diff --git a/codes/models/gpt_voice/gpt_tts_hf.py b/codes/models/gpt_voice/gpt_tts_hf.py
index 733bc8bc..405fb64b 100644
--- a/codes/models/gpt_voice/gpt_tts_hf.py
+++ b/codes/models/gpt_voice/gpt_tts_hf.py
@@ -108,8 +108,8 @@ class GptTtsHf(nn.Module):
         loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
         return loss_text.mean(), loss_mel.mean(), mel_logits
 
-    def inference(self, text_inputs, cond_inputs, do_sample=False, temperature=1.0, num_beams=8):
-        text_inputs, cond_inputs = torch.load("debug_text_and_cond.pt")
+    def inference(self, text_inputs, cond_inputs, do_sample=False, temperature=1.0, num_beams=8, repetition_penalty=1):
+        #text_inputs, cond_inputs = torch.load("debug_text_and_cond.pt")
 
         if not hasattr(self, 'inference_model'):
             self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.mel_pos_embedding, self.final_norm, self.mel_head)
@@ -134,8 +134,8 @@ class GptTtsHf(nn.Module):
         fake_inputs[:,-1] = self.START_MEL_TOKEN
 
         gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.START_MEL_TOKEN, pad_token_id=self.STOP_MEL_TOKEN, eos_token_id=self.STOP_MEL_TOKEN,
-                          max_length=emb.shape[1]+self.max_mel_tokens, temperature=temperature, num_beams=num_beams, use_cache=True)
-        return gen[:, fake_inputs.shape[1]:]
+                          max_length=emb.shape[1]+self.max_mel_tokens, temperature=temperature, num_beams=num_beams, use_cache=True, repetition_penalty=repetition_penalty)
+        return gen[:, fake_inputs.shape[1]:-1]
 
 
 @register_model
diff --git a/codes/scripts/audio/gen/use_gpt_tts.py b/codes/scripts/audio/gen/use_gpt_tts.py
index 08a451d7..798cbbf0 100644
--- a/codes/scripts/audio/gen/use_gpt_tts.py
+++ b/codes/scripts/audio/gen/use_gpt_tts.py
@@ -46,9 +46,9 @@ if __name__ == '__main__':
     parser.add_argument('-dvae_model_name', type=str, help='Name of the DVAE model in opt.', default='dvae')
     parser.add_argument('-opt_gpt_tts', type=str, help='Path to options YAML file used to train the GPT-TTS model', default='X:\\dlas\\experiments\\train_gpt_tts.yml')
     parser.add_argument('-gpt_tts_model_name', type=str, help='Name of the GPT TTS model in opt.', default='gpt')
-    parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts\\models\\32000_gpt.pth')
-    parser.add_argument('-text', type=str, help='Text to speak.', default="I'm a language model that has learned to speak.")
-    parser.add_argument('-cond_path', type=str, help='Folder containing conditioning samples.', default='Z:\\clips\\books1\\3042_18_Holden__000000000')
+    parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts\\models\\48000_gpt.pth')
+    parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
+    parser.add_argument('-cond_path', type=str, help='Folder containing conditioning samples.', default='Y:\\clips\\podcasts-0\\8816_20210511-Pay Taxes Less Frequently_ We\'re Interested')
     parser.add_argument('-num_cond', type=int, help='Number of conditioning samples to load.', default=3)
     args = parser.parse_args()
 
@@ -63,7 +63,7 @@ if __name__ == '__main__':
     conds, cond_wav = load_conditioning_candidates(args.cond_path, args.num_cond)
 
     print("Performing GPT inference..")
-    codes = gpt.inference(text, conds, num_beams=32)
+    codes = gpt.inference(text, conds, num_beams=32, repetition_penalty=10.0)
 
     # Delete the GPT TTS model to free up GPU memory
     del gpt
@@ -72,7 +72,7 @@ if __name__ == '__main__':
     dvae = load_model_from_config(args.opt_diffuse, args.dvae_model_name)
     print("Loading Diffusion Model..")
     diffusion = load_model_from_config(args.opt_diffuse, args.diffusion_model_name, also_load_savepoint=False, load_path=args.diffusion_model_path)
-    diffuser = load_discrete_vocoder_diffuser()
+    diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=50)
 
     print("Performing vocoding..")
     wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, codes, cond_wav, spectrogram_compression_factor=128, plt_spec=False)