hparams.py:rewrite

hparams.py

@@ -10,7 +10,7 @@ def create_hparams(hparams_string=None, verbose=False):
         # Experiment Parameters        #
         ################################
         epochs=500,
-        iters_per_checkpoint=500,
+        iters_per_checkpoint=1000,
         seed=1234,
         dynamic_loss_scaling=True,
         fp16_run=False,
@@ -24,10 +24,9 @@ def create_hparams(hparams_string=None, verbose=False):
         # 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',
+        training_files='filelists/ljs_audio22khz_text_train_filelist.txt',
+        validation_files='filelists/ljs_audio22khz_text_val_filelist.txt',
         text_cleaners=['english_cleaners'],
-        sort_by_length=False,
 
         ################################
         # Audio Parameters             #
@@ -39,7 +38,7 @@ def create_hparams(hparams_string=None, verbose=False):
         win_length=1024,
         n_mel_channels=80,
         mel_fmin=0.0,
-        mel_fmax=None,  # if None, half the sampling rate
+        mel_fmax=8000.0,
 
         ################################
         # Model Parameters             #
@@ -57,7 +56,9 @@ def create_hparams(hparams_string=None, verbose=False):
         decoder_rnn_dim=1024,
         prenet_dim=256,
         max_decoder_steps=1000,
-        gate_threshold=0.6,
+        gate_threshold=0.5,
+        p_attention_dropout=0.1,
+        p_decoder_dropout=0.1,
 
         # Attention parameters
         attention_rnn_dim=1024,
@@ -78,9 +79,9 @@ def create_hparams(hparams_string=None, verbose=False):
         use_saved_learning_rate=False,
         learning_rate=1e-3,
         weight_decay=1e-6,
-        grad_clip_thresh=1,
-        batch_size=48,
-        mask_padding=False  # set model's padded outputs to padded values
+        grad_clip_thresh=1.0,
+        batch_size=64,
+        mask_padding=True  # set model's padded outputs to padded values
     )
 
     if hparams_string: