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RVC-Boss
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@ -1,2 +1,2 @@
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runtime\python.exe infer-web.py --pycmd runtime\python.exe
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runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897
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pause
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37
infer-web.py
37
infer-web.py
@ -1,5 +1,5 @@
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from multiprocessing import cpu_count
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import threading
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import threading,pdb,librosa
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from time import sleep
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from subprocess import Popen
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from time import sleep
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@ -17,6 +17,7 @@ os.environ["TEMP"] = tmp
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warnings.filterwarnings("ignore")
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torch.manual_seed(114514)
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from i18n import I18nAuto
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import ffmpeg
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i18n = I18nAuto()
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# 判断是否有能用来训练和加速推理的N卡
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@ -235,7 +236,7 @@ def vc_multi(
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yield traceback.format_exc()
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def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins):
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def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins,agg):
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infos = []
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try:
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inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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@ -246,6 +247,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins):
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save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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)
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pre_fun = _audio_pre_(
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agg=int(agg),
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model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
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device=device,
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is_half=is_half,
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@ -254,10 +256,25 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins):
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paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
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else:
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paths = [path.name for path in paths]
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for name in paths:
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inp_path = os.path.join(inp_root, name)
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for path in paths:
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inp_path = os.path.join(inp_root, path)
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need_reformat=1
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done=0
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try:
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info = ffmpeg.probe(inp_path, cmd="ffprobe")
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if(info["streams"][0]["channels"]==2 and info["streams"][0]["sample_rate"]=="44100"):
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need_reformat=0
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pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
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done=1
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except:
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need_reformat = 1
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traceback.print_exc()
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if(need_reformat==1):
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tmp_path="%s/%s.reformatted.wav"%(tmp,os.path.basename(inp_path))
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os.system("ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"%(inp_path,tmp_path))
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inp_path=tmp_path
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try:
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if(done==0):pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
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infos.append("%s->Success" % (os.path.basename(inp_path)))
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yield "\n".join(infos)
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except:
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@ -1147,6 +1164,15 @@ with gr.Blocks() as app:
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)
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with gr.Column():
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model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names)
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agg = gr.Slider(
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minimum=0,
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maximum=20,
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step=1,
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label="人声提取激进程度",
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value=10,
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interactive=True,
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visible=False#先不开放调整
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)
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opt_vocal_root = gr.Textbox(
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label=i18n("指定输出人声文件夹"), value="opt"
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)
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@ -1161,6 +1187,7 @@ with gr.Blocks() as app:
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opt_vocal_root,
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wav_inputs,
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opt_ins_root,
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agg
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],
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[vc_output4],
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)
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@ -1246,7 +1273,7 @@ with gr.Blocks() as app:
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with gr.Row():
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save_epoch10 = gr.Slider(
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minimum=0,
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maximum=200,
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maximum=50,
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step=1,
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label=i18n("保存频率save_every_epoch"),
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value=5,
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@ -13,7 +13,7 @@ from scipy.io import wavfile
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class _audio_pre_:
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def __init__(self, model_path, device, is_half):
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def __init__(self, agg,model_path, device, is_half):
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self.model_path = model_path
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self.device = device
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self.data = {
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@ -22,7 +22,7 @@ class _audio_pre_:
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"tta": False,
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# Constants
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"window_size": 512,
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"agg": 10,
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"agg": agg,
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"high_end_process": "mirroring",
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}
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nn_arch_sizes = [
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@ -139,7 +139,7 @@ class _audio_pre_:
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wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
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print("%s instruments done" % name)
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wavfile.write(
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os.path.join(ins_root, "instrument_{}.wav".format(name)),
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os.path.join(ins_root, "instrument_{}_{}.wav".format(name,self.data["agg"])),
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self.mp.param["sr"],
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(np.array(wav_instrument) * 32768).astype("int16"),
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) #
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@ -155,7 +155,7 @@ class _audio_pre_:
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wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
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print("%s vocals done" % name)
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wavfile.write(
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os.path.join(vocal_root, "vocal_{}.wav".format(name)),
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os.path.join(vocal_root, "vocal_{}_{}.wav".format(name,self.data["agg"])),
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self.mp.param["sr"],
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(np.array(wav_vocals) * 32768).astype("int16"),
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)
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@ -45,7 +45,7 @@ global_step = 0
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def main():
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# n_gpus = torch.cuda.device_count()
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = "51515"
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os.environ["MASTER_PORT"] = "51545"
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mp.spawn(
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run,
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@ -123,7 +123,6 @@ class VC(object):
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# _, I = index.search(npy, 1)
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# npy = big_npy[I.squeeze()]
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#by github @nadare881
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score, ix = index.search(npy, k=8)
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weight = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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