diff --git a/TTS/vocoder/utils/generic_utils.py b/TTS/vocoder/utils/generic_utils.py
index 63a0af44..98a0a939 100644
--- a/TTS/vocoder/utils/generic_utils.py
+++ b/TTS/vocoder/utils/generic_utils.py
@@ -3,9 +3,10 @@ from typing import Dict
 import numpy as np
 import torch
 from matplotlib import pyplot as plt
+from TTS.utils.audio.numpy_transforms import amp_to_db, build_mel_basis, wav_to_mel
 
 from TTS.tts.utils.visual import plot_spectrogram
-from TTS.utils.audio import AudioProcessor
+from TTS.utils.audio.processor import AudioProcessor
 
 
 def interpolate_vocoder_input(scale_factor, spec):
@@ -29,13 +30,14 @@ def interpolate_vocoder_input(scale_factor, spec):
     return spec
 
 
-def plot_results(y_hat: torch.tensor, y: torch.tensor, ap: AudioProcessor, name_prefix: str = None) -> Dict:
+def plot_results(y_hat: torch.tensor, y: torch.tensor, ap: AudioProcessor=None, audio_config: "Coqpit"= None,  name_prefix: str = None) -> Dict:
     """Plot the predicted and the real waveform and their spectrograms.
 
     Args:
         y_hat (torch.tensor): Predicted waveform.
         y (torch.tensor): Real waveform.
-        ap (AudioProcessor): Audio processor used to process the waveform.
+        ap (AudioProcessor): Audio processor used to process the waveform. Defaults to None.
+        audio_config (Coqpit): Audio configuration. Only used when ```ap``` is None. Defaults to None.
         name_prefix (str, optional): Name prefix used to name the figures. Defaults to None.
 
     Returns:
@@ -48,8 +50,17 @@ def plot_results(y_hat: torch.tensor, y: torch.tensor, ap: AudioProcessor, name_
     y_hat = y_hat[0].squeeze().detach().cpu().numpy()
     y = y[0].squeeze().detach().cpu().numpy()
 
-    spec_fake = ap.melspectrogram(y_hat).T
-    spec_real = ap.melspectrogram(y).T
+    if ap is not None:
+        spec_fake = ap.melspectrogram(y_hat).T
+        spec_real = ap.melspectrogram(y).T
+    elif audio_config is not None:
+        mel_basis = build_mel_basis(**audio_config)
+        spec_fake = wav_to_mel(y=y_hat, mel_basis=mel_basis, **audio_config).T
+        spec_real = wav_to_mel(y=y, mel_basis=mel_basis, **audio_config).T
+        spec_fake = amp_to_db(x=spec_fake, gain=1.0, base=10.0)
+        spec_real = amp_to_db(x=spec_real, gain=1.0, base=10.0)
+    else:
+        raise ValueError(" [!] Either `ap` or `audio_config` must be provided.")
     spec_diff = np.abs(spec_fake - spec_real)
 
     # plot figure and save it