|
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(" ") |
|
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(" ") |
|
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="127.0.0.1",server_port=7860) |
