bugfix in DDC now DDC work on Tacotron1

TTS/tts/configs/config.json

@@ -1,5 +1,5 @@
 {
-    "model": "Tacotron2",
+    "model": "Tacotron",
     "run_name": "ljspeech-ddc-bn",
     "run_description": "tacotron2 with ddc and batch-normalization",
 
@@ -113,7 +113,7 @@
     "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
 
     // DATA LOADING
-    "text_cleaner": "phoneme_cleaners",
+    "text_cleaner": "portuguese_cleaners",
     "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
     "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
     "num_val_loader_workers": 4,    // number of evaluation data loader processes.
@@ -122,15 +122,15 @@
     "max_seq_len": 153,     // DATASET-RELATED: maximum text length
 
     // PATHS
-    "output_path": "/home/erogol/Models/LJSpeech/",
+    "output_path": "../../Mozilla-TTS/vctk-test/",
 
     // PHONEMES
-    "phoneme_cache_path": "/media/erogol/data_ssd2/mozilla_us_phonemes_3",  // phoneme computation is slow, therefore, it caches results in the given folder.
+    "phoneme_cache_path": "../../Mozilla-TTS/vctk-test/",  // phoneme computation is slow, therefore, it caches results in the given folder.
     "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
     "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
 
     // MULTI-SPEAKER and GST
-    "use_speaker_embedding": false,     // use speaker embedding to enable multi-speaker learning.
+    "use_speaker_embedding": true,     // use speaker embedding to enable multi-speaker learning.
     "use_gst": true,       			    // use global style tokens
     "gst":	{			                // gst parameter if gst is enabled
         "gst_style_input": null,        // Condition the style input either on a 
@@ -146,9 +146,9 @@
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
         [
             {
-                "name": "ljspeech",
-                "path": "/home/erogol/Data/LJSpeech-1.1/",
-                "meta_file_train": "metadata.csv",
+                "name": "vctk",
+                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
+                "meta_file_train": ["p225", "p234", "p238", "p245", "p248", "p261", "p294", "p302", "p326", "p335", "p347"], // for vtck if list, ignore speakers id in list for train, its useful for test cloning with new speakers
                 "meta_file_val": null
             }
         ]

TTS/tts/datasets/TTSDataset.py

@@ -70,7 +70,7 @@ def __init__(self,
         self.sort_items()
 
     def load_wav(self, filename):
-        audio = self.ap.load_wav(filename)
+        audio = self.ap.load_wav(filename, sr=self.sample_rate)
         return audio
 
     @staticmethod

TTS/tts/layers/tacotron.py

@@ -278,7 +278,7 @@ def __init__(self, in_features, memory_dim, r, memory_size, attn_type, attn_wind
         self.separate_stopnet = separate_stopnet
         self.query_dim = 256
         # memory -> |Prenet| -> processed_memory
-        prenet_dim = memory_dim * self.memory_size + speaker_embedding_dim if self.use_memory_queue else memory_dim + speaker_embedding_dim
+        prenet_dim = memory_dim * self.memory_size if self.use_memory_queue else memory_dim
         self.prenet = Prenet(
             prenet_dim,
             prenet_type,
@@ -405,7 +405,7 @@ def _update_memory_input(self, new_memory):
             # assert new_memory.shape[-1] == self.r * self.memory_dim
             self.memory_input = new_memory[:, self.memory_dim * (self.r - 1):]
 
-    def forward(self, inputs, memory, mask, speaker_embeddings=None):
+    def forward(self, inputs, memory, mask):
         """
         Args:
             inputs: Encoder outputs.
@@ -430,8 +430,7 @@ def forward(self, inputs, memory, mask, speaker_embeddings=None):
             if t > 0:
                 new_memory = memory[t - 1]
                 self._update_memory_input(new_memory)
-            if speaker_embeddings is not None:
-                self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
+
             output, stop_token, attention = self.decode(inputs, mask)
             outputs += [output]
             attentions += [attention]

TTS/tts/layers/tacotron2.py

@@ -246,13 +246,11 @@ def decode(self, memory):
         decoder_output = decoder_output[:, :self.r * self.frame_dim]
         return decoder_output, self.attention.attention_weights, stop_token
 
-    def forward(self, inputs, memories, mask, speaker_embeddings=None):
+    def forward(self, inputs, memories, mask):
         memory = self.get_go_frame(inputs).unsqueeze(0)
         memories = self._reshape_memory(memories)
         memories = torch.cat((memory, memories), dim=0)
         memories = self._update_memory(memories)
-        if speaker_embeddings is not None:
-            memories = torch.cat([memories, speaker_embeddings], dim=-1)
         memories = self.prenet(memories)
 
         self._init_states(inputs, mask=mask)

TTS/tts/models/tacotron.py

@@ -6,7 +6,6 @@
 from TTS.tts.layers.tacotron import Decoder, Encoder, PostCBHG
 from TTS.tts.models.tacotron_abstract import TacotronAbstract
 
-
 class Tacotron(TacotronAbstract):
     def __init__(self,
                  num_chars,
@@ -41,10 +40,19 @@ def __init__(self,
                              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
+
+
+        # init layer dims
+        decoder_in_features = 256
+        encoder_in_features = 256
         speaker_embedding_dim = 256
         proj_speaker_dim = 80 if num_speakers > 1 else 0
+
+        if num_speakers > 1: 
+            decoder_in_features = decoder_in_features + speaker_embedding_dim # add speaker embedding dim
+        if self.gst:
+            decoder_in_features = decoder_in_features + gst_embedding_dim # add gst embedding dim
+
         # base model layers
         self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
         self.embedding.weight.data.normal_(0, 0.3)
@@ -98,10 +106,6 @@ def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, 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
-            inputs = self._concat_speaker_embedding(inputs,
-                                                    self.speaker_embeddings)
         # B x T_in x encoder_in_features
         encoder_outputs = self.encoder(inputs)
         # sequence masking
@@ -117,8 +121,7 @@ def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker
         # 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, input_mask,
-            self.speaker_embeddings_projected)
+            encoder_outputs, mel_specs, input_mask)
         # sequence masking
         if output_mask is not None:
             decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
@@ -145,9 +148,6 @@ def inference(self, characters, speaker_ids=None, style_mel=None):
         self._init_states()
         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)
         encoder_outputs = self.encoder(inputs)
         if self.gst and style_mel is not None:
             encoder_outputs = self.compute_gst(encoder_outputs, style_mel)

TTS/tts/models/tacotron2.py

@@ -5,7 +5,6 @@
 from TTS.tts.layers.tacotron2 import Decoder, Encoder, Postnet
 from TTS.tts.models.tacotron_abstract import TacotronAbstract
 
-
 # TODO: match function arguments with tacotron
 class Tacotron2(TacotronAbstract):
     def __init__(self,
@@ -86,24 +85,6 @@ def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
         mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
         return mel_outputs, mel_outputs_postnet, alignments
 
-    def compute_gst(self, inputs, style_input):
-        """ Compute global style token """
-        device = inputs.device
-        if isinstance(style_input, dict):
-            query = torch.zeros(1, 1, self.gst_embedding_dim//2).to(device)
-            _GST = torch.tanh(self.gst_layer.style_token_layer.style_tokens)
-            gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
-            for k_token, v_amplifier in style_input.items():
-                key = _GST[int(k_token)].unsqueeze(0).expand(1, -1, -1)
-                gst_outputs_att = self.gst_layer.style_token_layer.attention(query, key)
-                gst_outputs = gst_outputs + gst_outputs_att * v_amplifier
-        elif style_input is None:
-            gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
-        else:
-            gst_outputs = self.gst_layer(style_input) # pylint: disable=not-callable
-        embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)
-        return inputs, embedded_gst
-
     def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
         # compute mask for padding
         # B x T_in_max (boolean)
@@ -113,20 +94,13 @@ def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_
         # B x T_in_max x D_en
         encoder_outputs = self.encoder(embedded_inputs, text_lengths)
 
+        if self.gst:
+            # B x gst_dim
+            encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
+
         if self.num_speakers > 1:
             embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
-            embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
-            if self.gst:
-                # B x gst_dim
-                encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, mel_specs)
-                encoder_outputs = torch.cat([encoder_outputs, embedded_gst, embedded_speakers], dim=-1)
-            else:
-                encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
-        else:
-            if self.gst:
-                # B x gst_dim
-                encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, mel_specs)
-                encoder_outputs = torch.cat([encoder_outputs, embedded_gst], dim=-1)
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, embedded_speakers)
 
         encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
 
@@ -163,15 +137,14 @@ def inference(self, text, speaker_ids=None, style_mel=None):
             embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
             if self.gst:
                 # B x gst_dim
-                encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, style_mel)
-                encoder_outputs = torch.cat([encoder_outputs, embedded_gst, embedded_speakers], dim=-1)
+                encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
+                encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
             else:
                 encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
         else:
             if self.gst:
                 # B x gst_dim
-                encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, style_mel)
-                encoder_outputs = torch.cat([encoder_outputs, embedded_gst], dim=-1)
+                encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
 
         decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
@@ -193,15 +166,13 @@ def inference_truncated(self, text, speaker_ids=None, style_mel=None):
             embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
             if self.gst:
                 # B x gst_dim
-                encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, style_mel)
-                encoder_outputs = torch.cat([encoder_outputs, embedded_gst, embedded_speakers], dim=-1)
+                encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
             else:
                 encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
         else:
             if self.gst:
                 # B x gst_dim
-                encoder_outputs, embedded_gst = self.compute_gst(encoder_outputs, style_mel)
-                encoder_outputs = torch.cat([encoder_outputs, embedded_gst], dim=-1)
+                encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
 
         mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(
             encoder_outputs)

TTS/tts/models/tacotron_abstract.py

@@ -164,11 +164,22 @@ def compute_speaker_embedding(self, speaker_ids):
             self.speaker_embeddings_projected = self.speaker_project_mel(
                 self.speaker_embeddings).squeeze(1)
 
-    def compute_gst(self, inputs, mel_specs):
+    def compute_gst(self, inputs, style_input):
         """ Compute global style token """
-        # pylint: disable=not-callable
-        gst_outputs = self.gst_layer(mel_specs)
-        inputs = self._add_speaker_embedding(inputs, gst_outputs)
+        device = inputs.device
+        if isinstance(style_input, dict):
+            query = torch.zeros(1, 1, self.gst_embedding_dim//2).to(device)
+            _GST = torch.tanh(self.gst_layer.style_token_layer.style_tokens)
+            gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
+            for k_token, v_amplifier in style_input.items():
+                key = _GST[int(k_token)].unsqueeze(0).expand(1, -1, -1)
+                gst_outputs_att = self.gst_layer.style_token_layer.attention(query, key)
+                gst_outputs = gst_outputs + gst_outputs_att * v_amplifier
+        elif style_input is None:
+            gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
+        else:
+            gst_outputs = self.gst_layer(style_input) # pylint: disable=not-callable
+        inputs = self._concat_speaker_embedding(inputs, gst_outputs)
         return inputs
 
     @staticmethod

TTS/tts/utils/generic_utils.py

@@ -245,13 +245,18 @@ def check_config(c):
 
     # multi-speaker gst
     check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
-    check_argument('style_wav_for_test', c, restricted=True, val_type=str)
     check_argument('use_gst', c, restricted=True, val_type=bool)
 
+    check_argument('gst', c, restricted=True, val_type=dict)
+    check_argument('gst_style_input', c['gst'], restricted=True, val_type=[str, dict])
+    check_argument('gst_embedding_dim', c['gst'], restricted=True, val_type=int, min_val=0, max_val=1000)
+    check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=2, max_val=10)
+    check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1, max_val=1000)
+
     # datasets - checking only the first entry
     check_argument('datasets', c, restricted=True, val_type=list)
     for dataset_entry in c['datasets']:
         check_argument('name', dataset_entry, restricted=True, val_type=str)
         check_argument('path', dataset_entry, restricted=True, val_type=str)
-        check_argument('meta_file_train', dataset_entry, restricted=True, val_type=str)
+        check_argument('meta_file_train', dataset_entry, restricted=True, val_type=[str, list])
         check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)

TTS/tts/utils/text/cleaners.py

@@ -107,7 +107,6 @@ def basic_turkish_cleaners(text):
     text = collapse_whitespace(text)
     return text
 
-
 def english_cleaners(text):
     '''Pipeline for English text, including number and abbreviation expansion.'''
     text = convert_to_ascii(text)

TTS/utils/generic_utils.py

@@ -150,5 +150,12 @@ def check_argument(name, c, enum_list=None, max_val=None, min_val=None, restrict
             assert c[name] >= min_val, f' [!] {name} is smaller than min value {min_val}'
         if enum_list:
             assert c[name].lower() in enum_list, f' [!] {name} is not a valid value'
-        if val_type:
+        if isinstance(val_type, list):
+            valid_types = val_type
+            is_valid = False
+            for typ in val_type:
+                if isinstance(c[name], typ):
+                    is_valid = True
+            assert is_valid or c[name] is None, f' [!] {name} has wrong type - {type(c[name])} vs {val_type}'
+        elif val_type:
             assert isinstance(c[name], val_type) or c[name] is None, f' [!] {name} has wrong type - {type(c[name])} vs {val_type}'