grave attention config update:

config.json

@@ -1,7 +1,7 @@
 {
     "model": "Tacotron2",          // one of the model in models/  
-    "run_name": "ljspeech",
-    "run_description": "tacotron2 without bidirectional decoder",
+    "run_name": "ljspeech-graves",
+    "run_description": "tacotron2 wuth graves attention",
 
     // AUDIO PARAMETERS
     "audio":{
@@ -38,7 +38,7 @@
     "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
     "eval_batch_size":16,   
     "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.  
-    "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 16], [290000, 1, 8]], // ONLY TACOTRON - set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled.  
+    "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], // ONLY TACOTRON - set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled.  
     "loss_masking": true,         // enable / disable loss masking against the sequence padding.
 
     // VALIDATION
@@ -47,6 +47,7 @@
     "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
 
     // OPTIMIZER
+    "noam_schedule": false,
     "grad_clip": 1,                // upper limit for gradients for clipping.
     "epochs": 1000,                // total number of epochs to train.
     "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
@@ -60,7 +61,7 @@
     "prenet_dropout": true,        // enable/disable dropout at prenet. 
 
     // ATTENTION
-    "attention_type": "original",  // 'original' or 'graves'
+    "attention_type": "graves",  // 'original' or 'graves'
     "attention_heads": 5,          // number of attention heads (only for 'graves')
     "attention_norm": "sigmoid",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
     "windowing": false,            // Enables attention windowing. Used only in eval mode.
@@ -90,8 +91,8 @@
     "max_seq_len": 150,     // DATASET-RELATED: maximum text length
 
     // PATHS
-    // "output_path": "../keep/",      // DATASET-RELATED: output path for all training outputs.
-    "output_path": "/media/erogol/data_ssd/Models/runs/",
+    "output_path": "/data5/rw/pit/keep/",      // DATASET-RELATED: output path for all training outputs.
+    // "output_path": "/media/erogol/data_ssd/Models/runs/",
  
     // PHONEMES
     "phoneme_cache_path": "mozilla_us_phonemes",  // phoneme computation is slow, therefore, it caches results in the given folder.
@@ -108,8 +109,8 @@
         [
             {
                 "name": "ljspeech",
-                // "path": "/data/ro/shared/data/keithito/LJSpeech-1.1/",
-                "path": "/home/erogol/Data/LJSpeech-1.1",
+                "path": "/data5/ro/shared/data/keithito/LJSpeech-1.1/",
+                // "path": "/home/erogol/Data/LJSpeech-1.1",
                 "meta_file_train": "metadata_train.csv",
                 "meta_file_val": "metadata_val.csv"
             }

layers/common_layers.py

@@ -159,7 +159,7 @@ class GravesAttention(nn.Module):
         k_t = gbk_t[:, 2, :]
 
         # attention GMM parameters
-        sig_t = torch.nn.functional.softplus(b_t)+self.eps
+        sig_t = torch.nn.functional.softplus(b_t) + self.eps
 
         mu_t = self.mu_prev + torch.nn.functional.softplus(k_t)
         g_t = torch.softmax(g_t, dim=-1) / sig_t + self.eps

notebooks/ExtractTTSpectrogram.ipynb

@@ -303,9 +303,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }