diff --git a/hparams.py b/hparams.py
index 8886f18fe..a7f707df3 100644
--- a/hparams.py
+++ b/hparams.py
@@ -1,89 +1,93 @@
 import tensorflow as tf
 from text import symbols
 
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
 
 def create_hparams(hparams_string=None, verbose=False):
     """Create model hyperparameters. Parse nondefault from given string."""
 
-    hparams = tf.contrib.training.HParams(
+    hparams = AttrDict({
         ################################
         # Experiment Parameters        #
         ################################
-        epochs=500,
-        iters_per_checkpoint=1000,
-        seed=1234,
-        dynamic_loss_scaling=True,
-        fp16_run=False,
-        distributed_run=False,
-        dist_backend="nccl",
-        dist_url="tcp://localhost:54321",
-        cudnn_enabled=True,
-        cudnn_benchmark=False,
-        ignore_layers=['embedding.weight'],
+        "epochs":500,
+        "iters_per_checkpoint":1000,
+        "seed":1234,
+        "dynamic_loss_scaling":True,
+        "fp16_run":False,
+        "distributed_run":False,
+        "dist_backend":"nccl",
+        "dist_url":"tcp://localhost:54321",
+        "cudnn_enabled":True,
+        "cudnn_benchmark":False,
+        "ignore_layers":['embedding.weight'],
 
         ################################
         # Data Parameters             #
         ################################
-        load_mel_from_disk=False,
-        training_files='filelists/ljs_audio_text_train_filelist.txt',
-        validation_files='filelists/ljs_audio_text_val_filelist.txt',
-        text_cleaners=['english_cleaners'],
+        "load_mel_from_disk":False,
+        "training_files":'filelists/ljs_audio_text_train_filelist.txt',
+        "validation_files":'filelists/ljs_audio_text_val_filelist.txt',
+        "text_cleaners":['english_cleaners'],
 
         ################################
         # Audio Parameters             #
         ################################
-        max_wav_value=32768.0,
-        sampling_rate=22050,
-        filter_length=1024,
-        hop_length=256,
-        win_length=1024,
-        n_mel_channels=80,
-        mel_fmin=0.0,
-        mel_fmax=8000.0,
+        "max_wav_value":32768.0,
+        "sampling_rate":22050,
+        "filter_length":1024,
+        "hop_length":256,
+        "win_length":1024,
+        "n_mel_channels":80,
+        "mel_fmin":0.0,
+        "mel_fmax":8000.0,
 
         ################################
         # Model Parameters             #
         ################################
-        n_symbols=len(symbols),
-        symbols_embedding_dim=512,
+        "n_symbols":len(symbols),
+        "symbols_embedding_dim":512,
 
         # Encoder parameters
-        encoder_kernel_size=5,
-        encoder_n_convolutions=3,
-        encoder_embedding_dim=512,
+        "encoder_kernel_size":5,
+        "encoder_n_convolutions":3,
+        "encoder_embedding_dim":512,
 
         # Decoder parameters
-        n_frames_per_step=1,  # currently only 1 is supported
-        decoder_rnn_dim=1024,
-        prenet_dim=256,
-        max_decoder_steps=1000,
-        gate_threshold=0.5,
-        p_attention_dropout=0.1,
-        p_decoder_dropout=0.1,
+        "n_frames_per_step":1,  # currently only 1 is supported
+        "decoder_rnn_dim":1024,
+        "prenet_dim":256,
+        "max_decoder_steps":1000,
+        "gate_threshold":0.5,
+        "p_attention_dropout":0.1,
+        "p_decoder_dropout":0.1,
 
         # Attention parameters
-        attention_rnn_dim=1024,
-        attention_dim=128,
+        "attention_rnn_dim":1024,
+        "attention_dim":128,
 
         # Location Layer parameters
-        attention_location_n_filters=32,
-        attention_location_kernel_size=31,
+        "attention_location_n_filters":32,
+        "attention_location_kernel_size":31,
 
         # Mel-post processing network parameters
-        postnet_embedding_dim=512,
-        postnet_kernel_size=5,
-        postnet_n_convolutions=5,
+        "postnet_embedding_dim":512,
+        "postnet_kernel_size":5,
+        "postnet_n_convolutions":5,
 
         ################################
         # Optimization Hyperparameters #
         ################################
-        use_saved_learning_rate=False,
-        learning_rate=1e-3,
-        weight_decay=1e-6,
-        grad_clip_thresh=1.0,
-        batch_size=64,
-        mask_padding=True  # set model's padded outputs to padded values
-    )
+        "use_saved_learning_rate":False,
+        "learning_rate":1e-3,
+        "weight_decay":1e-6,
+        "grad_clip_thresh":1.0,
+        "batch_size":64,
+        "mask_padding":True  # set model's padded outputs to padded values
+    })
 
     if hparams_string:
         tf.logging.info('Parsing command line hparams: %s', hparams_string)
diff --git a/requirements.txt b/requirements.txt
index 9b23dca72..0e1b42c47 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 matplotlib==2.1.0
-tensorflow==1.15.2
+tensorflow==2.15.0
 numpy==1.13.3
 inflect==0.2.5
 librosa==0.6.0
diff --git a/train.py b/train.py
index 88b09494c..3b74113d6 100644
--- a/train.py
+++ b/train.py
@@ -71,7 +71,10 @@ def prepare_directories_and_logger(output_directory, log_directory, rank):
 
 
 def load_model(hparams):
-    model = Tacotron2(hparams).cuda()
+    if torch.cuda.is_available():
+        model = Tacotron2(hparams).cuda()
+    else:
+        model = Tacotron2(hparams)
     if hparams.fp16_run:
         model.decoder.attention_layer.score_mask_value = finfo('float16').min
 
diff --git a/utils.py b/utils.py
index 439520184..098572a61 100644
--- a/utils.py
+++ b/utils.py
@@ -5,7 +5,10 @@
 
 def get_mask_from_lengths(lengths):
     max_len = torch.max(lengths).item()
-    ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
+    if torch.cuda.is_available():
+        ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
+    else:
+        ids = torch.arange(0, max_len, out=torch.LongTensor(max_len))
     mask = (ids < lengths.unsqueeze(1)).bool()
     return mask