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VC_inference.py

@@ -3,7 +3,7 @@ import numpy as np
 import torch
 from torch import no_grad, LongTensor
 import argparse
-from mel_processing import spectrogram_torch
+from models_infer import spectrogram_torch
 import utils
 from models_infer import SynthesizerTrn
 import gradio as gr

models_infer.py

@@ -398,4 +398,26 @@ class SynthesizerTrn(nn.Module):
     z_p = self.flow(z, y_mask, g=g_src)
     z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
     o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
+    return o_hat, y_mask, (z, z_p, z_hat)
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+  if torch.min(y) < -1.:
+    print('min value is ', torch.min(y))
+  if torch.max(y) > 1.:
+    print('max value is ', torch.max(y))
+
+  global hann_window
+  dtype_device = str(y.dtype) + '_' + str(y.device)
+  wnsize_dtype_device = str(win_size) + '_' + dtype_device
+  if wnsize_dtype_device not in hann_window:
+    hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+  y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+                              mode='reflect')
+  y = y.squeeze(1)
+
+  spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+                    center=center, pad_mode='reflect', normalized=False, onesided=True)
+
+  spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+  return spec

requirements_infer.txt

@@ -1,5 +1,4 @@
 Cython
-librosa
 numpy
 scipy
 torch