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WIP: refactor

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wataru 2023-04-08 05:17:57 +09:00
parent 4e0d2a24a3
commit e780af7fc2
1 changed files with 7 additions and 43 deletions
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server/voice_changer/RVC/custom_vc_infer_pipeline.py
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@ -17,7 +17,6 @@ class VC(object):
self.sr = 16000 # hubert输入采样率 self.sr = 16000 # hubert输入采样率
self.window = 160 # 每帧点数 self.window = 160 # 每帧点数
self.t_pad = self.sr * x_pad # 每条前后pad时间 self.t_pad = self.sr * x_pad # 每条前后pad时间
print("INITIALIZE", self.sr, x_pad, self.t_pad)
self.t_pad_tgt = tgt_sr * x_pad self.t_pad_tgt = tgt_sr * x_pad
self.t_pad2 = self.t_pad * 2 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_query = self.sr * x_query # 查询切点前后查询时间
@ -28,10 +27,8 @@ class VC(object):
def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None): def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
time_step = self.window / self.sr * 1000 time_step = self.window / self.sr * 1000
# f0_min = 50 f0_min = 50
# f0_max = 1100 f0_max = 1100
f0_min = 70
f0_max = 1000
f0_mel_min = 1127 * np.log(1 + f0_min / 700) f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700) f0_mel_max = 1127 * np.log(1 + f0_max / 700)
if (f0_method == "pm"): if (f0_method == "pm"):
@ -69,7 +66,6 @@ class VC(object):
return f0_coarse, f0bak # 1-0 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 def vc(self, model, net_g, sid, audio0, pitch, pitchf, times, index, big_npy, index_rate): # ,file_index,file_big_npy
print("vc audio len 1,", len(audio0))
feats = torch.from_numpy(audio0) feats = torch.from_numpy(audio0)
if (self.is_half == True): if (self.is_half == True):
feats = feats.half() feats = feats.half()
@ -142,13 +138,6 @@ class VC(object):
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode='reflect') audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode='reflect')
print("audio_pad len 1,", len(audio_pad)) print("audio_pad len 1,", len(audio_pad))
opt_ts = [] 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])
print("audio_pad len 2,", len(audio_pad), opt_ts) print("audio_pad len 2,", len(audio_pad), opt_ts)
@ -159,19 +148,7 @@ class VC(object):
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode='reflect') audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode='reflect')
p_len = audio_pad.shape[0] // self.window p_len = audio_pad.shape[0] // self.window
inp_f0 = None inp_f0 = None
print("audio_pad len 3,", len(audio_pad), self.t_pad, len(audio))
# if (hasattr(f0_file, 'name') == True):
# print("load pitch !!!!!!!!!!!!", f0_file.name)
# 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() 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):
@ -183,26 +160,13 @@ class VC(object):
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)
print("opt start") if self.t_pad_tgt == 0:
# for t in opt_ts:
# print("opt exec")
# 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
print("opt end")
if (if_f0 == 1):
print("TTTTT", t, self.t_pad_tgt)
# 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])
audio_opt.append(self.vc(model, net_g, sid, audio_pad[t:], pitch[:, t // self.window:]if t is not None else pitch, pitchf[:, 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)) t // self.window:]if t is not None else pitchf, times, index, big_npy, index_rate))
else: 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.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])
audio_opt = np.concatenate(audio_opt) audio_opt = np.concatenate(audio_opt)
del pitch, pitchf, sid del pitch, pitchf, sid
torch.cuda.empty_cache() torch.cuda.empty_cache()