inherit TacotronAbstact with both tacotron and tacotron2

models/tacotron.py

@@ -1,23 +1,21 @@
 # coding: utf-8
 import torch
-import copy
 from torch import nn
-from TTS.layers.tacotron import Encoder, Decoder, PostCBHG
+
-from TTS.utils.generic_utils import sequence_mask
 from TTS.layers.gst_layers import GST
+from TTS.layers.tacotron import Decoder, Encoder, PostCBHG
+from TTS.models.tacotron_abstract import TacotronAbstract
 
 
-class Tacotron(nn.Module):
+class Tacotron(TacotronAbstract):
     def __init__(self,
                  num_chars,
                  num_speakers,
                  r=5,
                  postnet_output_dim=1025,
                  decoder_output_dim=80,
-                 memory_size=5,
                  attn_type='original',
                  attn_win=False,
-                 gst=False,
                  attn_norm="sigmoid",
                  prenet_type="original",
                  prenet_dropout=True,
@@ -27,38 +25,41 @@ class Tacotron(nn.Module):
                  location_attn=True,
                  attn_K=5,
                  separate_stopnet=True,
-                 bidirectional_decoder=False):
+                 bidirectional_decoder=False,
-        super(Tacotron, self).__init__()
+                 double_decoder_consistency=False,
-        self.r = r
+                 ddc_r=None,
-        self.decoder_output_dim = decoder_output_dim
+                 gst=False,
-        self.postnet_output_dim = postnet_output_dim
+                 memory_size=5):
-        self.gst = gst
+        super(Tacotron,
-        self.num_speakers = num_speakers
+              self).__init__(num_chars, num_speakers, r, postnet_output_dim,
-        self.bidirectional_decoder = bidirectional_decoder
+                             decoder_output_dim, attn_type, attn_win,
-        decoder_dim = 512 if num_speakers > 1 else 256
+                             attn_norm, prenet_type, prenet_dropout,
-        encoder_dim = 512 if num_speakers > 1 else 256
+                             forward_attn, trans_agent, forward_attn_mask,
+                             location_attn, attn_K, separate_stopnet,
+                             bidirectional_decoder, double_decoder_consistency,
+                             ddc_r, gst)
+        decoder_in_features = 512 if num_speakers > 1 else 256
+        encoder_in_features = 512 if num_speakers > 1 else 256
+        speaker_embedding_dim = 256
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # embedding layer
+        # base model layers
         self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
         self.embedding.weight.data.normal_(0, 0.3)
-        # boilerplate model
+        self.encoder = Encoder(encoder_in_features)
-        self.encoder = Encoder(encoder_dim)
+        self.decoder = Decoder(decoder_in_features, decoder_output_dim, r, memory_size, attn_type, attn_win,
-        self.decoder = Decoder(decoder_dim, decoder_output_dim, r, memory_size, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
                                forward_attn, trans_agent, forward_attn_mask,
                                location_attn, attn_K, separate_stopnet,
                                proj_speaker_dim)
-        if self.bidirectional_decoder:
-            self.decoder_backward = copy.deepcopy(self.decoder)
         self.postnet = PostCBHG(decoder_output_dim)
         self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2,
                                      postnet_output_dim)
         # speaker embedding layers
         if num_speakers > 1:
-            self.speaker_embedding = nn.Embedding(num_speakers, 256)
+            self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
             self.speaker_project_mel = nn.Sequential(
-                nn.Linear(256, proj_speaker_dim), nn.Tanh())
+                nn.Linear(speaker_embedding_dim, proj_speaker_dim), nn.Tanh())
             self.speaker_embeddings = None
             self.speaker_embeddings_projected = None
         # global style token layers
@@ -68,28 +69,15 @@ class Tacotron(nn.Module):
                                  num_heads=4,
                                  num_style_tokens=10,
                                  embedding_dim=gst_embedding_dim)
+        # backward pass decoder
+        if self.bidirectional_decoder:
+            self._init_backward_decoder()
+        # setup DDC
+        if self.double_decoder_consistency:
+            self._init_coarse_decoder()
 
-    def _init_states(self):
-        self.speaker_embeddings = None
-        self.speaker_embeddings_projected = None
 
-    def compute_speaker_embedding(self, speaker_ids):
+    def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None):
-        if hasattr(self, "speaker_embedding") and speaker_ids is None:
-            raise RuntimeError(
-                " [!] Model has speaker embedding layer but speaker_id is not provided"
-            )
-        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
-            self.speaker_embeddings = self._compute_speaker_embedding(
-                speaker_ids)
-            self.speaker_embeddings_projected = self.speaker_project_mel(
-                self.speaker_embeddings).squeeze(1)
-
-    def compute_gst(self, inputs, mel_specs):
-        gst_outputs = self.gst_layer(mel_specs)
-        inputs = self._add_speaker_embedding(inputs, gst_outputs)
-        return inputs
-
-    def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
         """
         Shapes:
             - characters: B x T_in
@@ -98,45 +86,59 @@ class Tacotron(nn.Module):
             - speaker_ids: B x 1
         """
         self._init_states()
-        mask = sequence_mask(text_lengths).to(characters.device)
+        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
         # B x T_in x embed_dim
         inputs = self.embedding(characters)
         # B x speaker_embed_dim
-        self.compute_speaker_embedding(speaker_ids)
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
         if self.num_speakers > 1:
             # B x T_in x embed_dim + speaker_embed_dim
             inputs = self._concat_speaker_embedding(inputs,
                                                     self.speaker_embeddings)
-        # B x T_in x encoder_dim
+        # B x T_in x encoder_in_features
         encoder_outputs = self.encoder(inputs)
+        # sequence masking
+        encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
+        # global style token
         if self.gst:
             # B x gst_dim
             encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
         if self.num_speakers > 1:
             encoder_outputs = self._concat_speaker_embedding(
                 encoder_outputs, self.speaker_embeddings)
-        # decoder_outputs: B x decoder_dim x T_out
+        # decoder_outputs: B x decoder_in_features x T_out
-        # alignments: B x T_in x encoder_dim
+        # alignments: B x T_in x encoder_in_features
         # stop_tokens: B x T_in
         decoder_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, mask,
+            encoder_outputs, mel_specs, input_mask,
             self.speaker_embeddings_projected)
-        # B x T_out x decoder_dim
+        # sequence masking
+        if output_mask is not None:
+            decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
+        # B x T_out x decoder_in_features
         postnet_outputs = self.postnet(decoder_outputs)
+        # sequence masking
+        if output_mask is not None:
+            postnet_outputs = postnet_outputs * output_mask.unsqueeze(2).expand_as(postnet_outputs)
         # B x T_out x posnet_dim
         postnet_outputs = self.last_linear(postnet_outputs)
-        # B x T_out x decoder_dim
+        # B x T_out x decoder_in_features
         decoder_outputs = decoder_outputs.transpose(1, 2).contiguous()
         if self.bidirectional_decoder:
-            decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, mask)
+            decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
             return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
+        if self.double_decoder_consistency:
+            decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
+            return  decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
     def inference(self, characters, speaker_ids=None, style_mel=None):
         inputs = self.embedding(characters)
         self._init_states()
-        self.compute_speaker_embedding(speaker_ids)
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
         if self.num_speakers > 1:
             inputs = self._concat_speaker_embedding(inputs,
                                                     self.speaker_embeddings)
@@ -152,28 +154,3 @@ class Tacotron(nn.Module):
         postnet_outputs = self.last_linear(postnet_outputs)
         decoder_outputs = decoder_outputs.transpose(1, 2)
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
-
-    def _backward_inference(self, mel_specs, encoder_outputs, mask):
-        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
-            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
-            self.speaker_embeddings_projected)
-        decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
-        return decoder_outputs_b, alignments_b
-
-    def _compute_speaker_embedding(self, speaker_ids):
-        speaker_embeddings = self.speaker_embedding(speaker_ids)
-        return speaker_embeddings.unsqueeze_(1)
-
-    @staticmethod
-    def _add_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = outputs + speaker_embeddings_
-        return outputs
-
-    @staticmethod
-    def _concat_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
-        return outputs

models/tacotron2.py

@@ -1,13 +1,15 @@
-import copy
-import torch
 from math import sqrt
+
+import torch
 from torch import nn
-from TTS.layers.tacotron2 import Encoder, Decoder, Postnet
+
-from TTS.utils.generic_utils import sequence_mask
+from TTS.layers.gst_layers import GST
+from TTS.layers.tacotron2 import Decoder, Encoder, Postnet
+from TTS.models.tacotron_abstract import TacotronAbstract
 
 
 # TODO: match function arguments with tacotron
-class Tacotron2(nn.Module):
+class Tacotron2(TacotronAbstract):
     def __init__(self,
                  num_chars,
                  num_speakers,
@@ -25,16 +27,22 @@ class Tacotron2(nn.Module):
                  location_attn=True,
                  attn_K=5,
                  separate_stopnet=True,
-                 bidirectional_decoder=False):
+                 bidirectional_decoder=False,
-        super(Tacotron2, self).__init__()
+                 double_decoder_consistency=False,
-        self.postnet_output_dim = postnet_output_dim
+                 ddc_r=None,
-        self.decoder_output_dim = decoder_output_dim
+                 gst=False):
-        self.r = r
+        super(Tacotron2,
-        self.bidirectional_decoder = bidirectional_decoder
+              self).__init__(num_chars, num_speakers, r, postnet_output_dim,
-        decoder_dim = 512 if num_speakers > 1 else 512
+                             decoder_output_dim, attn_type, attn_win,
-        encoder_dim = 512 if num_speakers > 1 else 512
+                             attn_norm, prenet_type, prenet_dropout,
+                             forward_attn, trans_agent, forward_attn_mask,
+                             location_attn, attn_K, separate_stopnet,
+                             bidirectional_decoder, double_decoder_consistency,
+                             ddc_r, gst)
+        decoder_in_features = 512 if num_speakers > 1 else 512
+        encoder_in_features = 512 if num_speakers > 1 else 512
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # embedding layer
+        # base layers
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
         std = sqrt(2.0 / (num_chars + 512))
         val = sqrt(3.0) * std  # uniform bounds for std
@@ -42,20 +50,25 @@ class Tacotron2(nn.Module):
         if num_speakers > 1:
             self.speaker_embedding = nn.Embedding(num_speakers, 512)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
-            self.speaker_embeddings = None
+        self.encoder = Encoder(encoder_in_features)
-            self.speaker_embeddings_projected = None
+        self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
-        self.encoder = Encoder(encoder_dim)
-        self.decoder = Decoder(decoder_dim, self.decoder_output_dim, r, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
                                forward_attn, trans_agent, forward_attn_mask,
                                location_attn, attn_K, separate_stopnet, proj_speaker_dim)
-        if self.bidirectional_decoder:
-            self.decoder_backward = copy.deepcopy(self.decoder)
         self.postnet = Postnet(self.postnet_output_dim)
-
+        # global style token layers
-    def _init_states(self):
+        if self.gst:
-        self.speaker_embeddings = None
+            gst_embedding_dim = encoder_in_features
-        self.speaker_embeddings_projected = None
+            self.gst_layer = GST(num_mel=80,
+                                 num_heads=4,
+                                 num_style_tokens=10,
+                                 embedding_dim=gst_embedding_dim)
+        # backward pass decoder
+        if self.bidirectional_decoder:
+            self._init_backward_decoder()
+        # setup DDC
+        if self.double_decoder_consistency:
+            self._init_coarse_decoder()
 
     @staticmethod
     def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
@@ -63,31 +76,60 @@ class Tacotron2(nn.Module):
         mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
         return mel_outputs, mel_outputs_postnet, alignments
 
-    def forward(self, text, text_lengths, mel_specs=None, speaker_ids=None):
+    def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
         self._init_states()
         # compute mask for padding
-        mask = sequence_mask(text_lengths).to(text.device)
+        # B x T_in_max (boolean)
+        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
+        # B x D_embed x T_in_max
         embedded_inputs = self.embedding(text).transpose(1, 2)
+        # B x T_in_max x D_en
         encoder_outputs = self.encoder(embedded_inputs, text_lengths)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+        # adding speaker embeddding to encoder output
-                                                      speaker_ids)
+        # TODO: multi-speaker
+        # B x speaker_embed_dim
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
+        if self.num_speakers > 1:
+            # B x T_in x embed_dim + speaker_embed_dim
+            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+                                                          self.speaker_embeddings)
+        encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
+        # global style token
+        if self.gst:
+            # B x gst_dim
+            encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
+        # B x mel_dim x T_out -- B x T_out//r x T_in -- B x T_out//r
         decoder_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, mask)
+            encoder_outputs, mel_specs, input_mask)
+        # sequence masking
+        if mel_lengths is not None:
+            decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
+        # B x mel_dim x T_out
         postnet_outputs = self.postnet(decoder_outputs)
-        postnet_outputs = decoder_outputs + postnet_outputs
+        # sequence masking
+        if output_mask is not None:
+            postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs)
+        # B x T_out x mel_dim -- B x T_out x mel_dim -- B x T_out//r x T_in
         decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
             decoder_outputs, postnet_outputs, alignments)
         if self.bidirectional_decoder:
-            decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, mask)
+            decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
             return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
+        if self.double_decoder_consistency:
+            decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
+            return  decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
     def inference(self, text, speaker_ids=None):
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference(embedded_inputs)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+        if speaker_ids is not None:
-                                                      speaker_ids)
+            self.compute_speaker_embedding(speaker_ids)
+        if self.num_speakers > 1:
+            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+                                                    self.speaker_embeddings)
         mel_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
         mel_outputs_postnet = self.postnet(mel_outputs)
@@ -112,22 +154,16 @@ class Tacotron2(nn.Module):
             mel_outputs, mel_outputs_postnet, alignments)
         return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
 
-    def _backward_inference(self, mel_specs, encoder_outputs, mask):
-        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
-            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
-            self.speaker_embeddings_projected)
-        decoder_outputs_b = decoder_outputs_b.transpose(1, 2)
-        return decoder_outputs_b, alignments_b
 
-    def _add_speaker_embedding(self, encoder_outputs, speaker_ids):
+    def _speaker_embedding_pass(self, encoder_outputs, speaker_ids):
-        if hasattr(self, "speaker_embedding") and speaker_ids is None:
+        # TODO: multi-speaker
-            raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
+        # if hasattr(self, "speaker_embedding") and speaker_ids is None:
-        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
+        #     raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
-            speaker_embeddings = self.speaker_embedding(speaker_ids)
+        # if hasattr(self, "speaker_embedding") and speaker_ids is not None:
 
-            speaker_embeddings.unsqueeze_(1)
+        #     speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
-            speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
+        #                                                    encoder_outputs.size(1),
-                                                           encoder_outputs.size(1),
+        #                                                    -1)
-                                                           -1)
+        #     encoder_outputs = encoder_outputs + speaker_embeddings
-            encoder_outputs = encoder_outputs + speaker_embeddings
+        # return encoder_outputs
-        return encoder_outputs
+        pass