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import numpy as np, parselmouth, torch, pdb | |
from time import time as ttime | |
import torch.nn.functional as F | |
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, config): | |
self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = ( | |
config.x_pad, | |
config.x_query, | |
config.x_center, | |
config.x_max, | |
config.is_half, | |
) | |
self.sr = 16000 # hubert输入采样率 | |
self.window = 160 # 每帧点数 | |
self.t_pad = self.sr * self.x_pad # 每条前后pad时间 | |
self.t_pad_tgt = tgt_sr * self.x_pad | |
self.t_pad2 = self.t_pad * 2 | |
self.t_query = self.sr * self.x_query # 查询切点前后查询时间 | |
self.t_center = self.sr * self.x_center # 查询切点位置 | |
self.t_max = self.sr * self.x_max # 免查询时长阈值 | |
self.device = config.device | |
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[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0] | |
f0[self.x_pad * tf0 : self.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 | |