diff --git a/TTS/bin/synthesize.py b/TTS/bin/synthesize.py
index b5ab409c..f93f062c 100755
--- a/TTS/bin/synthesize.py
+++ b/TTS/bin/synthesize.py
@@ -436,6 +436,8 @@ If you don't specify any models, then it uses LJSpeech based English model.
             source_wav=args.source_wav,
             target_wav=args.target_wav,
         )
+    elif model_dir is not None:
+        wav = synthesizer.tts(args.text)
 
     # save the results
     print(" > Saving output to {}".format(args.out_path))
diff --git a/TTS/tts/models/tortoise.py b/TTS/tts/models/tortoise.py
index c44c7ec5..5566d720 100644
--- a/TTS/tts/models/tortoise.py
+++ b/TTS/tts/models/tortoise.py
@@ -1,3 +1,4 @@
+import os
 import random
 from contextlib import contextmanager
 from dataclasses import dataclass
@@ -18,7 +19,6 @@ from TTS.tts.layers.tortoise.diffusion import SpacedDiffusion, get_named_beta_sc
 from TTS.tts.layers.tortoise.diffusion_decoder import DiffusionTts
 from TTS.tts.layers.tortoise.random_latent_generator import RandomLatentConverter
 from TTS.tts.layers.tortoise.tokenizer import VoiceBpeTokenizer
-from TTS.tts.layers.tortoise.utils import get_model_path
 from TTS.tts.layers.tortoise.vocoder import VocConf
 from TTS.tts.layers.tortoise.wav2vec_alignment import Wav2VecAlignment
 from TTS.tts.models.base_tts import BaseTTS
@@ -147,7 +147,7 @@ def do_spectrogram_diffusion(
         return denormalize_tacotron_mel(mel)[:, :, :output_seq_len]
 
 
-def classify_audio_clip(clip):
+def classify_audio_clip(clip, model_dir):
     """
     Returns whether or not Tortoises' classifier thinks the given clip came from Tortoise.
     :param clip: torch tensor containing audio waveform data (get it from load_audio)
@@ -167,7 +167,7 @@ def classify_audio_clip(clip):
         kernel_size=5,
         distribute_zero_label=False,
     )
-    classifier.load_state_dict(torch.load(get_model_path("classifier.pth"), map_location=torch.device("cpu")))
+    classifier.load_state_dict(torch.load(os.path.join(model_dir, "classifier.pth"), map_location=torch.device("cpu")))
     clip = clip.cpu().unsqueeze(0)
     results = F.softmax(classifier(clip), dim=-1)
     return results[0][0]
@@ -315,11 +315,11 @@ class Tortoise(BaseTTS):
             .cpu()
             .eval()
         )
-        ar_path = self.args.ar_checkpoint or get_model_path("autoregressive.pth", self.models_dir)
+        ar_path = self.args.ar_checkpoint or os.path.join(self.models_dir, "autoregressive.pth")
         self.autoregressive.load_state_dict(torch.load(ar_path))
         self.autoregressive.post_init_gpt2_config(self.args.kv_cache)
 
-        diff_path = self.args.diff_checkpoint or get_model_path("diffusion_decoder.pth", self.models_dir)
+        diff_path = self.args.diff_checkpoint or os.path.join(self.models_dir, "diffusion_decoder.pth")
         self.diffusion = (
             DiffusionTts(
                 model_channels=self.args.diff_model_channels,
@@ -357,14 +357,14 @@ class Tortoise(BaseTTS):
             .cpu()
             .eval()
         )
-        clvp_path = self.args.clvp_checkpoint or get_model_path("clvp2.pth", self.models_dir)
+        clvp_path = self.args.clvp_checkpoint or os.path.join(self.models_dir, "clvp2.pth")
         self.clvp.load_state_dict(torch.load(clvp_path))
 
         self.vocoder = vocoder.value.constructor().cpu()
         self.vocoder.load_state_dict(
             vocoder.value.optionally_index(
                 torch.load(
-                    get_model_path(vocoder.value.model_path, self.models_dir),
+                    os.path.join(self.models_dir, vocoder.value.model_path),
                     map_location=torch.device("cpu"),
                 )
             )
@@ -472,14 +472,14 @@ class Tortoise(BaseTTS):
             self.rlg_auto = RandomLatentConverter(1024).eval()
             self.rlg_auto.load_state_dict(
                 torch.load(
-                    get_model_path("rlg_auto.pth", self.models_dir),
+                    os.path.join(self.models_dir, "rlg_auto.pth"),
                     map_location=torch.device("cpu"),
                 )
             )
             self.rlg_diffusion = RandomLatentConverter(2048).eval()
             self.rlg_diffusion.load_state_dict(
                 torch.load(
-                    get_model_path("rlg_diffuser.pth", self.models_dir),
+                    os.path.join(self.models_dir, "rlg_diffuser.pth"),
                     map_location=torch.device("cpu"),
                 )
             )
@@ -555,8 +555,9 @@ class Tortoise(BaseTTS):
                 "diffusion_iterations": 400,
             },
         }
-        settings.update(presets[kwargs["preset"]])
-        kwargs.pop("preset")
+        if hasattr(kwargs, "preset"):
+            settings.update(presets[kwargs["preset"]])
+            kwargs.pop("preset")
         settings.update(kwargs)  # allow overriding of preset settings with kwargs
         return self.inference(text, **settings)