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()]

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_big_npy != ""

and file_index != ""

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)

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()