diff --git a/TTS/tts/models/forward_tts_e2e.py b/TTS/tts/models/forward_tts_e2e.py
index ef7bb155..5bda50de 100644
--- a/TTS/tts/models/forward_tts_e2e.py
+++ b/TTS/tts/models/forward_tts_e2e.py
@@ -89,7 +89,10 @@ class ForwardTTSE2eF0Dataset(F0Dataset):
     def _compute_and_save_pitch(audio_config, wav_file, pitch_file=None):
         wav, _ = load_audio(wav_file)
         f0 = compute_f0(
-            x=wav.numpy()[0], sample_rate=audio_config.sample_rate, hop_length=audio_config.hop_length, pitch_fmax=audio_config.pitch_fmax
+            x=wav.numpy()[0],
+            sample_rate=audio_config.sample_rate,
+            hop_length=audio_config.hop_length,
+            pitch_fmax=audio_config.pitch_fmax,
         )
         # skip the last F0 value to align with the spectrogram
         if wav.shape[1] % audio_config.hop_length != 0:
@@ -104,7 +107,9 @@ class ForwardTTSE2eF0Dataset(F0Dataset):
         """
         pitch_file = self.create_pitch_file_path(wav_file, self.cache_path)
         if not os.path.exists(pitch_file):
-            pitch = self._compute_and_save_pitch(audio_config=self.audio_config, wav_file=wav_file, pitch_file=pitch_file)
+            pitch = self._compute_and_save_pitch(
+                audio_config=self.audio_config, wav_file=wav_file, pitch_file=pitch_file
+            )
         else:
             pitch = np.load(pitch_file)
         return pitch.astype(np.float32)
@@ -300,6 +305,9 @@ class ForwardTTSE2eArgs(ForwardTTSArgs):
     upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2])
     upsample_initial_channel_decoder: int = 512
     upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4])
+    # discriminator
+    upsampling_rates_discriminator: List[int] = field(default_factory=lambda: [4, 4, 4, 4])
+    periods_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11])
     # multi-speaker params
     use_speaker_embedding: bool = False
     num_speakers: int = 0
@@ -359,7 +367,18 @@ class ForwardTTSE2e(BaseTTSE2E):
 
         # use Vits Discriminator for limiting VRAM use
         if self.args.init_discriminator:
-            self.disc = VitsDiscriminator(use_spectral_norm=self.args.use_spectral_norm_discriminator)
+            self.disc = VitsDiscriminator(
+                use_spectral_norm=self.args.use_spectral_norm_discriminator,
+                periods=self.args.periods_discriminator,
+                upsampling_rates=self.args.upsampling_rates_discriminator,
+            )
+
+    # def check_model_args(self):
+    #     upsample_rate = torch.prod(torch.as_tensor(self.args.upsample_rates_decoder)).item()
+    #     if s
+    #     assert (
+    #         upsample_rate == self.config.audio.hop_length
+    #     ), f" [!] Product of upsample rates must be equal to the hop length - {upsample_rate} vs {config.audio.hop_length}"
 
     def init_multispeaker(self, config: Coqpit):
         """Init for multi-speaker training.
@@ -440,9 +459,10 @@ class ForwardTTSE2e(BaseTTSE2E):
             let_short_samples=True,
             pad_short=True,
         )
+
         vocoder_output = self.waveform_decoder(
             x=o_en_ex_slices.detach() if self.args.detach_vocoder_input else o_en_ex_slices,
-            g=encoder_outputs["g"],
+            g=encoder_outputs["spk_emb"],
         )
         wav_seg = segment(
             waveform,
@@ -461,7 +481,7 @@ class ForwardTTSE2e(BaseTTSE2E):
     def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}):
         encoder_outputs = self.encoder_model.inference(x=x, aux_input=aux_input, skip_decoder=True)
         o_en_ex = encoder_outputs["o_en_ex"]
-        vocoder_output = self.waveform_decoder(x=o_en_ex, g=encoder_outputs["g"])
+        vocoder_output = self.waveform_decoder(x=o_en_ex, g=encoder_outputs["spk_emb"])
         model_outputs = {**encoder_outputs}
         model_outputs["model_outputs"] = vocoder_output
         return model_outputs
@@ -860,9 +880,11 @@ class ForwardTTSE2e(BaseTTSE2E):
             center=False,
         )
 
-        assert (
-            batch["pitch"].shape[2] == batch["mel_input"].shape[2]
-        ), f"{batch['pitch'].shape[2]}, {batch['mel'].shape[2]}"
+        # TODO: Align pitch properly
+        # assert (
+        #     batch["pitch"].shape[2] == batch["mel_input"].shape[2]
+        # ), f"{batch['pitch'].shape[2]}, {batch['mel_input'].shape[2]}"
+        batch["pitch"] = batch["pitch"][:, :, : batch["mel_input"].shape[2]]
         batch["mel_lengths"] = (batch["mel_input"].shape[2] * batch["waveform_rel_lens"]).int()
 
         # zero the padding frames
@@ -990,9 +1012,7 @@ class ForwardTTSE2e(BaseTTSE2E):
         # language_manager = LanguageManager.init_from_config(config)
         return ForwardTTSE2e(config=new_config, tokenizer=tokenizer, speaker_manager=speaker_manager)
 
-    def load_checkpoint(
-        self, config, checkpoint_path, eval=False
-    ):
+    def load_checkpoint(self, config, checkpoint_path, eval=False):
         """Load model from a checkpoint created by the 👟"""
         # pylint: disable=unused-argument, redefined-builtin
         state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
@@ -1003,11 +1023,7 @@ class ForwardTTSE2e(BaseTTSE2E):
 
     def get_state_dict(self):
         """Custom state dict of the model with all the necessary components for inference."""
-        save_state = {
-            "config": self.config.to_dict(),
-            "args": self.args.to_dict(),
-            "model": self.state_dict
-        }
+        save_state = {"config": self.config.to_dict(), "args": self.args.to_dict(), "model": self.state_dict}
 
         if hasattr(self, "emb_g"):
             save_state["speaker_ids"] = self.speaker_manager.speaker_ids