Merge branch 'dev' of https://github.com/mozilla/TTS into dev

layers/common_layers.py

@@ -138,7 +138,7 @@ class GravesAttention(nn.Module):
 
     def init_states(self, inputs):
         if self.J is None or inputs.shape[1]+1 > self.J.shape[-1]:
-            self.J = torch.arange(0, inputs.shape[1]+2).to(inputs.device) + 0.5
+            self.J = torch.arange(0, inputs.shape[1]+2.0).to(inputs.device) + 0.5
         self.attention_weights = torch.zeros(inputs.shape[0], inputs.shape[1]).to(inputs.device)
         self.mu_prev = torch.zeros(inputs.shape[0], self.K).to(inputs.device)
 

server/synthesizer.py

@@ -164,16 +164,20 @@ class Synthesizer(object):
         sentences = list(filter(None, [s.strip() for s in sentences])) # remove empty sentences
         return sentences
 
-    def tts(self, text):
+    def tts(self, text, speaker_id=None):
         wavs = []
         sens = self.split_into_sentences(text)
         print(sens)
+        speaker_id = id_to_torch(speaker_id)
+        if speaker_id is not None and self.use_cuda:
+            speaker_id = speaker_id.cuda()
+        
         for sen in sens:
             # preprocess the given text
             inputs = text_to_seqvec(sen, self.tts_config, self.use_cuda)
             # synthesize voice
             decoder_output, postnet_output, alignments, _ = run_model(
-                self.tts_model, inputs, self.tts_config, False, None, None)
+                self.tts_model, inputs, self.tts_config, False, speaker_id, None)
             # convert outputs to numpy
             postnet_output, decoder_output, _ = parse_outputs(
                 postnet_output, decoder_output, alignments)

speaker_encoder/config.json

@@ -34,6 +34,7 @@
     "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
     "print_step": 1, // Number of steps to log traning on console.
     "output_path": "/media/erogol/data_ssd/Models/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
+    "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
     "model": {
         "input_dim": 40,
         "proj_dim": 128,

speaker_encoder/train.py

@@ -44,7 +44,7 @@ def setup_loader(ap, is_val=False, verbose=False):
         loader = DataLoader(dataset,
                             batch_size=c.num_speakers_in_batch,
                             shuffle=False,
-                            num_workers=0,
+                            num_workers=c.num_loader_workers,
                             collate_fn=dataset.collate_fn)
     return loader
 

train.py

@@ -356,7 +356,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
                                   mel_lengths, decoder_backward_output,
                                   alignments, alignment_lengths, text_lengths)
             if c.bidirectional_decoder:
-                keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_backward_loss'].item(),
+                keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_b_loss'].item(),
                                         'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})
             if c.ga_alpha > 0:
                 keep_avg.update_values({'avg_ga_loss': loss_dict['ga_loss'].item()})