diff --git a/TTS/tts/models/xtts.py b/TTS/tts/models/xtts.py
index 3e413114..de38f7c8 100644
--- a/TTS/tts/models/xtts.py
+++ b/TTS/tts/models/xtts.py
@@ -190,7 +190,6 @@ class XttsArgs(Coqpit):
         decoder_checkpoint (str, optional): The checkpoint for the DiffTTS model. Defaults to None.
         num_chars (int, optional): The maximum number of characters to generate. Defaults to 255.
         use_hifigan (bool, optional): Whether to use hifigan with implicit enhancement or diffusion + univnet as a decoder. Defaults to True.
-        use_ne_hifigan (bool, optional): Whether to use regular hifigan or diffusion + univnet as a decoder. Defaults to False.
 
         For GPT model:
         gpt_max_audio_tokens (int, optional): The maximum mel tokens for the autoregressive model. Defaults to 604.
@@ -229,7 +228,6 @@ class XttsArgs(Coqpit):
     decoder_checkpoint: str = None
     num_chars: int = 255
     use_hifigan: bool = True
-    use_ne_hifigan: bool = False
 
     # XTTS GPT Encoder params
     tokenizer_file: str = ""
@@ -337,18 +335,7 @@ class Xtts(BaseTTS):
                 cond_d_vector_in_each_upsampling_layer=self.args.cond_d_vector_in_each_upsampling_layer,
             )
 
-        if self.args.use_ne_hifigan:
-            self.ne_hifigan_decoder = HifiDecoder(
-                input_sample_rate=self.args.input_sample_rate,
-                output_sample_rate=self.args.output_sample_rate,
-                output_hop_length=self.args.output_hop_length,
-                ar_mel_length_compression=self.args.gpt_code_stride_len,
-                decoder_input_dim=self.args.decoder_input_dim,
-                d_vector_dim=self.args.d_vector_dim,
-                cond_d_vector_in_each_upsampling_layer=self.args.cond_d_vector_in_each_upsampling_layer,
-            )
-
-        if not (self.args.use_hifigan or self.args.use_ne_hifigan):
+        if not self.args.use_hifigan:
             self.diffusion_decoder = DiffusionTts(
                 model_channels=self.args.diff_model_channels,
                 num_layers=self.args.diff_num_layers,
@@ -454,7 +441,7 @@ class Xtts(BaseTTS):
         if librosa_trim_db is not None:
             audio = librosa.effects.trim(audio, top_db=librosa_trim_db)[0]
 
-        if self.args.use_hifigan or self.args.use_ne_hifigan:
+        if self.args.use_hifigan or self.args.use_hifigan:
             speaker_embedding = self.get_speaker_embedding(audio, sr)
         else:
             diffusion_cond_latents = self.get_diffusion_cond_latents(audio, sr)
@@ -706,19 +693,14 @@ class Xtts(BaseTTS):
                     break
 
             if decoder == "hifigan":
-                assert hasattr(
-                    self, "hifigan_decoder"
+                assert (
+                    hasattr(self, "hifigan_decoder") and self.hifigan_decoder is not None
                 ), "You must enable hifigan decoder to use it by setting config `use_hifigan: true`"
                 wav = self.hifigan_decoder(gpt_latents, g=speaker_embedding)
-            elif decoder == "ne_hifigan":
-                assert hasattr(
-                    self, "ne_hifigan_decoder"
-                ), "You must enable ne_hifigan decoder to use it by setting config `use_ne_hifigan: true`"
-                wav = self.ne_hifigan_decoder(gpt_latents, g=speaker_embedding)
             else:
                 assert hasattr(
                     self, "diffusion_decoder"
-                ), "You must disable hifigan decoders to use difffusion by setting config `use_ne_hifigan: false` and `use_hifigan: false`"
+                ), "You must disable hifigan decoders to use difffusion by setting `use_hifigan: false`"
                 mel = do_spectrogram_diffusion(
                     self.diffusion_decoder,
                     diffuser,
@@ -816,11 +798,6 @@ class Xtts(BaseTTS):
                         self, "hifigan_decoder"
                     ), "You must enable hifigan decoder to use it by setting config `use_hifigan: true`"
                     wav_gen = self.hifigan_decoder(gpt_latents, g=speaker_embedding.to(self.device))
-                elif decoder == "ne_hifigan":
-                    assert hasattr(
-                        self, "ne_hifigan_decoder"
-                    ), "You must enable ne_hifigan decoder to use it by setting config `use_ne_hifigan: true`"
-                    wav_gen = self.ne_hifigan_decoder(gpt_latents, g=speaker_embedding.to(self.device))
                 else:
                     raise NotImplementedError("Diffusion for streaming inference not implemented.")
                 wav_chunk, wav_gen_prev, wav_overlap = self.handle_chunks(
@@ -850,9 +827,8 @@ class Xtts(BaseTTS):
 
     def get_compatible_checkpoint_state_dict(self, model_path):
         checkpoint = load_fsspec(model_path, map_location=torch.device("cpu"))["model"]
-        ignore_keys = ["diffusion_decoder", "vocoder"] if self.args.use_hifigan or self.args.use_ne_hifigan else []
+        ignore_keys = ["diffusion_decoder", "vocoder"] if self.args.use_hifigan else []
         ignore_keys += [] if self.args.use_hifigan else ["hifigan_decoder"]
-        ignore_keys += [] if self.args.use_ne_hifigan else ["ne_hifigan_decoder"]
         # remove xtts gpt trainer extra keys
         ignore_keys += ["torch_mel_spectrogram_style_encoder", "torch_mel_spectrogram_dvae", "dvae"]
         for key in list(checkpoint.keys()):
@@ -915,8 +891,6 @@ class Xtts(BaseTTS):
         if eval:
             if hasattr(self, "hifigan_decoder"):
                 self.hifigan_decoder.eval()
-            if hasattr(self, "ne_hifigan_decoder"):
-                self.ne_hifigan_decoder.eval()
             if hasattr(self, "diffusion_decoder"):
                 self.diffusion_decoder.eval()
             if hasattr(self, "vocoder"):