%s"%(os.path.basename(path),info))
return "\n".join(infos)
except:
return traceback.format_exc()
finally:
print("clean_empty_cache")
del net_g,dv,vc
torch.cuda.empty_cache()
def uvr(model_name,inp_root,save_root_vocal,save_root_ins):
infos = []
try:
inp_root = inp_root.strip(" ")# 防止小白拷路径头尾带了空格
save_root_vocal = save_root_vocal.strip(" ")
save_root_ins = save_root_ins.strip(" ")
pre_fun = _audio_pre_(model_path=os.path.join(weight_uvr5_root,model_name+".pth"), device=device, is_half=is_half)
for name in os.listdir(inp_root):
inp_path=os.path.join(inp_root,name)
try:
pre_fun._path_audio_(inp_path , save_root_ins,save_root_vocal)
infos.append("%s->Success"%(os.path.basename(inp_path)))
except:
infos.append("%s->%s" % (os.path.basename(inp_path),traceback.format_exc()))
except:
infos.append(traceback.format_exc())
finally:
try:
del pre_fun.model
del pre_fun
except:
traceback.print_exc()
print("clean_empty_cache")
torch.cuda.empty_cache()
return "\n".join(infos)
with gr.Blocks() as app:
with gr.Tabs():
with gr.TabItem("推理"):
with gr.Group():
gr.Markdown(value="""
使用软件者、传播软件导出的声音者自负全责。如不认可该条款,则不能使用/引用软件包内所有代码和文件。<br>
目前仅开放白菜音色,后续将扩展为本地训练推理工具,用户可训练自己的音色进行社区共享。<br>
男转女推荐+12key,女转男推荐-12key,如果音域爆炸导致音色失真也可以自己调整到合适音域
""")
with gr.Row():
with gr.Column():
sid0 = gr.Dropdown(label="音色", choices=names)
vc_transform0 = gr.Number(label="变调(整数,半音数量,升八度12降八度-12)", value=12)
f0_file = gr.File(label="F0曲线文件,可选,一行一个音高,代替默认F0及升降调")
input_audio0 = gr.Audio(label="上传音频")
but0=gr.Button("转换", variant="primary")
with gr.Column():
vc_output1 = gr.Textbox(label="输出信息")
vc_output2 = gr.Audio(label="输出音频")
but0.click(vc_single, [sid0, input_audio0, vc_transform0,f0_file], [vc_output1, vc_output2])
with gr.Group():
gr.Markdown(value="""
批量转换,上传多个音频文件,在指定文件夹(默认opt)下输出转换的音频。<br>
合格的文件夹路径格式举例:E:\codes\py39\\vits_vc_gpu\白鹭霜华测试样例(去文件管理器地址栏拷就行了)
""")
with gr.Row():
with gr.Column():
sid1 = gr.Dropdown(label="音色", choices=names)
vc_transform1 = gr.Number(label="变调(整数,半音数量,升八度12降八度-12)", value=12)
opt_input = gr.Textbox(label="指定输出文件夹",value="opt")
with gr.Column():
dir_input = gr.Textbox(label="输入待处理音频文件夹路径")
inputs = gr.File(file_count="multiple", label="也可批量输入音频文件,二选一,优先读文件夹")
but1=gr.Button("转换", variant="primary")
vc_output3 = gr.Textbox(label="输出信息")
but1.click(vc_multi, [sid1, dir_input,opt_input,inputs, vc_transform1], [vc_output3])
with gr.TabItem("数据处理"):
with gr.Group():
gr.Markdown(value="""
人声伴奏分离批量处理,使用UVR5模型。<br>
不带和声用HP2,带和声且提取的人声不需要和声用HP5<br>
合格的文件夹路径格式举例:E:\codes\py39\\vits_vc_gpu\白鹭霜华测试样例(去文件管理器地址栏拷就行了)
""")
with gr.Row():
with gr.Column():
dir_wav_input = gr.Textbox(label="输入待处理音频文件夹路径")
wav_inputs = gr.File(file_count="multiple", label="也可批量输入音频文件,二选一,优先读文件夹")
with gr.Column():
model_choose = gr.Dropdown(label="模型", choices=uvr5_names)
opt_vocal_root = gr.Textbox(label="指定输出人声文件夹",value="opt")
opt_ins_root = gr.Textbox(label="指定输出乐器文件夹",value="opt")
but2=gr.Button("转换", variant="primary")
vc_output4 = gr.Textbox(label="输出信息")
but2.click(uvr, [model_choose, dir_wav_input,opt_vocal_root,opt_ins_root], [vc_output4])
with gr.TabItem("训练-待开放"):pass
# app.launch(server_name="0.0.0.0",server_port=7860)
app.launch(server_name="127.0.0.1",server_port=7860) |