diff --git a/tortoise/models/arch_util.py b/tortoise/models/arch_util.py
index 5d8c36e..6a79194 100644
--- a/tortoise/models/arch_util.py
+++ b/tortoise/models/arch_util.py
@@ -1,3 +1,4 @@
+import os
 import functools
 import math
 
@@ -288,9 +289,12 @@ class AudioMiniEncoder(nn.Module):
         return h[:, :, 0]
 
 
+DEFAULT_MEL_NORM_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/mel_norms.pth')
+
+
 class TorchMelSpectrogram(nn.Module):
     def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, mel_fmin=0, mel_fmax=8000,
-                 sampling_rate=22050, normalize=False, mel_norm_file='tortoise/data/mel_norms.pth'):
+                 sampling_rate=22050, normalize=False, mel_norm_file=DEFAULT_MEL_NORM_FILE):
         super().__init__()
         # These are the default tacotron values for the MEL spectrogram.
         self.filter_length = filter_length
diff --git a/tortoise/utils/audio.py b/tortoise/utils/audio.py
index 7d5390c..b125258 100644
--- a/tortoise/utils/audio.py
+++ b/tortoise/utils/audio.py
@@ -10,6 +10,9 @@ from scipy.io.wavfile import read
 from tortoise.utils.stft import STFT
 
 
+BUILTIN_VOICES_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../voices')
+
+
 def load_wav_to_torch(full_path):
     sampling_rate, data = read(full_path)
     if data.dtype == np.int32:
@@ -83,7 +86,7 @@ def dynamic_range_decompression(x, C=1):
 
 
 def get_voices(extra_voice_dirs=[]):
-    dirs = ['tortoise/voices'] + extra_voice_dirs
+    dirs = [BUILTIN_VOICES_DIR] + extra_voice_dirs
     voices = {}
     for d in dirs:
         subs = os.listdir(d)
diff --git a/tortoise/utils/tokenizer.py b/tortoise/utils/tokenizer.py
index a8959d8..3ab1c31 100644
--- a/tortoise/utils/tokenizer.py
+++ b/tortoise/utils/tokenizer.py
@@ -1,3 +1,4 @@
+import os
 import re
 
 import inflect
@@ -165,8 +166,11 @@ def lev_distance(s1, s2):
   return distances[-1]
 
 
+DEFAULT_VOCAB_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/tokenizer.json')
+
+
 class VoiceBpeTokenizer:
-    def __init__(self, vocab_file='tortoise/data/tokenizer.json'):
+    def __init__(self, vocab_file=DEFAULT_VOCAB_FILE):
         if vocab_file is not None:
             self.tokenizer = Tokenizer.from_file(vocab_file)