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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
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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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39
infer-web.py
39
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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pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
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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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@ -1,314 +1,313 @@
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import numpy as np, parselmouth, torch, pdb
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from time import time as ttime
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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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self.sr = 16000 # hubert输入采样率
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self.window = 160 # 每帧点数
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self.t_pad = self.sr * x_pad # 每条前后pad时间
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self.t_pad_tgt = tgt_sr * x_pad
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self.t_pad2 = self.t_pad * 2
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self.t_query = self.sr * x_query # 查询切点前后查询时间
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self.t_center = self.sr * x_center # 查询切点位置
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self.t_max = self.sr * x_max # 免查询时长阈值
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self.device = device
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self.is_half = is_half
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def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
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time_step = self.window / self.sr * 1000
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f0_min = 50
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f0_max = 1100
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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if f0_method == "pm":
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f0 = (
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parselmouth.Sound(x, self.sr)
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.to_pitch_ac(
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time_step=time_step / 1000,
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voicing_threshold=0.6,
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pitch_floor=f0_min,
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pitch_ceiling=f0_max,
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)
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.selected_array["frequency"]
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)
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pad_size = (p_len - len(f0) + 1) // 2
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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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f0 = np.pad(
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f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
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)
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elif f0_method == "harvest":
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f0, t = pyworld.harvest(
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x.astype(np.double),
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fs=self.sr,
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f0_ceil=f0_max,
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f0_floor=f0_min,
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frame_period=10,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
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f0 = signal.medfilt(f0, 3)
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f0 *= pow(2, f0_up_key / 12)
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# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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tf0 = self.sr // self.window # 每秒f0点数
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if inp_f0 is not None:
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delta_t = np.round(
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(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
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).astype("int16")
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replace_f0 = np.interp(
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list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
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)
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shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
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f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
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# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
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f0_mel_max - f0_mel_min
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) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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f0_coarse = np.rint(f0_mel).astype(np.int)
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return f0_coarse, f0bak # 1-0
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def vc(
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self,
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model,
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net_g,
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sid,
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audio0,
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pitch,
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pitchf,
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times,
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index,
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big_npy,
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index_rate,
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): # ,file_index,file_big_npy
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feats = torch.from_numpy(audio0)
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if self.is_half:
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feats = feats.half()
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else:
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feats = feats.float()
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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inputs = {
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"source": feats.to(self.device),
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"padding_mask": padding_mask,
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"output_layer": 9, # layer 9
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}
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t0 = ttime()
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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feats = model.final_proj(logits[0])
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if (
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isinstance(index, type(None)) == False
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and isinstance(big_npy, type(None)) == False
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and index_rate != 0
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):
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npy = feats[0].cpu().numpy()
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if self.is_half:
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npy = npy.astype("float32")
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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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npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
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if self.is_half:
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npy = npy.astype("float16")
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feats = (
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torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
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+ (1 - index_rate) * feats
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)
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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t1 = ttime()
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p_len = audio0.shape[0] // self.window
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if feats.shape[1] < p_len:
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p_len = feats.shape[1]
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if pitch != None and pitchf != None:
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pitch = pitch[:, :p_len]
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pitchf = pitchf[:, :p_len]
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p_len = torch.tensor([p_len], device=self.device).long()
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with torch.no_grad():
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if pitch != None and pitchf != None:
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audio1 = (
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(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
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.data.cpu()
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.float()
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.numpy()
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.astype(np.int16)
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)
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else:
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audio1 = (
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(net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
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.data.cpu()
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.float()
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.numpy()
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.astype(np.int16)
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)
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del feats, p_len, padding_mask
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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t2 = ttime()
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times[0] += t1 - t0
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times[2] += t2 - t1
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return audio1
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def pipeline(
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self,
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model,
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net_g,
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sid,
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audio,
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times,
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f0_up_key,
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f0_method,
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file_index,
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# file_big_npy,
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index_rate,
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if_f0,
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f0_file=None,
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):
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if (
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file_index != ""
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# and file_big_npy != ""
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# and os.path.exists(file_big_npy) == True
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and os.path.exists(file_index) == True
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and index_rate != 0
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):
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try:
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index = faiss.read_index(file_index)
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# big_npy = np.load(file_big_npy)
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big_npy = index.reconstruct_n(0, index.ntotal)
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except:
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traceback.print_exc()
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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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audio_sum = np.zeros_like(audio)
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for i in range(self.window):
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audio_sum += audio_pad[i : i - self.window]
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for t in range(self.t_center, audio.shape[0], self.t_center):
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opt_ts.append(
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t
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- self.t_query
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+ np.where(
|
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np.abs(audio_sum[t - self.t_query : t + self.t_query])
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== np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
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)[0][0]
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)
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s = 0
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audio_opt = []
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t = None
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t1 = ttime()
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audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
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p_len = audio_pad.shape[0] // self.window
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inp_f0 = None
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if hasattr(f0_file, "name") == True:
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try:
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with open(f0_file.name, "r") as f:
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lines = f.read().strip("\n").split("\n")
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inp_f0 = []
|
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for line in lines:
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inp_f0.append([float(i) for i in line.split(",")])
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inp_f0 = np.array(inp_f0, dtype="float32")
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||||
except:
|
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traceback.print_exc()
|
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sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
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pitch, pitchf = None, None
|
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if if_f0 == 1:
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pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
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pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
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t2 = ttime()
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times[1] += t2 - t1
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for t in opt_ts:
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t = t // self.window * self.window
|
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if if_f0 == 1:
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audio_opt.append(
|
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self.vc(
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model,
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net_g,
|
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sid,
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audio_pad[s : t + self.t_pad2 + self.window],
|
||||
pitch[:, s // self.window : (t + self.t_pad2) // self.window],
|
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pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
else:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[s : t + self.t_pad2 + self.window],
|
||||
None,
|
||||
None,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
s = t
|
||||
if if_f0 == 1:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[t:],
|
||||
pitch[:, t // self.window :] if t is not None else pitch,
|
||||
pitchf[:, t // self.window :] if t is not None else pitchf,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
else:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[t:],
|
||||
None,
|
||||
None,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
del pitch, pitchf, sid
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
return audio_opt
|
||||
import numpy as np, parselmouth, torch, pdb
|
||||
from time import time as ttime
|
||||
import torch.nn.functional as F
|
||||
from config import x_pad, x_query, x_center, x_max
|
||||
import scipy.signal as signal
|
||||
import pyworld, os, traceback, faiss
|
||||
from scipy import signal
|
||||
|
||||
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
|
||||
|
||||
|
||||
class VC(object):
|
||||
def __init__(self, tgt_sr, device, is_half):
|
||||
self.sr = 16000 # hubert输入采样率
|
||||
self.window = 160 # 每帧点数
|
||||
self.t_pad = self.sr * x_pad # 每条前后pad时间
|
||||
self.t_pad_tgt = tgt_sr * x_pad
|
||||
self.t_pad2 = self.t_pad * 2
|
||||
self.t_query = self.sr * x_query # 查询切点前后查询时间
|
||||
self.t_center = self.sr * x_center # 查询切点位置
|
||||
self.t_max = self.sr * x_max # 免查询时长阈值
|
||||
self.device = device
|
||||
self.is_half = is_half
|
||||
|
||||
def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
|
||||
time_step = self.window / self.sr * 1000
|
||||
f0_min = 50
|
||||
f0_max = 1100
|
||||
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
||||
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
||||
if f0_method == "pm":
|
||||
f0 = (
|
||||
parselmouth.Sound(x, self.sr)
|
||||
.to_pitch_ac(
|
||||
time_step=time_step / 1000,
|
||||
voicing_threshold=0.6,
|
||||
pitch_floor=f0_min,
|
||||
pitch_ceiling=f0_max,
|
||||
)
|
||||
.selected_array["frequency"]
|
||||
)
|
||||
pad_size = (p_len - len(f0) + 1) // 2
|
||||
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
|
||||
f0 = np.pad(
|
||||
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
|
||||
)
|
||||
elif f0_method == "harvest":
|
||||
f0, t = pyworld.harvest(
|
||||
x.astype(np.double),
|
||||
fs=self.sr,
|
||||
f0_ceil=f0_max,
|
||||
f0_floor=f0_min,
|
||||
frame_period=10,
|
||||
)
|
||||
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
|
||||
tf0 = self.sr // self.window # 每秒f0点数
|
||||
if inp_f0 is not None:
|
||||
delta_t = np.round(
|
||||
(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
|
||||
).astype("int16")
|
||||
replace_f0 = np.interp(
|
||||
list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
|
||||
)
|
||||
shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
|
||||
f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
|
||||
# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
|
||||
f0bak = f0.copy()
|
||||
f0_mel = 1127 * np.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
|
||||
f0_mel_max - f0_mel_min
|
||||
) + 1
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > 255] = 255
|
||||
f0_coarse = np.rint(f0_mel).astype(np.int)
|
||||
return f0_coarse, f0bak # 1-0
|
||||
|
||||
def vc(
|
||||
self,
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio0,
|
||||
pitch,
|
||||
pitchf,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
): # ,file_index,file_big_npy
|
||||
feats = torch.from_numpy(audio0)
|
||||
if self.is_half:
|
||||
feats = feats.half()
|
||||
else:
|
||||
feats = feats.float()
|
||||
if feats.dim() == 2: # double channels
|
||||
feats = feats.mean(-1)
|
||||
assert feats.dim() == 1, feats.dim()
|
||||
feats = feats.view(1, -1)
|
||||
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
|
||||
|
||||
inputs = {
|
||||
"source": feats.to(self.device),
|
||||
"padding_mask": padding_mask,
|
||||
"output_layer": 9, # layer 9
|
||||
}
|
||||
t0 = ttime()
|
||||
with torch.no_grad():
|
||||
logits = model.extract_features(**inputs)
|
||||
feats = model.final_proj(logits[0])
|
||||
|
||||
if (
|
||||
isinstance(index, type(None)) == False
|
||||
and isinstance(big_npy, type(None)) == False
|
||||
and index_rate != 0
|
||||
):
|
||||
npy = feats[0].cpu().numpy()
|
||||
if self.is_half:
|
||||
npy = npy.astype("float32")
|
||||
|
||||
# _, I = index.search(npy, 1)
|
||||
# npy = big_npy[I.squeeze()]
|
||||
|
||||
score, ix = index.search(npy, k=8)
|
||||
weight = np.square(1 / score)
|
||||
weight /= weight.sum(axis=1, keepdims=True)
|
||||
npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
|
||||
|
||||
if self.is_half:
|
||||
npy = npy.astype("float16")
|
||||
feats = (
|
||||
torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
|
||||
+ (1 - index_rate) * feats
|
||||
)
|
||||
|
||||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||||
t1 = ttime()
|
||||
p_len = audio0.shape[0] // self.window
|
||||
if feats.shape[1] < p_len:
|
||||
p_len = feats.shape[1]
|
||||
if pitch != None and pitchf != None:
|
||||
pitch = pitch[:, :p_len]
|
||||
pitchf = pitchf[:, :p_len]
|
||||
p_len = torch.tensor([p_len], device=self.device).long()
|
||||
with torch.no_grad():
|
||||
if pitch != None and pitchf != None:
|
||||
audio1 = (
|
||||
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
else:
|
||||
audio1 = (
|
||||
(net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
del feats, p_len, padding_mask
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
t2 = ttime()
|
||||
times[0] += t1 - t0
|
||||
times[2] += t2 - t1
|
||||
return audio1
|
||||
|
||||
def pipeline(
|
||||
self,
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio,
|
||||
times,
|
||||
f0_up_key,
|
||||
f0_method,
|
||||
file_index,
|
||||
# file_big_npy,
|
||||
index_rate,
|
||||
if_f0,
|
||||
f0_file=None,
|
||||
):
|
||||
if (
|
||||
file_index != ""
|
||||
# and file_big_npy != ""
|
||||
# and os.path.exists(file_big_npy) == True
|
||||
and os.path.exists(file_index) == True
|
||||
and index_rate != 0
|
||||
):
|
||||
try:
|
||||
index = faiss.read_index(file_index)
|
||||
# big_npy = np.load(file_big_npy)
|
||||
big_npy = index.reconstruct_n(0, index.ntotal)
|
||||
except:
|
||||
traceback.print_exc()
|
||||
index = big_npy = None
|
||||
else:
|
||||
index = big_npy = None
|
||||
audio = signal.filtfilt(bh, ah, audio)
|
||||
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
|
||||
opt_ts = []
|
||||
if audio_pad.shape[0] > self.t_max:
|
||||
audio_sum = np.zeros_like(audio)
|
||||
for i in range(self.window):
|
||||
audio_sum += audio_pad[i : i - self.window]
|
||||
for t in range(self.t_center, audio.shape[0], self.t_center):
|
||||
opt_ts.append(
|
||||
t
|
||||
- self.t_query
|
||||
+ np.where(
|
||||
np.abs(audio_sum[t - self.t_query : t + self.t_query])
|
||||
== np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
|
||||
)[0][0]
|
||||
)
|
||||
s = 0
|
||||
audio_opt = []
|
||||
t = None
|
||||
t1 = ttime()
|
||||
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
|
||||
p_len = audio_pad.shape[0] // self.window
|
||||
inp_f0 = None
|
||||
if hasattr(f0_file, "name") == True:
|
||||
try:
|
||||
with open(f0_file.name, "r") as f:
|
||||
lines = f.read().strip("\n").split("\n")
|
||||
inp_f0 = []
|
||||
for line in lines:
|
||||
inp_f0.append([float(i) for i in line.split(",")])
|
||||
inp_f0 = np.array(inp_f0, dtype="float32")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
||||
pitch, pitchf = None, None
|
||||
if if_f0 == 1:
|
||||
pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
|
||||
pitch = pitch[:p_len]
|
||||
pitchf = pitchf[:p_len]
|
||||
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
|
||||
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
|
||||
t2 = ttime()
|
||||
times[1] += t2 - t1
|
||||
for t in opt_ts:
|
||||
t = t // self.window * self.window
|
||||
if if_f0 == 1:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[s : t + self.t_pad2 + self.window],
|
||||
pitch[:, s // self.window : (t + self.t_pad2) // self.window],
|
||||
pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
else:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[s : t + self.t_pad2 + self.window],
|
||||
None,
|
||||
None,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
s = t
|
||||
if if_f0 == 1:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[t:],
|
||||
pitch[:, t // self.window :] if t is not None else pitch,
|
||||
pitchf[:, t // self.window :] if t is not None else pitchf,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
else:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[t:],
|
||||
None,
|
||||
None,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
del pitch, pitchf, sid
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
return audio_opt
|
||||
|
||||
Loading…
Reference in New Issue
Block a user