add mk annealing (mk attn loss contribution)

config.json

@@ -1,33 +1,34 @@
 {
-  "num_mels": 80,
-  "num_freq": 1025,
-  "sample_rate": 22050,
-  "frame_length_ms": 50,
-  "frame_shift_ms": 12.5,
-  "preemphasis": 0.97,
-  "min_level_db": -100,
-  "ref_level_db": 20,
-  "embedding_size": 256,
-  "text_cleaner": "english_cleaners",
+    "num_mels": 80,
+    "num_freq": 1025,
+    "sample_rate": 22050,
+    "frame_length_ms": 50,
+    "frame_shift_ms": 12.5,
+    "preemphasis": 0.97,
+    "min_level_db": -100,
+    "ref_level_db": 20,
+    "embedding_size": 256,
+    "text_cleaner": "english_cleaners",
 
-  "epochs": 500,
-  "lr": 0.002,
-  "warmup_steps": 4000,
-  "batch_size": 32,
-  "eval_batch_size":32,
-  "r": 5,
+    "epochs": 500,
+    "lr": 0.002,
+    "warmup_steps": 4000,
+    "batch_size": 32,
+    "eval_batch_size":32,
+    "r": 5,
+    "mk": 1,
 
-  "griffin_lim_iters": 60,
-  "power": 1.2,
+    "griffin_lim_iters": 60,
+    "power": 1.2,
 
-  "dataset": "LJSpeech",
-  "meta_file_train": "metadata_train.csv",
-  "meta_file_val": "metadata_val.csv",
-  "data_path": "/data/shared/KeithIto/LJSpeech-1.0/",
-  "min_seq_len": 0, 
-  "num_loader_workers": 8,
+    "dataset": "LJSpeech",
+    "meta_file_train": "metadata_train.csv",
+    "meta_file_val": "metadata_val.csv",
+    "data_path": "/data/shared/KeithIto/LJSpeech-1.0/",
+    "min_seq_len": 0, 
+    "num_loader_workers": 8,
 
-  "checkpoint": true,
-  "save_step": 908,
-  "output_path": "/data/shared/erogol_models/"
+    "checkpoint": true,
+    "save_step": 908,
+    "output_path": "/data/shared/erogol_models/"
 }

train.py

@@ -19,7 +19,8 @@ from tensorboardX import SummaryWriter
 from utils.generic_utils import (Progbar, remove_experiment_folder,
                                  create_experiment_folder, save_checkpoint,
                                  save_best_model, load_config, lr_decay,
-                                 count_parameters, check_update, get_commit_hash)
+                                 count_parameters, check_update, get_commit_hash,
+                                 create_attn_mask)
 from utils.model import get_param_size
 from utils.visual import plot_alignment, plot_spectrogram
 from models.tacotron import Tacotron
@@ -90,6 +91,9 @@ def train(model, criterion, data_loader, optimizer, epoch):
             params_group['lr'] = current_lr
 
         optimizer.zero_grad()
+        
+        # setup mk
+        mk = mk_decay(c.mk, c.epochs, epoch)
 
         # convert inputs to variables
         text_input_var = Variable(text_input)
@@ -105,19 +109,10 @@ def train(model, criterion, data_loader, optimizer, epoch):
             linear_spec_var = linear_spec_var.cuda()
             
         # create attention mask
-        # TODO: vectorize
         N = text_input_var.shape[1]
         T = mel_spec_var.shape[1] // c.r
-        M = np.zeros([N, T])
-        for t in range(T):
-            for n in range(N):
-                val = 20 * np.exp(-pow((n/N)-(t/T), 2.0)/0.05)
-                M[n, t] = val
-        e_x = np.exp(M - np.max(M))
-        M = e_x / e_x.sum(axis=0) # only difference
-        M = Variable(torch.FloatTensor(M).t()).cuda()
-        M = torch.stack([M]*32)
-
+        M = create_attn_mask(N, T, g)
+        
         # forward pass
         mel_output, linear_output, alignments =\
             model.forward(text_input_var, mel_spec_var)
@@ -129,7 +124,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
                               linear_spec_var[:, :, :n_priority_freq],
                               mel_lengths_var)
         attention_loss = criterion(alignments, M, mel_lengths_var)
-        loss = mel_loss + linear_loss + attention_loss
+        loss = mel_loss + linear_loss + mk * attention_loss
 
         # backpass and check the grad norm
         loss.backward()

utils/generic_utils.py

@@ -131,6 +131,24 @@ def lr_decay(init_lr, global_step, warmup_steps):
     return lr
 
 
+def create_attn_mask(N, T, g=0.05):
+    r'''creating attn mask for guided attention'''
+    M = np.zeros([N, T])
+    for t in range(T):
+        for n in range(N):
+            val = 20 * np.exp(-pow((n/N)-(t/T), 2.0)/g)
+            M[n, t] = val
+    e_x = np.exp(M - np.max(M))
+    M = e_x / e_x.sum(axis=0) # only difference
+    M = Variable(torch.FloatTensor(M).t()).cuda()
+    M = torch.stack([M]*32)
+    return M
+
+
+def mk_decay(init_mk, max_epoch, n_epoch):
+    return init_mk * ((max_epoch - n_epoch) / max_epoch)
+
+
 def count_parameters(model):
     r"""Count number of trainable parameters in a network"""
     return sum(p.numel() for p in model.parameters() if p.requires_grad)