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go-realtime-gui.bat
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2
go-realtime-gui.bat
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@ -0,0 +1,2 @@
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runtime\python.exe gui.py
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pause
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5
gui.py
5
gui.py
@ -1,3 +1,6 @@
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import os,sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import PySimpleGUI as sg
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import sounddevice as sd
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import noisereduce as nr
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@ -12,7 +15,7 @@ from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFs
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from i18n import I18nAuto
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i18n = I18nAuto()
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print(i18n.language_map)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@ -139,6 +139,8 @@ def vc_single(
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if hubert_model == None:
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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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
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file_big_npy = file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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audio_opt = vc.pipeline(
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hubert_model,
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net_g,
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@ -936,7 +938,7 @@ with gr.Blocks() as app:
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minimum=0,
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maximum=1,
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label="检索特征占比",
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value=1,
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value=0.6,
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interactive=True,
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)
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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"))
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@ -21,7 +21,7 @@ import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.cuda.amp import autocast, GradScaler
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from infer_pack import commons
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from time import sleep
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from time import time as ttime
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from data_utils import (
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TextAudioLoaderMultiNSFsid,
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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"] = "5555"
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os.environ["MASTER_PORT"] = "51515"
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mp.spawn(
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run,
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@ -676,6 +676,7 @@ def train_and_evaluate(
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"saving final ckpt:%s"
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% (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch))
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)
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sleep(1)
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os._exit(2333333)
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@ -1,4 +1,5 @@
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import sys, os, multiprocessing
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from scipy import signal
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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@ -38,6 +39,7 @@ class PreProcess:
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max_sil_kept=150,
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)
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self.sr = sr
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self.bh, self.ah = signal.butter(N=5, Wn=48, btype='high', fs=self.sr)
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self.per = 3.7
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self.overlap = 0.3
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self.tail = self.per + self.overlap
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@ -69,6 +71,7 @@ class PreProcess:
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def pipeline(self, path, idx0):
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try:
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audio = load_audio(path, self.sr)
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audio = signal.filtfilt(self.bh, self.ah, audio)
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idx1 = 0
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for audio in self.slicer.slice(audio):
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i = 0
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@ -4,7 +4,8 @@ import torch.nn.functional as F
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from config import x_pad, x_query, x_center, x_max
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import scipy.signal as signal
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import pyworld, os, traceback, faiss
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from scipy import signal
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bh, ah = signal.butter(N=5, Wn=48, btype='high', fs=16000)
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class VC(object):
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def __init__(self, tgt_sr, device, is_half):
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@ -189,6 +190,7 @@ class VC(object):
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index = big_npy = None
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else:
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index = big_npy = None
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audio = signal.filtfilt(bh, ah, audio)
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audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
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opt_ts = []
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if audio_pad.shape[0] > self.t_max:
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