Update vc_infer_pipeline.py
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RVC-Boss
GitHub
parent
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commit
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@ -1,315 +1,314 @@
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import numpy as np, parselmouth, torch, pdb
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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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from time import time as ttime
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import torch.nn.functional as F
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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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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 scipy.signal as signal
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import pyworld, os, traceback, faiss
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import pyworld, os, traceback, faiss
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from scipy import signal
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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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bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
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class VC(object):
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class VC(object):
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def __init__(self, tgt_sr, device, is_half):
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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.sr = 16000 # hubert输入采样率
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self.window = 160 # 每帧点数
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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 = 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_pad_tgt = tgt_sr * x_pad
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self.t_pad2 = self.t_pad * 2
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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_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_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.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.device = device
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self.is_half = is_half
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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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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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time_step = self.window / self.sr * 1000
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f0_min = 50
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f0_min = 50
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f0_max = 1100
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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_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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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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if f0_method == "pm":
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if f0_method == "pm":
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f0 = (
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f0 = (
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parselmouth.Sound(x, self.sr)
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parselmouth.Sound(x, self.sr)
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.to_pitch_ac(
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.to_pitch_ac(
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time_step=time_step / 1000,
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time_step=time_step / 1000,
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voicing_threshold=0.6,
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voicing_threshold=0.6,
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pitch_floor=f0_min,
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pitch_floor=f0_min,
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pitch_ceiling=f0_max,
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pitch_ceiling=f0_max,
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)
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)
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.selected_array["frequency"]
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.selected_array["frequency"]
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)
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)
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pad_size = (p_len - len(f0) + 1) // 2
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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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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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f0 = np.pad(
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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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f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
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)
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)
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elif f0_method == "harvest":
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elif f0_method == "harvest":
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f0, t = pyworld.harvest(
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f0, t = pyworld.harvest(
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x.astype(np.double),
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x.astype(np.double),
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fs=self.sr,
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fs=self.sr,
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f0_ceil=f0_max,
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f0_ceil=f0_max,
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f0_floor=f0_min,
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f0_floor=f0_min,
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frame_period=10,
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frame_period=10,
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)
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
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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 = signal.medfilt(f0, 3)
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f0 *= pow(2, f0_up_key / 12)
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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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# 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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tf0 = self.sr // self.window # 每秒f0点数
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if inp_f0 is not None:
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if inp_f0 is not None:
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delta_t = np.round(
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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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(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
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).astype("int16")
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).astype("int16")
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replace_f0 = np.interp(
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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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list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
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)
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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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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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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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# 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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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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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[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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f0_mel_max - f0_mel_min
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) + 1
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) + 1
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f0_mel[f0_mel <= 1] = 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_mel[f0_mel > 255] = 255
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f0_coarse = np.rint(f0_mel).astype(np.int)
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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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return f0_coarse, f0bak # 1-0
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def vc(
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def vc(
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self,
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self,
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model,
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model,
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net_g,
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net_g,
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sid,
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sid,
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audio0,
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audio0,
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pitch,
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pitch,
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pitchf,
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pitchf,
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times,
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times,
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index,
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index,
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big_npy,
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big_npy,
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index_rate,
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index_rate,
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): # ,file_index,file_big_npy
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): # ,file_index,file_big_npy
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feats = torch.from_numpy(audio0)
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feats = torch.from_numpy(audio0)
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if self.is_half:
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if self.is_half:
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feats = feats.half()
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feats = feats.half()
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else:
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else:
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feats = feats.float()
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feats = feats.float()
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if feats.dim() == 2: # double channels
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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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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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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inputs = {
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inputs = {
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"source": feats.to(self.device),
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"source": feats.to(self.device),
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"padding_mask": padding_mask,
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"padding_mask": padding_mask,
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"output_layer": 9, # layer 9
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"output_layer": 9, # layer 9
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}
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}
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t0 = ttime()
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t0 = ttime()
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with torch.no_grad():
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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logits = model.extract_features(**inputs)
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feats = model.final_proj(logits[0])
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feats = model.final_proj(logits[0])
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if (
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if (
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isinstance(index, type(None)) == False
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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 isinstance(big_npy, type(None)) == False
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and index_rate != 0
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and index_rate != 0
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):
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):
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npy = feats[0].cpu().numpy()
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npy = feats[0].cpu().numpy()
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if self.is_half:
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if self.is_half:
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npy = npy.astype("float32")
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npy = npy.astype("float32")
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# _, I = index.search(npy, 1)
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# _, I = index.search(npy, 1)
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# npy = big_npy[I.squeeze()]
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# npy = big_npy[I.squeeze()]
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#by github @nadare881
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#by github @nadare881
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score, ix = index.search(npy, k=8)
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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 = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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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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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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if self.is_half:
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npy = npy.astype("float16")
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npy = npy.astype("float16")
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feats = (
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feats = (
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torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
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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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+ (1 - index_rate) * feats
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)
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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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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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t1 = ttime()
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p_len = audio0.shape[0] // self.window
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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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if feats.shape[1] < p_len:
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p_len = feats.shape[1]
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p_len = feats.shape[1]
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if pitch != None and pitchf != None:
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if pitch != None and pitchf != None:
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pitch = pitch[:, :p_len]
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pitch = pitch[:, :p_len]
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pitchf = pitchf[:, :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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p_len = torch.tensor([p_len], device=self.device).long()
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with torch.no_grad():
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with torch.no_grad():
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if pitch != None and pitchf != None:
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if pitch != None and pitchf != None:
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audio1 = (
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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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(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
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.data.cpu()
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.data.cpu()
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.float()
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.float()
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.numpy()
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.numpy()
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.astype(np.int16)
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.astype(np.int16)
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)
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)
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else:
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else:
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audio1 = (
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audio1 = (
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(net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
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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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.data.cpu()
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.float()
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.float()
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.numpy()
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.numpy()
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.astype(np.int16)
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.astype(np.int16)
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)
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)
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del feats, p_len, padding_mask
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del feats, p_len, padding_mask
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if torch.cuda.is_available():
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.empty_cache()
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t2 = ttime()
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t2 = ttime()
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times[0] += t1 - t0
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times[0] += t1 - t0
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times[2] += t2 - t1
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times[2] += t2 - t1
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return audio1
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return audio1
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def pipeline(
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def pipeline(
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self,
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self,
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model,
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model,
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net_g,
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net_g,
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sid,
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sid,
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audio,
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audio,
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times,
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times,
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f0_up_key,
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f0_up_key,
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f0_method,
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f0_method,
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file_index,
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file_index,
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# file_big_npy,
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# file_big_npy,
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index_rate,
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index_rate,
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if_f0,
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if_f0,
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f0_file=None,
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f0_file=None,
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):
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):
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print(file_index!= "",os.path.exists(file_index) == True,index_rate != 0)
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if (
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if (
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file_index != ""
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file_index != ""
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# and file_big_npy != ""
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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_big_npy) == True
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and os.path.exists(file_index) == True
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and os.path.exists(file_index) == True
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and index_rate != 0
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and index_rate != 0
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):
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):
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try:
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try:
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index = faiss.read_index(file_index)
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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 = np.load(file_big_npy)
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big_npy = index.reconstruct_n(0, index.ntotal)
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big_npy = index.reconstruct_n(0, index.ntotal)
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except:
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except:
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traceback.print_exc()
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traceback.print_exc()
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index = big_npy = None
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index = big_npy = None
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else:
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else:
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index = big_npy = None
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index = big_npy = None
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audio = signal.filtfilt(bh, ah, audio)
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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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audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
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opt_ts = []
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opt_ts = []
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if audio_pad.shape[0] > self.t_max:
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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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audio_sum = np.zeros_like(audio)
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for i in range(self.window):
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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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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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for t in range(self.t_center, audio.shape[0], self.t_center):
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opt_ts.append(
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opt_ts.append(
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t
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t
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- self.t_query
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- self.t_query
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+ np.where(
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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])
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== np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
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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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)[0][0]
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)
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)
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s = 0
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s = 0
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audio_opt = []
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audio_opt = []
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t = None
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t = None
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t1 = ttime()
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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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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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p_len = audio_pad.shape[0] // self.window
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inp_f0 = None
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inp_f0 = None
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if hasattr(f0_file, "name") == True:
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if hasattr(f0_file, "name") == True:
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try:
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try:
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with open(f0_file.name, "r") as f:
|
||||||
with open(f0_file.name, "r") as f:
|
lines = f.read().strip("\n").split("\n")
|
||||||
lines = f.read().strip("\n").split("\n")
|
inp_f0 = []
|
||||||
inp_f0 = []
|
for line in lines:
|
||||||
for line in lines:
|
inp_f0.append([float(i) for i in line.split(",")])
|
||||||
inp_f0.append([float(i) for i in line.split(",")])
|
inp_f0 = np.array(inp_f0, dtype="float32")
|
||||||
inp_f0 = np.array(inp_f0, dtype="float32")
|
except:
|
||||||
except:
|
traceback.print_exc()
|
||||||
traceback.print_exc()
|
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
||||||
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
pitch, pitchf = None, None
|
||||||
pitch, pitchf = None, None
|
if if_f0 == 1:
|
||||||
if if_f0 == 1:
|
pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
|
||||||
pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
|
pitch = pitch[:p_len]
|
||||||
pitch = pitch[:p_len]
|
pitchf = pitchf[:p_len]
|
||||||
pitchf = pitchf[:p_len]
|
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
|
||||||
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
|
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
|
||||||
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
|
t2 = ttime()
|
||||||
t2 = ttime()
|
times[1] += t2 - t1
|
||||||
times[1] += t2 - t1
|
for t in opt_ts:
|
||||||
for t in opt_ts:
|
t = t // self.window * self.window
|
||||||
t = t // self.window * self.window
|
if if_f0 == 1:
|
||||||
if if_f0 == 1:
|
audio_opt.append(
|
||||||
audio_opt.append(
|
self.vc(
|
||||||
self.vc(
|
model,
|
||||||
model,
|
net_g,
|
||||||
net_g,
|
sid,
|
||||||
sid,
|
audio_pad[s : t + self.t_pad2 + self.window],
|
||||||
audio_pad[s : t + self.t_pad2 + self.window],
|
pitch[:, s // self.window : (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],
|
||||||
pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
|
times,
|
||||||
times,
|
index,
|
||||||
index,
|
big_npy,
|
||||||
big_npy,
|
index_rate,
|
||||||
index_rate,
|
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
)
|
||||||
)
|
else:
|
||||||
else:
|
audio_opt.append(
|
||||||
audio_opt.append(
|
self.vc(
|
||||||
self.vc(
|
model,
|
||||||
model,
|
net_g,
|
||||||
net_g,
|
sid,
|
||||||
sid,
|
audio_pad[s : t + self.t_pad2 + self.window],
|
||||||
audio_pad[s : t + self.t_pad2 + self.window],
|
None,
|
||||||
None,
|
None,
|
||||||
None,
|
times,
|
||||||
times,
|
index,
|
||||||
index,
|
big_npy,
|
||||||
big_npy,
|
index_rate,
|
||||||
index_rate,
|
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
)
|
||||||
)
|
s = t
|
||||||
s = t
|
if if_f0 == 1:
|
||||||
if if_f0 == 1:
|
audio_opt.append(
|
||||||
audio_opt.append(
|
self.vc(
|
||||||
self.vc(
|
model,
|
||||||
model,
|
net_g,
|
||||||
net_g,
|
sid,
|
||||||
sid,
|
audio_pad[t:],
|
||||||
audio_pad[t:],
|
pitch[:, t // self.window :] if t is not None else pitch,
|
||||||
pitch[:, t // self.window :] if t is not None else pitch,
|
pitchf[:, t // self.window :] if t is not None else pitchf,
|
||||||
pitchf[:, t // self.window :] if t is not None else pitchf,
|
times,
|
||||||
times,
|
index,
|
||||||
index,
|
big_npy,
|
||||||
big_npy,
|
index_rate,
|
||||||
index_rate,
|
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
)
|
||||||
)
|
else:
|
||||||
else:
|
audio_opt.append(
|
||||||
audio_opt.append(
|
self.vc(
|
||||||
self.vc(
|
model,
|
||||||
model,
|
net_g,
|
||||||
net_g,
|
sid,
|
||||||
sid,
|
audio_pad[t:],
|
||||||
audio_pad[t:],
|
None,
|
||||||
None,
|
None,
|
||||||
None,
|
times,
|
||||||
times,
|
index,
|
||||||
index,
|
big_npy,
|
||||||
big_npy,
|
index_rate,
|
||||||
index_rate,
|
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
)
|
||||||
)
|
audio_opt = np.concatenate(audio_opt)
|
||||||
audio_opt = np.concatenate(audio_opt)
|
del pitch, pitchf, sid
|
||||||
del pitch, pitchf, sid
|
if torch.cuda.is_available():
|
||||||
if torch.cuda.is_available():
|
torch.cuda.empty_cache()
|
||||||
torch.cuda.empty_cache()
|
return audio_opt
|
||||||
return audio_opt
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue