Update src/infer_pack/predictor/RMVPE.py

src/infer_pack/predictor/RMVPE.py

@@ -334,28 +334,23 @@ class RMVPE:
  ckpt = torch.load(model_path, map_location="cpu")
  model.load_state_dict(ckpt)
  model.eval()
- if is_half == True:
+ if is_half:
  model = model.half()
  self.model = model
- self.resample_kernel = {}
  self.is_half = is_half
- if device is None:
- device = "cuda" if torch.cuda.is_available() else "cpu"
- self.device = device
- self.mel_extractor = MelSpectrogram(
- is_half, 128, 16000, 1024, 160, None, 30, 8000
- ).to(device)
- self.model = self.model.to(device)
+ self.device = device if device else "cuda" if torch.cuda.is_available() else "cpu"
+ self.mel_extractor = MelSpectrogram(is_half, 128, 16000, 1024, 160, None, 30, 8000).to(self.device)
+ self.model = self.model.to(self.device)
  cents_mapping = 20 * np.arange(360) + 1997.3794084376191
- self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
+ self.cents_mapping = np.pad(cents_mapping, (4, 4))
 
  def mel2hidden(self, mel):
  with torch.no_grad():
  n_frames = mel.shape[-1]
- mel = mel.float() # Приведение к float32 перед паддингом
- mel = F.pad(
- mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
- )
+ mel = mel.float()
+ mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect")
+ if self.is_half:
+ mel = mel.half()
  hidden = self.model(mel)
  return hidden[:, :n_frames]
 
@@ -370,7 +365,7 @@ class RMVPE:
  mel = self.mel_extractor(audio, center=True)
  hidden = self.mel2hidden(mel)
  hidden = hidden.squeeze(0).cpu().numpy()
- if self.is_half == True:
+ if self.is_half:
  hidden = hidden.astype("float32")
  f0 = self.decode(hidden, thred=thred)
  return f0
@@ -384,23 +379,23 @@ class RMVPE:
  starts = center - 4
  ends = center + 5
  for idx in range(salience.shape[0]):
- todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
- todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
+ todo_salience.append(salience[:, starts[idx]:ends[idx]][idx])
+ todo_cents_mapping.append(self.cents_mapping[starts[idx]:ends[idx]])
  todo_salience = np.array(todo_salience)
  todo_cents_mapping = np.array(todo_cents_mapping)
  product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
  weight_sum = np.sum(todo_salience, 1)
- devided = product_sum / weight_sum
+ divided = product_sum / weight_sum
  maxx = np.max(salience, axis=1)
- devided[maxx <= thred] = 0
- return devided
+ divided[maxx <= thred] = 0
+ return divided
 
  def infer_from_audio_with_pitch(self, audio, thred=0.03, f0_min=50, f0_max=1100):
  audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
  mel = self.mel_extractor(audio, center=True)
  hidden = self.mel2hidden(mel)
  hidden = hidden.squeeze(0).cpu().numpy()
- if self.is_half == True:
+ if self.is_half:
  hidden = hidden.astype("float32")
  f0 = self.decode(hidden, thred=thred)
  f0[(f0 < f0_min) | (f0 > f0_max)] = 0