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import os
import traceback,gradio as gr
import logging
from tools.i18n.i18n import I18nAuto
from tools.my_utils import clean_path
i18n = I18nAuto()
logger = logging.getLogger(__name__)
import librosa,ffmpeg
import soundfile as sf
import torch
import sys
from mdxnet import MDXNetDereverb
from vr import AudioPre, AudioPreDeEcho
from bsroformer import BsRoformer_Loader
weight_uvr5_root = "tools/uvr5/uvr5_weights"
uvr5_names = []
for name in os.listdir(weight_uvr5_root):
if name.endswith(".pth") or name.endswith(".ckpt") or "onnx" in name:
uvr5_names.append(name.replace(".pth", "").replace(".ckpt", ""))
device=sys.argv[1]
is_half=eval(sys.argv[2])
webui_port_uvr5=int(sys.argv[3])
is_share=eval(sys.argv[4])
def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
infos = []
try:
inp_root = clean_path(inp_root)
save_root_vocal = clean_path(save_root_vocal)
save_root_ins = clean_path(save_root_ins)
is_hp3 = "HP3" in model_name
if model_name == "onnx_dereverb_By_FoxJoy":
pre_fun = MDXNetDereverb(15)
elif model_name == "Bs_Roformer" or "bs_roformer" in model_name.lower():
func = BsRoformer_Loader
pre_fun = func(
model_path = os.path.join(weight_uvr5_root, model_name + ".ckpt"),
device = device,
is_half=is_half
)
else:
func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho
pre_fun = func(
agg=int(agg),
model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
device=device,
is_half=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)
if(os.path.isfile(inp_path)==False):continue
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,is_hp3
)
done = 1
except:
need_reformat = 1
traceback.print_exc()
if need_reformat == 1:
tmp_path = "%s/%s.reformatted.wav" % (
os.path.join(os.environ["TEMP"]),
os.path.basename(inp_path),
)
os.system(
f'ffmpeg -i "{inp_path}" -vn -acodec pcm_s16le -ac 2 -ar 44100 "{tmp_path}" -y'
)
inp_path = tmp_path
try:
if done == 0:
pre_fun._path_audio_(
inp_path, save_root_ins, save_root_vocal, format0,is_hp3
)
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)
with gr.Blocks(title="UVR5 WebUI") as app:
gr.Markdown(
value=
i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
)
with gr.Tabs():
with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
with gr.Group():
gr.Markdown(
value=i18n("人声伴奏分离批量处理， 使用UVR5模型。") + "<br>" + \
i18n("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)。")+ "<br>" + \
i18n("模型分为三类：") + "<br>" + \
i18n("1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；") + "<br>" + \
i18n("2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；") + "<br>" + \
i18n("3、去混响、去延迟模型（by FoxJoy）：") + "<br> " + \
i18n("(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；") + "<br> " + \
i18n("(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。") + "<br>" + \
i18n("去混响/去延迟，附：") + "<br>" + \
i18n("1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；") + "<br>" + \
i18n("2、MDX-Net-Dereverb模型挺慢的；") + "<br>" + \
i18n("3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。")
)
with gr.Row():
with gr.Column():
dir_wav_input = gr.Textbox(
label=i18n("输入待处理音频文件夹路径"),
placeholder="C:\\Users\\Desktop\\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")
)
with gr.Column():
model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names)
agg = gr.Slider(
minimum=0,
maximum=20,
step=1,
label=i18n("人声提取激进程度"),
value=10,
interactive=True,
visible=False, # 先不开放调整
)
opt_vocal_root = gr.Textbox(
label=i18n("指定输出主人声文件夹"), value="output/uvr5_opt"
)
opt_ins_root = gr.Textbox(
label=i18n("指定输出非主人声文件夹"), value="output/uvr5_opt"
)
format0 = gr.Radio(
label=i18n("导出文件格式"),
choices=["wav", "flac", "mp3", "m4a"],
value="flac",
interactive=True,
)
but2 = gr.Button(i18n("转换"), variant="primary")
vc_output4 = gr.Textbox(label=i18n("输出信息"))
but2.click(
uvr,
[
model_choose,
dir_wav_input,
opt_vocal_root,
wav_inputs,
opt_ins_root,
agg,
format0,
],
[vc_output4],
api_name="uvr_convert",
)
app.queue(concurrency_count=511, max_size=1022).launch(
server_name="0.0.0.0",
inbrowser=True,
share=is_share,
server_port=webui_port_uvr5,
quiet=True,
)
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