Set attention norm method by config.json

config_cluster.json

@@ -39,6 +39,7 @@
     "warmup_steps": 4000,   // Noam decay steps to increase the learning rate from 0 to "lr"
     "windowing": false,      // Enables attention windowing. Used only in eval mode.
     "memory_size": 5,       // TO BE IMPLEMENTED -- memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5. 
+    "attention_norm": "softmax",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
 
     "batch_size": 16,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
     "eval_batch_size":16,   

layers/attention.py

@@ -100,7 +100,7 @@ class LocationSensitiveAttention(nn.Module):
 
 
 class AttentionRNNCell(nn.Module):
-    def __init__(self, out_dim, rnn_dim, annot_dim, memory_dim, align_model, windowing=False):
+    def __init__(self, out_dim, rnn_dim, annot_dim, memory_dim, align_model, windowing=False, norm="sigmoid"):
         r"""
         General Attention RNN wrapper
 
@@ -112,6 +112,7 @@ class AttentionRNNCell(nn.Module):
             align_model (str): 'b' for Bahdanau, 'ls' Location Sensitive alignment.
             windowing (bool): attention windowing forcing monotonic attention.
                 It is only active in eval mode.
+            norm (str): norm method to compute alignment weights.
         """
         super(AttentionRNNCell, self).__init__()
         self.align_model = align_model
@@ -121,7 +122,7 @@ class AttentionRNNCell(nn.Module):
             self.win_back = 3
             self.win_front = 6
             self.win_idx = None
-        # pick bahdanau or location sensitive attention
+        self.norm = norm
         if align_model == 'b':
             self.alignment_model = BahdanauAttention(annot_dim, rnn_dim,
                                                      out_dim)
@@ -164,7 +165,12 @@ class AttentionRNNCell(nn.Module):
                 alignment[:, front_win:] = -float("inf")
             # Update the window
             self.win_idx = torch.argmax(alignment,1).long()[0].item()
-        alignment = torch.sigmoid(alignment) / torch.sigmoid(alignment).sum(dim=1).unsqueeze(1)
+        if self.norm == "softmax":
+            alignment = torch.softmax(alignment, dim=-1)
+        elif self.norm == "sigmoid":
+            alignment = torch.sigmoid(alignment) / torch.sigmoid(alignment).sum(dim=1).unsqueeze(1)
+        else:
+            raise RuntimeError("Unknown value for attention norm type")
         context = torch.bmm(alignment.unsqueeze(1), annots)
         context = context.squeeze(1)
         return rnn_output, context, alignment

layers/tacotron.py

@@ -302,7 +302,7 @@ class Decoder(nn.Module):
     """
 
     def __init__(self, in_features, memory_dim, r, memory_size,
-                 attn_windowing):
+                 attn_windowing, attn_norm):
         super(Decoder, self).__init__()
         self.r = r
         self.in_features = in_features
@@ -319,7 +319,8 @@ class Decoder(nn.Module):
             annot_dim=in_features,
             memory_dim=128,
             align_model='ls',
-            windowing=attn_windowing)
+            windowing=attn_windowing,
+            norm=attn_norm)
         # (processed_memory | attention context) -> |Linear| -> decoder_RNN_input
         self.project_to_decoder_in = nn.Linear(256 + in_features, 256)
         # decoder_RNN_input -> |RNN| -> RNN_state

layers/tacotron2.py

@@ -112,7 +112,7 @@ class LocationLayer(nn.Module):
 class Attention(nn.Module):
     def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
                  attention_location_n_filters, attention_location_kernel_size,
-                 windowing):
+                 windowing, norm):
         super(Attention, self).__init__()
         self.query_layer = Linear(
             attention_rnn_dim, attention_dim, bias=False, init_gain='tanh')
@@ -128,6 +128,7 @@ class Attention(nn.Module):
             self.win_back = 1
             self.win_front = 3
             self.win_idx = None
+        self.norm = norm
 
     def init_win_idx(self):
         self.win_idx = -1
@@ -163,8 +164,13 @@ class Attention(nn.Module):
                 attention[:, 0] = attention.max()
             # Update the window
             self.win_idx = torch.argmax(attention, 1).long()[0].item()
-        alignment = torch.sigmoid(attention) / torch.sigmoid(
-            attention).sum(dim=1).unsqueeze(1)
+        if self.norm == "softmax":
+            alignment = torch.softmax(attention, dim=-1)
+        elif self.norm == "sigmoid":
+            alignment = torch.sigmoid(attention) / torch.sigmoid(
+                    attention).sum(dim=1).unsqueeze(1)
+        else:
+            raise RuntimeError("Unknown value for attention norm type")
         context = torch.bmm(alignment.unsqueeze(1), inputs)
         context = context.squeeze(1)
         return context, alignment
@@ -237,7 +243,7 @@ class Encoder(nn.Module):
 
 # adapted from https://github.com/NVIDIA/tacotron2/
 class Decoder(nn.Module):
-    def __init__(self, in_features, inputs_dim, r, attn_win):
+    def __init__(self, in_features, inputs_dim, r, attn_win, attn_norm):
         super(Decoder, self).__init__()
         self.mel_channels = inputs_dim
         self.r = r
@@ -257,7 +263,7 @@ class Decoder(nn.Module):
                                          self.attention_rnn_dim)
 
         self.attention_layer = Attention(self.attention_rnn_dim, in_features,
-                                         128, 32, 31, attn_win)
+                                         128, 32, 31, attn_win, attn_norm)
 
         self.decoder_rnn = nn.LSTMCell(self.attention_rnn_dim + in_features,
                                        self.decoder_rnn_dim, 1)

models/tacotron.py

@@ -14,7 +14,8 @@ class Tacotron(nn.Module):
                  r=5,
                  padding_idx=None,
                  memory_size=5,
-                 attn_windowing=False):
+                 attn_windowing=False,
+                 attn_norm="sigmoid"):
         super(Tacotron, self).__init__()
         self.r = r
         self.mel_dim = mel_dim
@@ -22,7 +23,7 @@ class Tacotron(nn.Module):
         self.embedding = nn.Embedding(num_chars, 256, padding_idx=padding_idx)
         self.embedding.weight.data.normal_(0, 0.3)
         self.encoder = Encoder(256)
-        self.decoder = Decoder(256, mel_dim, r, memory_size, attn_windowing)
+        self.decoder = Decoder(256, mel_dim, r, memory_size, attn_windowing, attn_norm)
         self.postnet = PostCBHG(mel_dim)
         self.last_linear = nn.Sequential(
             nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim),

models/tacotron2.py

@@ -9,7 +9,7 @@ from utils.generic_utils import sequence_mask
 
 # TODO: match function arguments with tacotron
 class Tacotron2(nn.Module):
-    def __init__(self, num_chars, r, attn_win=False):
+    def __init__(self, num_chars, r, attn_win=False, attn_norm="softmax"):
         super(Tacotron2, self).__init__()
         self.n_mel_channels = 80
         self.n_frames_per_step = r
@@ -18,7 +18,7 @@ class Tacotron2(nn.Module):
         val = sqrt(3.0) * std  # uniform bounds for std
         self.embedding.weight.data.uniform_(-val, val)
         self.encoder = Encoder(512)
-        self.decoder = Decoder(512, self.n_mel_channels, r, attn_win)
+        self.decoder = Decoder(512, self.n_mel_channels, r, attn_win, attn_norm)
         self.postnet = Postnet(self.n_mel_channels)
 
     def shape_outputs(self, mel_outputs, mel_outputs_postnet, alignments):

tests/layers_tests.py

@@ -38,7 +38,7 @@ class CBHGTests(unittest.TestCase):
 
 class DecoderTests(unittest.TestCase):
     def test_in_out(self):
-        layer = Decoder(in_features=256, memory_dim=80, r=2, memory_size=4, attn_windowing=False)
+        layer = Decoder(in_features=256, memory_dim=80, r=2, memory_size=4, attn_windowing=False, attn_norm="sigmoid")
         dummy_input = T.rand(4, 8, 256)
         dummy_memory = T.rand(4, 2, 80)
 

train.py

@@ -375,7 +375,7 @@ def main(args):
         init_distributed(args.rank, num_gpus, args.group_id,
                          c.distributed["backend"], c.distributed["url"])
     num_chars = len(phonemes) if c.use_phonemes else len(symbols)
-    model = MyModel(num_chars=num_chars, r=c.r)
+    model = MyModel(num_chars=num_chars, r=c.r, attention_norm=c.attention_norm)
 
     print(" | > Num output units : {}".format(ap.num_freq), flush=True)