diff --git a/models/tacotron.py b/models/tacotron.py
index b3b04205..435e11d7 100644
--- a/models/tacotron.py
+++ b/models/tacotron.py
@@ -2,12 +2,12 @@
 import torch
 from torch import nn
 from math import sqrt
-from utils.text.symbols import symbols, phonemes
 from layers.tacotron import Prenet, Encoder, Decoder, PostCBHG
 
 
 class Tacotron(nn.Module):
     def __init__(self,
+                 num_chars,
                  embedding_dim=256,
                  linear_dim=1025,
                  mel_dim=80,
@@ -19,8 +19,8 @@ class Tacotron(nn.Module):
         self.mel_dim = mel_dim
         self.linear_dim = linear_dim
         self.embedding = nn.Embedding(
-            len(phonemes), embedding_dim, padding_idx=padding_idx)
-        print(" | > Number of characters : {}".format(len(phonemes)))
+            num_chars, embedding_dim, padding_idx=padding_idx)
+        print(" | > Number of characters : {}".format(num_chars))
         self.embedding.weight.data.normal_(0, 0.3)
         self.encoder = Encoder(embedding_dim)
         self.decoder = Decoder(256, mel_dim, r, attn_windowing)
diff --git a/train.py b/train.py
index 3e8bf9ed..910f9d8f 100644
--- a/train.py
+++ b/train.py
@@ -17,6 +17,7 @@ from utils.generic_utils import (
     remove_experiment_folder, create_experiment_folder, save_checkpoint,
     save_best_model, load_config, lr_decay, count_parameters, check_update,
     get_commit_hash, sequence_mask, NoamLR)
+from utils.text.symbols import symbols, phonemes
 from utils.visual import plot_alignment, plot_spectrogram
 from models.tacotron import Tacotron
 from layers.losses import L1LossMasked
@@ -355,7 +356,8 @@ def evaluate(model, criterion, criterion_st, ap, current_step):
 
 
 def main(args):
-    model = Tacotron(c.embedding_size, ap.num_freq, ap.num_mels, c.r)
+    num_chars = len(phonemes) if c.use_phonemes else len(symbols)
+    model = Tacotron(num_chars, c.embedding_size, ap.num_freq, ap.num_mels, c.r)
     print(" | > Num output units : {}".format(ap.num_freq), flush=True)
 
     optimizer = optim.Adam(model.parameters(), lr=c.lr, weight_decay=0)