Update src/vc_infer_pipeline.py

src/vc_infer_pipeline.py

@@ -1,13 +1,14 @@
+from functools import lru_cache
 import numpy as np, parselmouth, torch, pdb, sys, os
 from time import time as ttime
 import torch.nn.functional as F
 import torchcrepe
-from torch import Tensor
-import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa, torchcrepe
 from scipy import signal
-from functools import lru_cache
-import gc, re
+from torch import Tensor
+import pyworld, os, faiss, librosa, torchcrepe
+import random
+import gc
+import re
 
 BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 now_dir = os.path.join(BASE_DIR, 'src')
@@ -36,19 +37,20 @@ def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
 
 
 def change_rms(data1, sr1, data2, sr2, rate):
- rms1 = librosa.feature.rms(
- y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
- )
+ rms1 = librosa.feature.rms(y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2)
  rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
+
  rms1 = torch.from_numpy(rms1)
  rms1 = F.interpolate(
  rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
  ).squeeze()
+
  rms2 = torch.from_numpy(rms2)
  rms2 = F.interpolate(
  rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
  ).squeeze()
  rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
+
  data2 *= (
  torch.pow(rms1, torch.tensor(1 - rate))
  * torch.pow(rms2, torch.tensor(rate - 1))
@@ -78,9 +80,7 @@ class VC(object):
 
  def get_optimal_torch_device(self, index: int = 0) -> torch.device:
  if torch.cuda.is_available():
- return torch.device(
- f"cuda:{index % torch.cuda.device_count()}"
- )
+ return torch.device(f"cuda:{index % torch.cuda.device_count()}")
  elif torch.backends.mps.is_available():
  return torch.device("mps")
  return torch.device("cpu")
@@ -94,9 +94,7 @@ class VC(object):
  hop_length=160,
  model="full",
  ):
- x = x.astype(
- np.float32
- )
+ x = x.astype(np.float32)
  x /= np.quantile(np.abs(x), 0.999)
  torch_device = self.get_optimal_torch_device()
  audio = torch.from_numpy(x).to(torch_device, copy=True)
@@ -152,12 +150,6 @@ class VC(object):
  f0 = f0[0].cpu().numpy()
  return f0
 
- def get_f0_pyin_computation(self, x, f0_min, f0_max):
- y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
- f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
- f0 = f0[1:]
- return f0
- 
  def get_f0_hybrid_computation(
  self,
  methods_str,
@@ -180,8 +172,9 @@ class VC(object):
  for method in methods:
  f0 = None
  if method == "crepe":
- f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
- f0 = f0[1:]
+ f0 = self.get_f0_crepe_computation(
+ x, f0_min, f0_max, p_len
+ )
  elif method == "mangio-crepe":
  f0 = self.get_f0_crepe_computation(
  x, f0_min, f0_max, p_len, crepe_hop_length
@@ -228,11 +221,13 @@ class VC(object):
  filter_radius,
  crepe_hop_length,
  inp_f0=None,
+ f0_min=50,
+ f0_max=1100,
  ):
  global input_audio_path2wav
  time_step = self.window / self.sr * 1000
- f0_min = 50
- f0_max = 1100
+ #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":
@@ -248,9 +243,7 @@ class VC(object):
  )
  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"
- )
+ f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
  
  elif f0_method == "harvest":
  input_audio_path2wav[input_audio_path] = x.astype(np.double)
@@ -268,10 +261,10 @@ class VC(object):
  )
  f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
  f0 = signal.medfilt(f0, 3)
- 
- elif f0_method == "crepe":
- f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
 
+ elif f0_method == "crepe":
+ f0 = self.get_f0_crepe_computation(x, f0_min, f0_max, p_len)
+ 
  elif f0_method == "mangio-crepe":
  f0 = self.get_f0_crepe_computation(x, f0_min, f0_max, p_len, crepe_hop_length)
  
@@ -476,17 +469,15 @@ class VC(object):
  protect,
  crepe_hop_length,
  f0_file=None,
+ f0_min=50,
+ f0_max=1100,
  ):
- if (
- file_index != ""
- and os.path.exists(file_index) == True
- and index_rate != 0
- ):
+ if file_index != "" and os.path.exists(file_index) == True and index_rate != 0:
  try:
  index = faiss.read_index(file_index)
  big_npy = index.reconstruct_n(0, index.ntotal)
- except:
- traceback.print_exc()
+ except Exception as error:
+ print(error)
  index = big_npy = None
  else:
  index = big_npy = None
@@ -521,8 +512,8 @@ class VC(object):
  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()
+ except Exception as error:
+ print(error)
  sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
  pitch, pitchf = None, None
  if if_f0 == 1:
@@ -535,6 +526,8 @@ class VC(object):
  filter_radius,
  crepe_hop_length,
  inp_f0,
+ f0_min,
+ f0_max,
  )
  pitch = pitch[:p_len]
  pitchf = pitchf[:p_len]