diff --git a/train.py b/train.py
index 16245ac0..9b7df10a 100644
--- a/train.py
+++ b/train.py
@@ -43,7 +43,9 @@ def setup_loader(is_val=False):
             preprocessor=preprocessor,
             ap=ap,
             batch_group_size=0 if is_val else 8 * c.batch_size,
-            min_seq_len=0 if is_val else c.min_seq_len)
+            min_seq_len=0 if is_val else c.min_seq_len,
+            max_seq_len=float("inf") if is_val else c.max_seq_len
+            cached=False if c.dataset ~= "tts_cache" else True)
         loader = DataLoader(
             dataset,
             batch_size=c.eval_batch_size if is_val else c.batch_size,
@@ -164,8 +166,8 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st,
                     grad_norm, grad_norm_st, avg_text_length, avg_spec_length, step_time, current_lr),
                 flush=True)
 
-        avg_linear_loss += linear_loss.item()
-        avg_mel_loss += mel_loss.item()
+        avg_linear_loss += float(linear_loss.item())
+        avg_mel_loss += float(mel_loss.item())
         avg_stop_loss += stop_loss.item()
         avg_step_time += step_time
 
@@ -198,7 +200,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st,
             # Sample audio
             tb_logger.tb_train_audios(current_step, 
                                         {'TrainAudio': ap.inv_spectrogram(const_spec.T)},
-                                        c.sample_rate)
+                                        c.audio["sample_rate"])
 
     avg_linear_loss /= (num_iter + 1)
     avg_mel_loss /= (num_iter + 1)
@@ -295,8 +297,8 @@ def evaluate(model, criterion, criterion_st, ap, current_step):
                                                     stop_loss.item()),
                         flush=True)
 
-                avg_linear_loss += linear_loss.item()
-                avg_mel_loss += mel_loss.item()
+                avg_linear_loss += float(linear_loss.item())
+                avg_mel_loss += float(mel_loss.item())
                 avg_stop_loss += stop_loss.item()
 
             # Diagnostic visualizations