From b96e74dd4906df02a4fba23519eb947491f96716 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 5 Aug 2020 17:35:58 +0200
Subject: [PATCH 01/56] add multi-speaker arguments to the model def

---
 mozilla_voice_tts/bin/synthesize.py           |   2 +-
 mozilla_voice_tts/tts/configs/config.json     |  13 ++-
 mozilla_voice_tts/tts/models/tacotron.py      |   8 +-
 mozilla_voice_tts/tts/models/tacotron2.py     | 102 +++++++++++-------
 .../tts/models/tacotron_abstract.py           |  36 +++++--
 mozilla_voice_tts/tts/utils/generic_utils.py  |  17 ++-
 mozilla_voice_tts/tts/utils/synthesis.py      |  25 +++--
 mozilla_voice_tts/tts/utils/text/cleaners.py  |   7 ++
 8 files changed, 145 insertions(+), 65 deletions(-)

diff --git a/mozilla_voice_tts/bin/synthesize.py b/mozilla_voice_tts/bin/synthesize.py
index 6f139433..b52db37e 100644
--- a/mozilla_voice_tts/bin/synthesize.py
+++ b/mozilla_voice_tts/bin/synthesize.py
@@ -20,7 +20,7 @@ from mozilla_voice_tts.vocoder.utils.generic_utils import setup_generator
 
 def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_id):
     t_1 = time.time()
-    waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, None, False, CONFIG.enable_eos_bos_chars, use_gl)
+    waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, CONFIG.gst['gst_style_input'], False, CONFIG.enable_eos_bos_chars, use_gl)
     if CONFIG.model == "Tacotron" and not use_gl:
         mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T
     if not use_gl:
diff --git a/mozilla_voice_tts/tts/configs/config.json b/mozilla_voice_tts/tts/configs/config.json
index cd4595b9..9068e2c4 100644
--- a/mozilla_voice_tts/tts/configs/config.json
+++ b/mozilla_voice_tts/tts/configs/config.json
@@ -132,8 +132,16 @@
 
     // MULTI-SPEAKER and GST
     "use_speaker_embedding": false,     // use speaker embedding to enable multi-speaker learning.
-    "style_wav_for_test": null,          // path to style wav file to be used in TacotronGST inference.
-    "use_gst": false,       // TACOTRON ONLY: use global style tokens
+    "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 
+                                        // -> wave file [path to wave] or 
+                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}  
+                                        // with the dictionary being len(dict) == len(gst_style_tokens).
+        "gst_embedding_dim": 512,       
+        "gst_num_heads": 4,
+        "gst_style_tokens": 10
+	},  
 
     // DATASETS
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
@@ -145,6 +153,5 @@
                 "meta_file_val": null
             }
         ]
-
 }
 
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index 295dbeda..6e0ba09b 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -29,6 +29,9 @@ class Tacotron(TacotronAbstract):
                  double_decoder_consistency=False,
                  ddc_r=None,
                  gst=False,
+                 gst_embedding_dim=256,
+                 gst_num_heads=4,
+                 gst_style_tokens=10,
                  memory_size=5):
         super(Tacotron,
               self).__init__(num_chars, num_speakers, r, postnet_output_dim,
@@ -64,10 +67,9 @@ class Tacotron(TacotronAbstract):
             self.speaker_embeddings_projected = None
         # global style token layers
         if self.gst:
-            gst_embedding_dim = 256
             self.gst_layer = GST(num_mel=80,
-                                 num_heads=4,
-                                 num_style_tokens=10,
+                                 num_heads=gst_num_heads,
+                                 num_style_tokens=gst_style_tokens,
                                  embedding_dim=gst_embedding_dim)
         # backward pass decoder
         if self.bidirectional_decoder:
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 327e1bd9..52da2f39 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -28,7 +28,10 @@ class Tacotron2(TacotronAbstract):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
-                 gst=False):
+                 gst=False,
+                 gst_embedding_dim=512,
+                 gst_num_heads=4,
+                 gst_style_tokens=10):
         super(Tacotron2,
               self).__init__(num_chars, num_speakers, r, postnet_output_dim,
                              decoder_output_dim, attn_type, attn_win,
@@ -37,13 +40,17 @@ class Tacotron2(TacotronAbstract):
                              location_attn, attn_K, separate_stopnet,
                              bidirectional_decoder, double_decoder_consistency,
                              ddc_r, gst)
-        decoder_in_features = 512 if num_speakers > 1 else 512
+
+        # init layer dims
+        speaker_embedding_dim = 512 if num_speakers > 1 else 0
+        gst_embedding_dim = gst_embedding_dim if self.gst else 0
+        decoder_in_features = 512+speaker_embedding_dim+gst_embedding_dim
         encoder_in_features = 512 if num_speakers > 1 else 512
         proj_speaker_dim = 80 if num_speakers > 1 else 0
         # base layers
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
         if num_speakers > 1:
-            self.speaker_embedding = nn.Embedding(num_speakers, 512)
+            self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
@@ -53,10 +60,9 @@ class Tacotron2(TacotronAbstract):
         self.postnet = Postnet(self.postnet_output_dim)
         # global style token layers
         if self.gst:
-            gst_embedding_dim = encoder_in_features
             self.gst_layer = GST(num_mel=80,
-                                 num_heads=4,
-                                 num_style_tokens=10,
+                                 num_heads=gst_num_heads,
+                                 num_style_tokens=gst_style_tokens,
                                  embedding_dim=gst_embedding_dim)
         # backward pass decoder
         if self.bidirectional_decoder:
@@ -76,7 +82,6 @@ class Tacotron2(TacotronAbstract):
         return mel_outputs, mel_outputs_postnet, alignments
 
     def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
-        self._init_states()
         # compute mask for padding
         # B x T_in_max (boolean)
         input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
@@ -84,20 +89,24 @@ class Tacotron2(TacotronAbstract):
         embedded_inputs = self.embedding(text).transpose(1, 2)
         # B x T_in_max x D_en
         encoder_outputs = self.encoder(embedded_inputs, text_lengths)
-        # adding speaker embeddding to encoder output
-        # 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)
+            embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
+            embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
+            if hasattr(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 hasattr(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 = 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, input_mask)
@@ -122,14 +131,25 @@ class Tacotron2(TacotronAbstract):
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
-    def inference(self, text, speaker_ids=None):
+    def inference(self, text, speaker_ids=None, style_mel=None):
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference(embedded_inputs)
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
+
         if self.num_speakers > 1:
-            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                          self.speaker_embeddings)
+            embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
+            embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
+            if hasattr(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)
+            else:
+                encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
+        else:
+            if hasattr(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)
+
         decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
         postnet_outputs = self.postnet(decoder_outputs)
@@ -138,14 +158,28 @@ class Tacotron2(TacotronAbstract):
             decoder_outputs, postnet_outputs, alignments)
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
-    def inference_truncated(self, text, speaker_ids=None):
+    def inference_truncated(self, text, speaker_ids=None, style_mel=None):
         """
         Preserve model states for continuous inference
         """
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference_truncated(embedded_inputs)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                      speaker_ids)
+
+        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 hasattr(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)
+            else:
+                encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
+        else:
+            if hasattr(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)
+
         mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(
             encoder_outputs)
         mel_outputs_postnet = self.postnet(mel_outputs)
@@ -153,17 +187,3 @@ class Tacotron2(TacotronAbstract):
         mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
             mel_outputs, mel_outputs_postnet, alignments)
         return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
-
-
-    def _speaker_embedding_pass(self, encoder_outputs, speaker_ids):
-        # TODO: multi-speaker
-        # 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:
-
-        #     speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
-        #                                                    encoder_outputs.size(1),
-        #                                                    -1)
-        #     encoder_outputs = encoder_outputs + speaker_embeddings
-        # return encoder_outputs
-        pass
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index c9ae9b83..bc794d49 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -28,7 +28,10 @@ class TacotronAbstract(ABC, nn.Module):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
-                 gst=False):
+                 gst=False,
+                 gst_embedding_dim=512,
+                 gst_num_heads=4,
+                 gst_style_tokens=10):
         """ Abstract Tacotron class """
         super().__init__()
         self.num_chars = num_chars
@@ -36,6 +39,9 @@ class TacotronAbstract(ABC, nn.Module):
         self.decoder_output_dim = decoder_output_dim
         self.postnet_output_dim = postnet_output_dim
         self.gst = gst
+        self.gst_embedding_dim = gst_embedding_dim
+        self.gst_num_heads = gst_num_heads
+        self.gst_style_tokens = gst_style_tokens
         self.num_speakers = num_speakers
         self.bidirectional_decoder = bidirectional_decoder
         self.double_decoder_consistency = double_decoder_consistency
@@ -158,12 +164,28 @@ class TacotronAbstract(ABC, nn.Module):
             self.speaker_embeddings_projected = self.speaker_project_mel(
                 self.speaker_embeddings).squeeze(1)
 
-    def compute_gst(self, inputs, mel_specs):
-        """ Compute global style token """
-        # pylint: disable=not-callable
-        gst_outputs = self.gst_layer(mel_specs)
-        inputs = self._add_speaker_embedding(inputs, gst_outputs)
-        return inputs
+    def compute_gst(self, inputs, style_input):
+        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:
+            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 in range(self.gst_style_tokens):
+                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 * 0
+        else:
+            gst_outputs = self.gst_layer(style_input)
+        embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)
+        return inputs, embedded_gst
 
     @staticmethod
     def _add_speaker_embedding(outputs, speaker_embeddings):
diff --git a/mozilla_voice_tts/tts/utils/generic_utils.py b/mozilla_voice_tts/tts/utils/generic_utils.py
index e98c267d..212379a3 100644
--- a/mozilla_voice_tts/tts/utils/generic_utils.py
+++ b/mozilla_voice_tts/tts/utils/generic_utils.py
@@ -55,6 +55,9 @@ def setup_model(num_chars, num_speakers, c):
                         postnet_output_dim=int(c.audio['fft_size'] / 2 + 1),
                         decoder_output_dim=c.audio['num_mels'],
                         gst=c.use_gst,
+                        gst_embedding_dim=c.gst['gst_embedding_dim'],
+                        gst_num_heads=c.gst['gst_num_heads'],
+                        gst_style_tokens=c.gst['gst_style_tokens'],
                         memory_size=c.memory_size,
                         attn_type=c.attention_type,
                         attn_win=c.windowing,
@@ -77,6 +80,9 @@ def setup_model(num_chars, num_speakers, c):
                         postnet_output_dim=c.audio['num_mels'],
                         decoder_output_dim=c.audio['num_mels'],
                         gst=c.use_gst,
+                        gst_embedding_dim=c.gst['gst_embedding_dim'],
+                        gst_num_heads=c.gst['gst_num_heads'],
+                        gst_style_tokens=c.gst['gst_style_tokens'],
                         attn_type=c.attention_type,
                         attn_win=c.windowing,
                         attn_norm=c.attention_norm,
@@ -93,6 +99,7 @@ def setup_model(num_chars, num_speakers, c):
                         ddc_r=c.ddc_r)
     return model
 
+
 class KeepAverage():
     def __init__(self):
         self.avg_values = {}
@@ -239,10 +246,16 @@ def check_config(c):
     # paths
     check_argument('output_path', c, restricted=True, val_type=str)
 
-    # multi-speaker gst
+    # multi-speaker
     check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
-    check_argument('style_wav_for_test', c, restricted=True, val_type=str)
+
+    # GST
     check_argument('use_gst', c, restricted=True, val_type=bool)
+    check_argument('gst_style_input', c, restricted=True, val_type=str)
+    check_argument('gst', c, restricted=True, val_type=dict)
+    check_argument('gst_embedding_dim', c['gst'], restricted=True, val_type=int, min_val=1)
+    check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=1)
+    check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1)
 
     # datasets - checking only the first entry
     check_argument('datasets', c, restricted=True, val_type=list)
diff --git a/mozilla_voice_tts/tts/utils/synthesis.py b/mozilla_voice_tts/tts/utils/synthesis.py
index fef2348d..6fed8f89 100644
--- a/mozilla_voice_tts/tts/utils/synthesis.py
+++ b/mozilla_voice_tts/tts/utils/synthesis.py
@@ -37,9 +37,11 @@ def numpy_to_tf(np_array, dtype):
     return tensor
 
 
-def compute_style_mel(style_wav, ap):
-    style_mel = ap.melspectrogram(
-        ap.load_wav(style_wav)).expand_dims(0)
+def compute_style_mel(style_wav, ap, cuda=False):
+    style_mel = torch.FloatTensor(ap.melspectrogram(
+        ap.load_wav(style_wav))).unsqueeze(0)
+    if cuda:
+        return style_mel.cuda()
     return style_mel
 
 
@@ -129,10 +131,12 @@ def inv_spectrogram(postnet_output, ap, CONFIG):
     return wav
 
 
-def id_to_torch(speaker_id):
+def id_to_torch(speaker_id, cuda=False):
     if speaker_id is not None:
         speaker_id = np.asarray(speaker_id)
         speaker_id = torch.from_numpy(speaker_id).unsqueeze(0)
+    if cuda:
+        return speaker_id.cuda()
     return speaker_id
 
 
@@ -185,14 +189,19 @@ def synthesis(model,
     """
     # GST processing
     style_mel = None
-    if CONFIG.model == "TacotronGST" and style_wav is not None:
-        style_mel = compute_style_mel(style_wav, ap)
+    if CONFIG.use_gst and style_wav is not None:
+        if isinstance(style_wav, dict):
+            style_mel = style_wav
+        else:
+            style_mel = compute_style_mel(style_wav, ap, cuda=use_cuda)
     # preprocess the given text
     inputs = text_to_seqvec(text, CONFIG)
     # pass tensors to backend
     if backend == 'torch':
-        speaker_id = id_to_torch(speaker_id)
-        style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
+        if speaker_id is not None:
+            speaker_id = id_to_torch(speaker_id, cuda=use_cuda)
+        if not isinstance(style_mel, dict):
+            style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
         inputs = numpy_to_torch(inputs, torch.long, cuda=use_cuda)
         inputs = inputs.unsqueeze(0)
     elif backend == 'tf':
diff --git a/mozilla_voice_tts/tts/utils/text/cleaners.py b/mozilla_voice_tts/tts/utils/text/cleaners.py
index f0a66f57..dd329f9c 100644
--- a/mozilla_voice_tts/tts/utils/text/cleaners.py
+++ b/mozilla_voice_tts/tts/utils/text/cleaners.py
@@ -91,6 +91,13 @@ def transliteration_cleaners(text):
     return text
 
 
+def basic_german_cleaners(text):
+    '''Pipeline for Turkish text'''
+    text = lowercase(text)
+    text = collapse_whitespace(text)
+    return text
+
+
 # TODO: elaborate it
 def basic_turkish_cleaners(text):
     '''Pipeline for Turkish text'''

From 8d0d4919fdce201b138deb774ef5aec5cd60d04e Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Fri, 10 Jul 2020 12:46:43 +0200
Subject: [PATCH 02/56] No need to query every token when none were passed

---
 mozilla_voice_tts/tts/configs/config.json         | 2 +-
 mozilla_voice_tts/tts/models/tacotron_abstract.py | 6 ------
 2 files changed, 1 insertion(+), 7 deletions(-)

diff --git a/mozilla_voice_tts/tts/configs/config.json b/mozilla_voice_tts/tts/configs/config.json
index 9068e2c4..8f56816e 100644
--- a/mozilla_voice_tts/tts/configs/config.json
+++ b/mozilla_voice_tts/tts/configs/config.json
@@ -137,7 +137,7 @@
         "gst_style_input": null,        // Condition the style input either on a 
                                         // -> wave file [path to wave] or 
                                         // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}  
-                                        // with the dictionary being len(dict) == len(gst_style_tokens).
+                                        // with the dictionary being len(dict) <= len(gst_style_tokens).
         "gst_embedding_dim": 512,       
         "gst_num_heads": 4,
         "gst_style_tokens": 10
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index bc794d49..13c3e948 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -175,13 +175,7 @@ class TacotronAbstract(ABC, nn.Module):
                 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:
-            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 in range(self.gst_style_tokens):
-                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 * 0
         else:
             gst_outputs = self.gst_layer(style_input)
         embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)

From bdf69446653fe84e6fd69fb91c2bf99adfd7efff Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Sun, 12 Jul 2020 10:40:33 +0200
Subject: [PATCH 03/56] fix fft_size key error

---
 mozilla_voice_tts/tts/models/tacotron_abstract.py |  1 +
 tests/inputs/test_config.json                     | 14 ++++++++++++--
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index 13c3e948..d1148be5 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -177,6 +177,7 @@ class TacotronAbstract(ABC, nn.Module):
         elif style_input is None:
             gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
         else:
+            # pylint: disable=not-callable
             gst_outputs = self.gst_layer(style_input)
         embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)
         return inputs, embedded_gst
diff --git a/tests/inputs/test_config.json b/tests/inputs/test_config.json
index 6da13bfc..b34a53a8 100644
--- a/tests/inputs/test_config.json
+++ b/tests/inputs/test_config.json
@@ -2,7 +2,7 @@
     "audio":{
         "audio_processor": "audio",     // to use dictate different audio processors, if available.
         "num_mels": 80,         // size of the mel spec frame.
-        "num_freq": 513,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "fft_size": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
         "sample_rate": 22050,   // wav sample-rate. If different than the original data, it is resampled.
         "frame_length_ms": null,  // stft window length in ms.
         "frame_shift_ms": null, // stft window hop-lengh in ms.
@@ -51,5 +51,15 @@
     "output_path": "result",
     "min_seq_len": 0,
     "max_seq_len": 300,
-    "log_dir": "tests/outputs/"
+    "log_dir": "tests/outputs/",
+
+    "use_speaker_embedding": false,     
+    "use_gst": false,       			    
+    "gst":	{			                
+        "gst_style_input": null,        
+        "gst_embedding_dim": 512,       
+        "gst_num_heads": 4,
+        "gst_style_tokens": 10
+	},  
+
     }

From eb51d5409af0b70f48cfb817241159d691fd81d6 Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Sun, 12 Jul 2020 12:33:13 +0200
Subject: [PATCH 04/56] pylint

---
 mozilla_voice_tts/tts/models/tacotron_abstract.py | 3 +--
 tests/inputs/test_config.json                     | 3 +--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index d1148be5..9b2ef148 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -177,8 +177,7 @@ class TacotronAbstract(ABC, nn.Module):
         elif style_input is None:
             gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
         else:
-            # pylint: disable=not-callable
-            gst_outputs = self.gst_layer(style_input)
+            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
 
diff --git a/tests/inputs/test_config.json b/tests/inputs/test_config.json
index b34a53a8..450cb23a 100644
--- a/tests/inputs/test_config.json
+++ b/tests/inputs/test_config.json
@@ -60,6 +60,5 @@
         "gst_embedding_dim": 512,       
         "gst_num_heads": 4,
         "gst_style_tokens": 10
-	},  
-
+	}
     }

From e206ff8a28f28beed5b44be363025f5cdd4ecd87 Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Mon, 13 Jul 2020 08:50:39 +0200
Subject: [PATCH 05/56] override compute_gst in tacotron2 model

---
 mozilla_voice_tts/tts/models/tacotron2.py     | 25 ++++++++++++++++++-
 .../tts/models/tacotron_abstract.py           | 22 +++++-----------
 2 files changed, 30 insertions(+), 17 deletions(-)

diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 52da2f39..59e44fb2 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -47,17 +47,22 @@ class Tacotron2(TacotronAbstract):
         decoder_in_features = 512+speaker_embedding_dim+gst_embedding_dim
         encoder_in_features = 512 if num_speakers > 1 else 512
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # base layers
+
+        # embedding layer
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
+
+        # speaker embedding layer
         if num_speakers > 1:
             self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
+
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, 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)
         self.postnet = Postnet(self.postnet_output_dim)
+
         # global style token layers
         if self.gst:
             self.gst_layer = GST(num_mel=80,
@@ -81,6 +86,24 @@ class Tacotron2(TacotronAbstract):
         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)
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index 9b2ef148..a4b8c227 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -164,22 +164,12 @@ class TacotronAbstract(ABC, nn.Module):
             self.speaker_embeddings_projected = self.speaker_project_mel(
                 self.speaker_embeddings).squeeze(1)
 
-    def compute_gst(self, inputs, style_input):
-        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 compute_gst(self, inputs, mel_specs):
+        """ Compute global style token """
+        # pylint: disable=not-callable
+        gst_outputs = self.gst_layer(mel_specs)
+        inputs = self._add_speaker_embedding(inputs, gst_outputs)
+        return inputs
 
     @staticmethod
     def _add_speaker_embedding(outputs, speaker_embeddings):

From b0c4a77aea67b0ea93a4c2419f2e2c4dba518577 Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Mon, 13 Jul 2020 08:51:37 +0200
Subject: [PATCH 06/56] small gst config change

---
 tests/test_tacotron2_model.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index 2faccd75..a0c5e59a 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -20,7 +20,12 @@ c = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
 
 class TacotronTrainTest(unittest.TestCase):
+<<<<<<< HEAD
     def test_train_step(self):  # pylint: disable=no-self-use
+=======
+    @staticmethod
+    def test_train_step():
+>>>>>>> small gst config change
         input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
         input_lengths = torch.randint(100, 128, (8, )).long().to(device)
         input_lengths = torch.sort(input_lengths, descending=True)[0]

From 571f5761c950008f343c59282455ba1c5fc292e6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Eren=20G=C3=B6lge?= <erogol@hotmail.com>
Date: Mon, 13 Jul 2020 10:33:55 +0200
Subject: [PATCH 07/56] update comment

---
 mozilla_voice_tts/tts/utils/text/cleaners.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mozilla_voice_tts/tts/utils/text/cleaners.py b/mozilla_voice_tts/tts/utils/text/cleaners.py
index dd329f9c..6d1ace08 100644
--- a/mozilla_voice_tts/tts/utils/text/cleaners.py
+++ b/mozilla_voice_tts/tts/utils/text/cleaners.py
@@ -92,7 +92,7 @@ def transliteration_cleaners(text):
 
 
 def basic_german_cleaners(text):
-    '''Pipeline for Turkish text'''
+    '''Pipeline for German text'''
     text = lowercase(text)
     text = collapse_whitespace(text)
     return text

From 93a9cc4683316eafbe6d9a3e951bd13938527fec Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Mon, 27 Jul 2020 16:59:59 -0300
Subject: [PATCH 08/56] add support fot VCTK and BRSpeech dataset

---
 mozilla_voice_tts/tts/datasets/preprocess.py | 35 ++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/mozilla_voice_tts/tts/datasets/preprocess.py b/mozilla_voice_tts/tts/datasets/preprocess.py
index c3cf34e5..2ad414fb 100644
--- a/mozilla_voice_tts/tts/datasets/preprocess.py
+++ b/mozilla_voice_tts/tts/datasets/preprocess.py
@@ -205,3 +205,38 @@ def custom_turkish(root_path, meta_file):
             items.append([text, wav_file, speaker_name])
     print(f" [!] {len(skipped_files)} files skipped. They don't exist...")
     return items
+
+# ToDo: add the dataset link when the dataset is released publicly
+def brspeech(root_path, meta_file):
+    '''BRSpeech 3.0 beta'''
+    txt_file = os.path.join(root_path, meta_file)
+    items = []
+    with open(txt_file, 'r') as ttf:
+        for line in ttf:
+            if line.startswith("wav_filename"):
+                continue
+            cols = line.split('|')
+            #print(cols)
+            wav_file = os.path.join(root_path, cols[0])
+            text = cols[2]
+            speaker_name = cols[3]
+            items.append([text, wav_file, speaker_name])
+    return items
+
+def vctk(root_path, meta_files=None, wavs_path='wav48'):
+    """homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
+    test_speakers = meta_files
+    items = []
+    meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
+    for meta_file in meta_files:
+        txt, speaker_id, txt_file = os.path.relpath(meta_file,root_path).split(os.sep)
+        file_id = txt_file.split('.')[0]
+        if isinstance(test_speakers, list): # if is list ignore this speakers ids
+            if speaker_id in test_speakers:
+                continue
+        with open(meta_file) as file_text:
+            text = file_text.readlines()[0]
+        wav_file = os.path.join(root_path, wavs_path, speaker_id,file_id+'.wav')
+        items.append([text, wav_file, speaker_id])
+    
+    return items
\ No newline at end of file

From b7504527828eb3e2377b932fe6dd5bcf1aa50a58 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Mon, 27 Jul 2020 17:20:51 -0300
Subject: [PATCH 09/56] add Portuguese Cleaner

---
 mozilla_voice_tts/tts/utils/text/cleaners.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/mozilla_voice_tts/tts/utils/text/cleaners.py b/mozilla_voice_tts/tts/utils/text/cleaners.py
index 6d1ace08..227118e6 100644
--- a/mozilla_voice_tts/tts/utils/text/cleaners.py
+++ b/mozilla_voice_tts/tts/utils/text/cleaners.py
@@ -67,15 +67,16 @@ def remove_aux_symbols(text):
     text = re.sub(r'[\<\>\(\)\[\]\"]+', '', text)
     return text
 
-
-def replace_symbols(text):
+def replace_symbols(text, lang='en'):
     text = text.replace(';', ',')
     text = text.replace('-', ' ')
-    text = text.replace(':', ',')
-    text = text.replace('&', 'and')
+    text = text.replace(':', ' ')
+    if lang == 'en':
+        text = text.replace('&', 'and')
+    elif lang == 'pt':
+        text = text.replace('&', ' e ')
     return text
 
-
 def basic_cleaners(text):
     '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
     text = lowercase(text)
@@ -118,6 +119,14 @@ def english_cleaners(text):
     text = collapse_whitespace(text)
     return text
 
+def portuguese_cleaners(text):
+    '''Basic pipeline for Portuguese text. There is no need to expand abbreviation and 
+        numbers, phonemizer already does that'''
+    text = lowercase(text)
+    text = replace_symbols(text, lang='pt')
+    text = remove_aux_symbols(text)
+    text = collapse_whitespace(text)
+    return text
 
 def phoneme_cleaners(text):
     '''Pipeline for phonemes mode, including number and abbreviation expansion.'''

From e265810e8c430be60e3775a41fff4de70e4ecb1e Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Tue, 28 Jul 2020 17:11:32 -0300
Subject: [PATCH 10/56] bugfix in DDC now DDC work on Tacotron1

---
 mozilla_voice_tts/tts/configs/config.json     | 16 +++---
 mozilla_voice_tts/tts/datasets/TTSDataset.py  |  2 +-
 mozilla_voice_tts/tts/layers/tacotron.py      |  7 ++-
 mozilla_voice_tts/tts/layers/tacotron2.py     |  4 +-
 mozilla_voice_tts/tts/models/tacotron.py      | 24 ++++-----
 mozilla_voice_tts/tts/models/tacotron2.py     | 49 ++++---------------
 .../tts/models/tacotron_abstract.py           | 19 +++++--
 mozilla_voice_tts/tts/utils/generic_utils.py  |  8 ++-
 mozilla_voice_tts/tts/utils/text/cleaners.py  |  1 -
 mozilla_voice_tts/utils/generic_utils.py      |  9 +++-
 10 files changed, 65 insertions(+), 74 deletions(-)

diff --git a/mozilla_voice_tts/tts/configs/config.json b/mozilla_voice_tts/tts/configs/config.json
index 8f56816e..154e1961 100644
--- a/mozilla_voice_tts/tts/configs/config.json
+++ b/mozilla_voice_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",
 
@@ -114,7 +114,7 @@
     "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.
@@ -123,15 +123,15 @@
     "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 
@@ -147,9 +147,9 @@
     "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
             }
         ]
diff --git a/mozilla_voice_tts/tts/datasets/TTSDataset.py b/mozilla_voice_tts/tts/datasets/TTSDataset.py
index ac524e55..cc6b72b5 100644
--- a/mozilla_voice_tts/tts/datasets/TTSDataset.py
+++ b/mozilla_voice_tts/tts/datasets/TTSDataset.py
@@ -70,7 +70,7 @@ class MyDataset(Dataset):
         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
diff --git a/mozilla_voice_tts/tts/layers/tacotron.py b/mozilla_voice_tts/tts/layers/tacotron.py
index 2fc9e86a..bbeee95f 100644
--- a/mozilla_voice_tts/tts/layers/tacotron.py
+++ b/mozilla_voice_tts/tts/layers/tacotron.py
@@ -303,7 +303,7 @@ class Decoder(nn.Module):
         self.separate_stopnet = separate_stopnet
         self.query_dim = 256
         # memory -> |Prenet| -> processed_memory
-        prenet_dim = frame_channels * self.memory_size + speaker_embedding_dim if self.use_memory_queue else frame_channels + 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,
@@ -429,7 +429,7 @@ class Decoder(nn.Module):
             # assert new_memory.shape[-1] == self.r * self.frame_channels
             self.memory_input = new_memory[:, self.frame_channels * (self.r - 1):]
 
-    def forward(self, inputs, memory, mask, speaker_embeddings=None):
+    def forward(self, inputs, memory, mask):
         """
         Args:
             inputs: Encoder outputs.
@@ -454,8 +454,7 @@ class Decoder(nn.Module):
             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]
diff --git a/mozilla_voice_tts/tts/layers/tacotron2.py b/mozilla_voice_tts/tts/layers/tacotron2.py
index 395a10ea..7c0dd443 100644
--- a/mozilla_voice_tts/tts/layers/tacotron2.py
+++ b/mozilla_voice_tts/tts/layers/tacotron2.py
@@ -300,7 +300,7 @@ class Decoder(nn.Module):
         decoder_output = decoder_output[:, :self.r * self.frame_channels]
         return decoder_output, self.attention.attention_weights, stop_token
 
-    def forward(self, inputs, memories, mask, speaker_embeddings=None):
+    def forward(self, inputs, memories, mask):
         r"""Train Decoder with teacher forcing.
         Args:
             inputs: Encoder outputs.
@@ -318,8 +318,6 @@ class Decoder(nn.Module):
         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)
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index 6e0ba09b..8eda83b3 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -6,7 +6,6 @@ from mozilla_voice_tts.tts.layers.gst_layers import GST
 from mozilla_voice_tts.tts.layers.tacotron import Decoder, Encoder, PostCBHG
 from mozilla_voice_tts.tts.models.tacotron_abstract import TacotronAbstract
 
-
 class Tacotron(TacotronAbstract):
     def __init__(self,
                  num_chars,
@@ -41,10 +40,19 @@ class Tacotron(TacotronAbstract):
                              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 @@ class Tacotron(TacotronAbstract):
         # 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 @@ class Tacotron(TacotronAbstract):
         # 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 @@ class Tacotron(TacotronAbstract):
         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)
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 59e44fb2..944138bc 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -5,7 +5,6 @@ from mozilla_voice_tts.tts.layers.gst_layers import GST
 from mozilla_voice_tts.tts.layers.tacotron2 import Decoder, Encoder, Postnet
 from mozilla_voice_tts.tts.models.tacotron_abstract import TacotronAbstract
 
-
 # TODO: match function arguments with tacotron
 class Tacotron2(TacotronAbstract):
     def __init__(self,
@@ -86,24 +85,6 @@ class Tacotron2(TacotronAbstract):
         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 @@ class Tacotron2(TacotronAbstract):
         # 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 hasattr(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 hasattr(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 @@ class Tacotron2(TacotronAbstract):
             embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
             if hasattr(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 hasattr(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 @@ class Tacotron2(TacotronAbstract):
             embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
             if hasattr(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 hasattr(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)
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index a4b8c227..6f3d32ad 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -164,11 +164,22 @@ class TacotronAbstract(ABC, nn.Module):
             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
diff --git a/mozilla_voice_tts/tts/utils/generic_utils.py b/mozilla_voice_tts/tts/utils/generic_utils.py
index 212379a3..9c3c618d 100644
--- a/mozilla_voice_tts/tts/utils/generic_utils.py
+++ b/mozilla_voice_tts/tts/utils/generic_utils.py
@@ -257,10 +257,16 @@ def check_config(c):
     check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=1)
     check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1)
 
+    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)
diff --git a/mozilla_voice_tts/tts/utils/text/cleaners.py b/mozilla_voice_tts/tts/utils/text/cleaners.py
index 227118e6..b1930834 100644
--- a/mozilla_voice_tts/tts/utils/text/cleaners.py
+++ b/mozilla_voice_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)
diff --git a/mozilla_voice_tts/utils/generic_utils.py b/mozilla_voice_tts/utils/generic_utils.py
index 478b4358..add5120d 100644
--- a/mozilla_voice_tts/utils/generic_utils.py
+++ b/mozilla_voice_tts/utils/generic_utils.py
@@ -146,5 +146,12 @@ 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}'

From 8a1c113df6403b0aae6d951fec8624643953e018 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Wed, 29 Jul 2020 00:49:00 -0300
Subject: [PATCH 11/56] add External Embedding per sample instead of
 nn.Embedding

---
 mozilla_voice_tts/bin/train_tts.py           | 89 +++++++++++++-------
 mozilla_voice_tts/tts/configs/config.json    |  8 +-
 mozilla_voice_tts/tts/datasets/TTSDataset.py | 18 +++-
 mozilla_voice_tts/tts/layers/tacotron.py     | 11 +--
 mozilla_voice_tts/tts/layers/tacotron2.py    |  9 +-
 mozilla_voice_tts/tts/models/tacotron.py     | 75 ++++++++++-------
 mozilla_voice_tts/tts/models/tacotron2.py    | 84 ++++++++++--------
 mozilla_voice_tts/tts/utils/generic_utils.py | 16 ++--
 mozilla_voice_tts/tts/utils/speakers.py      |  7 +-
 9 files changed, 190 insertions(+), 127 deletions(-)

diff --git a/mozilla_voice_tts/bin/train_tts.py b/mozilla_voice_tts/bin/train_tts.py
index 719b926f..0642f290 100644
--- a/mozilla_voice_tts/bin/train_tts.py
+++ b/mozilla_voice_tts/bin/train_tts.py
@@ -49,7 +49,7 @@ from mozilla_voice_tts.utils.training import (NoamLR, adam_weight_decay,
 use_cuda, num_gpus = setup_torch_training_env(True, False)
 
 
-def setup_loader(ap, r, is_val=False, verbose=False):
+def setup_loader(ap, r, is_val=False, verbose=False, speaker_mapping=None):
     if is_val and not c.run_eval:
         loader = None
     else:
@@ -68,7 +68,8 @@ def setup_loader(ap, r, is_val=False, verbose=False):
             use_phonemes=c.use_phonemes,
             phoneme_language=c.phoneme_language,
             enable_eos_bos=c.enable_eos_bos_chars,
-            verbose=verbose)
+            verbose=verbose,
+            speaker_mapping=speaker_mapping if c.use_speaker_embedding and c.use_external_speaker_embedding_file else None)
         sampler = DistributedSampler(dataset) if num_gpus > 1 else None
         loader = DataLoader(
             dataset,
@@ -82,9 +83,8 @@ def setup_loader(ap, r, is_val=False, verbose=False):
             pin_memory=False)
     return loader
 
-
-def format_data(data):
-    if c.use_speaker_embedding:
+def format_data(data, speaker_mapping=None):
+    if speaker_mapping is None and c.use_speaker_embedding and not c.use_external_speaker_embedding_file:
         speaker_mapping = load_speaker_mapping(OUT_PATH)
 
     # setup input data
@@ -99,13 +99,20 @@ def format_data(data):
     avg_spec_length = torch.mean(mel_lengths.float())
 
     if c.use_speaker_embedding:
-        speaker_ids = [
-            speaker_mapping[speaker_name] for speaker_name in speaker_names
-        ]
-        speaker_ids = torch.LongTensor(speaker_ids)
+        if c.use_external_speaker_embedding_file:
+            speaker_embeddings = data[8]
+            speaker_ids = None
+        else:
+            speaker_ids = [
+                speaker_mapping[speaker_name] for speaker_name in speaker_names
+            ]
+            speaker_ids = torch.LongTensor(speaker_ids)
+            speaker_embeddings = None
     else:
+        speaker_embeddings = None
         speaker_ids = None
 
+
     # set stop targets view, we predict a single stop token per iteration.
     stop_targets = stop_targets.view(text_input.shape[0],
                                      stop_targets.size(1) // c.r, -1)
@@ -122,13 +129,16 @@ def format_data(data):
         stop_targets = stop_targets.cuda(non_blocking=True)
         if speaker_ids is not None:
             speaker_ids = speaker_ids.cuda(non_blocking=True)
-    return text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, avg_text_length, avg_spec_length
+        if speaker_embeddings is not None:
+            speaker_embeddings = speaker_embeddings.cuda(non_blocking=True)
+
+    return text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, speaker_embeddings, avg_text_length, avg_spec_length
 
 
 def train(model, criterion, optimizer, optimizer_st, scheduler,
-          ap, global_step, epoch, amp):
+          ap, global_step, epoch, amp, speaker_mapping=None):
     data_loader = setup_loader(ap, model.decoder.r, is_val=False,
-                               verbose=(epoch == 0))
+                               verbose=(epoch == 0), speaker_mapping=speaker_mapping)
     model.train()
     epoch_time = 0
     keep_avg = KeepAverage()
@@ -143,7 +153,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         start_time = time.time()
 
         # format data
-        text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, avg_text_length, avg_spec_length = format_data(data)
+        text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, speaker_embeddings, avg_text_length, avg_spec_length = format_data(data, speaker_mapping)
         loader_time = time.time() - end_time
 
         global_step += 1
@@ -158,10 +168,10 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         # forward pass model
         if c.bidirectional_decoder or c.double_decoder_consistency:
             decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
-                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
+                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings)
         else:
             decoder_output, postnet_output, alignments, stop_tokens = model(
-                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
+                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings)
             decoder_backward_output = None
             alignments_backward = None
 
@@ -312,8 +322,8 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
 
 
 @torch.no_grad()
-def evaluate(model, criterion, ap, global_step, epoch):
-    data_loader = setup_loader(ap, model.decoder.r, is_val=True)
+def evaluate(model, criterion, ap, global_step, epoch, speaker_mapping=None):
+    data_loader = setup_loader(ap, model.decoder.r, is_val=True, speaker_mapping=speaker_mapping)
     model.eval()
     epoch_time = 0
     keep_avg = KeepAverage()
@@ -323,16 +333,16 @@ def evaluate(model, criterion, ap, global_step, epoch):
             start_time = time.time()
 
             # format data
-            text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, _, _ = format_data(data)
+            text_input, text_lengths, mel_input, mel_lengths, linear_input, stop_targets, speaker_ids, speaker_embeddings, _, _ = format_data(data, speaker_mapping)
             assert mel_input.shape[1] % model.decoder.r == 0
 
             # forward pass model
             if c.bidirectional_decoder or c.double_decoder_consistency:
                 decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
-                    text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
+                    text_input, text_lengths, mel_input, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings)
             else:
                 decoder_output, postnet_output, alignments, stop_tokens = model(
-                    text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
+                    text_input, text_lengths, mel_input, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings)
                 decoder_backward_output = None
                 alignments_backward = None
 
@@ -494,22 +504,41 @@ def main(args):  # pylint: disable=redefined-outer-name
     if c.use_speaker_embedding:
         speakers = get_speakers(meta_data_train)
         if args.restore_path:
-            prev_out_path = os.path.dirname(args.restore_path)
-            speaker_mapping = load_speaker_mapping(prev_out_path)
-            assert all([speaker in speaker_mapping
-                        for speaker in speakers]), "As of now you, you cannot " \
-                                                   "introduce new speakers to " \
-                                                   "a previously trained model."
-        else:
+            if c.use_external_speaker_embedding_file: # if restore checkpoint and use External Embedding file
+                prev_out_path = os.path.dirname(args.restore_path)
+                speaker_mapping = load_speaker_mapping(prev_out_path)
+                if not speaker_mapping:
+                    print("WARNING: speakers.json speakers.json was not found in restore_path, trying to use CONFIG.external_speaker_embedding_file")
+                    speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
+                    if not speaker_mapping:
+                        raise RuntimeError("You must copy the file speakers.json to restore_path, or set a valid file in CONFIG.external_speaker_embedding_file")
+                speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]['embedding'])
+            elif not c.use_external_speaker_embedding_file: # if restore checkpoint and don't use External Embedding file
+                prev_out_path = os.path.dirname(args.restore_path)
+                speaker_mapping = load_speaker_mapping(prev_out_path)
+                speaker_embedding_dim = None
+                assert all([speaker in speaker_mapping
+                            for speaker in speakers]), "As of now you, you cannot " \
+                                                    "introduce new speakers to " \
+                                                    "a previously trained model."
+        elif c.use_external_speaker_embedding_file and c.external_speaker_embedding_file: # if start new train using External Embedding file
+            speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
+            print(speaker_mapping)
+            speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]['embedding'])
+        elif c.use_external_speaker_embedding_file and not c.external_speaker_embedding_file: # if start new train using External Embedding file and don't pass external embedding file
+            raise "use_external_speaker_embedding_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
+        else: # if start new train and don't use External Embedding file
             speaker_mapping = {name: i for i, name in enumerate(speakers)}
+            speaker_embedding_dim = None
         save_speaker_mapping(OUT_PATH, speaker_mapping)
         num_speakers = len(speaker_mapping)
         print("Training with {} speakers: {}".format(num_speakers,
                                                      ", ".join(speakers)))
     else:
         num_speakers = 0
+        speaker_embedding_dim = None
 
-    model = setup_model(num_chars, num_speakers, c)
+    model = setup_model(num_chars, num_speakers, c, speaker_embedding_dim)
 
     params = set_weight_decay(model, c.wd)
     optimizer = RAdam(params, lr=c.lr, weight_decay=0)
@@ -530,6 +559,8 @@ def main(args):  # pylint: disable=redefined-outer-name
 
     # setup criterion
     criterion = TacotronLoss(c, stopnet_pos_weight=10.0, ga_sigma=0.4)
+    for name, _ in model.named_parameters():
+        print(name)
 
     if args.restore_path:
         checkpoint = torch.load(args.restore_path, map_location='cpu')
@@ -592,7 +623,7 @@ def main(args):  # pylint: disable=redefined-outer-name
             print("\n > Number of output frames:", model.decoder.r)
         train_avg_loss_dict, global_step = train(model, criterion, optimizer,
                                                  optimizer_st, scheduler, ap,
-                                                 global_step, epoch, amp)
+                                                 global_step, epoch, amp, speaker_mapping)
         eval_avg_loss_dict = evaluate(model, criterion, ap, global_step, epoch)
         c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
         target_loss = train_avg_loss_dict['avg_postnet_loss']
diff --git a/mozilla_voice_tts/tts/configs/config.json b/mozilla_voice_tts/tts/configs/config.json
index 154e1961..090540ab 100644
--- a/mozilla_voice_tts/tts/configs/config.json
+++ b/mozilla_voice_tts/tts/configs/config.json
@@ -1,5 +1,5 @@
 {
-    "model": "Tacotron",
+    "model": "Tacotron2",
     "run_name": "ljspeech-ddc-bn",
     "run_description": "tacotron2 with ddc and batch-normalization",
 
@@ -114,7 +114,7 @@
     "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": "portuguese_cleaners",
+    "text_cleaner": "phoneme_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.
@@ -131,7 +131,9 @@
     "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": true,     // use speaker embedding to enable multi-speaker learning.
+    "use_speaker_embedding": true,      // use speaker embedding to enable multi-speaker learning.
+    "use_external_speaker_embedding_file": false, // if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
+    "external_speaker_embedding_file": "../../speakers-vctk-en.json", // if not null and use_external_speaker_embedding_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
     "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 
diff --git a/mozilla_voice_tts/tts/datasets/TTSDataset.py b/mozilla_voice_tts/tts/datasets/TTSDataset.py
index cc6b72b5..1002a292 100644
--- a/mozilla_voice_tts/tts/datasets/TTSDataset.py
+++ b/mozilla_voice_tts/tts/datasets/TTSDataset.py
@@ -24,6 +24,7 @@ class MyDataset(Dataset):
                  phoneme_cache_path=None,
                  phoneme_language="en-us",
                  enable_eos_bos=False,
+                 speaker_mapping=None,
                  verbose=False):
         """
         Args:
@@ -58,6 +59,7 @@ class MyDataset(Dataset):
         self.phoneme_cache_path = phoneme_cache_path
         self.phoneme_language = phoneme_language
         self.enable_eos_bos = enable_eos_bos
+        self.speaker_mapping = speaker_mapping
         self.verbose = verbose
         if use_phonemes and not os.path.isdir(phoneme_cache_path):
             os.makedirs(phoneme_cache_path, exist_ok=True)
@@ -127,7 +129,8 @@ class MyDataset(Dataset):
             'text': text,
             'wav': wav,
             'item_idx': self.items[idx][1],
-            'speaker_name': speaker_name
+            'speaker_name': speaker_name,
+            'wav_file_name': os.path.basename(wav_file)
         }
         return sample
 
@@ -191,9 +194,15 @@ class MyDataset(Dataset):
                 batch[idx]['item_idx'] for idx in ids_sorted_decreasing
             ]
             text = [batch[idx]['text'] for idx in ids_sorted_decreasing]
+
             speaker_name = [batch[idx]['speaker_name']
                             for idx in ids_sorted_decreasing]
-
+            # get speaker embeddings
+            if self.speaker_mapping  is not None:
+                wav_files_names = [batch[idx]['wav_file_name'] for idx in ids_sorted_decreasing]       
+                speaker_embedding = [self.speaker_mapping[w]['embedding'] for w in wav_files_names]
+            else:
+                speaker_embedding = None
             # compute features
             mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
 
@@ -224,6 +233,9 @@ class MyDataset(Dataset):
             mel_lengths = torch.LongTensor(mel_lengths)
             stop_targets = torch.FloatTensor(stop_targets)
 
+            if speaker_embedding is not None:
+                speaker_embedding = torch.FloatTensor(speaker_embedding)
+
             # compute linear spectrogram
             if self.compute_linear_spec:
                 linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
@@ -234,7 +246,7 @@ class MyDataset(Dataset):
             else:
                 linear = None
             return text, text_lenghts, speaker_name, linear, mel, mel_lengths, \
-                   stop_targets, item_idxs
+                   stop_targets, item_idxs, speaker_embedding
 
         raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
                          found {}".format(type(batch[0]))))
diff --git a/mozilla_voice_tts/tts/layers/tacotron.py b/mozilla_voice_tts/tts/layers/tacotron.py
index bbeee95f..9dcebd0f 100644
--- a/mozilla_voice_tts/tts/layers/tacotron.py
+++ b/mozilla_voice_tts/tts/layers/tacotron.py
@@ -291,7 +291,7 @@ class Decoder(nn.Module):
     def __init__(self, in_channels, frame_channels, r, memory_size, attn_type, attn_windowing,
                  attn_norm, prenet_type, prenet_dropout, forward_attn,
                  trans_agent, forward_attn_mask, location_attn, attn_K,
-                 separate_stopnet, speaker_embedding_dim):
+                 separate_stopnet):
         super(Decoder, self).__init__()
         self.r_init = r
         self.r = r
@@ -462,15 +462,12 @@ class Decoder(nn.Module):
             t += 1
         return self._parse_outputs(outputs, attentions, stop_tokens)
 
-    def inference(self, inputs, speaker_embeddings=None):
+    def inference(self, inputs):
         """
         Args:
             inputs: encoder outputs.
-            speaker_embeddings: speaker vectors.
-
         Shapes:
-            - inputs: (B, T, D_out_enc)
-            - speaker_embeddings: (B, D_embed)
+            - inputs: batch x time x encoder_out_dim
         """
         outputs = []
         attentions = []
@@ -483,8 +480,6 @@ class Decoder(nn.Module):
             if t > 0:
                 new_memory = outputs[-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, None)
             stop_token = torch.sigmoid(stop_token.data)
             outputs += [output]
diff --git a/mozilla_voice_tts/tts/layers/tacotron2.py b/mozilla_voice_tts/tts/layers/tacotron2.py
index 7c0dd443..5d6ced25 100644
--- a/mozilla_voice_tts/tts/layers/tacotron2.py
+++ b/mozilla_voice_tts/tts/layers/tacotron2.py
@@ -147,8 +147,7 @@ class Decoder(nn.Module):
     #pylint: disable=attribute-defined-outside-init
     def __init__(self, in_channels, frame_channels, r, attn_type, attn_win, attn_norm,
                  prenet_type, prenet_dropout, forward_attn, trans_agent,
-                 forward_attn_mask, location_attn, attn_K, separate_stopnet,
-                 speaker_embedding_dim):
+                 forward_attn_mask, location_attn, attn_K, separate_stopnet):
         super(Decoder, self).__init__()
         self.frame_channels = frame_channels
         self.r_init = r
@@ -335,16 +334,14 @@ class Decoder(nn.Module):
             outputs, stop_tokens, alignments)
         return outputs, alignments, stop_tokens
 
-    def inference(self, inputs, speaker_embeddings=None):
+    def inference(self, inputs):
         r"""Decoder inference without teacher forcing and use
         Stopnet to stop decoder.
         Args:
             inputs: Encoder outputs.
-            speaker_embeddings: speaker embedding vectors.
 
         Shapes:
             - inputs: (B, T, D_out_enc)
-            - speaker_embeddings: (B, D_embed)
             - outputs: (B, T_mel, D_mel)
             - alignments: (B, T_in, T_out)
             - stop_tokens: (B, T_out)
@@ -358,8 +355,6 @@ class Decoder(nn.Module):
         outputs, stop_tokens, alignments, t = [], [], [], 0
         while True:
             memory = self.prenet(memory)
-            if speaker_embeddings is not None:
-                memory = torch.cat([memory, speaker_embeddings], dim=-1)
             decoder_output, alignment, stop_token = self.decode(memory)
             stop_token = torch.sigmoid(stop_token.data)
             outputs += [decoder_output.squeeze(1)]
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index 8eda83b3..9dfdbf63 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -27,6 +27,7 @@ class Tacotron(TacotronAbstract):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
+                 speaker_embedding_dim=None,
                  gst=False,
                  gst_embedding_dim=256,
                  gst_num_heads=4,
@@ -40,39 +41,46 @@ class Tacotron(TacotronAbstract):
                              location_attn, attn_K, separate_stopnet,
                              bidirectional_decoder, double_decoder_consistency,
                              ddc_r, gst)
-        
-        
+
         # 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 speaker_embedding_dim is None:
+            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
+            self.embeddings_per_sample = False
+            speaker_embedding_dim = 256
+        else:
+            # if speaker_embedding_dim is not None we need use speaker embedding per sample
+            self.embeddings_per_sample = True
+
+        # speaker and gst embeddings is concat in decoder input
         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
+        # embedding layer
         self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
+
+        # speaker embedding layers
+        if num_speakers > 1:
+            if not self.embeddings_per_sample:
+                self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
+                self.speaker_embedding.weight.data.normal_(0, 0.3)
+
+        # base model layers
         self.embedding.weight.data.normal_(0, 0.3)
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, 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)
+                               attn_K, separate_stopnet)
         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, speaker_embedding_dim)
-            self.speaker_embedding.weight.data.normal_(0, 0.3)
-            self.speaker_project_mel = nn.Sequential(
-                nn.Linear(speaker_embedding_dim, proj_speaker_dim), nn.Tanh())
-            self.speaker_embeddings = None
-            self.speaker_embeddings_projected = None
+
         # global style token layers
         if self.gst:
             self.gst_layer = GST(num_mel=80,
@@ -88,10 +96,9 @@ class Tacotron(TacotronAbstract):
                 decoder_in_features, decoder_output_dim, ddc_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)
+                attn_K, separate_stopnet)
 
-
-    def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None):
+    def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None, speaker_embeddings=None):
         """
         Shapes:
             - characters: B x T_in
@@ -99,24 +106,27 @@ class Tacotron(TacotronAbstract):
             - mel_specs: B x T_out x D
             - speaker_ids: B x 1
         """
-        self._init_states()
         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
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
         # 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)
+        # speaker embedding
         if self.num_speakers > 1:
-            encoder_outputs = self._concat_speaker_embedding(
-                encoder_outputs, self.speaker_embeddings)
+            if not self.embeddings_per_sample:
+                # B x 1 x speaker_embed_dim
+                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            else:
+                # B x 1 x speaker_embed_dim
+                speaker_embeddings = torch.unsqueeze(speaker_embeddings, 1)
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
         # decoder_outputs: B x decoder_in_features x T_out
         # alignments: B x T_in x encoder_in_features
         # stop_tokens: B x T_in
@@ -143,19 +153,22 @@ class Tacotron(TacotronAbstract):
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
-    def inference(self, characters, speaker_ids=None, style_mel=None):
+    def inference(self, characters, speaker_ids=None, style_mel=None, speaker_embeddings=None):
         inputs = self.embedding(characters)
-        self._init_states()
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
         encoder_outputs = self.encoder(inputs)
-        if self.gst and style_mel is not None:
+        if self.gst:
+            # B x gst_dim
             encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
         if self.num_speakers > 1:
-            encoder_outputs = self._concat_speaker_embedding(
-                encoder_outputs, self.speaker_embeddings)
+            if not self.embeddings_per_sample:
+                # B x 1 x speaker_embed_dim
+                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            else:
+                # B x 1 x speaker_embed_dim
+                speaker_embeddings = torch.unsqueeze(speaker_embeddings, 1)
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
         decoder_outputs, alignments, stop_tokens = self.decoder.inference(
-            encoder_outputs, self.speaker_embeddings_projected)
+            encoder_outputs)
         postnet_outputs = self.postnet(decoder_outputs)
         postnet_outputs = self.last_linear(postnet_outputs)
         decoder_outputs = decoder_outputs.transpose(1, 2)
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 944138bc..45e743e5 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -27,6 +27,7 @@ class Tacotron2(TacotronAbstract):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
+                 speaker_embedding_dim=None,
                  gst=False,
                  gst_embedding_dim=512,
                  gst_num_heads=4,
@@ -41,25 +42,38 @@ class Tacotron2(TacotronAbstract):
                              ddc_r, gst)
 
         # init layer dims
-        speaker_embedding_dim = 512 if num_speakers > 1 else 0
-        gst_embedding_dim = gst_embedding_dim if self.gst else 0
-        decoder_in_features = 512+speaker_embedding_dim+gst_embedding_dim
-        encoder_in_features = 512 if num_speakers > 1 else 512
-        proj_speaker_dim = 80 if num_speakers > 1 else 0
+        decoder_in_features = 512
+        encoder_in_features = 512
+
+        if speaker_embedding_dim is None:
+            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
+            self.embeddings_per_sample = False
+            speaker_embedding_dim = 512
+        else:
+            # if speaker_embedding_dim is not None we need use speaker embedding per sample
+            self.embeddings_per_sample = True
+
+        # speaker and gst embeddings is concat in decoder input
+        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
 
         # embedding layer
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
 
         # speaker embedding layer
         if num_speakers > 1:
-            self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
-            self.speaker_embedding.weight.data.normal_(0, 0.3)
+            if not self.embeddings_per_sample:
+                self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
+                self.speaker_embedding.weight.data.normal_(0, 0.3)
 
+        # base model layers
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, 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)
+                               location_attn, attn_K, separate_stopnet)
         self.postnet = Postnet(self.postnet_output_dim)
 
         # global style token layers
@@ -77,7 +91,7 @@ class Tacotron2(TacotronAbstract):
                 decoder_in_features, self.decoder_output_dim, ddc_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)
+                separate_stopnet)
 
     @staticmethod
     def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
@@ -85,7 +99,7 @@ class Tacotron2(TacotronAbstract):
         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, mel_lengths=None, speaker_ids=None):
+    def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None, speaker_embeddings=None):
         # compute mask for padding
         # B x T_in_max (boolean)
         input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
@@ -99,8 +113,13 @@ class Tacotron2(TacotronAbstract):
             encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
 
         if self.num_speakers > 1:
-            embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
-            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, embedded_speakers)
+            if not self.embeddings_per_sample:
+                # B x 1 x speaker_embed_dim
+                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            else:
+                # B x 1 x speaker_embed_dim
+                speaker_embeddings = torch.unsqueeze(speaker_embeddings, 1)
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
 
         encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
 
@@ -128,23 +147,18 @@ class Tacotron2(TacotronAbstract):
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
-    def inference(self, text, speaker_ids=None, style_mel=None):
+    def inference(self, text, speaker_ids=None, style_mel=None, speaker_embeddings=None):
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference(embedded_inputs)
 
+        if self.gst:
+            # B x gst_dim
+            encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
+
         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 hasattr(self, 'gst'):
-                # B x gst_dim
-                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 hasattr(self, 'gst'):
-                # B x gst_dim
-                encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
+            if not self.embeddings_per_sample:
+                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
 
         decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
@@ -154,25 +168,21 @@ class Tacotron2(TacotronAbstract):
             decoder_outputs, postnet_outputs, alignments)
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
-    def inference_truncated(self, text, speaker_ids=None, style_mel=None):
+    def inference_truncated(self, text, speaker_ids=None, style_mel=None, speaker_embeddings=None):
         """
         Preserve model states for continuous inference
         """
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference_truncated(embedded_inputs)
 
+        if self.gst:
+            # B x gst_dim
+            encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
+
         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 hasattr(self, 'gst'):
-                # B x gst_dim
-                encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
-            else:
-                encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
-        else:
-            if hasattr(self, 'gst'):
-                # B x gst_dim
-                encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
+            if not self.embeddings_per_sample:
+                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
 
         mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(
             encoder_outputs)
diff --git a/mozilla_voice_tts/tts/utils/generic_utils.py b/mozilla_voice_tts/tts/utils/generic_utils.py
index 9c3c618d..2d5044ef 100644
--- a/mozilla_voice_tts/tts/utils/generic_utils.py
+++ b/mozilla_voice_tts/tts/utils/generic_utils.py
@@ -44,7 +44,7 @@ def sequence_mask(sequence_length, max_len=None):
     return seq_range_expand < seq_length_expand
 
 
-def setup_model(num_chars, num_speakers, c):
+def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
     print(" > Using model: {}".format(c.model))
     MyModel = importlib.import_module('mozilla_voice_tts.tts.models.' + c.model.lower())
     MyModel = getattr(MyModel, c.model)
@@ -72,7 +72,8 @@ def setup_model(num_chars, num_speakers, c):
                         separate_stopnet=c.separate_stopnet,
                         bidirectional_decoder=c.bidirectional_decoder,
                         double_decoder_consistency=c.double_decoder_consistency,
-                        ddc_r=c.ddc_r)
+                        ddc_r=c.ddc_r,
+                        speaker_embedding_dim=speaker_embedding_dim)
     elif c.model.lower() == "tacotron2":
         model = MyModel(num_chars=num_chars,
                         num_speakers=num_speakers,
@@ -96,7 +97,8 @@ def setup_model(num_chars, num_speakers, c):
                         separate_stopnet=c.separate_stopnet,
                         bidirectional_decoder=c.bidirectional_decoder,
                         double_decoder_consistency=c.double_decoder_consistency,
-                        ddc_r=c.ddc_r)
+                        ddc_r=c.ddc_r,
+                        speaker_embedding_dim=speaker_embedding_dim)
     return model
 
 
@@ -175,7 +177,7 @@ def check_config(c):
     check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
     check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
     check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
-    check_argument('spec_gain', c['audio'], restricted=True, val_type=float, min_val=1, max_val=100)
+    check_argument('spec_gain', c['audio'], restricted=True, val_type=[int, float], min_val=1, max_val=100)
     check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
     check_argument('trim_db', c['audio'], restricted=True, val_type=int)
 
@@ -246,10 +248,10 @@ def check_config(c):
     # paths
     check_argument('output_path', c, restricted=True, val_type=str)
 
-    # multi-speaker
+    # multi-speaker and gst
     check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
-
-    # GST
+    check_argument('use_external_speaker_embedding_file', c, restricted=True, val_type=bool)
+    check_argument('external_speaker_embedding_file', c, restricted=True, val_type=str)
     check_argument('use_gst', c, restricted=True, val_type=bool)
     check_argument('gst_style_input', c, restricted=True, val_type=str)
     check_argument('gst', c, restricted=True, val_type=dict)
diff --git a/mozilla_voice_tts/tts/utils/speakers.py b/mozilla_voice_tts/tts/utils/speakers.py
index ff624b36..156e42af 100644
--- a/mozilla_voice_tts/tts/utils/speakers.py
+++ b/mozilla_voice_tts/tts/utils/speakers.py
@@ -10,12 +10,15 @@ def make_speakers_json_path(out_path):
 def load_speaker_mapping(out_path):
     """Loads speaker mapping if already present."""
     try:
-        with open(make_speakers_json_path(out_path)) as f:
+        if os.path.splitext(out_path)[1] == '.json':
+            json_file = out_path
+        else:
+            json_file = make_speakers_json_path(out_path)
+        with open(json_file) as f:
             return json.load(f)
     except FileNotFoundError:
         return {}
 
-
 def save_speaker_mapping(out_path, speaker_mapping):
     """Saves speaker mapping if not yet present."""
     speakers_json_path = make_speakers_json_path(out_path)

From 1d73566e4e2fe9a4f5e799b80f8506a0f92768e3 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Wed, 29 Jul 2020 18:22:13 -0300
Subject: [PATCH 12/56] bugfix in GST

---
 mozilla_voice_tts/bin/train_tts.py         | 6 +++---
 mozilla_voice_tts/tts/layers/gst_layers.py | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mozilla_voice_tts/bin/train_tts.py b/mozilla_voice_tts/bin/train_tts.py
index 0642f290..daa517b9 100644
--- a/mozilla_voice_tts/bin/train_tts.py
+++ b/mozilla_voice_tts/bin/train_tts.py
@@ -508,7 +508,7 @@ def main(args):  # pylint: disable=redefined-outer-name
                 prev_out_path = os.path.dirname(args.restore_path)
                 speaker_mapping = load_speaker_mapping(prev_out_path)
                 if not speaker_mapping:
-                    print("WARNING: speakers.json speakers.json was not found in restore_path, trying to use CONFIG.external_speaker_embedding_file")
+                    print("WARNING: speakers.json was not found in restore_path, trying to use CONFIG.external_speaker_embedding_file")
                     speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
                     if not speaker_mapping:
                         raise RuntimeError("You must copy the file speakers.json to restore_path, or set a valid file in CONFIG.external_speaker_embedding_file")
@@ -559,8 +559,6 @@ def main(args):  # pylint: disable=redefined-outer-name
 
     # setup criterion
     criterion = TacotronLoss(c, stopnet_pos_weight=10.0, ga_sigma=0.4)
-    for name, _ in model.named_parameters():
-        print(name)
 
     if args.restore_path:
         checkpoint = torch.load(args.restore_path, map_location='cpu')
@@ -575,6 +573,8 @@ def main(args):  # pylint: disable=redefined-outer-name
             print(" > Partial model initialization.")
             model_dict = model.state_dict()
             model_dict = set_init_dict(model_dict, checkpoint['model'], c)
+            # torch.save(model_dict, os.path.join(OUT_PATH, 'state_dict.pt'))
+            # print("State Dict saved for debug in: ", os.path.join(OUT_PATH, 'state_dict.pt'))
             model.load_state_dict(model_dict)
             del model_dict
 
diff --git a/mozilla_voice_tts/tts/layers/gst_layers.py b/mozilla_voice_tts/tts/layers/gst_layers.py
index 01f90697..a49b14a2 100644
--- a/mozilla_voice_tts/tts/layers/gst_layers.py
+++ b/mozilla_voice_tts/tts/layers/gst_layers.py
@@ -96,7 +96,7 @@ class StyleTokenLayer(nn.Module):
         self.key_dim = embedding_dim // num_heads
         self.style_tokens = nn.Parameter(
             torch.FloatTensor(num_style_tokens, self.key_dim))
-        nn.init.orthogonal_(self.style_tokens)
+        nn.init.normal_(self.style_tokens, mean=0, std=0.5)
         self.attention = MultiHeadAttention(
             query_dim=self.query_dim,
             key_dim=self.key_dim,

From 7c12e94ee4c922c79397b2abd496b5103f8cb83d Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Wed, 29 Jul 2020 21:21:42 -0300
Subject: [PATCH 13/56] fix Lint check

---
 mozilla_voice_tts/tts/datasets/TTSDataset.py | 2 +-
 mozilla_voice_tts/tts/datasets/preprocess.py | 4 ++--
 mozilla_voice_tts/tts/models/tacotron.py     | 2 +-
 mozilla_voice_tts/tts/utils/text/cleaners.py | 2 +-
 mozilla_voice_tts/utils/generic_utils.py     | 1 -
 5 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/mozilla_voice_tts/tts/datasets/TTSDataset.py b/mozilla_voice_tts/tts/datasets/TTSDataset.py
index 1002a292..2ef78e11 100644
--- a/mozilla_voice_tts/tts/datasets/TTSDataset.py
+++ b/mozilla_voice_tts/tts/datasets/TTSDataset.py
@@ -199,7 +199,7 @@ class MyDataset(Dataset):
                             for idx in ids_sorted_decreasing]
             # get speaker embeddings
             if self.speaker_mapping  is not None:
-                wav_files_names = [batch[idx]['wav_file_name'] for idx in ids_sorted_decreasing]       
+                wav_files_names = [batch[idx]['wav_file_name'] for idx in ids_sorted_decreasing]
                 speaker_embedding = [self.speaker_mapping[w]['embedding'] for w in wav_files_names]
             else:
                 speaker_embedding = None
diff --git a/mozilla_voice_tts/tts/datasets/preprocess.py b/mozilla_voice_tts/tts/datasets/preprocess.py
index 2ad414fb..317673e3 100644
--- a/mozilla_voice_tts/tts/datasets/preprocess.py
+++ b/mozilla_voice_tts/tts/datasets/preprocess.py
@@ -229,14 +229,14 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
     items = []
     meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
     for meta_file in meta_files:
-        txt, speaker_id, txt_file = os.path.relpath(meta_file,root_path).split(os.sep)
+        _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
         file_id = txt_file.split('.')[0]
         if isinstance(test_speakers, list): # if is list ignore this speakers ids
             if speaker_id in test_speakers:
                 continue
         with open(meta_file) as file_text:
             text = file_text.readlines()[0]
-        wav_file = os.path.join(root_path, wavs_path, speaker_id,file_id+'.wav')
+        wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id+'.wav')
         items.append([text, wav_file, speaker_id])
     
     return items
\ No newline at end of file
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index 9dfdbf63..f6bd07ed 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -55,7 +55,7 @@ class Tacotron(TacotronAbstract):
             self.embeddings_per_sample = True
 
         # speaker and gst embeddings is concat in decoder input
-        if num_speakers > 1: 
+        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
diff --git a/mozilla_voice_tts/tts/utils/text/cleaners.py b/mozilla_voice_tts/tts/utils/text/cleaners.py
index b1930834..a36ebe67 100644
--- a/mozilla_voice_tts/tts/utils/text/cleaners.py
+++ b/mozilla_voice_tts/tts/utils/text/cleaners.py
@@ -119,7 +119,7 @@ def english_cleaners(text):
     return text
 
 def portuguese_cleaners(text):
-    '''Basic pipeline for Portuguese text. There is no need to expand abbreviation and 
+    '''Basic pipeline for Portuguese text. There is no need to expand abbreviation and
         numbers, phonemizer already does that'''
     text = lowercase(text)
     text = replace_symbols(text, lang='pt')
diff --git a/mozilla_voice_tts/utils/generic_utils.py b/mozilla_voice_tts/utils/generic_utils.py
index add5120d..dcfbbdc3 100644
--- a/mozilla_voice_tts/utils/generic_utils.py
+++ b/mozilla_voice_tts/utils/generic_utils.py
@@ -147,7 +147,6 @@ def check_argument(name, c, enum_list=None, max_val=None, min_val=None, restrict
         if enum_list:
             assert c[name].lower() in enum_list, f' [!] {name} is not a valid value'
         if isinstance(val_type, list):
-            valid_types = val_type
             is_valid = False
             for typ in val_type:
                 if isinstance(c[name], typ):

From def7e49f5986ed8955a0befb3daddecb0bdb4392 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Wed, 29 Jul 2020 23:37:51 -0300
Subject: [PATCH 14/56] travis unit tests fix and add Tacotron and Tacotron 2
 GST and MultiSpeaker Tests

---
 tests/inputs/test_config.json |  15 +++++
 tests/test_layers.py          |  35 +---------
 tests/test_tacotron2_model.py | 119 ++++++++++++++++++++++++++++++++--
 tests/test_tacotron_model.py  |  79 +++++++++++++++++++++-
 4 files changed, 206 insertions(+), 42 deletions(-)

diff --git a/tests/inputs/test_config.json b/tests/inputs/test_config.json
index 450cb23a..b1e857c0 100644
--- a/tests/inputs/test_config.json
+++ b/tests/inputs/test_config.json
@@ -53,6 +53,7 @@
     "max_seq_len": 300,
     "log_dir": "tests/outputs/",
 
+<<<<<<< HEAD
     "use_speaker_embedding": false,     
     "use_gst": false,       			    
     "gst":	{			                
@@ -61,4 +62,18 @@
         "gst_num_heads": 4,
         "gst_style_tokens": 10
 	}
+=======
+    // MULTI-SPEAKER and GST
+    "use_speaker_embedding": false,     // 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 
+                                        // -> wave file [path to wave] or 
+                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}  
+                                        // with the dictionary being len(dict) <= len(gst_style_tokens).
+        "gst_embedding_dim": 512,       
+        "gst_num_heads": 4,
+        "gst_style_tokens": 10
+        }  
+>>>>>>> travis unit tests fix and add Tacotron and Tacotron 2 GST and MultiSpeaker Tests
     }
diff --git a/tests/test_layers.py b/tests/test_layers.py
index bf036f5c..0b5315c5 100644
--- a/tests/test_layers.py
+++ b/tests/test_layers.py
@@ -58,8 +58,7 @@ class DecoderTests(unittest.TestCase):
             trans_agent=True,
             forward_attn_mask=True,
             location_attn=True,
-            separate_stopnet=True,
-            speaker_embedding_dim=0)
+            separate_stopnet=True)
         dummy_input = T.rand(4, 8, 256)
         dummy_memory = T.rand(4, 2, 80)
 
@@ -71,38 +70,6 @@ class DecoderTests(unittest.TestCase):
         assert output.shape[2] == 2, "size not {}".format(output.shape[2])
         assert stop_tokens.shape[0] == 4
 
-    @staticmethod
-    def test_in_out_multispeaker():
-        layer = Decoder(
-            in_channels=256,
-            frame_channels=80,
-            r=2,
-            memory_size=4,
-            attn_windowing=False,
-            attn_norm="sigmoid",
-            attn_K=5,
-            attn_type="graves",
-            prenet_type='original',
-            prenet_dropout=True,
-            forward_attn=True,
-            trans_agent=True,
-            forward_attn_mask=True,
-            location_attn=True,
-            separate_stopnet=True,
-            speaker_embedding_dim=80)
-        dummy_input = T.rand(4, 8, 256)
-        dummy_memory = T.rand(4, 2, 80)
-        dummy_embed = T.rand(4, 80)
-
-        output, alignment, stop_tokens = layer(
-            dummy_input, dummy_memory, mask=None, speaker_embeddings=dummy_embed)
-
-        assert output.shape[0] == 4
-        assert output.shape[1] == 80, "size not {}".format(output.shape[1])
-        assert output.shape[2] == 2, "size not {}".format(output.shape[2])
-        assert stop_tokens.shape[0] == 4
-
-
 class EncoderTests(unittest.TestCase):
     def test_in_out(self):  #pylint: disable=no-self-use
         layer = Encoder(128)
diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index a0c5e59a..d4d5eb86 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -9,6 +9,7 @@ from torch import nn, optim
 from mozilla_voice_tts.tts.layers.losses import MSELossMasked
 from mozilla_voice_tts.tts.models.tacotron2 import Tacotron2
 from mozilla_voice_tts.utils.io import load_config
+from mozilla_voice_tts.utils.audio import AudioProcessor
 
 #pylint: disable=unused-variable
 
@@ -18,14 +19,12 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 c = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
+ap = AudioProcessor(**c.audio)
+WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
+
 
 class TacotronTrainTest(unittest.TestCase):
-<<<<<<< HEAD
     def test_train_step(self):  # pylint: disable=no-self-use
-=======
-    @staticmethod
-    def test_train_step():
->>>>>>> small gst config change
         input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
         input_lengths = torch.randint(100, 128, (8, )).long().to(device)
         input_lengths = torch.sort(input_lengths, descending=True)[0]
@@ -75,3 +74,113 @@ class TacotronTrainTest(unittest.TestCase):
             ), "param {} with shape {} not updated!! \n{}\n{}".format(
                 count, param.shape, param, param_ref)
             count += 1
+
+class TacotronGSTTrainTest(unittest.TestCase):
+    @staticmethod
+    def test_train_step():
+        # with random gst mel style
+        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 128, (8, )).long().to(device)
+        input_lengths = torch.sort(input_lengths, descending=True)[0]
+        mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
+        mel_lengths[0] = 30
+        stop_targets = torch.zeros(8, 30, 1).float().to(device)
+        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
+
+        for idx in mel_lengths:
+            stop_targets[:, int(idx.item()):, 0] = 1.0
+
+        stop_targets = stop_targets.view(input_dummy.shape[0],
+                                         stop_targets.size(1) // c.r, -1)
+        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
+
+        criterion = MSELossMasked(seq_len_norm=False).to(device)
+        criterion_st = nn.BCEWithLogitsLoss().to(device)
+        model = Tacotron2(num_chars=24, r=c.r, num_speakers=5, gst=True, gst_embedding_dim=c.gst['gst_embedding_dim'], gst_num_heads=c.gst['gst_num_heads'], gst_style_tokens=c.gst['gst_style_tokens']).to(device)
+        model.train()
+        model_ref = copy.deepcopy(model)
+        count = 0
+        for param, param_ref in zip(model.parameters(), model_ref.parameters()):
+            assert (param - param_ref).sum() == 0, param
+            count += 1
+        optimizer = optim.Adam(model.parameters(), lr=c.lr)
+        for i in range(10):
+            mel_out, mel_postnet_out, align, stop_tokens = model.forward(
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
+            assert torch.sigmoid(stop_tokens).data.max() <= 1.0
+            assert torch.sigmoid(stop_tokens).data.min() >= 0.0
+            optimizer.zero_grad()
+            loss = criterion(mel_out, mel_spec, mel_lengths)
+            stop_loss = criterion_st(stop_tokens, stop_targets)
+            loss = loss + criterion(mel_postnet_out, mel_postnet_spec, mel_lengths) + stop_loss
+            loss.backward()
+            optimizer.step()
+        # check parameter changes
+        count = 0
+        for name_param, param_ref in zip(model.named_parameters(), model_ref.parameters()):
+            # ignore pre-higway layer since it works conditional
+            # if count not in [145, 59]:
+            name, param = name_param
+            if name == 'gst_layer.encoder.recurrence.weight_hh_l0':
+                #print(param.grad)
+                continue
+            assert (param != param_ref).any(
+            ), "param {} {} with shape {} not updated!! \n{}\n{}".format(
+                name, count, param.shape, param, param_ref)
+            count += 1
+
+        # with file gst style
+        mel_spec = torch.FloatTensor(ap.melspectrogram(ap.load_wav(WAV_FILE)))[:, :30].unsqueeze(0).transpose(1, 2).to(device)
+        mel_spec = mel_spec.repeat(8, 1, 1)
+        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 128, (8, )).long().to(device)
+        input_lengths = torch.sort(input_lengths, descending=True)[0]
+        mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
+        mel_lengths[0] = 30
+        stop_targets = torch.zeros(8, 30, 1).float().to(device)
+        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
+
+        for idx in mel_lengths:
+            stop_targets[:, int(idx.item()):, 0] = 1.0
+
+        stop_targets = stop_targets.view(input_dummy.shape[0],
+                                         stop_targets.size(1) // c.r, -1)
+        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
+
+        criterion = MSELossMasked(seq_len_norm=False).to(device)
+        criterion_st = nn.BCEWithLogitsLoss().to(device)
+        model = Tacotron2(num_chars=24, r=c.r, num_speakers=5, gst=True, gst_embedding_dim=c.gst['gst_embedding_dim'], gst_num_heads=c.gst['gst_num_heads'], gst_style_tokens=c.gst['gst_style_tokens']).to(device)
+        model.train()
+        model_ref = copy.deepcopy(model)
+        count = 0
+        for param, param_ref in zip(model.parameters(), model_ref.parameters()):
+            assert (param - param_ref).sum() == 0, param
+            count += 1
+        optimizer = optim.Adam(model.parameters(), lr=c.lr)
+        for i in range(10):
+            mel_out, mel_postnet_out, align, stop_tokens = model.forward(
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
+            assert torch.sigmoid(stop_tokens).data.max() <= 1.0
+            assert torch.sigmoid(stop_tokens).data.min() >= 0.0
+            optimizer.zero_grad()
+            loss = criterion(mel_out, mel_spec, mel_lengths)
+            stop_loss = criterion_st(stop_tokens, stop_targets)
+            loss = loss + criterion(mel_postnet_out, mel_postnet_spec, mel_lengths) + stop_loss
+            loss.backward()
+            optimizer.step()
+        # check parameter changes
+        count = 0
+        for name_param, param_ref in zip(model.named_parameters(), model_ref.parameters()):
+            # ignore pre-higway layer since it works conditional
+            # if count not in [145, 59]:
+            name, param = name_param
+            if name == 'gst_layer.encoder.recurrence.weight_hh_l0':
+                #print(param.grad)
+                continue
+            assert (param != param_ref).any(
+            ), "param {} {} with shape {} not updated!! \n{}\n{}".format(
+                name, count, param.shape, param, param_ref)
+            count += 1
\ No newline at end of file
diff --git a/tests/test_tacotron_model.py b/tests/test_tacotron_model.py
index d15a6705..42880589 100644
--- a/tests/test_tacotron_model.py
+++ b/tests/test_tacotron_model.py
@@ -9,6 +9,7 @@ from torch import nn, optim
 from mozilla_voice_tts.tts.layers.losses import L1LossMasked
 from mozilla_voice_tts.tts.models.tacotron import Tacotron
 from mozilla_voice_tts.utils.io import load_config
+from mozilla_voice_tts.utils.audio import AudioProcessor
 
 #pylint: disable=unused-variable
 
@@ -18,6 +19,9 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 c = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
 
+ap = AudioProcessor(**c.audio)
+WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
+
 
 def count_parameters(model):
     r"""Count number of trainable parameters in a network"""
@@ -85,10 +89,10 @@ class TacotronTrainTest(unittest.TestCase):
                 count, param.shape, param, param_ref)
             count += 1
 
-
 class TacotronGSTTrainTest(unittest.TestCase):
     @staticmethod
     def test_train_step():
+        # with random gst mel style
         input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
         input_lengths = torch.randint(100, 129, (8, )).long().to(device)
         input_lengths[-1] = 128
@@ -113,13 +117,82 @@ class TacotronGSTTrainTest(unittest.TestCase):
             num_chars=32,
             num_speakers=5,
             gst=True,
-            postnet_output_dim=c.audio['num_freq'],
+            gst_embedding_dim=c.gst['gst_embedding_dim'],
+            gst_num_heads=c.gst['gst_num_heads'],
+            gst_style_tokens=c.gst['gst_style_tokens'],
+            postnet_output_dim=c.audio['fft_size'],
             decoder_output_dim=c.audio['num_mels'],
             r=c.r,
             memory_size=c.memory_size
         ).to(device)  #FIXME: missing num_speakers parameter to Tacotron ctor
         model.train()
-        print(model)
+        # print(model)
+        print(" > Num parameters for Tacotron GST model:%s" %
+              (count_parameters(model)))
+        model_ref = copy.deepcopy(model)
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            assert (param - param_ref).sum() == 0, param
+            count += 1
+        optimizer = optim.Adam(model.parameters(), lr=c.lr)
+        for _ in range(10):
+            mel_out, linear_out, align, stop_tokens = model.forward(
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
+            optimizer.zero_grad()
+            loss = criterion(mel_out, mel_spec, mel_lengths)
+            stop_loss = criterion_st(stop_tokens, stop_targets)
+            loss = loss + criterion(linear_out, linear_spec,
+                                    mel_lengths) + stop_loss
+            loss.backward()
+            optimizer.step()
+        # check parameter changes
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            # ignore pre-higway layer since it works conditional
+            assert (param != param_ref).any(
+            ), "param {} with shape {} not updated!! \n{}\n{}".format(
+                count, param.shape, param, param_ref)
+            count += 1
+
+        # with file gst style
+        mel_spec = torch.FloatTensor(ap.melspectrogram(ap.load_wav(WAV_FILE)))[:, :120].unsqueeze(0).transpose(1, 2).to(device)
+        mel_spec = mel_spec.repeat(8, 1, 1)
+
+        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 129, (8, )).long().to(device)
+        input_lengths[-1] = 128
+        linear_spec = torch.rand(8, mel_spec.size(1), c.audio['fft_size']).to(device)
+        mel_lengths = torch.randint(20, mel_spec.size(1), (8, )).long().to(device)
+        mel_lengths[-1] = mel_spec.size(1)
+        stop_targets = torch.zeros(8, mel_spec.size(1), 1).float().to(device)
+        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
+
+        for idx in mel_lengths:
+            stop_targets[:, int(idx.item()):, 0] = 1.0
+
+        stop_targets = stop_targets.view(input_dummy.shape[0],
+                                         stop_targets.size(1) // c.r, -1)
+        stop_targets = (stop_targets.sum(2) >
+                        0.0).unsqueeze(2).float().squeeze()
+
+        criterion = L1LossMasked(seq_len_norm=False).to(device)
+        criterion_st = nn.BCEWithLogitsLoss().to(device)
+        model = Tacotron(
+            num_chars=32,
+            num_speakers=5,
+            gst=True,
+            gst_embedding_dim=c.gst['gst_embedding_dim'],
+            gst_num_heads=c.gst['gst_num_heads'],
+            gst_style_tokens=c.gst['gst_style_tokens'],
+            postnet_output_dim=c.audio['fft_size'],
+            decoder_output_dim=c.audio['num_mels'],
+            r=c.r,
+            memory_size=c.memory_size
+        ).to(device)  #FIXME: missing num_speakers parameter to Tacotron ctor
+        model.train()
+        # print(model)
         print(" > Num parameters for Tacotron GST model:%s" %
               (count_parameters(model)))
         model_ref = copy.deepcopy(model)

From 6e7f33c798ce5277916efe6f0047d6ac20db48cb Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 03:51:20 -0300
Subject: [PATCH 15/56] add support for synthesize using variable size external
 embedding and add bugfix in scipy.io import

---
 mozilla_voice_tts/bin/synthesize.py      | 39 +++++++++++++++++-------
 mozilla_voice_tts/bin/train_tts.py       |  1 -
 mozilla_voice_tts/tts/utils/synthesis.py | 24 ++++++++++++---
 3 files changed, 47 insertions(+), 17 deletions(-)

diff --git a/mozilla_voice_tts/bin/synthesize.py b/mozilla_voice_tts/bin/synthesize.py
index b52db37e..527a3ce9 100644
--- a/mozilla_voice_tts/bin/synthesize.py
+++ b/mozilla_voice_tts/bin/synthesize.py
@@ -18,9 +18,9 @@ from mozilla_voice_tts.utils.io import load_config
 from mozilla_voice_tts.vocoder.utils.generic_utils import setup_generator
 
 
-def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_id):
+def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid, speaker_embedding=None):
     t_1 = time.time()
-    waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, CONFIG.gst['gst_style_input'], False, CONFIG.enable_eos_bos_chars, use_gl)
+    waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_fileid, None, False, CONFIG.enable_eos_bos_chars, use_gl, speaker_embedding=speaker_embedding)
     if CONFIG.model == "Tacotron" and not use_gl:
         mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T
     if not use_gl:
@@ -80,9 +80,9 @@ if __name__ == "__main__":
                         help="JSON file for multi-speaker model.",
                         default="")
     parser.add_argument(
-        '--speaker_id',
-        type=int,
-        help="target speaker_id if the model is multi-speaker.",
+        '--speaker_fileid',
+        type=str,
+        help="if CONFIG.use_external_speaker_embedding_file is true, name of speaker embedding reference file present in speakers.json, else target speaker_fileid if the model is multi-speaker.",
         default=None)
     args = parser.parse_args()
 
@@ -97,16 +97,24 @@ if __name__ == "__main__":
     if 'characters' in C.keys():
         symbols, phonemes = make_symbols(**C.characters)
 
+    speaker_embedding = None
+    speaker_embedding_dim = None
+    num_speakers = 0
+
     # load speakers
     if args.speakers_json != '':
-        speakers = json.load(open(args.speakers_json, 'r'))
-        num_speakers = len(speakers)
-    else:
-        num_speakers = 0
+        speaker_mapping = json.load(open(args.speakers_json, 'r'))
+        num_speakers = len(speaker_mapping)
+        if C.use_external_speaker_embedding_file:
+            if args.speaker_fileid is not None:
+                speaker_embedding = speaker_mapping[args.speaker_fileid]['embedding']
+            else: # if speaker_fileid is not specificated use the first sample in speakers.json
+                speaker_embedding = speaker_mapping[list(speaker_mapping.keys())[0]]['embedding']
+            speaker_embedding_dim = len(speaker_embedding)
 
     # load the model
     num_chars = len(phonemes) if C.use_phonemes else len(symbols)
-    model = setup_model(num_chars, num_speakers, C)
+    model = setup_model(num_chars, num_speakers, C, speaker_embedding_dim)
     cp = torch.load(args.model_path, map_location=torch.device('cpu'))
     model.load_state_dict(cp['model'])
     model.eval()
@@ -130,7 +138,16 @@ if __name__ == "__main__":
     # synthesize voice
     use_griffin_lim = args.vocoder_path == ""
     print(" > Text: {}".format(args.text))
-    wav = tts(model, vocoder_model, args.text, C, args.use_cuda, ap, use_griffin_lim, args.speaker_id)
+
+    if not C.use_external_speaker_embedding_file:
+        if args.speaker_fileid.isdigit():
+            args.speaker_fileid = int(args.speaker_fileid)
+        else:
+            args.speaker_fileid = None
+    else:
+        args.speaker_fileid = None
+
+    wav = tts(model, vocoder_model, args.text, C, args.use_cuda, ap, use_griffin_lim, args.speaker_fileid, speaker_embedding=speaker_embedding)
 
     # save the results
     file_name = args.text.replace(" ", "_")
diff --git a/mozilla_voice_tts/bin/train_tts.py b/mozilla_voice_tts/bin/train_tts.py
index daa517b9..1b9bc032 100644
--- a/mozilla_voice_tts/bin/train_tts.py
+++ b/mozilla_voice_tts/bin/train_tts.py
@@ -523,7 +523,6 @@ def main(args):  # pylint: disable=redefined-outer-name
                                                     "a previously trained model."
         elif c.use_external_speaker_embedding_file and c.external_speaker_embedding_file: # if start new train using External Embedding file
             speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
-            print(speaker_mapping)
             speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]['embedding'])
         elif c.use_external_speaker_embedding_file and not c.external_speaker_embedding_file: # if start new train using External Embedding file and don't pass external embedding file
             raise "use_external_speaker_embedding_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
diff --git a/mozilla_voice_tts/tts/utils/synthesis.py b/mozilla_voice_tts/tts/utils/synthesis.py
index 6fed8f89..2f746533 100644
--- a/mozilla_voice_tts/tts/utils/synthesis.py
+++ b/mozilla_voice_tts/tts/utils/synthesis.py
@@ -45,17 +45,17 @@ def compute_style_mel(style_wav, ap, cuda=False):
     return style_mel
 
 
-def run_model_torch(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
+def run_model_torch(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None, speaker_embeddings=None):
     if CONFIG.use_gst:
         decoder_output, postnet_output, alignments, stop_tokens = model.inference(
-            inputs, style_mel=style_mel, speaker_ids=speaker_id)
+            inputs, style_mel=style_mel, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
     else:
         if truncated:
             decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
-                inputs, speaker_ids=speaker_id)
+                inputs, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
         else:
             decoder_output, postnet_output, alignments, stop_tokens = model.inference(
-                inputs, speaker_ids=speaker_id)
+                inputs, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
     return decoder_output, postnet_output, alignments, stop_tokens
 
 
@@ -140,6 +140,15 @@ def id_to_torch(speaker_id, cuda=False):
     return speaker_id
 
 
+def embedding_to_torch(speaker_embedding, cuda=False):
+    if speaker_embedding is not None:
+        speaker_embedding = np.asarray(speaker_embedding)
+        speaker_embedding = torch.from_numpy(speaker_embedding).unsqueeze(0).type(torch.FloatTensor)
+    if cuda:
+        return speaker_embedding.cuda()
+    return speaker_embedding
+
+
 # TODO: perform GL with pytorch for batching
 def apply_griffin_lim(inputs, input_lens, CONFIG, ap):
     '''Apply griffin-lim to each sample iterating throught the first dimension.
@@ -169,6 +178,7 @@ def synthesis(model,
               enable_eos_bos_chars=False, #pylint: disable=unused-argument
               use_griffin_lim=False,
               do_trim_silence=False,
+              speaker_embedding=None,
               backend='torch'):
     """Synthesize voice for the given text.
 
@@ -200,6 +210,10 @@ def synthesis(model,
     if backend == 'torch':
         if speaker_id is not None:
             speaker_id = id_to_torch(speaker_id, cuda=use_cuda)
+
+        if speaker_embedding is not None:
+            speaker_embedding = embedding_to_torch(speaker_embedding, cuda=use_cuda)
+
         if not isinstance(style_mel, dict):
             style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
         inputs = numpy_to_torch(inputs, torch.long, cuda=use_cuda)
@@ -216,7 +230,7 @@ def synthesis(model,
     # synthesize voice
     if backend == 'torch':
         decoder_output, postnet_output, alignments, stop_tokens = run_model_torch(
-            model, inputs, CONFIG, truncated, speaker_id, style_mel)
+            model, inputs, CONFIG, truncated, speaker_id, style_mel, speaker_embeddings=speaker_embedding)
         postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_torch(
             postnet_output, decoder_output, alignments, stop_tokens)
     elif backend == 'tf':

From 5c752799aea6b04c5c98a773a44bb971b95fccb5 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 30 Jul 2020 11:48:12 +0200
Subject: [PATCH 16/56] linter update

---
 mozilla_voice_tts/tts/models/tacotron.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index f6bd07ed..682d2b59 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -113,7 +113,7 @@ class Tacotron(TacotronAbstract):
         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

From fe081d4f7c5078aef1df677680f10e79f18d6dfc Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 5 Aug 2020 18:33:22 +0200
Subject: [PATCH 17/56] fixing rebase issues

---
 mozilla_voice_tts/tts/layers/tacotron.py  |  2 +-
 mozilla_voice_tts/tts/layers/tacotron2.py |  2 --
 tests/inputs/test_config.json             | 23 ++++++-----------------
 tests/outputs/dummy_model_config.json     | 15 ++++++++++++++-
 tests/test_tacotron_model.py              |  6 +++---
 5 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/mozilla_voice_tts/tts/layers/tacotron.py b/mozilla_voice_tts/tts/layers/tacotron.py
index 9dcebd0f..807282b3 100644
--- a/mozilla_voice_tts/tts/layers/tacotron.py
+++ b/mozilla_voice_tts/tts/layers/tacotron.py
@@ -303,7 +303,7 @@ class Decoder(nn.Module):
         self.separate_stopnet = separate_stopnet
         self.query_dim = 256
         # memory -> |Prenet| -> processed_memory
-        prenet_dim = memory_dim * self.memory_size if self.use_memory_queue else memory_dim
+        prenet_dim = frame_channels * self.memory_size if self.use_memory_queue else frame_channels
         self.prenet = Prenet(
             prenet_dim,
             prenet_type,
diff --git a/mozilla_voice_tts/tts/layers/tacotron2.py b/mozilla_voice_tts/tts/layers/tacotron2.py
index 5d6ced25..490f3728 100644
--- a/mozilla_voice_tts/tts/layers/tacotron2.py
+++ b/mozilla_voice_tts/tts/layers/tacotron2.py
@@ -141,7 +141,6 @@ class Decoder(nn.Module):
         location_attn (bool): if true, use location sensitive attention.
         attn_K (int): number of attention heads for GravesAttention.
         separate_stopnet (bool): if true, detach stopnet input to prevent gradient flow.
-        speaker_embedding_dim (int): size of speaker embedding vector, for multi-speaker training.
     """
     # Pylint gets confused by PyTorch conventions here
     #pylint: disable=attribute-defined-outside-init
@@ -156,7 +155,6 @@ class Decoder(nn.Module):
         self.separate_stopnet = separate_stopnet
         self.max_decoder_steps = 1000
         self.stop_threshold = 0.5
-        self.speaker_embedding_dim = speaker_embedding_dim
 
         # model dimensions
         self.query_dim = 1024
diff --git a/tests/inputs/test_config.json b/tests/inputs/test_config.json
index b1e857c0..b2bba154 100644
--- a/tests/inputs/test_config.json
+++ b/tests/inputs/test_config.json
@@ -53,27 +53,16 @@
     "max_seq_len": 300,
     "log_dir": "tests/outputs/",
 
-<<<<<<< HEAD
-    "use_speaker_embedding": false,     
-    "use_gst": false,       			    
-    "gst":	{			                
-        "gst_style_input": null,        
-        "gst_embedding_dim": 512,       
-        "gst_num_heads": 4,
-        "gst_style_tokens": 10
-	}
-=======
     // MULTI-SPEAKER and GST
     "use_speaker_embedding": false,     // 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 
-                                        // -> wave file [path to wave] or 
-                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}  
+        "gst_style_input": null,        // Condition the style input either on a
+                                        // -> wave file [path to wave] or
+                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
                                         // with the dictionary being len(dict) <= len(gst_style_tokens).
-        "gst_embedding_dim": 512,       
+        "gst_embedding_dim": 512,
         "gst_num_heads": 4,
         "gst_style_tokens": 10
-        }  
->>>>>>> travis unit tests fix and add Tacotron and Tacotron 2 GST and MultiSpeaker Tests
-    }
+        }
+}
diff --git a/tests/outputs/dummy_model_config.json b/tests/outputs/dummy_model_config.json
index d2e2fca0..bf46be1c 100644
--- a/tests/outputs/dummy_model_config.json
+++ b/tests/outputs/dummy_model_config.json
@@ -83,6 +83,19 @@
     "use_phonemes": false,           // 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
     "text_cleaner": "phoneme_cleaners",
-    "use_speaker_embedding": false // whether to use additional embeddings for separate speakers
+    "use_speaker_embedding": false, // whether to use additional embeddings for separate speakers
+
+    // MULTI-SPEAKER and GST
+    "use_speaker_embedding": false,     // 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
+                                        // -> wave file [path to wave] or
+                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
+                                        // with the dictionary being len(dict) <= len(gst_style_tokens).
+        "gst_embedding_dim": 512,
+        "gst_num_heads": 4,
+        "gst_style_tokens": 10
+        }
 }
 
diff --git a/tests/test_tacotron_model.py b/tests/test_tacotron_model.py
index 42880589..2b55cbac 100644
--- a/tests/test_tacotron_model.py
+++ b/tests/test_tacotron_model.py
@@ -35,7 +35,7 @@ class TacotronTrainTest(unittest.TestCase):
         input_lengths = torch.randint(100, 129, (8, )).long().to(device)
         input_lengths[-1] = 128
         mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
-        linear_spec = torch.rand(8, 30, c.audio['num_freq']).to(device)
+        linear_spec = torch.rand(8, 30, c.audio['fft_size']).to(device)
         mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
         stop_targets = torch.zeros(8, 30, 1).float().to(device)
         speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
@@ -53,7 +53,7 @@ class TacotronTrainTest(unittest.TestCase):
         model = Tacotron(
             num_chars=32,
             num_speakers=5,
-            postnet_output_dim=c.audio['num_freq'],
+            postnet_output_dim=c.audio['fft_size'],
             decoder_output_dim=c.audio['num_mels'],
             r=c.r,
             memory_size=c.memory_size
@@ -97,7 +97,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
         input_lengths = torch.randint(100, 129, (8, )).long().to(device)
         input_lengths[-1] = 128
         mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
-        linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
+        linear_spec = torch.rand(8, 120, c.audio['fft_size']).to(device)
         mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
         mel_lengths[-1] = 120
         stop_targets = torch.zeros(8, 120, 1).float().to(device)

From 84b7ab6ee67a214fdac26458806886c319d430ed Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Fri, 10 Jul 2020 12:14:55 +0200
Subject: [PATCH 18/56] Added support for Tacotron2 GST + abbility to condition
 style input with wav or tokens

---
 mozilla_voice_tts/tts/models/tacotron2.py     | 100 +++--
 .../tts/models/tacotron_abstract.py           |  13 +-
 mozilla_voice_tts/tts/utils/synthesis.py      |   3 +
 synthesize.py                                 | 182 +++++++++
 tests/inputs/test_train_config.json           | 159 ++++++++
 utils/generic_utils.py                        | 374 ++++++++++++++++++
 6 files changed, 776 insertions(+), 55 deletions(-)
 create mode 100644 synthesize.py
 create mode 100644 utils/generic_utils.py

diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 45e743e5..7effdb3d 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -27,7 +27,6 @@ class Tacotron2(TacotronAbstract):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
-                 speaker_embedding_dim=None,
                  gst=False,
                  gst_embedding_dim=512,
                  gst_num_heads=4,
@@ -42,33 +41,18 @@ class Tacotron2(TacotronAbstract):
                              ddc_r, gst)
 
         # init layer dims
-        decoder_in_features = 512
-        encoder_in_features = 512
-
-        if speaker_embedding_dim is None:
-            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
-            self.embeddings_per_sample = False
-            speaker_embedding_dim = 512
-        else:
-            # if speaker_embedding_dim is not None we need use speaker embedding per sample
-            self.embeddings_per_sample = True
-
-        # speaker and gst embeddings is concat in decoder input
-        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
-
-        # embedding layer
+        speaker_embedding_dim = 512 if num_speakers > 1 else 0
+        gst_embedding_dim = gst_embedding_dim if self.gst else 0
+        decoder_in_features = 512+speaker_embedding_dim+gst_embedding_dim
+        encoder_in_features = 512 if num_speakers > 1 else 512
+        proj_speaker_dim = 80 if num_speakers > 1 else 0
+        # base layers
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
 
         # speaker embedding layer
         if num_speakers > 1:
-            if not self.embeddings_per_sample:
-                self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
-                self.speaker_embedding.weight.data.normal_(0, 0.3)
-
-        # base model layers
+            self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
+            self.speaker_embedding.weight.data.normal_(0, 0.3)
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
@@ -99,7 +83,7 @@ class Tacotron2(TacotronAbstract):
         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, mel_lengths=None, speaker_ids=None, speaker_embeddings=None):
+    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)
         input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
@@ -108,18 +92,20 @@ class Tacotron2(TacotronAbstract):
         # 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:
-            if not self.embeddings_per_sample:
-                # B x 1 x speaker_embed_dim
-                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
+            embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
+            if hasattr(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:
-                # B x 1 x speaker_embed_dim
-                speaker_embeddings = torch.unsqueeze(speaker_embeddings, 1)
-            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
+                encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
+        else:
+            if hasattr(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 = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
 
@@ -147,18 +133,24 @@ class Tacotron2(TacotronAbstract):
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
-    def inference(self, text, speaker_ids=None, style_mel=None, speaker_embeddings=None):
+    def inference(self, text, speaker_ids=None, style_mel=None):
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference(embedded_inputs)
 
-        if self.gst:
-            # B x gst_dim
-            encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
-
         if self.num_speakers > 1:
-            if not self.embeddings_per_sample:
-                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
-            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
+            embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
+            embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
+            if hasattr(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)
+            else:
+                encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
+        else:
+            if hasattr(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)
 
         decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
@@ -168,21 +160,27 @@ class Tacotron2(TacotronAbstract):
             decoder_outputs, postnet_outputs, alignments)
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
-    def inference_truncated(self, text, speaker_ids=None, style_mel=None, speaker_embeddings=None):
+    def inference_truncated(self, text, speaker_ids=None, style_mel=None):
         """
         Preserve model states for continuous inference
         """
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference_truncated(embedded_inputs)
 
-        if self.gst:
-            # B x gst_dim
-            encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
-
         if self.num_speakers > 1:
-            if not self.embeddings_per_sample:
-                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
-            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
+            embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
+            embedded_speakers = embedded_speakers.repeat(1, encoder_outputs.size(1), 1)
+            if hasattr(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)
+            else:
+                encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
+        else:
+            if hasattr(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)
 
         mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(
             encoder_outputs)
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index 6f3d32ad..bc794d49 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -165,7 +165,6 @@ class TacotronAbstract(ABC, nn.Module):
                 self.speaker_embeddings).squeeze(1)
 
     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)
@@ -176,11 +175,17 @@ class TacotronAbstract(ABC, nn.Module):
                 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:
+            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 in range(self.gst_style_tokens):
+                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 * 0
         else:
-            gst_outputs = self.gst_layer(style_input) # pylint: disable=not-callable
-        inputs = self._concat_speaker_embedding(inputs, gst_outputs)
-        return inputs
+            gst_outputs = self.gst_layer(style_input)
+        embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)
+        return inputs, embedded_gst
 
     @staticmethod
     def _add_speaker_embedding(outputs, speaker_embeddings):
diff --git a/mozilla_voice_tts/tts/utils/synthesis.py b/mozilla_voice_tts/tts/utils/synthesis.py
index 2f746533..52b33e86 100644
--- a/mozilla_voice_tts/tts/utils/synthesis.py
+++ b/mozilla_voice_tts/tts/utils/synthesis.py
@@ -210,10 +210,13 @@ def synthesis(model,
     if backend == 'torch':
         if speaker_id is not None:
             speaker_id = id_to_torch(speaker_id, cuda=use_cuda)
+<<<<<<< HEAD:mozilla_voice_tts/tts/utils/synthesis.py
 
         if speaker_embedding is not None:
             speaker_embedding = embedding_to_torch(speaker_embedding, cuda=use_cuda)
 
+=======
+>>>>>>> Added support for Tacotron2 GST + abbility to condition style input with wav or tokens:utils/synthesis.py
         if not isinstance(style_mel, dict):
             style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
         inputs = numpy_to_torch(inputs, torch.long, cuda=use_cuda)
diff --git a/synthesize.py b/synthesize.py
new file mode 100644
index 00000000..bd720123
--- /dev/null
+++ b/synthesize.py
@@ -0,0 +1,182 @@
+# pylint: disable=redefined-outer-name, unused-argument
+import os
+import time
+import argparse
+import torch
+import json
+import string
+
+from TTS.utils.synthesis import synthesis
+from TTS.utils.generic_utils import setup_model
+from TTS.utils.io import load_config
+from TTS.utils.text.symbols import make_symbols, symbols, phonemes
+from TTS.utils.audio import AudioProcessor
+
+
+def tts(model,
+        vocoder_model,
+        C,
+        VC,
+        text,
+        ap,
+        ap_vocoder,
+        use_cuda,
+        batched_vocoder,
+        speaker_id=None,
+        figures=False):
+    t_1 = time.time()
+    use_vocoder_model = vocoder_model is not None
+    waveform, alignment, _, postnet_output, stop_tokens, _ = synthesis(
+        model, text, C, use_cuda, ap, speaker_id, style_wav=C.gst['gst_style_input'],
+        truncated=False, enable_eos_bos_chars=C.enable_eos_bos_chars,
+        use_griffin_lim=(not use_vocoder_model), do_trim_silence=True)
+
+    if C.model == "Tacotron" and use_vocoder_model:
+        postnet_output = ap.out_linear_to_mel(postnet_output.T).T
+    # correct if there is a scale difference b/w two models
+    if use_vocoder_model:
+        postnet_output = ap._denormalize(postnet_output)
+        postnet_output = ap_vocoder._normalize(postnet_output)
+        vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
+        waveform = vocoder_model.generate(
+            vocoder_input.cuda() if use_cuda else vocoder_input,
+            batched=batched_vocoder,
+            target=8000,
+            overlap=400)
+    print(" >  Run-time: {}".format(time.time() - t_1))
+    return alignment, postnet_output, stop_tokens, waveform
+
+
+if __name__ == "__main__":
+
+    global symbols, phonemes
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('text', type=str, help='Text to generate speech.')
+    parser.add_argument('config_path',
+                        type=str,
+                        help='Path to model config file.')
+    parser.add_argument(
+        'model_path',
+        type=str,
+        help='Path to model file.',
+    )
+    parser.add_argument(
+        'out_path',
+        type=str,
+        help='Path to save final wav file. Wav file will be names as the text given.',
+    )
+    parser.add_argument('--use_cuda',
+                        type=bool,
+                        help='Run model on CUDA.',
+                        default=False)
+    parser.add_argument(
+        '--vocoder_path',
+        type=str,
+        help=
+        'Path to vocoder model file. If it is not defined, model uses GL as vocoder. Please make sure that you installed vocoder library before (WaveRNN).',
+        default="",
+    )
+    parser.add_argument('--vocoder_config_path',
+                        type=str,
+                        help='Path to vocoder model config file.',
+                        default="")
+    parser.add_argument(
+        '--batched_vocoder',
+        type=bool,
+        help="If True, vocoder model uses faster batch processing.",
+        default=True)
+    parser.add_argument('--speakers_json',
+                        type=str,
+                        help="JSON file for multi-speaker model.",
+                        default="")
+    parser.add_argument(
+        '--speaker_id',
+        type=int,
+        help="target speaker_id if the model is multi-speaker.",
+        default=None)
+    args = parser.parse_args()
+
+    if args.vocoder_path != "":
+        assert args.use_cuda, " [!] Enable cuda for vocoder."
+        from WaveRNN.models.wavernn import Model as VocoderModel
+
+    # load the config
+    C = load_config(args.config_path)
+    C.forward_attn_mask = True
+
+    # load the audio processor
+    ap = AudioProcessor(**C.audio)
+
+    # if the vocabulary was passed, replace the default
+    if 'characters' in C.keys():
+        symbols, phonemes = make_symbols(**C.characters)
+
+    # load speakers
+    if args.speakers_json != '':
+        speakers = json.load(open(args.speakers_json, 'r'))
+        num_speakers = len(speakers)
+    else:
+        num_speakers = 0
+
+    # load the model
+    num_chars = len(phonemes) if C.use_phonemes else len(symbols)
+    model = setup_model(num_chars, num_speakers, C)
+    cp = torch.load(args.model_path)
+    model.load_state_dict(cp['model'])
+    model.eval()
+    if args.use_cuda:
+        model.cuda()
+    model.decoder.set_r(cp['r'])
+
+    # load vocoder model
+    if args.vocoder_path != "":
+        VC = load_config(args.vocoder_config_path)
+        ap_vocoder = AudioProcessor(**VC.audio)
+        bits = 10
+        vocoder_model = VocoderModel(rnn_dims=512,
+                                     fc_dims=512,
+                                     mode=VC.mode,
+                                     mulaw=VC.mulaw,
+                                     pad=VC.pad,
+                                     upsample_factors=VC.upsample_factors,
+                                     feat_dims=VC.audio["num_mels"],
+                                     compute_dims=128,
+                                     res_out_dims=128,
+                                     res_blocks=10,
+                                     hop_length=ap.hop_length,
+                                     sample_rate=ap.sample_rate,
+                                     use_aux_net=True,
+                                     use_upsample_net=True)
+
+        check = torch.load(args.vocoder_path)
+        vocoder_model.load_state_dict(check['model'])
+        vocoder_model.eval()
+        if args.use_cuda:
+            vocoder_model.cuda()
+    else:
+        vocoder_model = None
+        VC = None
+        ap_vocoder = None
+
+    # synthesize voice
+    print(" > Text: {}".format(args.text))
+    _, _, _, wav = tts(model,
+                       vocoder_model,
+                       C,
+                       VC,
+                       args.text,
+                       ap,
+                       ap_vocoder,
+                       args.use_cuda,
+                       args.batched_vocoder,
+                       speaker_id=args.speaker_id,
+                       figures=False)
+
+    # save the results
+    file_name = args.text.replace(" ", "_")
+    file_name = file_name.translate(
+        str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
+    out_path = os.path.join(args.out_path, file_name)
+    print(" > Saving output to {}".format(out_path))
+    ap.save_wav(wav, out_path)
diff --git a/tests/inputs/test_train_config.json b/tests/inputs/test_train_config.json
index 951fe4a3..bea4cbb7 100644
--- a/tests/inputs/test_train_config.json
+++ b/tests/inputs/test_train_config.json
@@ -1,3 +1,4 @@
+<<<<<<< HEAD:tests/inputs/test_train_config.json
 {
     "model": "Tacotron2",
     "run_name": "test_sample_dataset_run",
@@ -150,3 +151,161 @@
 
 }
 
+=======
+{
+    "model": "Tacotron2",
+    "run_name": "ljspeech-ddc-bn",
+    "run_description": "tacotron2 with ddc and batch-normalization",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        // stft parameters
+        "fft_size": 1024,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // Griffin-Lim
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+        "spec_gain": 20,
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // VOCABULARY PARAMETERS
+    // if custom character set is not defined,
+    // default set in symbols.py is used
+    // "characters":{
+    //     "pad": "_",
+    //     "eos": "~",
+    //     "bos": "^",
+    //     "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
+    //     "punctuations":"!'(),-.:;? ",
+    //     "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
+    // },
+
+    // DISTRIBUTED TRAINING
+    "distributed":{
+        "backend": "nccl",
+        "url": "tcp:\/\/localhost:54321"
+    },
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "eval_batch_size":16,
+    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
+    "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
+    "loss_masking": true,         // enable / disable loss masking against the sequence padding.
+    "ga_alpha": 10.0,        // weight for guided attention loss. If > 0, guided attention is enabled.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "noam_schedule": false,        // use noam warmup and lr schedule.
+    "grad_clip": 1.0,              // upper limit for gradients for clipping.
+    "epochs": 1000,                // total number of epochs to train.
+    "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "wd": 0.000001,                // Weight decay weight.
+    "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "seq_len_norm": false,         // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
+
+    // TACOTRON PRENET
+    "memory_size": -1,             // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
+    "prenet_type": "bn",     // "original" or "bn".
+    "prenet_dropout": false,        // enable/disable dropout at prenet.
+
+    // TACOTRON ATTENTION
+    "attention_type": "original",  // 'original' or 'graves'
+    "attention_heads": 4,          // number of attention heads (only for 'graves')
+    "attention_norm": "sigmoid",   // softmax or sigmoid.
+    "windowing": false,            // Enables attention windowing. Used only in eval mode.
+    "use_forward_attn": false,     // if it uses forward attention. In general, it aligns faster.
+    "forward_attn_mask": false,    // Additional masking forcing monotonicity only in eval mode.
+    "transition_agent": false,     // enable/disable transition agent of forward attention.
+    "location_attn": true,         // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
+    "bidirectional_decoder": false,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
+    "double_decoder_consistency": true,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
+    "ddc_r": 7,                           // reduction rate for coarse decoder.
+
+    // STOPNET
+    "stopnet": true,               // Train stopnet predicting the end of synthesis.
+    "separate_stopnet": true,      // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log training on console.
+    "tb_plot_step:": 100,    // Number of steps to plot TB training figures.
+    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
+    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "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",
+    "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.
+    "batch_group_size": 0,  //Number of batches to shuffle after bucketing.
+    "min_seq_len": 6,       // DATASET-RELATED: minimum text length to use in training
+    "max_seq_len": 153,     // DATASET-RELATED: maximum text length
+
+    // PATHS
+    "output_path": "/home/erogol/Models/LJSpeech/",
+
+    // PHONEMES
+    "phoneme_cache_path": "/media/erogol/data_ssd2/mozilla_us_phonemes_3",  // 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_gst": true,       			    // use global style tokens
+    "gst":	{			                // gst parameter if gst is enabled
+        "gst_style_input": null,        // Condition the style input either on a 
+                                        // -> wave file [path to wave] or 
+                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}  
+                                        // with the dictionary being len(dict) == len(gst_style_tokens).
+        "gst_embedding_dim": 512,       
+        "gst_num_heads": 4,
+        "gst_style_tokens": 10
+	},  
+
+    // DATASETS
+    "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",
+                "meta_file_val": null
+            }
+        ]
+}
+
+>>>>>>> Added support for Tacotron2 GST + abbility to condition style input with wav or tokens:config.json
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
new file mode 100644
index 00000000..8b4b1f12
--- /dev/null
+++ b/utils/generic_utils.py
@@ -0,0 +1,374 @@
+import os
+import glob
+import torch
+import shutil
+import datetime
+import subprocess
+import importlib
+import numpy as np
+from collections import Counter
+
+
+def get_git_branch():
+    try:
+        out = subprocess.check_output(["git", "branch"]).decode("utf8")
+        current = next(line for line in out.split("\n")
+                       if line.startswith("*"))
+        current.replace("* ", "")
+    except subprocess.CalledProcessError:
+        current = "inside_docker"
+    return current
+
+
+def get_commit_hash():
+    """https://stackoverflow.com/questions/14989858/get-the-current-git-hash-in-a-python-script"""
+    # try:
+    #     subprocess.check_output(['git', 'diff-index', '--quiet',
+    #                              'HEAD'])  # Verify client is clean
+    # except:
+    #     raise RuntimeError(
+    #         " !! Commit before training to get the commit hash.")
+    try:
+        commit = subprocess.check_output(
+            ['git', 'rev-parse', '--short', 'HEAD']).decode().strip()
+    # Not copying .git folder into docker container
+    except subprocess.CalledProcessError:
+        commit = "0000000"
+    print(' > Git Hash: {}'.format(commit))
+    return commit
+
+
+def create_experiment_folder(root_path, model_name, debug):
+    """ Create a folder with the current date and time """
+    date_str = datetime.datetime.now().strftime("%B-%d-%Y_%I+%M%p")
+    if debug:
+        commit_hash = 'debug'
+    else:
+        commit_hash = get_commit_hash()
+    output_folder = os.path.join(
+        root_path, model_name + '-' + date_str + '-' + commit_hash)
+    os.makedirs(output_folder, exist_ok=True)
+    print(" > Experiment folder: {}".format(output_folder))
+    return output_folder
+
+
+def remove_experiment_folder(experiment_path):
+    """Check folder if there is a checkpoint, otherwise remove the folder"""
+
+    checkpoint_files = glob.glob(experiment_path + "/*.pth.tar")
+    if not checkpoint_files:
+        if os.path.exists(experiment_path):
+            shutil.rmtree(experiment_path, ignore_errors=True)
+            print(" ! Run is removed from {}".format(experiment_path))
+    else:
+        print(" ! Run is kept in {}".format(experiment_path))
+
+
+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)
+
+
+def split_dataset(items):
+    is_multi_speaker = False
+    speakers = [item[-1] for item in items]
+    is_multi_speaker = len(set(speakers)) > 1
+    eval_split_size = 500 if len(items) * 0.01 > 500 else int(
+        len(items) * 0.01)
+    assert eval_split_size > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
+    np.random.seed(0)
+    np.random.shuffle(items)
+    if is_multi_speaker:
+        items_eval = []
+        # most stupid code ever -- Fix it !
+        while len(items_eval) < eval_split_size:
+            speakers = [item[-1] for item in items]
+            speaker_counter = Counter(speakers)
+            item_idx = np.random.randint(0, len(items))
+            if speaker_counter[items[item_idx][-1]] > 1:
+                items_eval.append(items[item_idx])
+                del items[item_idx]
+        return items_eval, items
+    return items[:eval_split_size], items[eval_split_size:]
+
+
+# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
+def sequence_mask(sequence_length, max_len=None):
+    if max_len is None:
+        max_len = sequence_length.data.max()
+    batch_size = sequence_length.size(0)
+    seq_range = torch.arange(0, max_len).long()
+    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
+    if sequence_length.is_cuda:
+        seq_range_expand = seq_range_expand.to(sequence_length.device)
+    seq_length_expand = (
+        sequence_length.unsqueeze(1).expand_as(seq_range_expand))
+    # B x T_max
+    return seq_range_expand < seq_length_expand
+
+
+def set_init_dict(model_dict, checkpoint_state, c):
+    # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
+    for k, v in checkpoint_state.items():
+        if k not in model_dict:
+            print(" | > Layer missing in the model definition: {}".format(k))
+    # 1. filter out unnecessary keys
+    pretrained_dict = {
+        k: v
+        for k, v in checkpoint_state.items() if k in model_dict
+    }
+    # 2. filter out different size layers
+    pretrained_dict = {
+        k: v
+        for k, v in pretrained_dict.items()
+        if v.numel() == model_dict[k].numel()
+    }
+    # 3. skip reinit layers
+    if c.reinit_layers is not None:
+        for reinit_layer_name in c.reinit_layers:
+            pretrained_dict = {
+                k: v
+                for k, v in pretrained_dict.items()
+                if reinit_layer_name not in k
+            }
+    # 4. overwrite entries in the existing state dict
+    model_dict.update(pretrained_dict)
+    print(" | > {} / {} layers are restored.".format(len(pretrained_dict),
+                                                     len(model_dict)))
+    return model_dict
+
+
+def setup_model(num_chars, num_speakers, c):
+    print(" > Using model: {}".format(c.model))
+    MyModel = importlib.import_module('TTS.models.' + c.model.lower())
+    MyModel = getattr(MyModel, c.model)
+    if c.model.lower() in "tacotron":
+        model = MyModel(num_chars=num_chars,
+                        num_speakers=num_speakers,
+                        r=c.r,
+                        postnet_output_dim=int(c.audio['fft_size'] / 2 + 1),
+                        decoder_output_dim=c.audio['num_mels'],
+                        gst=c.use_gst,
+                        gst_embedding_dim=c.gst['gst_embedding_dim'],
+                        gst_num_heads=c.gst['gst_num_heads'],
+                        gst_style_tokens=c.gst['gst_style_tokens'],
+                        memory_size=c.memory_size,
+                        attn_type=c.attention_type,
+                        attn_win=c.windowing,
+                        attn_norm=c.attention_norm,
+                        prenet_type=c.prenet_type,
+                        prenet_dropout=c.prenet_dropout,
+                        forward_attn=c.use_forward_attn,
+                        trans_agent=c.transition_agent,
+                        forward_attn_mask=c.forward_attn_mask,
+                        location_attn=c.location_attn,
+                        attn_K=c.attention_heads,
+                        separate_stopnet=c.separate_stopnet,
+                        bidirectional_decoder=c.bidirectional_decoder,
+                        double_decoder_consistency=c.double_decoder_consistency,
+                        ddc_r=c.ddc_r)
+    elif c.model.lower() == "tacotron2":
+        model = MyModel(num_chars=num_chars,
+                        num_speakers=num_speakers,
+                        r=c.r,
+                        postnet_output_dim=c.audio['num_mels'],
+                        decoder_output_dim=c.audio['num_mels'],
+                        gst=c.use_gst,
+                        gst_embedding_dim=c.gst['gst_embedding_dim'],
+                        gst_num_heads=c.gst['gst_num_heads'],
+                        gst_style_tokens=c.gst['gst_style_tokens'],
+                        attn_type=c.attention_type,
+                        attn_win=c.windowing,
+                        attn_norm=c.attention_norm,
+                        prenet_type=c.prenet_type,
+                        prenet_dropout=c.prenet_dropout,
+                        forward_attn=c.use_forward_attn,
+                        trans_agent=c.transition_agent,
+                        forward_attn_mask=c.forward_attn_mask,
+                        location_attn=c.location_attn,
+                        attn_K=c.attention_heads,
+                        separate_stopnet=c.separate_stopnet,
+                        bidirectional_decoder=c.bidirectional_decoder,
+                        double_decoder_consistency=c.double_decoder_consistency,
+                        ddc_r=c.ddc_r)
+    return model
+
+class KeepAverage():
+    def __init__(self):
+        self.avg_values = {}
+        self.iters = {}
+
+    def __getitem__(self, key):
+        return self.avg_values[key]
+
+    def items(self):
+        return self.avg_values.items()
+
+    def add_value(self, name, init_val=0, init_iter=0):
+        self.avg_values[name] = init_val
+        self.iters[name] = init_iter
+
+    def update_value(self, name, value, weighted_avg=False):
+        if name not in self.avg_values:
+            # add value if not exist before
+            self.add_value(name, init_val=value)
+        else:
+            # else update existing value
+            if weighted_avg:
+                self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
+                self.iters[name] += 1
+            else:
+                self.avg_values[name] = self.avg_values[name] * \
+                    self.iters[name] + value
+                self.iters[name] += 1
+                self.avg_values[name] /= self.iters[name]
+
+    def add_values(self, name_dict):
+        for key, value in name_dict.items():
+            self.add_value(key, init_val=value)
+
+    def update_values(self, value_dict):
+        for key, value in value_dict.items():
+            self.update_value(key, value)
+
+
+def _check_argument(name, c, enum_list=None, max_val=None, min_val=None, restricted=False, val_type=None, alternative=None):
+    if alternative in c.keys() and c[alternative] is not None:
+        return
+    if restricted:
+        assert name in c.keys(), f' [!] {name} not defined in config.json'
+    if name in c.keys():
+        if max_val:
+            assert c[name] <= max_val, f' [!] {name} is larger than max value {max_val}'
+        if min_val:
+            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:
+            assert isinstance(c[name], val_type) or c[name] is None, f' [!] {name} has wrong type - {type(c[name])} vs {val_type}'
+
+
+def check_config(c):
+    _check_argument('model', c, enum_list=['tacotron', 'tacotron2'], restricted=True, val_type=str)
+    _check_argument('run_name', c, restricted=True, val_type=str)
+    _check_argument('run_description', c, val_type=str)
+
+    # AUDIO
+    _check_argument('audio', c, restricted=True, val_type=dict)
+
+    # audio processing parameters
+    _check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
+    _check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
+    _check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
+    _check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
+    _check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
+    _check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
+    _check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
+    _check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)
+    _check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
+    _check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
+
+    # vocabulary parameters
+    _check_argument('characters', c, restricted=False, val_type=dict)
+    _check_argument('pad', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
+    _check_argument('eos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
+    _check_argument('bos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
+    _check_argument('characters', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
+    _check_argument('phonemes', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
+    _check_argument('punctuations', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
+
+    # normalization parameters
+    _check_argument('signal_norm', c['audio'], restricted=True, val_type=bool)
+    _check_argument('symmetric_norm', c['audio'], restricted=True, val_type=bool)
+    _check_argument('max_norm', c['audio'], restricted=True, val_type=float, min_val=0.1, max_val=1000)
+    _check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
+    _check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
+    _check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
+    _check_argument('spec_gain', c['audio'], restricted=True, val_type=float, min_val=1, max_val=100)
+    _check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
+    _check_argument('trim_db', c['audio'], restricted=True, val_type=int)
+
+    # training parameters
+    _check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('r', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('gradual_training', c, restricted=False, val_type=list)
+    _check_argument('loss_masking', c, restricted=True, val_type=bool)
+    # _check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100)
+
+    # validation parameters
+    _check_argument('run_eval', c, restricted=True, val_type=bool)
+    _check_argument('test_delay_epochs', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('test_sentences_file', c, restricted=False, val_type=str)
+
+    # optimizer
+    _check_argument('noam_schedule', c, restricted=False, val_type=bool)
+    _check_argument('grad_clip', c, restricted=True, val_type=float, min_val=0.0)
+    _check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('lr', c, restricted=True, val_type=float, min_val=0)
+    _check_argument('wd', c, restricted=True, val_type=float, min_val=0)
+    _check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('seq_len_norm', c, restricted=True, val_type=bool)
+
+    # tacotron prenet
+    _check_argument('memory_size', c, restricted=True, val_type=int, min_val=-1)
+    _check_argument('prenet_type', c, restricted=True, val_type=str, enum_list=['original', 'bn'])
+    _check_argument('prenet_dropout', c, restricted=True, val_type=bool)
+
+    # attention
+    _check_argument('attention_type', c, restricted=True, val_type=str, enum_list=['graves', 'original'])
+    _check_argument('attention_heads', c, restricted=True, val_type=int)
+    _check_argument('attention_norm', c, restricted=True, val_type=str, enum_list=['sigmoid', 'softmax'])
+    _check_argument('windowing', c, restricted=True, val_type=bool)
+    _check_argument('use_forward_attn', c, restricted=True, val_type=bool)
+    _check_argument('forward_attn_mask', c, restricted=True, val_type=bool)
+    _check_argument('transition_agent', c, restricted=True, val_type=bool)
+    _check_argument('transition_agent', c, restricted=True, val_type=bool)
+    _check_argument('location_attn', c, restricted=True, val_type=bool)
+    _check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
+    _check_argument('double_decoder_consistency', c, restricted=True, val_type=bool)
+    _check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int)
+
+    # stopnet
+    _check_argument('stopnet', c, restricted=True, val_type=bool)
+    _check_argument('separate_stopnet', c, restricted=True, val_type=bool)
+
+    # tensorboard
+    _check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('tb_plot_step', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('save_step', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('checkpoint', c, restricted=True, val_type=bool)
+    _check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
+
+    # dataloading
+    # pylint: disable=import-outside-toplevel
+    from TTS.utils.text import cleaners
+    _check_argument('text_cleaner', c, restricted=True, val_type=str, enum_list=dir(cleaners))
+    _check_argument('enable_eos_bos_chars', c, restricted=True, val_type=bool)
+    _check_argument('num_loader_workers', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('num_val_loader_workers', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('batch_group_size', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('min_seq_len', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('max_seq_len', c, restricted=True, val_type=int, min_val=10)
+
+    # paths
+    _check_argument('output_path', c, restricted=True, val_type=str)
+
+    # multi-speaker
+    _check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
+
+    # GST
+    _check_argument('use_gst', c, restricted=True, val_type=bool)
+    _check_argument('gst_style_input', c, restricted=True, val_type=str)
+    _check_argument('gst', c, restricted=True, val_type=dict)
+    _check_argument('gst_embedding_dim', c['gst'], restricted=True, val_type=int, min_val=1)
+    _check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=1)
+    _check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1)
+
+    # 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_val', dataset_entry, restricted=True, val_type=str)

From c2d8a338a1254faade08a5b17645f8fa83d0b24c Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Fri, 10 Jul 2020 12:46:43 +0200
Subject: [PATCH 19/56] No need to query every token when none were passed

---
 mozilla_voice_tts/tts/models/tacotron_abstract.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index bc794d49..13c3e948 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -175,13 +175,7 @@ class TacotronAbstract(ABC, nn.Module):
                 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:
-            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 in range(self.gst_style_tokens):
-                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 * 0
         else:
             gst_outputs = self.gst_layer(style_input)
         embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)

From 447176258c5a91518e9a76858b6e8bb1f3dcc262 Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Sun, 12 Jul 2020 10:40:33 +0200
Subject: [PATCH 20/56] fix fft_size key error

---
 mozilla_voice_tts/tts/models/tacotron_abstract.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index 13c3e948..d1148be5 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -177,6 +177,7 @@ class TacotronAbstract(ABC, nn.Module):
         elif style_input is None:
             gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
         else:
+            # pylint: disable=not-callable
             gst_outputs = self.gst_layer(style_input)
         embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)
         return inputs, embedded_gst

From 89918c6e5351506395fad0545ca5af1cfda08d59 Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Sun, 12 Jul 2020 12:33:13 +0200
Subject: [PATCH 21/56] pylint

---
 mozilla_voice_tts/tts/models/tacotron_abstract.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index d1148be5..9b2ef148 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -177,8 +177,7 @@ class TacotronAbstract(ABC, nn.Module):
         elif style_input is None:
             gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
         else:
-            # pylint: disable=not-callable
-            gst_outputs = self.gst_layer(style_input)
+            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
 

From 77bfb881d74e753e25e2b40f8fa389620d169eca Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Sun, 12 Jul 2020 14:07:44 +0200
Subject: [PATCH 22/56] tacotrongst test + test fixes

---
 mozilla_voice_tts/tts/models/tacotron2.py | 12 ++---
 tests/outputs/dummy_model_config.json     |  1 +
 tests/test_tacotron2_model.py             | 56 +++++++++++++++++++++++
 3 files changed, 63 insertions(+), 6 deletions(-)

diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 7effdb3d..18eb17d1 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -95,14 +95,14 @@ class Tacotron2(TacotronAbstract):
         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 hasattr(self, 'gst'):
+            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 hasattr(self, 'gst'):
+            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)
@@ -140,14 +140,14 @@ class Tacotron2(TacotronAbstract):
         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 hasattr(self, 'gst'):
+            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)
             else:
                 encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
         else:
-            if hasattr(self, 'gst'):
+            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)
@@ -170,14 +170,14 @@ class Tacotron2(TacotronAbstract):
         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 hasattr(self, 'gst'):
+            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)
             else:
                 encoder_outputs = torch.cat([encoder_outputs, embedded_speakers], dim=-1)
         else:
-            if hasattr(self, 'gst'):
+            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)
diff --git a/tests/outputs/dummy_model_config.json b/tests/outputs/dummy_model_config.json
index bf46be1c..b032f191 100644
--- a/tests/outputs/dummy_model_config.json
+++ b/tests/outputs/dummy_model_config.json
@@ -99,3 +99,4 @@
         }
 }
 
+
diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index d4d5eb86..c6d08160 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -75,6 +75,62 @@ class TacotronTrainTest(unittest.TestCase):
                 count, param.shape, param, param_ref)
             count += 1
 
+
+class TacotronGSTTrainTest(unittest.TestCase):
+    def test_train_step(self):
+        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 128, (8, )).long().to(device)
+        input_lengths = torch.sort(input_lengths, descending=True)[0]
+        mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
+        mel_lengths[0] = 30
+        stop_targets = torch.zeros(8, 30, 1).float().to(device)
+        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
+
+        for idx in mel_lengths:
+            stop_targets[:, int(idx.item()):, 0] = 1.0
+
+        stop_targets = stop_targets.view(input_dummy.shape[0],
+                                         stop_targets.size(1) // c.r, -1)
+        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
+
+        criterion = MSELossMasked(seq_len_norm=False).to(device)
+        criterion_st = nn.BCEWithLogitsLoss().to(device)
+        model = Tacotron2(num_chars=24,
+                          gst=True,
+                          r=c.r,
+                          num_speakers=5).to(device)
+        model.train()
+        model_ref = copy.deepcopy(model)
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            assert (param - param_ref).sum() == 0, param
+            count += 1
+        optimizer = optim.Adam(model.parameters(), lr=c.lr)
+        for i in range(5):
+            mel_out, mel_postnet_out, align, stop_tokens = model.forward(
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
+            assert torch.sigmoid(stop_tokens).data.max() <= 1.0
+            assert torch.sigmoid(stop_tokens).data.min() >= 0.0
+            optimizer.zero_grad()
+            loss = criterion(mel_out, mel_spec, mel_lengths)
+            stop_loss = criterion_st(stop_tokens, stop_targets)
+            loss = loss + criterion(mel_postnet_out, mel_postnet_spec, mel_lengths) + stop_loss
+            loss.backward()
+            optimizer.step()
+        # check parameter changes
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            # ignore pre-higway layer since it works conditional
+            # if count not in [145, 59]:
+            assert (param != param_ref).any(
+            ), "param {} with shape {} not updated!! \n{}\n{}".format(
+                count, param.shape, param, param_ref)
+            count += 1
+
 class TacotronGSTTrainTest(unittest.TestCase):
     @staticmethod
     def test_train_step():

From 1436206224630253c42c153a00e20c7067eb7573 Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Mon, 13 Jul 2020 08:50:39 +0200
Subject: [PATCH 23/56] override compute_gst in tacotron2 model

---
 mozilla_voice_tts/tts/models/tacotron2.py     | 22 ++++++++++++++++++-
 .../tts/models/tacotron_abstract.py           | 22 +++++--------------
 2 files changed, 27 insertions(+), 17 deletions(-)

diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 18eb17d1..0aa237ff 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -46,13 +46,15 @@ class Tacotron2(TacotronAbstract):
         decoder_in_features = 512+speaker_embedding_dim+gst_embedding_dim
         encoder_in_features = 512 if num_speakers > 1 else 512
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # base layers
+
+        # embedding layer
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
 
         # speaker embedding layer
         if num_speakers > 1:
             self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
+
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
@@ -83,6 +85,24 @@ class Tacotron2(TacotronAbstract):
         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)
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index 9b2ef148..a4b8c227 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -164,22 +164,12 @@ class TacotronAbstract(ABC, nn.Module):
             self.speaker_embeddings_projected = self.speaker_project_mel(
                 self.speaker_embeddings).squeeze(1)
 
-    def compute_gst(self, inputs, style_input):
-        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 compute_gst(self, inputs, mel_specs):
+        """ Compute global style token """
+        # pylint: disable=not-callable
+        gst_outputs = self.gst_layer(mel_specs)
+        inputs = self._add_speaker_embedding(inputs, gst_outputs)
+        return inputs
 
     @staticmethod
     def _add_speaker_embedding(outputs, speaker_embeddings):

From c4828b2b9e94fa70e5f8097aae405077fbb24aca Mon Sep 17 00:00:00 2001
From: SanjaESC <alexx.korolev@gmail.com>
Date: Mon, 13 Jul 2020 08:51:37 +0200
Subject: [PATCH 24/56] small gst config change

---
 tests/test_tacotron2_model.py | 2 +-
 utils/generic_utils.py        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index c6d08160..92ffb9aa 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -239,4 +239,4 @@ class TacotronGSTTrainTest(unittest.TestCase):
             assert (param != param_ref).any(
             ), "param {} {} with shape {} not updated!! \n{}\n{}".format(
                 name, count, param.shape, param, param_ref)
-            count += 1
\ No newline at end of file
+            count += 1
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 8b4b1f12..3bb99e08 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -359,8 +359,8 @@ def check_config(c):
 
     # GST
     _check_argument('use_gst', c, restricted=True, val_type=bool)
-    _check_argument('gst_style_input', c, restricted=True, val_type=str)
     _check_argument('gst', c, restricted=True, val_type=dict)
+    _check_argument('gst_style_input', c['gst'], restricted=True, val_type=str)
     _check_argument('gst_embedding_dim', c['gst'], restricted=True, val_type=int, min_val=1)
     _check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=1)
     _check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1)

From 70c665b9c4c3716059b1bb774f93f775771eead4 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Mon, 27 Jul 2020 16:59:59 -0300
Subject: [PATCH 25/56] add support fot VCTK and BRSpeech dataset

---
 mozilla_voice_tts/tts/datasets/preprocess.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mozilla_voice_tts/tts/datasets/preprocess.py b/mozilla_voice_tts/tts/datasets/preprocess.py
index 317673e3..0509677c 100644
--- a/mozilla_voice_tts/tts/datasets/preprocess.py
+++ b/mozilla_voice_tts/tts/datasets/preprocess.py
@@ -229,14 +229,14 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
     items = []
     meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
     for meta_file in meta_files:
-        _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
+        txt, speaker_id, txt_file = os.path.relpath(meta_file,root_path).split(os.sep)
         file_id = txt_file.split('.')[0]
         if isinstance(test_speakers, list): # if is list ignore this speakers ids
             if speaker_id in test_speakers:
                 continue
         with open(meta_file) as file_text:
             text = file_text.readlines()[0]
-        wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id+'.wav')
+        wav_file = os.path.join(root_path, wavs_path, speaker_id,file_id+'.wav')
         items.append([text, wav_file, speaker_id])
-    
+
     return items
\ No newline at end of file

From be77e24a39dcc65c842096d9c289b4091811ddcf Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Tue, 28 Jul 2020 17:11:32 -0300
Subject: [PATCH 26/56] bugfix in DDC now DDC work on Tacotron1

---
 mozilla_voice_tts/tts/configs/config.json     |  8 ++-
 mozilla_voice_tts/tts/models/tacotron.py      | 18 +++----
 mozilla_voice_tts/tts/models/tacotron2.py     | 54 ++++++-------------
 .../tts/models/tacotron_abstract.py           | 19 +++++--
 mozilla_voice_tts/tts/utils/generic_utils.py  |  6 +++
 5 files changed, 52 insertions(+), 53 deletions(-)

diff --git a/mozilla_voice_tts/tts/configs/config.json b/mozilla_voice_tts/tts/configs/config.json
index 090540ab..70529fea 100644
--- a/mozilla_voice_tts/tts/configs/config.json
+++ b/mozilla_voice_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",
 
@@ -114,7 +114,7 @@
     "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.
@@ -131,9 +131,13 @@
     "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
 
     // MULTI-SPEAKER and GST
+<<<<<<< HEAD:mozilla_voice_tts/tts/configs/config.json
     "use_speaker_embedding": true,      // use speaker embedding to enable multi-speaker learning.
     "use_external_speaker_embedding_file": false, // if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
     "external_speaker_embedding_file": "../../speakers-vctk-en.json", // if not null and use_external_speaker_embedding_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
+=======
+    "use_speaker_embedding": true,     // use speaker embedding to enable multi-speaker learning.
+>>>>>>> bugfix in DDC now DDC work on Tacotron1:TTS/tts/configs/config.json
     "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 
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index 682d2b59..1395de97 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -42,19 +42,13 @@ class Tacotron(TacotronAbstract):
                              bidirectional_decoder, double_decoder_consistency,
                              ddc_r, gst)
 
+
         # 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 speaker_embedding_dim is None:
-            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
-            self.embeddings_per_sample = False
-            speaker_embedding_dim = 256
-        else:
-            # if speaker_embedding_dim is not None we need use speaker embedding per sample
-            self.embeddings_per_sample = True
-
-        # speaker and gst embeddings is concat in decoder input
         if num_speakers > 1:
             decoder_in_features = decoder_in_features + speaker_embedding_dim # add speaker embedding dim
         if self.gst:
@@ -109,6 +103,9 @@ class Tacotron(TacotronAbstract):
         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
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
         # B x T_in x encoder_in_features
         encoder_outputs = self.encoder(inputs)
         # sequence masking
@@ -155,6 +152,9 @@ class Tacotron(TacotronAbstract):
     @torch.no_grad()
     def inference(self, characters, speaker_ids=None, style_mel=None, speaker_embeddings=None):
         inputs = self.embedding(characters)
+        self._init_states()
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
         encoder_outputs = self.encoder(inputs)
         if self.gst:
             # B x gst_dim
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 0aa237ff..47057c56 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -1,9 +1,15 @@
 import torch
 from torch import nn
 
+<<<<<<< HEAD:mozilla_voice_tts/tts/models/tacotron2.py
 from mozilla_voice_tts.tts.layers.gst_layers import GST
 from mozilla_voice_tts.tts.layers.tacotron2 import Decoder, Encoder, Postnet
 from mozilla_voice_tts.tts.models.tacotron_abstract import TacotronAbstract
+=======
+from TTS.tts.layers.gst_layers import GST
+from TTS.tts.layers.tacotron2 import Decoder, Encoder, Postnet
+from TTS.tts.models.tacotron_abstract import TacotronAbstract
+>>>>>>> bugfix in DDC now DDC work on Tacotron1:TTS/tts/models/tacotron2.py
 
 # TODO: match function arguments with tacotron
 class Tacotron2(TacotronAbstract):
@@ -85,24 +91,6 @@ class Tacotron2(TacotronAbstract):
         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)
@@ -112,20 +100,13 @@ class Tacotron2(TacotronAbstract):
         # 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)
 
@@ -162,15 +143,14 @@ class Tacotron2(TacotronAbstract):
             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)
@@ -192,15 +172,13 @@ class Tacotron2(TacotronAbstract):
             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)
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index a4b8c227..6f3d32ad 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -164,11 +164,22 @@ class TacotronAbstract(ABC, nn.Module):
             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
diff --git a/mozilla_voice_tts/tts/utils/generic_utils.py b/mozilla_voice_tts/tts/utils/generic_utils.py
index 2d5044ef..fc35840d 100644
--- a/mozilla_voice_tts/tts/utils/generic_utils.py
+++ b/mozilla_voice_tts/tts/utils/generic_utils.py
@@ -265,6 +265,12 @@ def check_config(c):
     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)
 
+    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']:

From 89d338358e5c698876bb5c4a3ffeace98b98c0e4 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Wed, 29 Jul 2020 00:49:00 -0300
Subject: [PATCH 27/56] add External Embedding per sample instead of
 nn.Embedding

---
 mozilla_voice_tts/bin/train_tts.py        |  3 +
 mozilla_voice_tts/tts/configs/config.json | 18 ++---
 mozilla_voice_tts/tts/models/tacotron.py  | 19 +++---
 mozilla_voice_tts/tts/models/tacotron2.py | 82 +++++++++++++----------
 4 files changed, 65 insertions(+), 57 deletions(-)

diff --git a/mozilla_voice_tts/bin/train_tts.py b/mozilla_voice_tts/bin/train_tts.py
index 1b9bc032..f7bb0b60 100644
--- a/mozilla_voice_tts/bin/train_tts.py
+++ b/mozilla_voice_tts/bin/train_tts.py
@@ -523,6 +523,7 @@ def main(args):  # pylint: disable=redefined-outer-name
                                                     "a previously trained model."
         elif c.use_external_speaker_embedding_file and c.external_speaker_embedding_file: # if start new train using External Embedding file
             speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
+            print(speaker_mapping)
             speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]['embedding'])
         elif c.use_external_speaker_embedding_file and not c.external_speaker_embedding_file: # if start new train using External Embedding file and don't pass external embedding file
             raise "use_external_speaker_embedding_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
@@ -558,6 +559,8 @@ def main(args):  # pylint: disable=redefined-outer-name
 
     # setup criterion
     criterion = TacotronLoss(c, stopnet_pos_weight=10.0, ga_sigma=0.4)
+    for name, _ in model.named_parameters():
+        print(name)
 
     if args.restore_path:
         checkpoint = torch.load(args.restore_path, map_location='cpu')
diff --git a/mozilla_voice_tts/tts/configs/config.json b/mozilla_voice_tts/tts/configs/config.json
index 70529fea..2a61ba03 100644
--- a/mozilla_voice_tts/tts/configs/config.json
+++ b/mozilla_voice_tts/tts/configs/config.json
@@ -1,5 +1,5 @@
 {
-    "model": "Tacotron",
+    "model": "Tacotron2",
     "run_name": "ljspeech-ddc-bn",
     "run_description": "tacotron2 with ddc and batch-normalization",
 
@@ -114,7 +114,7 @@
     "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": "portuguese_cleaners",
+    "text_cleaner": "phoneme_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.
@@ -131,23 +131,19 @@
     "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
 
     // MULTI-SPEAKER and GST
-<<<<<<< HEAD:mozilla_voice_tts/tts/configs/config.json
     "use_speaker_embedding": true,      // use speaker embedding to enable multi-speaker learning.
     "use_external_speaker_embedding_file": false, // if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
     "external_speaker_embedding_file": "../../speakers-vctk-en.json", // if not null and use_external_speaker_embedding_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
-=======
-    "use_speaker_embedding": true,     // use speaker embedding to enable multi-speaker learning.
->>>>>>> bugfix in DDC now DDC work on Tacotron1:TTS/tts/configs/config.json
     "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 
-                                        // -> wave file [path to wave] or 
-                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}  
+        "gst_style_input": null,        // Condition the style input either on a
+                                        // -> wave file [path to wave] or
+                                        // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
                                         // with the dictionary being len(dict) <= len(gst_style_tokens).
-        "gst_embedding_dim": 512,       
+        "gst_embedding_dim": 512,
         "gst_num_heads": 4,
         "gst_style_tokens": 10
-	},  
+	},
 
     // DATASETS
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index 1395de97..bcc4a2a6 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -42,13 +42,19 @@ class Tacotron(TacotronAbstract):
                              bidirectional_decoder, double_decoder_consistency,
                              ddc_r, gst)
 
-
         # 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 speaker_embedding_dim is None:
+            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
+            self.embeddings_per_sample = False
+            speaker_embedding_dim = 256
+        else:
+            # if speaker_embedding_dim is not None we need use speaker embedding per sample
+            self.embeddings_per_sample = True
+
+        # speaker and gst embeddings is concat in decoder input
         if num_speakers > 1:
             decoder_in_features = decoder_in_features + speaker_embedding_dim # add speaker embedding dim
         if self.gst:
@@ -103,14 +109,10 @@ class Tacotron(TacotronAbstract):
         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
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
         # 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
@@ -152,9 +154,6 @@ class Tacotron(TacotronAbstract):
     @torch.no_grad()
     def inference(self, characters, speaker_ids=None, style_mel=None, speaker_embeddings=None):
         inputs = self.embedding(characters)
-        self._init_states()
-        if speaker_ids is not None:
-            self.compute_speaker_embedding(speaker_ids)
         encoder_outputs = self.encoder(inputs)
         if self.gst:
             # B x gst_dim
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 47057c56..c2fc8a32 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -33,6 +33,7 @@ class Tacotron2(TacotronAbstract):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
+                 speaker_embedding_dim=None,
                  gst=False,
                  gst_embedding_dim=512,
                  gst_num_heads=4,
@@ -47,20 +48,33 @@ class Tacotron2(TacotronAbstract):
                              ddc_r, gst)
 
         # init layer dims
-        speaker_embedding_dim = 512 if num_speakers > 1 else 0
-        gst_embedding_dim = gst_embedding_dim if self.gst else 0
-        decoder_in_features = 512+speaker_embedding_dim+gst_embedding_dim
-        encoder_in_features = 512 if num_speakers > 1 else 512
-        proj_speaker_dim = 80 if num_speakers > 1 else 0
+        decoder_in_features = 512
+        encoder_in_features = 512
+
+        if speaker_embedding_dim is None:
+            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
+            self.embeddings_per_sample = False
+            speaker_embedding_dim = 512
+        else:
+            # if speaker_embedding_dim is not None we need use speaker embedding per sample
+            self.embeddings_per_sample = True
+    
+        # speaker and gst embeddings is concat in decoder input
+        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
 
         # embedding layer
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
 
         # speaker embedding layer
         if num_speakers > 1:
-            self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
-            self.speaker_embedding.weight.data.normal_(0, 0.3)
-
+            if not self.embeddings_per_sample:
+                self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
+                self.speaker_embedding.weight.data.normal_(0, 0.3)
+            
+        # base model layers
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
@@ -91,7 +105,7 @@ class Tacotron2(TacotronAbstract):
         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, mel_lengths=None, speaker_ids=None):
+    def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None, speaker_embeddings=None):
         # compute mask for padding
         # B x T_in_max (boolean)
         input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
@@ -105,8 +119,13 @@ class Tacotron2(TacotronAbstract):
             encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
 
         if self.num_speakers > 1:
-            embedded_speakers = self.speaker_embedding(speaker_ids)[:, None]
-            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, embedded_speakers)
+            if not self.embeddings_per_sample:
+                # B x 1 x speaker_embed_dim
+                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            else:
+                # B x 1 x speaker_embed_dim
+                speaker_embeddings = torch.unsqueeze(speaker_embeddings, 1)
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
 
         encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
 
@@ -134,23 +153,18 @@ class Tacotron2(TacotronAbstract):
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
-    def inference(self, text, speaker_ids=None, style_mel=None):
+    def inference(self, text, speaker_ids=None, style_mel=None, speaker_embeddings=None):
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference(embedded_inputs)
 
+        if self.gst:
+            # B x gst_dim
+            encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
+
         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 = 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 = self.compute_gst(encoder_outputs, style_mel)
+            if not self.embeddings_per_sample:
+                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
 
         decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
@@ -160,25 +174,21 @@ class Tacotron2(TacotronAbstract):
             decoder_outputs, postnet_outputs, alignments)
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
-    def inference_truncated(self, text, speaker_ids=None, style_mel=None):
+    def inference_truncated(self, text, speaker_ids=None, style_mel=None, speaker_embeddings=None):
         """
         Preserve model states for continuous inference
         """
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference_truncated(embedded_inputs)
 
+        if self.gst:
+            # B x gst_dim
+            encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
+
         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 = 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 = self.compute_gst(encoder_outputs, style_mel)
+            if not self.embeddings_per_sample:
+                speaker_embeddings = self.speaker_embedding(speaker_ids)[:, None]
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs, speaker_embeddings)
 
         mel_outputs, alignments, stop_tokens = self.decoder.inference_truncated(
             encoder_outputs)

From a2ee48c28e6c4cf9d00063218154e3c3cd2747a4 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Wed, 29 Jul 2020 18:22:13 -0300
Subject: [PATCH 28/56] bugfix in GST

---
 mozilla_voice_tts/bin/train_tts.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/mozilla_voice_tts/bin/train_tts.py b/mozilla_voice_tts/bin/train_tts.py
index f7bb0b60..daa517b9 100644
--- a/mozilla_voice_tts/bin/train_tts.py
+++ b/mozilla_voice_tts/bin/train_tts.py
@@ -559,8 +559,6 @@ def main(args):  # pylint: disable=redefined-outer-name
 
     # setup criterion
     criterion = TacotronLoss(c, stopnet_pos_weight=10.0, ga_sigma=0.4)
-    for name, _ in model.named_parameters():
-        print(name)
 
     if args.restore_path:
         checkpoint = torch.load(args.restore_path, map_location='cpu')

From f91b9eeda1ab104c8ba6888c2599d7f0fa2e3a35 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Wed, 29 Jul 2020 21:21:42 -0300
Subject: [PATCH 29/56] fix Lint check

---
 mozilla_voice_tts/tts/datasets/preprocess.py | 4 ++--
 mozilla_voice_tts/tts/models/tacotron2.py    | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mozilla_voice_tts/tts/datasets/preprocess.py b/mozilla_voice_tts/tts/datasets/preprocess.py
index 0509677c..7865652a 100644
--- a/mozilla_voice_tts/tts/datasets/preprocess.py
+++ b/mozilla_voice_tts/tts/datasets/preprocess.py
@@ -229,14 +229,14 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
     items = []
     meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
     for meta_file in meta_files:
-        txt, speaker_id, txt_file = os.path.relpath(meta_file,root_path).split(os.sep)
+        _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
         file_id = txt_file.split('.')[0]
         if isinstance(test_speakers, list): # if is list ignore this speakers ids
             if speaker_id in test_speakers:
                 continue
         with open(meta_file) as file_text:
             text = file_text.readlines()[0]
-        wav_file = os.path.join(root_path, wavs_path, speaker_id,file_id+'.wav')
+        wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id+'.wav')
         items.append([text, wav_file, speaker_id])
 
     return items
\ No newline at end of file
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index c2fc8a32..2839cea7 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -58,7 +58,7 @@ class Tacotron2(TacotronAbstract):
         else:
             # if speaker_embedding_dim is not None we need use speaker embedding per sample
             self.embeddings_per_sample = True
-    
+
         # speaker and gst embeddings is concat in decoder input
         if num_speakers > 1:
             decoder_in_features = decoder_in_features + speaker_embedding_dim # add speaker embedding dim
@@ -73,7 +73,7 @@ class Tacotron2(TacotronAbstract):
             if not self.embeddings_per_sample:
                 self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
                 self.speaker_embedding.weight.data.normal_(0, 0.3)
-            
+
         # base model layers
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,

From 496a4be3e35e127b6df09033ff3b57a0a00c970e Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 03:51:20 -0300
Subject: [PATCH 30/56] add support for synthesize using variable size external
 embedding and add bugfix in scipy.io import

---
 mozilla_voice_tts/bin/train_tts.py       | 1 -
 mozilla_voice_tts/tts/utils/synthesis.py | 3 ---
 2 files changed, 4 deletions(-)

diff --git a/mozilla_voice_tts/bin/train_tts.py b/mozilla_voice_tts/bin/train_tts.py
index daa517b9..1b9bc032 100644
--- a/mozilla_voice_tts/bin/train_tts.py
+++ b/mozilla_voice_tts/bin/train_tts.py
@@ -523,7 +523,6 @@ def main(args):  # pylint: disable=redefined-outer-name
                                                     "a previously trained model."
         elif c.use_external_speaker_embedding_file and c.external_speaker_embedding_file: # if start new train using External Embedding file
             speaker_mapping = load_speaker_mapping(c.external_speaker_embedding_file)
-            print(speaker_mapping)
             speaker_embedding_dim = len(speaker_mapping[list(speaker_mapping.keys())[0]]['embedding'])
         elif c.use_external_speaker_embedding_file and not c.external_speaker_embedding_file: # if start new train using External Embedding file and don't pass external embedding file
             raise "use_external_speaker_embedding_file is True, so you need pass a external speaker embedding file, run GE2E-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb or AngularPrototypical-Speaker_Encoder-ExtractSpeakerEmbeddings-by-sample.ipynb notebook in notebooks/ folder"
diff --git a/mozilla_voice_tts/tts/utils/synthesis.py b/mozilla_voice_tts/tts/utils/synthesis.py
index 52b33e86..2f746533 100644
--- a/mozilla_voice_tts/tts/utils/synthesis.py
+++ b/mozilla_voice_tts/tts/utils/synthesis.py
@@ -210,13 +210,10 @@ def synthesis(model,
     if backend == 'torch':
         if speaker_id is not None:
             speaker_id = id_to_torch(speaker_id, cuda=use_cuda)
-<<<<<<< HEAD:mozilla_voice_tts/tts/utils/synthesis.py
 
         if speaker_embedding is not None:
             speaker_embedding = embedding_to_torch(speaker_embedding, cuda=use_cuda)
 
-=======
->>>>>>> Added support for Tacotron2 GST + abbility to condition style input with wav or tokens:utils/synthesis.py
         if not isinstance(style_mel, dict):
             style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
         inputs = numpy_to_torch(inputs, torch.long, cuda=use_cuda)

From ae62efd9eb0820fd430514ca2fde3f8b80c0aad2 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 19:48:27 -0300
Subject: [PATCH 31/56] Force the loading of the wav in synthesis using the AP
 sample rate to avoid breaking the demo

---
 mozilla_voice_tts/tts/utils/synthesis.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mozilla_voice_tts/tts/utils/synthesis.py b/mozilla_voice_tts/tts/utils/synthesis.py
index 2f746533..0952c936 100644
--- a/mozilla_voice_tts/tts/utils/synthesis.py
+++ b/mozilla_voice_tts/tts/utils/synthesis.py
@@ -39,7 +39,7 @@ def numpy_to_tf(np_array, dtype):
 
 def compute_style_mel(style_wav, ap, cuda=False):
     style_mel = torch.FloatTensor(ap.melspectrogram(
-        ap.load_wav(style_wav))).unsqueeze(0)
+        ap.load_wav(style_wav, sr=ap.sample_rate))).unsqueeze(0)
     if cuda:
         return style_mel.cuda()
     return style_mel

From 27ddef141dc38d38475dd543c2a630768d58e283 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 21:18:46 -0300
Subject: [PATCH 32/56] add gst suport in synthesize

---
 mozilla_voice_tts/bin/synthesize.py | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/mozilla_voice_tts/bin/synthesize.py b/mozilla_voice_tts/bin/synthesize.py
index 527a3ce9..3de97555 100644
--- a/mozilla_voice_tts/bin/synthesize.py
+++ b/mozilla_voice_tts/bin/synthesize.py
@@ -18,9 +18,9 @@ from mozilla_voice_tts.utils.io import load_config
 from mozilla_voice_tts.vocoder.utils.generic_utils import setup_generator
 
 
-def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid, speaker_embedding=None):
+def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid, speaker_embedding=None, gst_style=None):
     t_1 = time.time()
-    waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_fileid, None, False, CONFIG.enable_eos_bos_chars, use_gl, speaker_embedding=speaker_embedding)
+    waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_fileid, gst_style, False, CONFIG.enable_eos_bos_chars, use_gl, speaker_embedding=speaker_embedding)
     if CONFIG.model == "Tacotron" and not use_gl:
         mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T
     if not use_gl:
@@ -84,6 +84,11 @@ if __name__ == "__main__":
         type=str,
         help="if CONFIG.use_external_speaker_embedding_file is true, name of speaker embedding reference file present in speakers.json, else target speaker_fileid if the model is multi-speaker.",
         default=None)
+    parser.add_argument(
+        '--gst_style',
+        help="Wav path file for GST stylereference.",
+        default=None)
+
     args = parser.parse_args()
 
     # load the config
@@ -147,7 +152,12 @@ if __name__ == "__main__":
     else:
         args.speaker_fileid = None
 
-    wav = tts(model, vocoder_model, args.text, C, args.use_cuda, ap, use_griffin_lim, args.speaker_fileid, speaker_embedding=speaker_embedding)
+    if args.gst_style is None:
+        gst_style = C.gst['gst_style_input']
+    else:
+        gst_style = args.gst_style
+
+    wav = tts(model, vocoder_model, args.text, C, args.use_cuda, ap, use_griffin_lim, args.speaker_fileid, speaker_embedding=speaker_embedding, gst_style=gst_style)
 
     # save the results
     file_name = args.text.replace(" ", "_")

From d038a006020fbab70a0568835e7f2a7c646e5e36 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 21:51:43 -0300
Subject: [PATCH 33/56] add gst style dict  suport in synthesize

---
 mozilla_voice_tts/bin/synthesize.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/mozilla_voice_tts/bin/synthesize.py b/mozilla_voice_tts/bin/synthesize.py
index 3de97555..1e2886b8 100644
--- a/mozilla_voice_tts/bin/synthesize.py
+++ b/mozilla_voice_tts/bin/synthesize.py
@@ -155,7 +155,11 @@ if __name__ == "__main__":
     if args.gst_style is None:
         gst_style = C.gst['gst_style_input']
     else:
-        gst_style = args.gst_style
+        # check if gst_style string is a dict, if is dict convert  else use string
+        try:
+            gst_style = json.loads(args.gst_style)
+        except:
+            gst_style = args.gst_style
 
     wav = tts(model, vocoder_model, args.text, C, args.use_cuda, ap, use_griffin_lim, args.speaker_fileid, speaker_embedding=speaker_embedding, gst_style=gst_style)
 

From 0fa2544fa5a5c3b9021dcd6e17927379b11fea48 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 22:04:32 -0300
Subject: [PATCH 34/56] bugfix in tacotron and tacotron 2 gst inference

---
 mozilla_voice_tts/bin/synthesize.py       | 2 +-
 mozilla_voice_tts/tts/models/tacotron.py  | 2 +-
 mozilla_voice_tts/tts/models/tacotron2.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mozilla_voice_tts/bin/synthesize.py b/mozilla_voice_tts/bin/synthesize.py
index 1e2886b8..8fa27bf0 100644
--- a/mozilla_voice_tts/bin/synthesize.py
+++ b/mozilla_voice_tts/bin/synthesize.py
@@ -158,7 +158,7 @@ if __name__ == "__main__":
         # check if gst_style string is a dict, if is dict convert  else use string
         try:
             gst_style = json.loads(args.gst_style)
-        except:
+        except ValueError:
             gst_style = args.gst_style
 
     wav = tts(model, vocoder_model, args.text, C, args.use_cuda, ap, use_griffin_lim, args.speaker_fileid, speaker_embedding=speaker_embedding, gst_style=gst_style)
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index bcc4a2a6..3837e63c 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -40,7 +40,7 @@ class Tacotron(TacotronAbstract):
                              forward_attn, trans_agent, forward_attn_mask,
                              location_attn, attn_K, separate_stopnet,
                              bidirectional_decoder, double_decoder_consistency,
-                             ddc_r, gst)
+                             ddc_r, gst, gst_embedding_dim, gst_num_heads, gst_style_tokens)
 
         # init layer dims
         decoder_in_features = 256
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 2839cea7..9aeeb3d2 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -45,7 +45,7 @@ class Tacotron2(TacotronAbstract):
                              forward_attn, trans_agent, forward_attn_mask,
                              location_attn, attn_K, separate_stopnet,
                              bidirectional_decoder, double_decoder_consistency,
-                             ddc_r, gst)
+                             ddc_r, gst, gst_embedding_dim, gst_num_heads, gst_style_tokens)
 
         # init layer dims
         decoder_in_features = 512

From ec2de5c88d1012b6d8ddd5bf84b1d8e1aeeb9748 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 22:27:26 -0300
Subject: [PATCH 35/56] added integrity test for GST dictionary in synthesis

---
 mozilla_voice_tts/bin/synthesize.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/mozilla_voice_tts/bin/synthesize.py b/mozilla_voice_tts/bin/synthesize.py
index 8fa27bf0..7d68aef3 100644
--- a/mozilla_voice_tts/bin/synthesize.py
+++ b/mozilla_voice_tts/bin/synthesize.py
@@ -158,6 +158,8 @@ if __name__ == "__main__":
         # check if gst_style string is a dict, if is dict convert  else use string
         try:
             gst_style = json.loads(args.gst_style)
+            if max(map(int, gst_style.keys())) >= C.gst['gst_style_tokens']:
+                raise RuntimeError("The highest value of the gst_style dictionary key must be less than the number of GST Tokens, \n Highest dictionary key value: {} \n Number of GST tokens: {}".format(max(map(int, gst_style.keys())), C.gst['gst_style_tokens']))
         except ValueError:
             gst_style = args.gst_style
 

From d87c7227ddf16f5072cbde3da61b043a51756d67 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 22:29:06 -0300
Subject: [PATCH 36/56] add Colab Notebook from TTS Multi-Speaker

---
 ...illa_TTS_MultiSpeaker_jia_et_al_2018.ipynb | 1758 +++++++++++++++++
 1 file changed, 1758 insertions(+)
 create mode 100755 notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018.ipynb

diff --git a/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018.ipynb b/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018.ipynb
new file mode 100755
index 00000000..3581b6ab
--- /dev/null
+++ b/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018.ipynb
@@ -0,0 +1,1758 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Demo-Mozilla-TTS-MultiSpeaker-jia-et-al-2018.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "vnV-FigfvsS2",
+        "hkvv7gRcx4WV",
+        "QJ6VgT2a4vHW"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yZK6UdwSFnOO",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Download and install Mozilla TTS**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yvb0pX3WY6MN",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 139
+        },
+        "outputId": "5c5016ac-4f92-4b5b-bcf3-473363b2846d"
+      },
+      "source": [
+        "import os \n",
+        "!git clone https://github.com/Edresson/TTS -b dev-gst-embeddings"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Cloning into 'TTS'...\n",
+            "remote: Enumerating objects: 15, done.\u001b[K\n",
+            "remote: Counting objects:   6% (1/15)\u001b[K\rremote: Counting objects:  13% (2/15)\u001b[K\rremote: Counting objects:  20% (3/15)\u001b[K\rremote: Counting objects:  26% (4/15)\u001b[K\rremote: Counting objects:  33% (5/15)\u001b[K\rremote: Counting objects:  40% (6/15)\u001b[K\rremote: Counting objects:  46% (7/15)\u001b[K\rremote: Counting objects:  53% (8/15)\u001b[K\rremote: Counting objects:  60% (9/15)\u001b[K\rremote: Counting objects:  66% (10/15)\u001b[K\rremote: Counting objects:  73% (11/15)\u001b[K\rremote: Counting objects:  80% (12/15)\u001b[K\rremote: Counting objects:  86% (13/15)\u001b[K\rremote: Counting objects:  93% (14/15)\u001b[K\rremote: Counting objects: 100% (15/15)\u001b[K\rremote: Counting objects: 100% (15/15), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (14/14), done.\u001b[K\n",
+            "remote: Total 10370 (delta 1), reused 3 (delta 1), pack-reused 10355\u001b[K\n",
+            "Receiving objects: 100% (10370/10370), 120.77 MiB | 33.68 MiB/s, done.\n",
+            "Resolving deltas: 100% (6183/6183), done.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iB9nl2UEG3SY",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "36bd08c3-6f29-4c03-9dc6-3cc3cc538e30"
+      },
+      "source": [
+        "!apt-get install espeak\n",
+        "os.chdir('TTS')\n",
+        "!pip install -r requirements.txt\n",
+        "!python setup.py develop\n",
+        "os.chdir('..')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Reading package lists... Done\n",
+            "Building dependency tree       \n",
+            "Reading state information... Done\n",
+            "The following package was automatically installed and is no longer required:\n",
+            "  libnvidia-common-440\n",
+            "Use 'apt autoremove' to remove it.\n",
+            "The following additional packages will be installed:\n",
+            "  espeak-data libespeak1 libportaudio2 libsonic0\n",
+            "The following NEW packages will be installed:\n",
+            "  espeak espeak-data libespeak1 libportaudio2 libsonic0\n",
+            "0 upgraded, 5 newly installed, 0 to remove and 35 not upgraded.\n",
+            "Need to get 1,219 kB of archives.\n",
+            "After this operation, 3,031 kB of additional disk space will be used.\n",
+            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libportaudio2 amd64 19.6.0-1 [64.6 kB]\n",
+            "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsonic0 amd64 0.2.0-6 [13.4 kB]\n",
+            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 espeak-data amd64 1.48.04+dfsg-5 [934 kB]\n",
+            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libespeak1 amd64 1.48.04+dfsg-5 [145 kB]\n",
+            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 espeak amd64 1.48.04+dfsg-5 [61.6 kB]\n",
+            "Fetched 1,219 kB in 1s (820 kB/s)\n",
+            "Selecting previously unselected package libportaudio2:amd64.\n",
+            "(Reading database ... 144465 files and directories currently installed.)\n",
+            "Preparing to unpack .../libportaudio2_19.6.0-1_amd64.deb ...\n",
+            "Unpacking libportaudio2:amd64 (19.6.0-1) ...\n",
+            "Selecting previously unselected package libsonic0:amd64.\n",
+            "Preparing to unpack .../libsonic0_0.2.0-6_amd64.deb ...\n",
+            "Unpacking libsonic0:amd64 (0.2.0-6) ...\n",
+            "Selecting previously unselected package espeak-data:amd64.\n",
+            "Preparing to unpack .../espeak-data_1.48.04+dfsg-5_amd64.deb ...\n",
+            "Unpacking espeak-data:amd64 (1.48.04+dfsg-5) ...\n",
+            "Selecting previously unselected package libespeak1:amd64.\n",
+            "Preparing to unpack .../libespeak1_1.48.04+dfsg-5_amd64.deb ...\n",
+            "Unpacking libespeak1:amd64 (1.48.04+dfsg-5) ...\n",
+            "Selecting previously unselected package espeak.\n",
+            "Preparing to unpack .../espeak_1.48.04+dfsg-5_amd64.deb ...\n",
+            "Unpacking espeak (1.48.04+dfsg-5) ...\n",
+            "Setting up libportaudio2:amd64 (19.6.0-1) ...\n",
+            "Setting up espeak-data:amd64 (1.48.04+dfsg-5) ...\n",
+            "Setting up libsonic0:amd64 (0.2.0-6) ...\n",
+            "Setting up libespeak1:amd64 (1.48.04+dfsg-5) ...\n",
+            "Setting up espeak (1.48.04+dfsg-5) ...\n",
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+            "\n",
+            "Requirement already satisfied: torch>=1.5 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (1.5.1+cu101)\n",
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+            "Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (1.4.1)\n",
+            "Requirement already satisfied: numba==0.48 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (0.48.0)\n",
+            "Collecting librosa==0.7.2\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/b5/1817862d64a7c231afd15419d8418ae1f000742cac275e85c74b219cbccb/librosa-0.7.2.tar.gz (1.6MB)\n",
+            "\u001b[K     |████████████████████████████████| 1.6MB 4.7MB/s \n",
+            "\u001b[?25hCollecting phonemizer>=2.2.0\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e1/ec/eb95ec96dc5374ba339d47c5252d62e9a7b1fbd5c9e4eefebc0d7e25381d/phonemizer-2.2.1-py3-none-any.whl (49kB)\n",
+            "\u001b[K     |████████████████████████████████| 51kB 9.0MB/s \n",
+            "\u001b[?25hCollecting unidecode==0.4.20\n",
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+            "\u001b[?25hCollecting attrdict\n",
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+            "\u001b[K     |████████████████████████████████| 317kB 16.7MB/s \n",
+            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 11)) (3.2.2)\n",
+            "Requirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 12)) (7.0.0)\n",
+            "Requirement already satisfied: flask in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 13)) (1.1.2)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 14)) (4.41.1)\n",
+            "Requirement already satisfied: inflect in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 15)) (2.1.0)\n",
+            "Collecting bokeh==1.4.0\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/70/fdd4b186d8570a737372487cc5547aac885a1270626e3ebf03db1808e4ed/bokeh-1.4.0.tar.gz (32.4MB)\n",
+            "\u001b[K     |████████████████████████████████| 32.4MB 100kB/s \n",
+            "\u001b[?25hCollecting pysbd\n",
+            "  Downloading https://files.pythonhosted.org/packages/3b/49/4799b3cdf80aee5fa4562a3929eda738845900bbeef4ee60481196ad4d1a/pysbd-0.2.3-py3-none-any.whl\n",
+            "Collecting soundfile\n",
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+            "\u001b[?25hCollecting cardboardlint==1.3.0\n",
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+            "\u001b[?25hCollecting fuzzywuzzy\n",
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+            "Requirement already satisfied: gdown in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 23)) (3.6.4)\n",
+            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.5->-r requirements.txt (line 1)) (0.16.0)\n",
+            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.2->-r requirements.txt (line 2)) (1.1.0)\n",
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+            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.2->-r requirements.txt (line 2)) (1.1.2)\n",
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+            "Requirement already satisfied: tensorflow-estimator<2.3.0,>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.2->-r requirements.txt (line 2)) (2.2.0)\n",
+            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.2->-r requirements.txt (line 2)) (1.30.0)\n",
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+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba==0.48->-r requirements.txt (line 5)) (49.1.0)\n",
+            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa==0.7.2->-r requirements.txt (line 6)) (2.1.8)\n",
+            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa==0.7.2->-r requirements.txt (line 6)) (0.22.2.post1)\n",
+            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa==0.7.2->-r requirements.txt (line 6)) (0.16.0)\n",
+            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa==0.7.2->-r requirements.txt (line 6)) (4.4.2)\n",
+            "Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from librosa==0.7.2->-r requirements.txt (line 6)) (0.2.2)\n",
+            "Requirement already satisfied: attrs>=18.1 in /usr/local/lib/python3.6/dist-packages (from phonemizer>=2.2.0->-r requirements.txt (line 7)) (19.3.0)\n",
+            "Collecting segments\n",
+            "  Downloading https://files.pythonhosted.org/packages/5b/a0/0c3fe64787745c39eb3f2f5f5f9ed8d008d9ef22e9d7f9f52f71ea4712f7/segments-2.1.3-py2.py3-none-any.whl\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 11)) (0.10.0)\n",
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+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 11)) (2.4.7)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 11)) (1.2.0)\n",
+            "Requirement already satisfied: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from flask->-r requirements.txt (line 13)) (7.1.2)\n",
+            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from flask->-r requirements.txt (line 13)) (2.11.2)\n",
+            "Requirement already satisfied: itsdangerous>=0.24 in /usr/local/lib/python3.6/dist-packages (from flask->-r requirements.txt (line 13)) (1.1.0)\n",
+            "Requirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from flask->-r requirements.txt (line 13)) (1.0.1)\n",
+            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.6/dist-packages (from bokeh==1.4.0->-r requirements.txt (line 16)) (3.13)\n",
+            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.6/dist-packages (from bokeh==1.4.0->-r requirements.txt (line 16)) (20.4)\n",
+            "Requirement already satisfied: tornado>=4.3 in /usr/local/lib/python3.6/dist-packages (from bokeh==1.4.0->-r requirements.txt (line 16)) (5.1.1)\n",
+            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.6/dist-packages (from soundfile->-r requirements.txt (line 18)) (1.14.0)\n",
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+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl (42kB)\n",
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+            "\u001b[?25hCollecting astroid<=2.5,>=2.4.0\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/24/a8/5133f51967fb21e46ee50831c3f5dda49e976b7f915408d670b1603d41d6/astroid-2.4.2-py3-none-any.whl (213kB)\n",
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+            "\u001b[?25hCollecting toml>=0.7.1\n",
+            "  Downloading https://files.pythonhosted.org/packages/9f/e1/1b40b80f2e1663a6b9f497123c11d7d988c0919abbf3c3f2688e448c5363/toml-0.10.1-py2.py3-none-any.whl\n",
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+            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gdown->-r requirements.txt (line 23)) (2.23.0)\n",
+            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (1.17.2)\n",
+            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (0.4.1)\n",
+            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (1.7.0)\n",
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+            "  Downloading https://files.pythonhosted.org/packages/d1/b6/8fef6788b8f05b21424a17ae3881eff916d42e5c7e87f57a85d9d7abf0a1/csvw-1.7.0-py2.py3-none-any.whl\n",
+            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from segments->phonemizer>=2.2.0->-r requirements.txt (line 7)) (2019.12.20)\n",
+            "Collecting clldutils>=1.7.3\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7b/b3/05882a8d5c8a7f7c69a47500334ac99623928edca930278d6ab88ee6d99b/clldutils-3.5.2-py2.py3-none-any.whl (189kB)\n",
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+            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.0->soundfile->-r requirements.txt (line 18)) (2.20)\n",
+            "Collecting lazy-object-proxy==1.4.*\n",
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+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->gdown->-r requirements.txt (line 23)) (3.0.4)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gdown->-r requirements.txt (line 23)) (1.24.3)\n",
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+            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (4.6)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (0.2.8)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (1.3.0)\n",
+            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (1.7.0)\n",
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+            "Requirement already satisfied: uritemplate>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from csvw>=1.5.6->segments->phonemizer>=2.2.0->-r requirements.txt (line 7)) (3.0.1)\n",
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+            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.6/dist-packages (from clldutils>=1.7.3->segments->phonemizer>=2.2.0->-r requirements.txt (line 7)) (0.8.7)\n",
+            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (0.4.8)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (3.1.0)\n",
+            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (3.1.0)\n",
+            "Building wheels for collected packages: librosa, bokeh, cardboardlint\n",
+            "  Building wheel for librosa (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for librosa: filename=librosa-0.7.2-cp36-none-any.whl size=1612885 sha256=772045cdc2b1e22f5b5658822ab21ff12ed23871b729a83f7ab71702bd988fa2\n",
+            "  Stored in directory: /root/.cache/pip/wheels/4c/6e/d7/bb93911540d2d1e44d690a1561871e5b6af82b69e80938abef\n",
+            "  Building wheel for bokeh (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for bokeh: filename=bokeh-1.4.0-cp36-none-any.whl size=23689201 sha256=cad6fa791cc4e4304ea896a3ca0a192ca76b3e19fb6f4b9451c557f5488ff85e\n",
+            "  Stored in directory: /root/.cache/pip/wheels/fb/f8/47/09700d9a19cbcbf0b7a3130690b75c0d6ff80fbda0b1774c7c\n",
+            "  Building wheel for cardboardlint (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for cardboardlint: filename=cardboardlint-1.3.0-cp36-none-any.whl size=51334 sha256=784dae96378ea33bba49caa613246afb18bffc40892c8943e3dbfab99e312f7a\n",
+            "  Stored in directory: /root/.cache/pip/wheels/e6/fe/8e/fbeb2cda877a1f772c0462f9f07eed851f143020947a075ee9\n",
+            "Successfully built librosa bokeh cardboardlint\n",
+            "\u001b[31mERROR: panel 0.9.7 has requirement bokeh>=2.1, but you'll have bokeh 1.4.0 which is incompatible.\u001b[0m\n",
+            "Installing collected packages: soundfile, librosa, isodate, rfc3986, csvw, colorlog, clldutils, segments, phonemizer, unidecode, attrdict, tensorboardX, bokeh, pysbd, nose, cardboardlint, isort, lazy-object-proxy, typed-ast, astroid, toml, mccabe, pylint, fuzzywuzzy\n",
+            "  Found existing installation: librosa 0.6.3\n",
+            "    Uninstalling librosa-0.6.3:\n",
+            "      Successfully uninstalled librosa-0.6.3\n",
+            "  Found existing installation: bokeh 2.1.1\n",
+            "    Uninstalling bokeh-2.1.1:\n",
+            "      Successfully uninstalled bokeh-2.1.1\n",
+            "Successfully installed astroid-2.4.2 attrdict-2.0.1 bokeh-1.4.0 cardboardlint-1.3.0 clldutils-3.5.2 colorlog-4.2.1 csvw-1.7.0 fuzzywuzzy-0.18.0 isodate-0.6.0 isort-4.3.21 lazy-object-proxy-1.4.3 librosa-0.7.2 mccabe-0.6.1 nose-1.3.7 phonemizer-2.2.1 pylint-2.5.3 pysbd-0.2.3 rfc3986-1.4.0 segments-2.1.3 soundfile-0.10.3.post1 tensorboardX-2.1 toml-0.10.1 typed-ast-1.4.1 unidecode-0.4.20\n",
+            "running develop\n",
+            "-- Building version 0.0.4+f0284e8\n",
+            "running egg_info\n",
+            "creating TTS.egg-info\n",
+            "writing TTS.egg-info/PKG-INFO\n",
+            "writing dependency_links to TTS.egg-info/dependency_links.txt\n",
+            "writing entry points to TTS.egg-info/entry_points.txt\n",
+            "writing requirements to TTS.egg-info/requires.txt\n",
+            "writing top-level names to TTS.egg-info/top_level.txt\n",
+            "writing manifest file 'TTS.egg-info/SOURCES.txt'\n",
+            "writing manifest file 'TTS.egg-info/SOURCES.txt'\n",
+            "running build_ext\n",
+            "Creating /usr/local/lib/python3.6/dist-packages/TTS.egg-link (link to .)\n",
+            "Adding TTS 0.0.4+f0284e8 to easy-install.pth file\n",
+            "Installing tts-server script to /usr/local/bin\n",
+            "\n",
+            "Installed /content/TTS\n",
+            "Processing dependencies for TTS==0.0.4+f0284e8\n",
+            "Searching for gdown==3.6.4\n",
+            "Best match: gdown 3.6.4\n",
+            "Adding gdown 3.6.4 to easy-install.pth file\n",
+            "Installing gdown script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for fuzzywuzzy==0.18.0\n",
+            "Best match: fuzzywuzzy 0.18.0\n",
+            "Adding fuzzywuzzy 0.18.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for pylint==2.5.3\n",
+            "Best match: pylint 2.5.3\n",
+            "Adding pylint 2.5.3 to easy-install.pth file\n",
+            "Installing epylint script to /usr/local/bin\n",
+            "Installing pylint script to /usr/local/bin\n",
+            "Installing pyreverse script to /usr/local/bin\n",
+            "Installing symilar script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for cardboardlint==1.3.0\n",
+            "Best match: cardboardlint 1.3.0\n",
+            "Adding cardboardlint 1.3.0 to easy-install.pth file\n",
+            "Installing cardboardlinter script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for nose==1.3.7\n",
+            "Best match: nose 1.3.7\n",
+            "Adding nose 1.3.7 to easy-install.pth file\n",
+            "Installing nosetests script to /usr/local/bin\n",
+            "Installing nosetests-3.4 script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for phonemizer==2.2.1\n",
+            "Best match: phonemizer 2.2.1\n",
+            "Adding phonemizer 2.2.1 to easy-install.pth file\n",
+            "Installing phonemize script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for SoundFile==0.10.3.post1\n",
+            "Best match: SoundFile 0.10.3.post1\n",
+            "Adding SoundFile 0.10.3.post1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for bokeh==1.4.0\n",
+            "Best match: bokeh 1.4.0\n",
+            "Adding bokeh 1.4.0 to easy-install.pth file\n",
+            "Installing bokeh script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for pysbd==0.2.3\n",
+            "Best match: pysbd 0.2.3\n",
+            "Adding pysbd 0.2.3 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for inflect==2.1.0\n",
+            "Best match: inflect 2.1.0\n",
+            "Adding inflect 2.1.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for tqdm==4.41.1\n",
+            "Best match: tqdm 4.41.1\n",
+            "Adding tqdm 4.41.1 to easy-install.pth file\n",
+            "Installing tqdm script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for Flask==1.1.2\n",
+            "Best match: Flask 1.1.2\n",
+            "Adding Flask 1.1.2 to easy-install.pth file\n",
+            "Installing flask script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for Pillow==7.0.0\n",
+            "Best match: Pillow 7.0.0\n",
+            "Adding Pillow 7.0.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for matplotlib==3.2.2\n",
+            "Best match: matplotlib 3.2.2\n",
+            "Adding matplotlib 3.2.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for tensorboardX==2.1\n",
+            "Best match: tensorboardX 2.1\n",
+            "Adding tensorboardX 2.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for attrdict==2.0.1\n",
+            "Best match: attrdict 2.0.1\n",
+            "Adding attrdict 2.0.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for Unidecode==0.4.20\n",
+            "Best match: Unidecode 0.4.20\n",
+            "Adding Unidecode 0.4.20 to easy-install.pth file\n",
+            "Installing unidecode script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for librosa==0.7.2\n",
+            "Best match: librosa 0.7.2\n",
+            "Adding librosa 0.7.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for scipy==1.4.1\n",
+            "Best match: scipy 1.4.1\n",
+            "Adding scipy 1.4.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for numba==0.48.0\n",
+            "Best match: numba 0.48.0\n",
+            "Adding numba 0.48.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for numpy==1.18.5\n",
+            "Best match: numpy 1.18.5\n",
+            "Adding numpy 1.18.5 to easy-install.pth file\n",
+            "Installing f2py script to /usr/local/bin\n",
+            "Installing f2py3 script to /usr/local/bin\n",
+            "Installing f2py3.6 script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for torch==1.5.1+cu101\n",
+            "Best match: torch 1.5.1+cu101\n",
+            "Adding torch 1.5.1+cu101 to easy-install.pth file\n",
+            "Installing convert-caffe2-to-onnx script to /usr/local/bin\n",
+            "Installing convert-onnx-to-caffe2 script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for requests==2.23.0\n",
+            "Best match: requests 2.23.0\n",
+            "Adding requests 2.23.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for six==1.15.0\n",
+            "Best match: six 1.15.0\n",
+            "Adding six 1.15.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for toml==0.10.1\n",
+            "Best match: toml 0.10.1\n",
+            "Adding toml 0.10.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for isort==4.3.21\n",
+            "Best match: isort 4.3.21\n",
+            "Adding isort 4.3.21 to easy-install.pth file\n",
+            "Installing isort script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for mccabe==0.6.1\n",
+            "Best match: mccabe 0.6.1\n",
+            "Adding mccabe 0.6.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for astroid==2.4.2\n",
+            "Best match: astroid 2.4.2\n",
+            "Adding astroid 2.4.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for PyYAML==3.13\n",
+            "Best match: PyYAML 3.13\n",
+            "Adding PyYAML 3.13 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for joblib==0.16.0\n",
+            "Best match: joblib 0.16.0\n",
+            "Adding joblib 0.16.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for attrs==19.3.0\n",
+            "Best match: attrs 19.3.0\n",
+            "Adding attrs 19.3.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for segments==2.1.3\n",
+            "Best match: segments 2.1.3\n",
+            "Adding segments 2.1.3 to easy-install.pth file\n",
+            "Installing segments script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for cffi==1.14.0\n",
+            "Best match: cffi 1.14.0\n",
+            "Adding cffi 1.14.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for packaging==20.4\n",
+            "Best match: packaging 20.4\n",
+            "Adding packaging 20.4 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for tornado==5.1.1\n",
+            "Best match: tornado 5.1.1\n",
+            "Adding tornado 5.1.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for Jinja2==2.11.2\n",
+            "Best match: Jinja2 2.11.2\n",
+            "Adding Jinja2 2.11.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for python-dateutil==2.8.1\n",
+            "Best match: python-dateutil 2.8.1\n",
+            "Adding python-dateutil 2.8.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for click==7.1.2\n",
+            "Best match: click 7.1.2\n",
+            "Adding click 7.1.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for itsdangerous==1.1.0\n",
+            "Best match: itsdangerous 1.1.0\n",
+            "Adding itsdangerous 1.1.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for Werkzeug==1.0.1\n",
+            "Best match: Werkzeug 1.0.1\n",
+            "Adding Werkzeug 1.0.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for cycler==0.10.0\n",
+            "Best match: cycler 0.10.0\n",
+            "Adding cycler 0.10.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for kiwisolver==1.2.0\n",
+            "Best match: kiwisolver 1.2.0\n",
+            "Adding kiwisolver 1.2.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for pyparsing==2.4.7\n",
+            "Best match: pyparsing 2.4.7\n",
+            "Adding pyparsing 2.4.7 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for protobuf==3.12.2\n",
+            "Best match: protobuf 3.12.2\n",
+            "Adding protobuf 3.12.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for scikit-learn==0.22.2.post1\n",
+            "Best match: scikit-learn 0.22.2.post1\n",
+            "Adding scikit-learn 0.22.2.post1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for audioread==2.1.8\n",
+            "Best match: audioread 2.1.8\n",
+            "Adding audioread 2.1.8 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for resampy==0.2.2\n",
+            "Best match: resampy 0.2.2\n",
+            "Adding resampy 0.2.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for decorator==4.4.2\n",
+            "Best match: decorator 4.4.2\n",
+            "Adding decorator 4.4.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for llvmlite==0.31.0\n",
+            "Best match: llvmlite 0.31.0\n",
+            "Adding llvmlite 0.31.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for setuptools==49.1.0\n",
+            "Best match: setuptools 49.1.0\n",
+            "Adding setuptools 49.1.0 to easy-install.pth file\n",
+            "Installing easy_install script to /usr/local/bin\n",
+            "Installing easy_install-3.8 script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for future==0.16.0\n",
+            "Best match: future 0.16.0\n",
+            "Adding future 0.16.0 to easy-install.pth file\n",
+            "Installing futurize script to /usr/local/bin\n",
+            "Installing pasteurize script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for chardet==3.0.4\n",
+            "Best match: chardet 3.0.4\n",
+            "Adding chardet 3.0.4 to easy-install.pth file\n",
+            "Installing chardetect script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for urllib3==1.24.3\n",
+            "Best match: urllib3 1.24.3\n",
+            "Adding urllib3 1.24.3 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for certifi==2020.6.20\n",
+            "Best match: certifi 2020.6.20\n",
+            "Adding certifi 2020.6.20 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for idna==2.10\n",
+            "Best match: idna 2.10\n",
+            "Adding idna 2.10 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for wrapt==1.12.1\n",
+            "Best match: wrapt 1.12.1\n",
+            "Adding wrapt 1.12.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for typed-ast==1.4.1\n",
+            "Best match: typed-ast 1.4.1\n",
+            "Adding typed-ast 1.4.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for lazy-object-proxy==1.4.3\n",
+            "Best match: lazy-object-proxy 1.4.3\n",
+            "Adding lazy-object-proxy 1.4.3 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for csvw==1.7.0\n",
+            "Best match: csvw 1.7.0\n",
+            "Adding csvw 1.7.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for clldutils==3.5.2\n",
+            "Best match: clldutils 3.5.2\n",
+            "Adding clldutils 3.5.2 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for regex==2019.12.20\n",
+            "Best match: regex 2019.12.20\n",
+            "Adding regex 2019.12.20 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for pycparser==2.20\n",
+            "Best match: pycparser 2.20\n",
+            "Adding pycparser 2.20 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for MarkupSafe==1.1.1\n",
+            "Best match: MarkupSafe 1.1.1\n",
+            "Adding MarkupSafe 1.1.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for isodate==0.6.0\n",
+            "Best match: isodate 0.6.0\n",
+            "Adding isodate 0.6.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for rfc3986==1.4.0\n",
+            "Best match: rfc3986 1.4.0\n",
+            "Adding rfc3986 1.4.0 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for uritemplate==3.0.1\n",
+            "Best match: uritemplate 3.0.1\n",
+            "Adding uritemplate 3.0.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for tabulate==0.8.7\n",
+            "Best match: tabulate 0.8.7\n",
+            "Adding tabulate 0.8.7 to easy-install.pth file\n",
+            "Installing tabulate script to /usr/local/bin\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Searching for colorlog==4.2.1\n",
+            "Best match: colorlog 4.2.1\n",
+            "Adding colorlog 4.2.1 to easy-install.pth file\n",
+            "\n",
+            "Using /usr/local/lib/python3.6/dist-packages\n",
+            "Finished processing dependencies for TTS==0.0.4+f0284e8\n",
+            "Collecting tensorflow==2.3.0rc0\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8b/68/7c6c8e2b65ad4a3ff5ef658c04a6c2802ff7fe55fc7eecacb6efee1abc40/tensorflow-2.3.0rc0-cp36-cp36m-manylinux2010_x86_64.whl (320.3MB)\n",
+            "\u001b[K     |████████████████████████████████| 320.3MB 53kB/s \n",
+            "\u001b[?25hRequirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.4.1)\n",
+            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (3.12.2)\n",
+            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (0.3.3)\n",
+            "Collecting tf-estimator-nightly<2.3.0.dev2020062302,>=2.3.0.dev2020062301\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/17/3b/fb9aafd734da258411bff2a600cabff65c7d201782318791b72422bd973d/tf_estimator_nightly-2.3.0.dev2020062301-py2.py3-none-any.whl (459kB)\n",
+            "\u001b[K     |████████████████████████████████| 460kB 58.0MB/s \n",
+            "\u001b[?25hRequirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (2.10.0)\n",
+            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.15.0)\n",
+            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.18.5)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (3.3.0)\n",
+            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (0.34.2)\n",
+            "Requirement already satisfied: tensorboard<2.3.0,>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (2.2.2)\n",
+            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.1.0)\n",
+            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.30.0)\n",
+            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.6.3)\n",
+            "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.1.2)\n",
+            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.12.1)\n",
+            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (0.2.0)\n",
+            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (0.9.0)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.9.2->tensorflow==2.3.0rc0) (49.1.0)\n",
+            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (0.4.1)\n",
+            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.7.0)\n",
+            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (2.23.0)\n",
+            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.17.2)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (3.2.2)\n",
+            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.0.1)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.3.0)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (2.10)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (2020.6.20)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.24.3)\n",
+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (3.0.4)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (0.2.8)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (4.6)\n",
+            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (4.1.1)\n",
+            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.7.0)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (3.1.0)\n",
+            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (0.4.8)\n",
+            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (3.1.0)\n",
+            "Installing collected packages: tf-estimator-nightly, tensorflow\n",
+            "  Found existing installation: tensorflow 2.2.0\n",
+            "    Uninstalling tensorflow-2.2.0:\n",
+            "      Successfully uninstalled tensorflow-2.2.0\n",
+            "Successfully installed tensorflow-2.3.0rc0 tf-estimator-nightly-2.3.0.dev2020062301\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w6Krn8k1inC_",
+        "colab_type": "text"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "**Download Checkpoint**\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PiYHf3lKhi9z",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 104
+        },
+        "outputId": "75f7ecd0-3ee2-4ed2-8150-49f71a5ef4d5"
+      },
+      "source": [
+        "!wget -c -q --show-progress -O ./TTS-checkpoint.zip  https://github.com/Edresson/TTS/releases/download/v1.0.0/Checkpoints-TTS-MultiSpeaker-Jia-et-al-2018.zip\n",
+        "!unzip ./TTS-checkpoint.zip\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "./TTS-checkpoint.zi 100%[===================>] 367.68M  31.9MB/s    in 12s     \n",
+            "Archive:  ./TTS-checkpoint.zip\n",
+            "  inflating: best_model.pth.tar      \n",
+            "  inflating: speakers.json           \n",
+            "  inflating: config.json             \n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MpYNgqrZcJKn",
+        "colab_type": "text"
+      },
+      "source": [
+        "**Utils Functions**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4KZA4b_CbMqx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 52
+        },
+        "outputId": "c5d33fac-4682-4e72-8fc2-c1aa83408ef4"
+      },
+      "source": [
+        "%load_ext autoreload\n",
+        "%autoreload 2\n",
+        "import argparse\n",
+        "import json\n",
+        "# pylint: disable=redefined-outer-name, unused-argument\n",
+        "import os\n",
+        "import string\n",
+        "import time\n",
+        "import sys\n",
+        "import numpy as np\n",
+        "\n",
+        "TTS_PATH = \"../content/TTS\"\n",
+        "# add libraries into environment\n",
+        "sys.path.append(TTS_PATH) # set this if TTS is not installed globally\n",
+        "\n",
+        "import torch\n",
+        "\n",
+        "from TTS.tts.utils.generic_utils import setup_model\n",
+        "from TTS.tts.utils.synthesis import synthesis\n",
+        "from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols\n",
+        "from TTS.utils.audio import AudioProcessor\n",
+        "from TTS.utils.io import load_config\n",
+        "from TTS.vocoder.utils.generic_utils import setup_generator\n",
+        "\n",
+        "\n",
+        "def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid, speaker_embedding=None):\n",
+        "    t_1 = time.time()\n",
+        "    waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_fileid, None, False, CONFIG.enable_eos_bos_chars, use_gl, speaker_embedding=speaker_embedding)\n",
+        "    if CONFIG.model == \"Tacotron\" and not use_gl:\n",
+        "        mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n",
+        "    if not use_gl:\n",
+        "        waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
+        "    if use_cuda and not use_gl:\n",
+        "        waveform = waveform.cpu()\n",
+        "    if not use_gl:\n",
+        "        waveform = waveform.numpy()\n",
+        "    waveform = waveform.squeeze()\n",
+        "    rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
+        "    tps = (time.time() - t_1) / len(waveform)\n",
+        "    print(\" > Run-time: {}\".format(time.time() - t_1))\n",
+        "    print(\" > Real-time factor: {}\".format(rtf))\n",
+        "    print(\" > Time per step: {}\".format(tps))\n",
+        "    return waveform\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "The autoreload extension is already loaded. To reload it, use:\n",
+            "  %reload_ext autoreload\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ENA2OumIVeMA",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Vars definitions**\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jPD0d_XpVXmY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "TEXT = ''\n",
+        "OUT_PATH = 'tests-audios/'\n",
+        "# create output path\n",
+        "os.makedirs(OUT_PATH, exist_ok=True)\n",
+        "\n",
+        "SPEAKER_FILEID = None # if None use the first embedding from speakers.json\n",
+        "\n",
+        "# model vars \n",
+        "MODEL_PATH = 'best_model.pth.tar'\n",
+        "CONFIG_PATH = 'config.json'\n",
+        "SPEAKER_JSON = 'speakers.json'\n",
+        "\n",
+        "# vocoder vars\n",
+        "VOCODER_PATH = ''\n",
+        "VOCODER_CONFIG_PATH = ''\n",
+        "\n",
+        "USE_CUDA = True"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dV6cXXlfi72r",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Restore  TTS Model**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "x1WgLFauWUPe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 486
+        },
+        "outputId": "7cc2d0bd-f3c8-41dd-9f17-438f77c1f2d2"
+      },
+      "source": [
+        "# load the config\n",
+        "C = load_config(CONFIG_PATH)\n",
+        "C.forward_attn_mask = True\n",
+        "\n",
+        "# load the audio processor\n",
+        "ap = AudioProcessor(**C.audio)\n",
+        "\n",
+        "# if the vocabulary was passed, replace the default\n",
+        "if 'characters' in C.keys():\n",
+        "    symbols, phonemes = make_symbols(**C.characters)\n",
+        "\n",
+        "speaker_embedding = None\n",
+        "speaker_embedding_dim = None\n",
+        "num_speakers = 0\n",
+        "# load speakers\n",
+        "if SPEAKER_JSON != '':\n",
+        "    speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
+        "    num_speakers = len(speaker_mapping)\n",
+        "    if C.use_external_speaker_embedding_file:\n",
+        "        if SPEAKER_FILEID is not None:\n",
+        "            speaker_embedding = speaker_mapping[SPEAKER_FILEID]['embedding']\n",
+        "        else: # if speaker_fileid is not specificated use the first sample in speakers.json\n",
+        "            choise_speaker = list(speaker_mapping.keys())[0]\n",
+        "            print(\" Speaker: \",choise_speaker.split('_')[0],'was chosen automatically', \"(this speaker seen in training)\")\n",
+        "            speaker_embedding = speaker_mapping[choise_speaker]['embedding']\n",
+        "        speaker_embedding_dim = len(speaker_embedding)\n",
+        "\n",
+        "# load the model\n",
+        "num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
+        "model = setup_model(num_chars, num_speakers, C, speaker_embedding_dim)\n",
+        "cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))\n",
+        "model.load_state_dict(cp['model'])\n",
+        "model.eval()\n",
+        "\n",
+        "if USE_CUDA:\n",
+        "    model.cuda()\n",
+        "\n",
+        "model.decoder.set_r(cp['r'])\n",
+        "\n",
+        "# load vocoder model\n",
+        "if VOCODER_PATH!= \"\":\n",
+        "    VC = load_config(VOCODER_CONFIG_PATH)\n",
+        "    vocoder_model = setup_generator(VC)\n",
+        "    vocoder_model.load_state_dict(torch.load(VOCODER_PATH, map_location=\"cpu\")[\"model\"])\n",
+        "    vocoder_model.remove_weight_norm()\n",
+        "    if USE_CUDA:\n",
+        "        vocoder_model.cuda()\n",
+        "    vocoder_model.eval()\n",
+        "else:\n",
+        "    vocoder_model = None\n",
+        "    VC = None\n",
+        "\n",
+        "# synthesize voice\n",
+        "use_griffin_lim = VOCODER_PATH== \"\"\n",
+        "\n",
+        "if not C.use_external_speaker_embedding_file:\n",
+        "    if SPEAKER_FILEID.isdigit():\n",
+        "        SPEAKER_FILEID = int(SPEAKER_FILEID)\n",
+        "    else:\n",
+        "        SPEAKER_FILEID = None\n",
+        "else:\n",
+        "    SPEAKER_FILEID = None\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            " > Setting up Audio Processor...\n",
+            " | > sample_rate:22050\n",
+            " | > num_mels:80\n",
+            " | > min_level_db:-100\n",
+            " | > frame_shift_ms:None\n",
+            " | > frame_length_ms:None\n",
+            " | > ref_level_db:20\n",
+            " | > fft_size:1024\n",
+            " | > power:1.5\n",
+            " | > preemphasis:0.98\n",
+            " | > griffin_lim_iters:60\n",
+            " | > signal_norm:True\n",
+            " | > symmetric_norm:True\n",
+            " | > mel_fmin:0\n",
+            " | > mel_fmax:8000.0\n",
+            " | > spec_gain:20.0\n",
+            " | > stft_pad_mode:constant\n",
+            " | > max_norm:4.0\n",
+            " | > clip_norm:True\n",
+            " | > do_trim_silence:False\n",
+            " | > trim_db:60\n",
+            " | > do_sound_norm:False\n",
+            " | > stats_path:None\n",
+            " | > hop_length:256\n",
+            " | > win_length:1024\n",
+            " Speaker:  p262 was chosen automatically (this speaker seen in training)\n",
+            " > Using model: Tacotron2\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tNvVEoE30qY6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Synthesize sentence with Speaker\n",
+        "\n",
+        "> Stop running the cell to leave!\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2o8fXkVSyXOa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 704
+        },
+        "outputId": "b093ab6c-57a9-4595-8ff6-edee226cb0fd"
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with Speaker: \",choise_speaker.split('_')[0], \"(this speaker seen in training)\")\n",
+        "while True:\n",
+        "  TEXT = input(\"Enter sentence: \")\n",
+        "  print(\" > Text: {}\".format(TEXT))\n",
+        "  wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding)\n",
+        "  IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "  # save the results\n",
+        "  file_name = TEXT.replace(\" \", \"_\")\n",
+        "  file_name = file_name.translate(\n",
+        "      str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "  out_path = os.path.join(OUT_PATH, file_name)\n",
+        "  print(\" > Saving output to {}\".format(out_path))\n",
+        "  ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Synthesize sentence with Speaker:  p262 (this speaker seen in training)\n",
+            "Enter sentence: teste this demonstration :)\n",
+            " > Text: teste this demonstration :)\n",
+            " > Run-time: 2.143589496612549\n",
+            " > Real-time factor: 1.6784820231524382\n",
+            " > Time per step: 7.61217437684536e-05\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "                <audio controls=\"controls\" >\n",
+              "                    <source 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\" type=\"audio/wav\" />\n",
+              "                    Your browser does not support the audio element.\n",
+              "                </audio>\n",
+              "              "
+            ],
+            "text/plain": [
+              "<IPython.lib.display.Audio object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            " > Saving output to tests-audios/teste_this_demonstration_.wav\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
+            "\nDuring handling of the above exception, another exception occurred:\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-14-a0b8edea98d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Synthesize sentence with Speaker: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchoise_speaker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"(this speaker seen in training)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mTEXT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter sentence: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Text: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocoder_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSE_CUDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_griffin_lim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSPEAKER_FILEID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vnV-FigfvsS2",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Select Speaker**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RuCGOnJ_fgDV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "\n",
+        "# VCTK speakers not seen in training (new speakers)\n",
+        "VCTK_test_Speakers = [\"p225\", \"p234\", \"p238\", \"p245\", \"p248\", \"p261\", \"p294\", \"p302\", \"p326\", \"p335\", \"p347\"]\n",
+        "\n",
+        "# VCTK speakers seen in training\n",
+        "VCTK_train_Speakers = ['p244', 'p300', 'p303', 'p273', 'p292', 'p252', 'p254', 'p269', 'p345', 'p274', 'p363', 'p285', 'p351', 'p361', 'p295', 'p266', 'p307', 'p230', 'p339', 'p253', 'p310', 'p241', 'p256', 'p323', 'p237', 'p229', 'p298', 'p336', 'p276', 'p305', 'p255', 'p278', 'p299', 'p265', 'p267', 'p280', 'p260', 'p272', 'p262', 'p334', 'p283', 'p247', 'p246', 'p374', 'p297', 'p249', 'p250', 'p304', 'p240', 'p236', 'p312', 'p286', 'p263', 'p258', 'p313', 'p376', 'p279', 'p340', 'p362', 'p284', 'p231', 'p308', 'p277', 'p275', 'p333', 'p314', 'p330', 'p264', 'p226', 'p288', 'p343', 'p239', 'p232', 'p268', 'p270', 'p329', 'p227', 'p271', 'p228', 'p311', 'p301', 'p293', 'p364', 'p251', 'p317', 'p360', 'p281', 'p243', 'p287', 'p233', 'p259', 'p316', 'p257', 'p282', 'p306', 'p341', 'p318']\n",
+        "\n",
+        "\n",
+        "num_samples_speaker = 2 # In theory the more samples of the speaker the more similar to the real voice it will be!\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hkvv7gRcx4WV",
+        "colab_type": "text"
+      },
+      "source": [
+        "## **Example select  a VCTK seen speaker in training**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BviNMI9UyCYz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# get embedding\n",
+        "Speaker_choise = VCTK_train_Speakers[0] # choise one of training speakers\n",
+        "# load speakers\n",
+        "if SPEAKER_JSON != '':\n",
+        "    speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
+        "    if C.use_external_speaker_embedding_file:\n",
+        "        speaker_embeddings = []\n",
+        "        for key in list(speaker_mapping.keys()):\n",
+        "          if Speaker_choise in key:\n",
+        "            if len(speaker_embeddings) < num_samples_speaker:\n",
+        "              speaker_embeddings.append(speaker_mapping[key]['embedding'])\n",
+        "        # takes the average of the embedings samples of the announcers\n",
+        "        speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()\n",
+        "        "
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5e5_XnLsx3jg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 704
+        },
+        "outputId": "3c82a3cd-f4b9-4493-aa4c-275c34d3294d"
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker seen in training)\")\n",
+        "while True:\n",
+        "  TEXT = input(\"Enter sentence: \")\n",
+        "  print(\" > Text: {}\".format(TEXT))\n",
+        "  wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding)\n",
+        "  IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "  # save the results\n",
+        "  file_name = TEXT.replace(\" \", \"_\")\n",
+        "  file_name = file_name.translate(\n",
+        "      str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "  out_path = os.path.join(OUT_PATH, file_name)\n",
+        "  print(\" > Saving output to {}\".format(out_path))\n",
+        "  ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Synthesize sentence with Speaker:  p244 (this speaker seen in training)\n",
+            "Enter sentence: Test this demonstration \n",
+            " > Text: Test this demonstration \n",
+            " > Run-time: 1.06947922706604\n",
+            " > Real-time factor: 0.8609054588373298\n",
+            " > Time per step: 3.904344461788641e-05\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "                <audio controls=\"controls\" >\n",
+              "                    <source 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\" type=\"audio/wav\" />\n",
+              "                    Your browser does not support the audio element.\n",
+              "                </audio>\n",
+              "              "
+            ],
+            "text/plain": [
+              "<IPython.lib.display.Audio object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            " > Saving output to tests-audios/Test_this_demonstration_.wav\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
+            "\nDuring handling of the above exception, another exception occurred:\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-32-390fa572c62e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Synthesize sentence with Speaker: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSpeaker_choise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"(this speaker seen in training)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mTEXT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter sentence: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Text: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocoder_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSE_CUDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_griffin_lim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSPEAKER_FILEID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "QJ6VgT2a4vHW"
+      },
+      "source": [
+        "## **Example select  a VCTK not seen speaker in training (new Speakers)**\n",
+        "\n",
+        "\n",
+        "> Fitting new Speakers :)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "SZS57ZK-4vHa",
+        "colab": {}
+      },
+      "source": [
+        "# get embedding\n",
+        "Speaker_choise = VCTK_test_Speakers[0] # choise one of training speakers\n",
+        "# load speakers\n",
+        "if SPEAKER_JSON != '':\n",
+        "    speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
+        "    if C.use_external_speaker_embedding_file:\n",
+        "        speaker_embeddings = []\n",
+        "        for key in list(speaker_mapping.keys()):\n",
+        "          if Speaker_choise in key:\n",
+        "            if len(speaker_embeddings) < num_samples_speaker:\n",
+        "              speaker_embeddings.append(speaker_mapping[key]['embedding'])\n",
+        "        # takes the average of the embedings samples of the announcers\n",
+        "        speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()\n",
+        "        "
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "bbs85vzz4vHo",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 704
+        },
+        "outputId": "3c82a3cd-f4b9-4493-aa4c-275c34d3294d"
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker  not seen in training (new speaker))\")\n",
+        "while True:\n",
+        "  TEXT = input(\"Enter sentence: \")\n",
+        "  print(\" > Text: {}\".format(TEXT))\n",
+        "  wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding)\n",
+        "  IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "  # save the results\n",
+        "  file_name = TEXT.replace(\" \", \"_\")\n",
+        "  file_name = file_name.translate(\n",
+        "      str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "  out_path = os.path.join(OUT_PATH, file_name)\n",
+        "  print(\" > Saving output to {}\".format(out_path))\n",
+        "  ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Synthesize sentence with Speaker:  p244 (this speaker seen in training)\n",
+            "Enter sentence: Test this demonstration \n",
+            " > Text: Test this demonstration \n",
+            " > Run-time: 1.06947922706604\n",
+            " > Real-time factor: 0.8609054588373298\n",
+            " > Time per step: 3.904344461788641e-05\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "                <audio controls=\"controls\" >\n",
+              "                    <source 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\" type=\"audio/wav\" />\n",
+              "                    Your browser does not support the audio element.\n",
+              "                </audio>\n",
+              "              "
+            ],
+            "text/plain": [
+              "<IPython.lib.display.Audio object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            " > Saving output to tests-audios/Test_this_demonstration_.wav\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
+            "\nDuring handling of the above exception, another exception occurred:\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-32-390fa572c62e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Synthesize sentence with Speaker: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSpeaker_choise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"(this speaker seen in training)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mTEXT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter sentence: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Text: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocoder_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSE_CUDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_griffin_lim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSPEAKER_FILEID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "LEE6mQLh5Who"
+      },
+      "source": [
+        "# **Example Synthesizing with your own voice :)**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "La70gSB65nrs",
+        "colab_type": "text"
+      },
+      "source": [
+        " Download and load GE2E Speaker Encoder "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "r0IEFZ0B5vQg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 121
+        },
+        "outputId": "93ee404d-3fdc-48ac-f867-9ef7f7ecaa0c"
+      },
+      "source": [
+        "!wget -c -q --show-progress -O ./SpeakerEncoder-checkpoint.zip  https://github.com/Edresson/TTS/releases/download/v1.0.0/GE2E-SpeakerEncoder-iter25k.zip\n",
+        "!unzip ./SpeakerEncoder-checkpoint.zip"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "./SpeakerEncoder-ch 100%[===================>]  25.35M  25.1MB/s    in 1.0s    \n",
+            "Archive:  ./SpeakerEncoder-checkpoint.zip\n",
+            "   creating: GE2E-SpeakerEncoder/\n",
+            "  inflating: GE2E-SpeakerEncoder/best_model.pth.tar  \n",
+            "  inflating: GE2E-SpeakerEncoder/config.json  \n",
+            "  inflating: GE2E-SpeakerEncoder/README.MD  \n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jEH8HCTh5mF6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "SE_MODEL_RUN_PATH = \"GE2E-SpeakerEncoder/\"\n",
+        "SE_MODEL_PATH = os.path.join(SE_MODEL_RUN_PATH, \"best_model.pth.tar\")\n",
+        "SE_CONFIG_PATH =os.path.join(SE_MODEL_RUN_PATH, \"config.json\")\n",
+        "USE_CUDA = True"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tOwkfQqT6-Qo",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 451
+        },
+        "outputId": "091c9020-302a-429e-bc7e-7fd2bb6f68f9"
+      },
+      "source": [
+        "from TTS.utils.audio import AudioProcessor\n",
+        "from TTS.speaker_encoder.model import SpeakerEncoder\n",
+        "se_config = load_config(SE_CONFIG_PATH)\n",
+        "se_ap = AudioProcessor(**se_config['audio'])\n",
+        "\n",
+        "se_model = SpeakerEncoder(**se_config.model)\n",
+        "se_model.load_state_dict(torch.load(SE_MODEL_PATH)['model'])\n",
+        "se_model.eval()\n",
+        "if USE_CUDA:\n",
+        "    se_model.cuda()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            " > Setting up Audio Processor...\n",
+            " | > sample_rate:16000\n",
+            " | > num_mels:40\n",
+            " | > min_level_db:-100\n",
+            " | > frame_shift_ms:12.5\n",
+            " | > frame_length_ms:50\n",
+            " | > ref_level_db:20\n",
+            " | > fft_size:1024\n",
+            " | > power:None\n",
+            " | > preemphasis:0.98\n",
+            " | > griffin_lim_iters:None\n",
+            " | > signal_norm:True\n",
+            " | > symmetric_norm:True\n",
+            " | > mel_fmin:0\n",
+            " | > mel_fmax:8000.0\n",
+            " | > spec_gain:20.0\n",
+            " | > stft_pad_mode:reflect\n",
+            " | > max_norm:4.0\n",
+            " | > clip_norm:True\n",
+            " | > do_trim_silence:False\n",
+            " | > trim_db:60\n",
+            " | > do_sound_norm:False\n",
+            " | > stats_path:None\n",
+            " | > hop_length:200\n",
+            " | > win_length:800\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0TLlbUFG8O36",
+        "colab_type": "text"
+      },
+      "source": [
+        "Upload a wav audio file in your voice.\n",
+        "\n",
+        "\n",
+        "> We recommend files longer than 3 seconds, the bigger the file the closer to your voice :)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_FWwHPjJ8NXl",
+        "colab_type": "code",
+        "colab": {
+          "resources": {
+            "http://localhost:8080/nbextensions/google.colab/files.js": {
+              "data": "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",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/javascript"
+                ]
+              ],
+              "status": 200,
+              "status_text": "OK"
+            }
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 246
+        },
+        "outputId": "cb0ca2ad-73b9-44ce-d0bd-6139552232d8"
+      },
+      "source": [
+        "from google.colab import files\n",
+        "file_list = files.upload()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-1890223f-cc4a-4274-a149-c6368625bb38\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-1890223f-cc4a-4274-a149-c6368625bb38\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Saving p238_001.wav to p238_001.wav\n",
+            "Saving p238_002.wav to p238_002.wav\n",
+            "Saving p238_003.wav to p238_003.wav\n",
+            "Saving p238_004.wav to p238_004.wav\n",
+            "Saving p238_005.wav to p238_005.wav\n",
+            "Saving p238_006.wav to p238_006.wav\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WWOf6sgbBbGY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# extract embedding from wav files\n",
+        "speaker_embeddings = []\n",
+        "for name in file_list.keys():\n",
+        "    if '.wav' in name:\n",
+        "      mel_spec = se_ap.melspectrogram(se_ap.load_wav(name, sr=se_ap.sample_rate)).T\n",
+        "      mel_spec = torch.FloatTensor(mel_spec[None, :, :])\n",
+        "      if USE_CUDA:\n",
+        "          mel_spec = mel_spec.cuda()\n",
+        "      embedd = se_model.compute_embedding(mel_spec).cpu().detach().numpy().reshape(-1)\n",
+        "      speaker_embeddings.append(embedd)\n",
+        "    else:\n",
+        "      print(\" You need upload Wav files, others files is not supported !!\")\n",
+        "\n",
+        "# takes the average of the embedings samples of the announcers\n",
+        "speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "xmItcGac5WiG",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 872
+        },
+        "outputId": "4940027a-a303-4782-ef47-0c91716665a1"
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with New Speaker using files: \",file_list.keys(), \"(this speaker not seen in training (new speaker))\")\n",
+        "while True:\n",
+        "  TEXT = input(\"Enter sentence: \")\n",
+        "  print(\" > Text: {}\".format(TEXT))\n",
+        "  wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding)\n",
+        "  IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "  # save the results\n",
+        "  file_name = TEXT.replace(\" \", \"_\")\n",
+        "  file_name = file_name.translate(\n",
+        "      str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "  out_path = os.path.join(OUT_PATH, file_name)\n",
+        "  print(\" > Saving output to {}\".format(out_path))\n",
+        "  ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Synthesize sentence with New Speaker using files:  dict_keys(['p238_001.wav', 'p238_002.wav', 'p238_003.wav', 'p238_004.wav', 'p238_005.wav', 'p238_006.wav']) (this speaker not seen in training (new speaker))\n",
+            "Enter sentence: Test this demonstration\n",
+            " > Text: Test this demonstration\n",
+            " > Run-time: 1.1401546001434326\n",
+            " > Real-time factor: 0.8927653497084975\n",
+            " > Time per step: 4.0488326075402176e-05\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "                <audio controls=\"controls\" >\n",
+              "                    <source 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type=\"audio/wav\" />\n",
+              "                    Your browser does not support the audio element.\n",
+              "                </audio>\n",
+              "              "
+            ],
+            "text/plain": [
+              "<IPython.lib.display.Audio object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            " > Saving output to tests-audios/Test_this_demonstration.wav\n",
+            "Enter sentence: Ask her to bring these things with her from the store.\n",
+            " > Text: Ask her to bring these things with her from the store.\n",
+            " > Run-time: 1.594754934310913\n",
+            " > Real-time factor: 0.6868010717444122\n",
+            " > Time per step: 3.1147492118179796e-05\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "\n",
+              "                <audio controls=\"controls\" >\n",
+              "                    <source 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\" type=\"audio/wav\" />\n",
+              "                    Your browser does not support the audio element.\n",
+              "                </audio>\n",
+              "              "
+            ],
+            "text/plain": [
+              "<IPython.lib.display.Audio object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            " > Saving output to tests-audios/Ask_her_to_bring_these_things_with_her_from_the_store.wav\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
+            "\nDuring handling of the above exception, another exception occurred:\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-59-d2d06555a22b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Synthesize sentence with New Speaker using files: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfile_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"(this speaker not seen in training (new speaker))\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mTEXT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter sentence: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Text: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocoder_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSE_CUDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_griffin_lim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSPEAKER_FILEID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 66452918b784c71b5c3621c57b74b1276476d3a4 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 22:33:47 -0300
Subject: [PATCH 37/56] clean Colab Notebook outputs

---
 ...illa_TTS_MultiSpeaker_jia_et_al_2018.ipynb | 1169 +----------------
 1 file changed, 24 insertions(+), 1145 deletions(-)

diff --git a/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018.ipynb b/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018.ipynb
index 3581b6ab..458422c0 100755
--- a/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018.ipynb
+++ b/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018.ipynb
@@ -33,43 +33,21 @@
       "metadata": {
         "id": "yvb0pX3WY6MN",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 139
-        },
-        "outputId": "5c5016ac-4f92-4b5b-bcf3-473363b2846d"
+        "colab": {}
       },
       "source": [
         "import os \n",
         "!git clone https://github.com/Edresson/TTS -b dev-gst-embeddings"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Cloning into 'TTS'...\n",
-            "remote: Enumerating objects: 15, done.\u001b[K\n",
-            "remote: Counting objects:   6% (1/15)\u001b[K\rremote: Counting objects:  13% (2/15)\u001b[K\rremote: Counting objects:  20% (3/15)\u001b[K\rremote: Counting objects:  26% (4/15)\u001b[K\rremote: Counting objects:  33% (5/15)\u001b[K\rremote: Counting objects:  40% (6/15)\u001b[K\rremote: Counting objects:  46% (7/15)\u001b[K\rremote: Counting objects:  53% (8/15)\u001b[K\rremote: Counting objects:  60% (9/15)\u001b[K\rremote: Counting objects:  66% (10/15)\u001b[K\rremote: Counting objects:  73% (11/15)\u001b[K\rremote: Counting objects:  80% (12/15)\u001b[K\rremote: Counting objects:  86% (13/15)\u001b[K\rremote: Counting objects:  93% (14/15)\u001b[K\rremote: Counting objects: 100% (15/15)\u001b[K\rremote: Counting objects: 100% (15/15), done.\u001b[K\n",
-            "remote: Compressing objects: 100% (14/14), done.\u001b[K\n",
-            "remote: Total 10370 (delta 1), reused 3 (delta 1), pack-reused 10355\u001b[K\n",
-            "Receiving objects: 100% (10370/10370), 120.77 MiB | 33.68 MiB/s, done.\n",
-            "Resolving deltas: 100% (6183/6183), done.\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "code",
       "metadata": {
         "id": "iB9nl2UEG3SY",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "outputId": "36bd08c3-6f29-4c03-9dc6-3cc3cc538e30"
+        "colab": {}
       },
       "source": [
         "!apt-get install espeak\n",
@@ -79,639 +57,7 @@
         "os.chdir('..')"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Reading package lists... Done\n",
-            "Building dependency tree       \n",
-            "Reading state information... Done\n",
-            "The following package was automatically installed and is no longer required:\n",
-            "  libnvidia-common-440\n",
-            "Use 'apt autoremove' to remove it.\n",
-            "The following additional packages will be installed:\n",
-            "  espeak-data libespeak1 libportaudio2 libsonic0\n",
-            "The following NEW packages will be installed:\n",
-            "  espeak espeak-data libespeak1 libportaudio2 libsonic0\n",
-            "0 upgraded, 5 newly installed, 0 to remove and 35 not upgraded.\n",
-            "Need to get 1,219 kB of archives.\n",
-            "After this operation, 3,031 kB of additional disk space will be used.\n",
-            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libportaudio2 amd64 19.6.0-1 [64.6 kB]\n",
-            "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsonic0 amd64 0.2.0-6 [13.4 kB]\n",
-            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 espeak-data amd64 1.48.04+dfsg-5 [934 kB]\n",
-            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libespeak1 amd64 1.48.04+dfsg-5 [145 kB]\n",
-            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 espeak amd64 1.48.04+dfsg-5 [61.6 kB]\n",
-            "Fetched 1,219 kB in 1s (820 kB/s)\n",
-            "Selecting previously unselected package libportaudio2:amd64.\n",
-            "(Reading database ... 144465 files and directories currently installed.)\n",
-            "Preparing to unpack .../libportaudio2_19.6.0-1_amd64.deb ...\n",
-            "Unpacking libportaudio2:amd64 (19.6.0-1) ...\n",
-            "Selecting previously unselected package libsonic0:amd64.\n",
-            "Preparing to unpack .../libsonic0_0.2.0-6_amd64.deb ...\n",
-            "Unpacking libsonic0:amd64 (0.2.0-6) ...\n",
-            "Selecting previously unselected package espeak-data:amd64.\n",
-            "Preparing to unpack .../espeak-data_1.48.04+dfsg-5_amd64.deb ...\n",
-            "Unpacking espeak-data:amd64 (1.48.04+dfsg-5) ...\n",
-            "Selecting previously unselected package libespeak1:amd64.\n",
-            "Preparing to unpack .../libespeak1_1.48.04+dfsg-5_amd64.deb ...\n",
-            "Unpacking libespeak1:amd64 (1.48.04+dfsg-5) ...\n",
-            "Selecting previously unselected package espeak.\n",
-            "Preparing to unpack .../espeak_1.48.04+dfsg-5_amd64.deb ...\n",
-            "Unpacking espeak (1.48.04+dfsg-5) ...\n",
-            "Setting up libportaudio2:amd64 (19.6.0-1) ...\n",
-            "Setting up espeak-data:amd64 (1.48.04+dfsg-5) ...\n",
-            "Setting up libsonic0:amd64 (0.2.0-6) ...\n",
-            "Setting up libespeak1:amd64 (1.48.04+dfsg-5) ...\n",
-            "Setting up espeak (1.48.04+dfsg-5) ...\n",
-            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
-            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
-            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
-            "\n",
-            "Requirement already satisfied: torch>=1.5 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (1.5.1+cu101)\n",
-            "Requirement already satisfied: tensorflow>=2.2 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 2)) (2.2.0)\n",
-            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (1.18.5)\n",
-            "Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (1.4.1)\n",
-            "Requirement already satisfied: numba==0.48 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (0.48.0)\n",
-            "Collecting librosa==0.7.2\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/b5/1817862d64a7c231afd15419d8418ae1f000742cac275e85c74b219cbccb/librosa-0.7.2.tar.gz (1.6MB)\n",
-            "\u001b[K     |████████████████████████████████| 1.6MB 4.7MB/s \n",
-            "\u001b[?25hCollecting phonemizer>=2.2.0\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e1/ec/eb95ec96dc5374ba339d47c5252d62e9a7b1fbd5c9e4eefebc0d7e25381d/phonemizer-2.2.1-py3-none-any.whl (49kB)\n",
-            "\u001b[K     |████████████████████████████████| 51kB 9.0MB/s \n",
-            "\u001b[?25hCollecting unidecode==0.4.20\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c3/6f/05f5deb753d0594583aa1cc0d2fe9d631d9a00e9b28d0da49f8d3763755b/Unidecode-0.04.20-py2.py3-none-any.whl (228kB)\n",
-            "\u001b[K     |████████████████████████████████| 235kB 30.0MB/s \n",
-            "\u001b[?25hCollecting attrdict\n",
-            "  Downloading https://files.pythonhosted.org/packages/ef/97/28fe7e68bc7adfce67d4339756e85e9fcf3c6fd7f0c0781695352b70472c/attrdict-2.0.1-py2.py3-none-any.whl\n",
-            "Collecting tensorboardX\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/af/0c/4f41bcd45db376e6fe5c619c01100e9b7531c55791b7244815bac6eac32c/tensorboardX-2.1-py2.py3-none-any.whl (308kB)\n",
-            "\u001b[K     |████████████████████████████████| 317kB 16.7MB/s \n",
-            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 11)) (3.2.2)\n",
-            "Requirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 12)) (7.0.0)\n",
-            "Requirement already satisfied: flask in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 13)) (1.1.2)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 14)) (4.41.1)\n",
-            "Requirement already satisfied: inflect in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 15)) (2.1.0)\n",
-            "Collecting bokeh==1.4.0\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/70/fdd4b186d8570a737372487cc5547aac885a1270626e3ebf03db1808e4ed/bokeh-1.4.0.tar.gz (32.4MB)\n",
-            "\u001b[K     |████████████████████████████████| 32.4MB 100kB/s \n",
-            "\u001b[?25hCollecting pysbd\n",
-            "  Downloading https://files.pythonhosted.org/packages/3b/49/4799b3cdf80aee5fa4562a3929eda738845900bbeef4ee60481196ad4d1a/pysbd-0.2.3-py3-none-any.whl\n",
-            "Collecting soundfile\n",
-            "  Downloading https://files.pythonhosted.org/packages/eb/f2/3cbbbf3b96fb9fa91582c438b574cff3f45b29c772f94c400e2c99ef5db9/SoundFile-0.10.3.post1-py2.py3-none-any.whl\n",
-            "Collecting nose==1.3.7\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/d8/dd071918c040f50fa1cf80da16423af51ff8ce4a0f2399b7bf8de45ac3d9/nose-1.3.7-py3-none-any.whl (154kB)\n",
-            "\u001b[K     |████████████████████████████████| 163kB 54.4MB/s \n",
-            "\u001b[?25hCollecting cardboardlint==1.3.0\n",
-            "  Downloading https://files.pythonhosted.org/packages/a8/d4/02c9ad87226867995e8cc89791ba3a5a653e1d25c04263adabe87b7e1472/cardboardlint-1.3.0.tar.gz\n",
-            "Collecting pylint==2.5.3\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e8/fb/734960c55474c8f74e6ad4c8588fc44073fb9d69e223269d26a3c2435d16/pylint-2.5.3-py3-none-any.whl (324kB)\n",
-            "\u001b[K     |████████████████████████████████| 327kB 55.3MB/s \n",
-            "\u001b[?25hCollecting fuzzywuzzy\n",
-            "  Downloading https://files.pythonhosted.org/packages/43/ff/74f23998ad2f93b945c0309f825be92e04e0348e062026998b5eefef4c33/fuzzywuzzy-0.18.0-py2.py3-none-any.whl\n",
-            "Requirement already satisfied: gdown in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 23)) (3.6.4)\n",
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-            "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.2->-r requirements.txt (line 2)) (2.10.0)\n",
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-            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.2->-r requirements.txt (line 2)) (0.34.2)\n",
-            "Requirement already satisfied: tensorflow-estimator<2.3.0,>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.2->-r requirements.txt (line 2)) (2.2.0)\n",
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-            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa==0.7.2->-r requirements.txt (line 6)) (0.22.2.post1)\n",
-            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa==0.7.2->-r requirements.txt (line 6)) (0.16.0)\n",
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-            "Collecting segments\n",
-            "  Downloading https://files.pythonhosted.org/packages/5b/a0/0c3fe64787745c39eb3f2f5f5f9ed8d008d9ef22e9d7f9f52f71ea4712f7/segments-2.1.3-py2.py3-none-any.whl\n",
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-            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->-r requirements.txt (line 11)) (1.2.0)\n",
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-            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from flask->-r requirements.txt (line 13)) (2.11.2)\n",
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-            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.6/dist-packages (from bokeh==1.4.0->-r requirements.txt (line 16)) (3.13)\n",
-            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.6/dist-packages (from bokeh==1.4.0->-r requirements.txt (line 16)) (20.4)\n",
-            "Requirement already satisfied: tornado>=4.3 in /usr/local/lib/python3.6/dist-packages (from bokeh==1.4.0->-r requirements.txt (line 16)) (5.1.1)\n",
-            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.6/dist-packages (from soundfile->-r requirements.txt (line 18)) (1.14.0)\n",
-            "Collecting isort<5,>=4.2.5\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl (42kB)\n",
-            "\u001b[K     |████████████████████████████████| 51kB 7.2MB/s \n",
-            "\u001b[?25hCollecting astroid<=2.5,>=2.4.0\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/24/a8/5133f51967fb21e46ee50831c3f5dda49e976b7f915408d670b1603d41d6/astroid-2.4.2-py3-none-any.whl (213kB)\n",
-            "\u001b[K     |████████████████████████████████| 215kB 55.8MB/s \n",
-            "\u001b[?25hCollecting toml>=0.7.1\n",
-            "  Downloading https://files.pythonhosted.org/packages/9f/e1/1b40b80f2e1663a6b9f497123c11d7d988c0919abbf3c3f2688e448c5363/toml-0.10.1-py2.py3-none-any.whl\n",
-            "Collecting mccabe<0.7,>=0.6\n",
-            "  Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gdown->-r requirements.txt (line 23)) (2.23.0)\n",
-            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (1.17.2)\n",
-            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (0.4.1)\n",
-            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (1.7.0)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (3.2.2)\n",
-            "Collecting csvw>=1.5.6\n",
-            "  Downloading https://files.pythonhosted.org/packages/d1/b6/8fef6788b8f05b21424a17ae3881eff916d42e5c7e87f57a85d9d7abf0a1/csvw-1.7.0-py2.py3-none-any.whl\n",
-            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from segments->phonemizer>=2.2.0->-r requirements.txt (line 7)) (2019.12.20)\n",
-            "Collecting clldutils>=1.7.3\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7b/b3/05882a8d5c8a7f7c69a47500334ac99623928edca930278d6ab88ee6d99b/clldutils-3.5.2-py2.py3-none-any.whl (189kB)\n",
-            "\u001b[K     |████████████████████████████████| 194kB 49.8MB/s \n",
-            "\u001b[?25hRequirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->flask->-r requirements.txt (line 13)) (1.1.1)\n",
-            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.0->soundfile->-r requirements.txt (line 18)) (2.20)\n",
-            "Collecting lazy-object-proxy==1.4.*\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0b/dd/b1e3407e9e6913cf178e506cd0dee818e58694d9a5cd1984e3f6a8b9a10f/lazy_object_proxy-1.4.3-cp36-cp36m-manylinux1_x86_64.whl (55kB)\n",
-            "\u001b[K     |████████████████████████████████| 61kB 8.6MB/s \n",
-            "\u001b[?25hCollecting typed-ast<1.5,>=1.4.0; implementation_name == \"cpython\" and python_version < \"3.8\"\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/90/ed/5459080d95eb87a02fe860d447197be63b6e2b5e9ff73c2b0a85622994f4/typed_ast-1.4.1-cp36-cp36m-manylinux1_x86_64.whl (737kB)\n",
-            "\u001b[K     |████████████████████████████████| 747kB 54.2MB/s \n",
-            "\u001b[?25hRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->gdown->-r requirements.txt (line 23)) (2020.6.20)\n",
-            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->gdown->-r requirements.txt (line 23)) (3.0.4)\n",
-            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gdown->-r requirements.txt (line 23)) (1.24.3)\n",
-            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->gdown->-r requirements.txt (line 23)) (2.10)\n",
-            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (4.1.1)\n",
-            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (4.6)\n",
-            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (0.2.8)\n",
-            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (1.3.0)\n",
-            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (1.7.0)\n",
-            "Collecting isodate\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/9f/b36f7774ff5ea8e428fdcfc4bb332c39ee5b9362ddd3d40d9516a55221b2/isodate-0.6.0-py2.py3-none-any.whl (45kB)\n",
-            "\u001b[K     |████████████████████████████████| 51kB 9.4MB/s \n",
-            "\u001b[?25hCollecting rfc3986\n",
-            "  Downloading https://files.pythonhosted.org/packages/78/be/7b8b99fd74ff5684225f50dd0e865393d2265656ef3b4ba9eaaaffe622b8/rfc3986-1.4.0-py2.py3-none-any.whl\n",
-            "Requirement already satisfied: uritemplate>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from csvw>=1.5.6->segments->phonemizer>=2.2.0->-r requirements.txt (line 7)) (3.0.1)\n",
-            "Collecting colorlog\n",
-            "  Downloading https://files.pythonhosted.org/packages/2a/81/12d77537c82c5d46aa2721dfee25a0e873ef5920ebd0827152f411effb57/colorlog-4.2.1-py2.py3-none-any.whl\n",
-            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.6/dist-packages (from clldutils>=1.7.3->segments->phonemizer>=2.2.0->-r requirements.txt (line 7)) (0.8.7)\n",
-            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (0.4.8)\n",
-            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (3.1.0)\n",
-            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow>=2.2->-r requirements.txt (line 2)) (3.1.0)\n",
-            "Building wheels for collected packages: librosa, bokeh, cardboardlint\n",
-            "  Building wheel for librosa (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for librosa: filename=librosa-0.7.2-cp36-none-any.whl size=1612885 sha256=772045cdc2b1e22f5b5658822ab21ff12ed23871b729a83f7ab71702bd988fa2\n",
-            "  Stored in directory: /root/.cache/pip/wheels/4c/6e/d7/bb93911540d2d1e44d690a1561871e5b6af82b69e80938abef\n",
-            "  Building wheel for bokeh (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for bokeh: filename=bokeh-1.4.0-cp36-none-any.whl size=23689201 sha256=cad6fa791cc4e4304ea896a3ca0a192ca76b3e19fb6f4b9451c557f5488ff85e\n",
-            "  Stored in directory: /root/.cache/pip/wheels/fb/f8/47/09700d9a19cbcbf0b7a3130690b75c0d6ff80fbda0b1774c7c\n",
-            "  Building wheel for cardboardlint (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for cardboardlint: filename=cardboardlint-1.3.0-cp36-none-any.whl size=51334 sha256=784dae96378ea33bba49caa613246afb18bffc40892c8943e3dbfab99e312f7a\n",
-            "  Stored in directory: /root/.cache/pip/wheels/e6/fe/8e/fbeb2cda877a1f772c0462f9f07eed851f143020947a075ee9\n",
-            "Successfully built librosa bokeh cardboardlint\n",
-            "\u001b[31mERROR: panel 0.9.7 has requirement bokeh>=2.1, but you'll have bokeh 1.4.0 which is incompatible.\u001b[0m\n",
-            "Installing collected packages: soundfile, librosa, isodate, rfc3986, csvw, colorlog, clldutils, segments, phonemizer, unidecode, attrdict, tensorboardX, bokeh, pysbd, nose, cardboardlint, isort, lazy-object-proxy, typed-ast, astroid, toml, mccabe, pylint, fuzzywuzzy\n",
-            "  Found existing installation: librosa 0.6.3\n",
-            "    Uninstalling librosa-0.6.3:\n",
-            "      Successfully uninstalled librosa-0.6.3\n",
-            "  Found existing installation: bokeh 2.1.1\n",
-            "    Uninstalling bokeh-2.1.1:\n",
-            "      Successfully uninstalled bokeh-2.1.1\n",
-            "Successfully installed astroid-2.4.2 attrdict-2.0.1 bokeh-1.4.0 cardboardlint-1.3.0 clldutils-3.5.2 colorlog-4.2.1 csvw-1.7.0 fuzzywuzzy-0.18.0 isodate-0.6.0 isort-4.3.21 lazy-object-proxy-1.4.3 librosa-0.7.2 mccabe-0.6.1 nose-1.3.7 phonemizer-2.2.1 pylint-2.5.3 pysbd-0.2.3 rfc3986-1.4.0 segments-2.1.3 soundfile-0.10.3.post1 tensorboardX-2.1 toml-0.10.1 typed-ast-1.4.1 unidecode-0.4.20\n",
-            "running develop\n",
-            "-- Building version 0.0.4+f0284e8\n",
-            "running egg_info\n",
-            "creating TTS.egg-info\n",
-            "writing TTS.egg-info/PKG-INFO\n",
-            "writing dependency_links to TTS.egg-info/dependency_links.txt\n",
-            "writing entry points to TTS.egg-info/entry_points.txt\n",
-            "writing requirements to TTS.egg-info/requires.txt\n",
-            "writing top-level names to TTS.egg-info/top_level.txt\n",
-            "writing manifest file 'TTS.egg-info/SOURCES.txt'\n",
-            "writing manifest file 'TTS.egg-info/SOURCES.txt'\n",
-            "running build_ext\n",
-            "Creating /usr/local/lib/python3.6/dist-packages/TTS.egg-link (link to .)\n",
-            "Adding TTS 0.0.4+f0284e8 to easy-install.pth file\n",
-            "Installing tts-server script to /usr/local/bin\n",
-            "\n",
-            "Installed /content/TTS\n",
-            "Processing dependencies for TTS==0.0.4+f0284e8\n",
-            "Searching for gdown==3.6.4\n",
-            "Best match: gdown 3.6.4\n",
-            "Adding gdown 3.6.4 to easy-install.pth file\n",
-            "Installing gdown script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for fuzzywuzzy==0.18.0\n",
-            "Best match: fuzzywuzzy 0.18.0\n",
-            "Adding fuzzywuzzy 0.18.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for pylint==2.5.3\n",
-            "Best match: pylint 2.5.3\n",
-            "Adding pylint 2.5.3 to easy-install.pth file\n",
-            "Installing epylint script to /usr/local/bin\n",
-            "Installing pylint script to /usr/local/bin\n",
-            "Installing pyreverse script to /usr/local/bin\n",
-            "Installing symilar script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for cardboardlint==1.3.0\n",
-            "Best match: cardboardlint 1.3.0\n",
-            "Adding cardboardlint 1.3.0 to easy-install.pth file\n",
-            "Installing cardboardlinter script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for nose==1.3.7\n",
-            "Best match: nose 1.3.7\n",
-            "Adding nose 1.3.7 to easy-install.pth file\n",
-            "Installing nosetests script to /usr/local/bin\n",
-            "Installing nosetests-3.4 script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for phonemizer==2.2.1\n",
-            "Best match: phonemizer 2.2.1\n",
-            "Adding phonemizer 2.2.1 to easy-install.pth file\n",
-            "Installing phonemize script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for SoundFile==0.10.3.post1\n",
-            "Best match: SoundFile 0.10.3.post1\n",
-            "Adding SoundFile 0.10.3.post1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for bokeh==1.4.0\n",
-            "Best match: bokeh 1.4.0\n",
-            "Adding bokeh 1.4.0 to easy-install.pth file\n",
-            "Installing bokeh script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for pysbd==0.2.3\n",
-            "Best match: pysbd 0.2.3\n",
-            "Adding pysbd 0.2.3 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for inflect==2.1.0\n",
-            "Best match: inflect 2.1.0\n",
-            "Adding inflect 2.1.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for tqdm==4.41.1\n",
-            "Best match: tqdm 4.41.1\n",
-            "Adding tqdm 4.41.1 to easy-install.pth file\n",
-            "Installing tqdm script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for Flask==1.1.2\n",
-            "Best match: Flask 1.1.2\n",
-            "Adding Flask 1.1.2 to easy-install.pth file\n",
-            "Installing flask script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for Pillow==7.0.0\n",
-            "Best match: Pillow 7.0.0\n",
-            "Adding Pillow 7.0.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for matplotlib==3.2.2\n",
-            "Best match: matplotlib 3.2.2\n",
-            "Adding matplotlib 3.2.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for tensorboardX==2.1\n",
-            "Best match: tensorboardX 2.1\n",
-            "Adding tensorboardX 2.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for attrdict==2.0.1\n",
-            "Best match: attrdict 2.0.1\n",
-            "Adding attrdict 2.0.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for Unidecode==0.4.20\n",
-            "Best match: Unidecode 0.4.20\n",
-            "Adding Unidecode 0.4.20 to easy-install.pth file\n",
-            "Installing unidecode script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for librosa==0.7.2\n",
-            "Best match: librosa 0.7.2\n",
-            "Adding librosa 0.7.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for scipy==1.4.1\n",
-            "Best match: scipy 1.4.1\n",
-            "Adding scipy 1.4.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for numba==0.48.0\n",
-            "Best match: numba 0.48.0\n",
-            "Adding numba 0.48.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for numpy==1.18.5\n",
-            "Best match: numpy 1.18.5\n",
-            "Adding numpy 1.18.5 to easy-install.pth file\n",
-            "Installing f2py script to /usr/local/bin\n",
-            "Installing f2py3 script to /usr/local/bin\n",
-            "Installing f2py3.6 script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for torch==1.5.1+cu101\n",
-            "Best match: torch 1.5.1+cu101\n",
-            "Adding torch 1.5.1+cu101 to easy-install.pth file\n",
-            "Installing convert-caffe2-to-onnx script to /usr/local/bin\n",
-            "Installing convert-onnx-to-caffe2 script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for requests==2.23.0\n",
-            "Best match: requests 2.23.0\n",
-            "Adding requests 2.23.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for six==1.15.0\n",
-            "Best match: six 1.15.0\n",
-            "Adding six 1.15.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for toml==0.10.1\n",
-            "Best match: toml 0.10.1\n",
-            "Adding toml 0.10.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for isort==4.3.21\n",
-            "Best match: isort 4.3.21\n",
-            "Adding isort 4.3.21 to easy-install.pth file\n",
-            "Installing isort script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for mccabe==0.6.1\n",
-            "Best match: mccabe 0.6.1\n",
-            "Adding mccabe 0.6.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for astroid==2.4.2\n",
-            "Best match: astroid 2.4.2\n",
-            "Adding astroid 2.4.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for PyYAML==3.13\n",
-            "Best match: PyYAML 3.13\n",
-            "Adding PyYAML 3.13 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for joblib==0.16.0\n",
-            "Best match: joblib 0.16.0\n",
-            "Adding joblib 0.16.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for attrs==19.3.0\n",
-            "Best match: attrs 19.3.0\n",
-            "Adding attrs 19.3.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for segments==2.1.3\n",
-            "Best match: segments 2.1.3\n",
-            "Adding segments 2.1.3 to easy-install.pth file\n",
-            "Installing segments script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for cffi==1.14.0\n",
-            "Best match: cffi 1.14.0\n",
-            "Adding cffi 1.14.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for packaging==20.4\n",
-            "Best match: packaging 20.4\n",
-            "Adding packaging 20.4 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for tornado==5.1.1\n",
-            "Best match: tornado 5.1.1\n",
-            "Adding tornado 5.1.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for Jinja2==2.11.2\n",
-            "Best match: Jinja2 2.11.2\n",
-            "Adding Jinja2 2.11.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for python-dateutil==2.8.1\n",
-            "Best match: python-dateutil 2.8.1\n",
-            "Adding python-dateutil 2.8.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for click==7.1.2\n",
-            "Best match: click 7.1.2\n",
-            "Adding click 7.1.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for itsdangerous==1.1.0\n",
-            "Best match: itsdangerous 1.1.0\n",
-            "Adding itsdangerous 1.1.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for Werkzeug==1.0.1\n",
-            "Best match: Werkzeug 1.0.1\n",
-            "Adding Werkzeug 1.0.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for cycler==0.10.0\n",
-            "Best match: cycler 0.10.0\n",
-            "Adding cycler 0.10.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for kiwisolver==1.2.0\n",
-            "Best match: kiwisolver 1.2.0\n",
-            "Adding kiwisolver 1.2.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for pyparsing==2.4.7\n",
-            "Best match: pyparsing 2.4.7\n",
-            "Adding pyparsing 2.4.7 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for protobuf==3.12.2\n",
-            "Best match: protobuf 3.12.2\n",
-            "Adding protobuf 3.12.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for scikit-learn==0.22.2.post1\n",
-            "Best match: scikit-learn 0.22.2.post1\n",
-            "Adding scikit-learn 0.22.2.post1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for audioread==2.1.8\n",
-            "Best match: audioread 2.1.8\n",
-            "Adding audioread 2.1.8 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for resampy==0.2.2\n",
-            "Best match: resampy 0.2.2\n",
-            "Adding resampy 0.2.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for decorator==4.4.2\n",
-            "Best match: decorator 4.4.2\n",
-            "Adding decorator 4.4.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for llvmlite==0.31.0\n",
-            "Best match: llvmlite 0.31.0\n",
-            "Adding llvmlite 0.31.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for setuptools==49.1.0\n",
-            "Best match: setuptools 49.1.0\n",
-            "Adding setuptools 49.1.0 to easy-install.pth file\n",
-            "Installing easy_install script to /usr/local/bin\n",
-            "Installing easy_install-3.8 script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for future==0.16.0\n",
-            "Best match: future 0.16.0\n",
-            "Adding future 0.16.0 to easy-install.pth file\n",
-            "Installing futurize script to /usr/local/bin\n",
-            "Installing pasteurize script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for chardet==3.0.4\n",
-            "Best match: chardet 3.0.4\n",
-            "Adding chardet 3.0.4 to easy-install.pth file\n",
-            "Installing chardetect script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for urllib3==1.24.3\n",
-            "Best match: urllib3 1.24.3\n",
-            "Adding urllib3 1.24.3 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for certifi==2020.6.20\n",
-            "Best match: certifi 2020.6.20\n",
-            "Adding certifi 2020.6.20 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for idna==2.10\n",
-            "Best match: idna 2.10\n",
-            "Adding idna 2.10 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for wrapt==1.12.1\n",
-            "Best match: wrapt 1.12.1\n",
-            "Adding wrapt 1.12.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for typed-ast==1.4.1\n",
-            "Best match: typed-ast 1.4.1\n",
-            "Adding typed-ast 1.4.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for lazy-object-proxy==1.4.3\n",
-            "Best match: lazy-object-proxy 1.4.3\n",
-            "Adding lazy-object-proxy 1.4.3 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for csvw==1.7.0\n",
-            "Best match: csvw 1.7.0\n",
-            "Adding csvw 1.7.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for clldutils==3.5.2\n",
-            "Best match: clldutils 3.5.2\n",
-            "Adding clldutils 3.5.2 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for regex==2019.12.20\n",
-            "Best match: regex 2019.12.20\n",
-            "Adding regex 2019.12.20 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for pycparser==2.20\n",
-            "Best match: pycparser 2.20\n",
-            "Adding pycparser 2.20 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for MarkupSafe==1.1.1\n",
-            "Best match: MarkupSafe 1.1.1\n",
-            "Adding MarkupSafe 1.1.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for isodate==0.6.0\n",
-            "Best match: isodate 0.6.0\n",
-            "Adding isodate 0.6.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for rfc3986==1.4.0\n",
-            "Best match: rfc3986 1.4.0\n",
-            "Adding rfc3986 1.4.0 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for uritemplate==3.0.1\n",
-            "Best match: uritemplate 3.0.1\n",
-            "Adding uritemplate 3.0.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for tabulate==0.8.7\n",
-            "Best match: tabulate 0.8.7\n",
-            "Adding tabulate 0.8.7 to easy-install.pth file\n",
-            "Installing tabulate script to /usr/local/bin\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Searching for colorlog==4.2.1\n",
-            "Best match: colorlog 4.2.1\n",
-            "Adding colorlog 4.2.1 to easy-install.pth file\n",
-            "\n",
-            "Using /usr/local/lib/python3.6/dist-packages\n",
-            "Finished processing dependencies for TTS==0.0.4+f0284e8\n",
-            "Collecting tensorflow==2.3.0rc0\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8b/68/7c6c8e2b65ad4a3ff5ef658c04a6c2802ff7fe55fc7eecacb6efee1abc40/tensorflow-2.3.0rc0-cp36-cp36m-manylinux2010_x86_64.whl (320.3MB)\n",
-            "\u001b[K     |████████████████████████████████| 320.3MB 53kB/s \n",
-            "\u001b[?25hRequirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.4.1)\n",
-            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (3.12.2)\n",
-            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (0.3.3)\n",
-            "Collecting tf-estimator-nightly<2.3.0.dev2020062302,>=2.3.0.dev2020062301\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/17/3b/fb9aafd734da258411bff2a600cabff65c7d201782318791b72422bd973d/tf_estimator_nightly-2.3.0.dev2020062301-py2.py3-none-any.whl (459kB)\n",
-            "\u001b[K     |████████████████████████████████| 460kB 58.0MB/s \n",
-            "\u001b[?25hRequirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (2.10.0)\n",
-            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.15.0)\n",
-            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.18.5)\n",
-            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (3.3.0)\n",
-            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (0.34.2)\n",
-            "Requirement already satisfied: tensorboard<2.3.0,>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (2.2.2)\n",
-            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.1.0)\n",
-            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.30.0)\n",
-            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.6.3)\n",
-            "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.1.2)\n",
-            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (1.12.1)\n",
-            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (0.2.0)\n",
-            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3.0rc0) (0.9.0)\n",
-            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.9.2->tensorflow==2.3.0rc0) (49.1.0)\n",
-            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (0.4.1)\n",
-            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.7.0)\n",
-            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (2.23.0)\n",
-            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.17.2)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (3.2.2)\n",
-            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.0.1)\n",
-            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.3.0)\n",
-            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (2.10)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (2020.6.20)\n",
-            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.24.3)\n",
-            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (3.0.4)\n",
-            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (0.2.8)\n",
-            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (4.6)\n",
-            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (4.1.1)\n",
-            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (1.7.0)\n",
-            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (3.1.0)\n",
-            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (0.4.8)\n",
-            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.3.0rc0) (3.1.0)\n",
-            "Installing collected packages: tf-estimator-nightly, tensorflow\n",
-            "  Found existing installation: tensorflow 2.2.0\n",
-            "    Uninstalling tensorflow-2.2.0:\n",
-            "      Successfully uninstalled tensorflow-2.2.0\n",
-            "Successfully installed tensorflow-2.3.0rc0 tf-estimator-nightly-2.3.0.dev2020062301\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -732,30 +78,14 @@
       "metadata": {
         "id": "PiYHf3lKhi9z",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 104
-        },
-        "outputId": "75f7ecd0-3ee2-4ed2-8150-49f71a5ef4d5"
+        "colab": {}
       },
       "source": [
         "!wget -c -q --show-progress -O ./TTS-checkpoint.zip  https://github.com/Edresson/TTS/releases/download/v1.0.0/Checkpoints-TTS-MultiSpeaker-Jia-et-al-2018.zip\n",
         "!unzip ./TTS-checkpoint.zip\n"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "./TTS-checkpoint.zi 100%[===================>] 367.68M  31.9MB/s    in 12s     \n",
-            "Archive:  ./TTS-checkpoint.zip\n",
-            "  inflating: best_model.pth.tar      \n",
-            "  inflating: speakers.json           \n",
-            "  inflating: config.json             \n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -772,11 +102,7 @@
       "metadata": {
         "id": "4KZA4b_CbMqx",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 52
-        },
-        "outputId": "c5d33fac-4682-4e72-8fc2-c1aa83408ef4"
+        "colab": {}
       },
       "source": [
         "%load_ext autoreload\n",
@@ -825,16 +151,7 @@
         "\n"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "The autoreload extension is already loaded. To reload it, use:\n",
-            "  %reload_ext autoreload\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -890,11 +207,7 @@
       "metadata": {
         "id": "x1WgLFauWUPe",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 486
-        },
-        "outputId": "7cc2d0bd-f3c8-41dd-9f17-438f77c1f2d2"
+        "colab": {}
       },
       "source": [
         "# load the config\n",
@@ -961,41 +274,7 @@
         "    SPEAKER_FILEID = None\n"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            " > Setting up Audio Processor...\n",
-            " | > sample_rate:22050\n",
-            " | > num_mels:80\n",
-            " | > min_level_db:-100\n",
-            " | > frame_shift_ms:None\n",
-            " | > frame_length_ms:None\n",
-            " | > ref_level_db:20\n",
-            " | > fft_size:1024\n",
-            " | > power:1.5\n",
-            " | > preemphasis:0.98\n",
-            " | > griffin_lim_iters:60\n",
-            " | > signal_norm:True\n",
-            " | > symmetric_norm:True\n",
-            " | > mel_fmin:0\n",
-            " | > mel_fmax:8000.0\n",
-            " | > spec_gain:20.0\n",
-            " | > stft_pad_mode:constant\n",
-            " | > max_norm:4.0\n",
-            " | > clip_norm:True\n",
-            " | > do_trim_silence:False\n",
-            " | > trim_db:60\n",
-            " | > do_sound_norm:False\n",
-            " | > stats_path:None\n",
-            " | > hop_length:256\n",
-            " | > win_length:1024\n",
-            " Speaker:  p262 was chosen automatically (this speaker seen in training)\n",
-            " > Using model: Tacotron2\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -1015,11 +294,7 @@
       "metadata": {
         "id": "2o8fXkVSyXOa",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 704
-        },
-        "outputId": "b093ab6c-57a9-4595-8ff6-edee226cb0fd"
+        "colab": {}
       },
       "source": [
         "import IPython\n",
@@ -1039,69 +314,7 @@
         "  ap.save_wav(wav, out_path)"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Synthesize sentence with Speaker:  p262 (this speaker seen in training)\n",
-            "Enter sentence: teste this demonstration :)\n",
-            " > Text: teste this demonstration :)\n",
-            " > Run-time: 2.143589496612549\n",
-            " > Real-time factor: 1.6784820231524382\n",
-            " > Time per step: 7.61217437684536e-05\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "\n",
-              "                <audio controls=\"controls\" >\n",
-              "                    <source 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8Ins+un86NHo5+he6kDu8/Ln9Ijx8+lQ4hje1t0o4PjjXOkj8Pb2yfxxAdAEdAZfBZYA1vhp8eTuofMX/aQGJQ61E+cWWxaUEYUK8gM8/jD4ffJu70fwj/Tc+okBtQb8BwIEwfyB9ln0dfYe+48AWgZuDZEWziAtKQAsyCdcH/oXRRWtFzId+iL0JhAo7CUKIVQa1hLmC9YF1f8I+n71mPL18JrvQu1y6YLkh9+J3CndKOHQ5kDswvCR9Lr3R/oq/Mb8XfzF/LT/aQVLDHURvRLLD7gJfQLm+wL3TvRV83rzufQh9p31E/JY7Krm/+Ls4PzehN1S3v3ilev69SD/WQTSA4L+tPiR9ub4p/3NAmIIEg+eFQEZOBdqEVcKnAMX/vP6gPrC+4j9LP+0ACcCSgMoBIIEkAP4AI39QfzFAOEKShbEHrIhYR+uG1saXhwIII4hdh5gGNUSvBC9Em0WfBhlFl0PjQaKAFj+qP1b+wX2FPDV7AHt3O+H86/1L/bv9Xb1xvUB92n40voP/yoE4QjXCwIMIwp0B0sE9gDl/U77dPkv+P72/vWD9bH0ZfJr70Dtn+sK6jPp1umB7M7wQfSu9Kvyo/AG8VP0A/nu/ZYCVwYUCdwKVwv5CbwF/v7O+Pn1HvfY+vj9tP4T/oP8C/lE8wfskuW+4bvg9eIZ6Xryqf2MCMIQpxQ9Ew8OZQp1DGkTBxv4H4IjPinZML01BDSCLK0k0iCXH2EdTBl/FZwTMxG5CcX8BPD26Qfrd+1w7O/opOdu6ybyAfeN95n1C/UJ+OD9QwX2DJgT5xhjHf0g/SIyIpce+BlKFQsQQAoCBTECxAIrBagG1gSm/lH1pOpY4HHZDdht2zDhFuZv6CXqPO288OLxru7A6I7k3+SS6brwvPec/V0CyAT7AwgBDP5D/Tj/FQJNBKcFqgU7A1b97vPU6NTe1tdX1CrUGdfS3KXkVu2A9PL2pvPk7Qfrdu7a9xEEzg/cGRojWyxeNF44SjfIMskssyZfIuUg8yDNH1AcTRhYFbYRWwtFA+j70/bO88bxhfH59EH7HwFTBEoEnQF0/Ub5/vct/EsERAwyEqcXEx+nJ6gsryqQI3gbnxTfDlsKPwkBDbkS7hRHEcQKmgWbAb36Ae994WLXs9Of1fzZN96j4VLkyeVz5bvjNOKL4lrlJ+pM8EH3xf5bB0ERvBquIcMlJycgJWIfkBdjELkKdgTJ++zxYOkA4x3dPtXoyi2/SrK9pFqZFpR8l46idbAHvLPDmcnl0BTbN+ea80H/kwqOF0Mo0zy4UmdlMHA6cDBlJlIOPk0wZivSK14swCpBJ60hqxhYC/T50eWK0brAL7dduFzGz98d/hsXWyI2HrUQdAOw/awBMQ53IH41cksAYeZypXsPdnRg/T7gGif+Uu807z35rwY8EjcYcBXBCPn0uN4qyRO2RqjnpG6wxchr5MX5fAUmCRMHKgEh+u/0afIB8qH1+ADcEwUoJjWxNr0tIR7UC9b65O135KrcetYU0z3SUNL90WrQZMxUxXO89bNvrT6pJ6dmpwKrBLMMv5PMktjB4XzokO348Rf38f60CnkY2CRwLs41ijugP0JBgD9EOmMyQClFIMEY5RO7EdUQIg9hCuAB8/fX733r4+ok7Obt1fBH9cn6DAE3B70MoREXFSYY2h7/KrU5ckZkTTJNkEf2Pxo6WjcPNXsvlyUpGsARTA6fDYUMrAhKACrzeeRp2gjaoeIN7sL1mffF9ffyK/HS8oX4LP9BAyoEawQxB2wM9RApEtIOKAfg/V32DPJg8Cfw5fBN8kbyHO775T3dtdYe0aLJBsKjv3fEZM1m1eDY7dcA1frRBtB3zznPxs7gzyjVWd+Z64j14Pr6+3f6n/iJ+DD79v8nBaYJOw3YD7MRDRPUE+YTpRJyD0cLOgjlByoKkgyoDKQKNQhRBroEDgM+AnwDvAaNC18RUxd9HCUfCx7jGucYNRpbHg8iMCPIIzYmOCrLLU0ugyqII6wbVhZwFvMaAx8WHk4YORJrDxsPvg3JCBECav1D+wj6y/kL+xH97/3m+1P4nvYd+If7mf7g/20ASQLjBREKqQzrC1MISgM5/UL2ge8w61nrKu9E8mHwZunN4N3ZW9Xr0QLOHcqVyELLkNG52F3eXOIx5U7mMOUa4xzjP+f67Tz0uvio+zf9Af2X+vP2TPQ89Ij2dPmy+0D9p/0F/MP40vSX8GTs/OhH6LXrzvGL+GT/HAU9CDYJawlaCkUNORKPGB4fmSSyKdovMDbGOqQ8HDw9O1I7ETq9NIoseiUaIjQhjB+AGwkW+RBkDf4KRAiPBK8BcAG4Au0CHwG8/kD9cfwp/FP9hP9PAeYCgwXtCYEPqBM+FLYRhg3pCAkFNQO5BKkIWwtJCiQGbQFC/rf74/aq7+noVeSS4Tjg2ODJ4yDnCefL4c3aV9fL2HPcwt9T4o3knObv6PPrg+6O7rHryufK5UznverF7bjvC/Df7WPpIOTt4LHhI+WN6dfuovTG+W78q/qb9dLwsu2464HryO7v9vYCKg+lGNAeXyHKH94ZthBfCLEF8AmnElUc5SSyLPIzjzj6Ny0xESZuGzIVThQ9GE0fiCeaL0M0ajHAJzUcjhLyCp4Dv/wf+WL6vv+dB6QP3RQjFVwQNQmHA/gAnQC6AWwEIgjrC98PKRQ2F+UW3hJtDPcEkP1G93/zmvOb9sT4H/hh9g31tPNx8dLtO+kU5Nve9duH3aTiuuln8dr2tPdX9CPvGOqK5QzihOCd30be8d2O34/iOOXq5E/hUNxi18PTrdFC0dnUo9ww5ZDs9/EN9UD3Fvg+9sPzzPLJ9DP7fQMCCpkO3xB4EAsPvwwqCWIF7AHZ/zQB7AUkDKYRwBQAFrkWzBbQFYwU4hTLFzwcUyHBJtMrhDDnM5Az5S+2K1MoHyZHJbYlVCcYKJ8lIyHMHJcYMRRvD20KWAbeAwUDLgSZBoYI3wj0BhUDGv+f/L38CACLBB4HTwcDB+4HQwrlC0oKDwbDATz+Lvtv+bv5gPrS+J3zFO1R6CHmQeVK5NnhVN3O2DrXodhq2o3aTNlz1znUEdCrzvbRVNdX21PcENtw2uHbn94g4lvl2+Yj58LnZelK7E7wkPSy96L42feg9i/1rPPO8/f2bvsF/ij+nf2R/T/+Mv9t/7b+cv1h/Nr8qf6tAOgDmgg8DN4NQA/MESoVohc8GSIc8B/1ImUmvCpzLSIt0iofKPAlcyQAJAMkkiLwHzAeUB1wHK8bmhqRGPMVFxM2EFYOLw6+DoAOlg3WDEkMCQymDAEO5A6eDS4Kbwd/B8MI7Aj9B54GoAR9AgIBo/+q/mX/t//g/CT5jPZO85PvDe246v7nRubf5VDmOOdX5/LlUeRf43jh2t2u2xzdXd/h3wfg5OEt5a7nDeg05xvm6ORO5Arll+aW57vnuuiJ68nuBPGR8cnwPPA/8KfwtvJb9XP1r/P28uDzC/YI+W/7Zvzd/K79df6+/0cCSwTiBJkFzAUtBfUGVQswD14SMxVIFiYWaRZ2FxcZIxozGRQXkxWrFAUUvRT4Fh0ZeRmxFiUToRN4FZ0SZA4oDpUPXRCnEZcTdhUnFkEVRRQ2E0IRFxCbEMAQQQ+RDaYNAg93DxwOkAtOCXcIGQgMB30F1QOsAmMBnf4A/Oj6B/kl9pP0vfMP8nbwvfDS8m/0UPS585XzdPOk8gfxMvCp8ZX0kPZF9Y7x3O9F8Wvxb+4m7H3t6u9a8GHwnPHo8TTwMO4T7d7s2OxA7TfvTfFT8nD0gfcI+cX5V/pc+ZT49PrV/okATAA6Ab0D7wUrB8cGvASyAzQEVQTnBKwGMQheCekJKgnRCDkJ8Ah5CE4IcQi9CAQH4QTSB20NMg8KDq8MCAstCjwKMArkCpwLzgpaCoIL8wyADp4PrQ5eDF8KAAlsCJQIqAmbC74KaAXMAloGoQcQA/7/RQG5AowC7AGvAa4BrAGhAeT/Tfxm+3n+aABF/xn+MP5T/g/+9P22/P/4Zvbd9+35gfog+1r7Rft6+/j5ivcm90/3fvbn9pX4F/m2+QP94f4z+y74Vfl594Tz2PVJ+tb5jfkS/Tn/Qv4o/gH+RPoL+OH6zvoN+DT8DQJ4AXABhwSmA8L/w/5nAFoA4v2G/Q0AcgHbAQkDzwQQB/YGjgN4AXUBHwGzAdYDUQUvBNYCkwV/CFUHOgcmCBAGMwWMBR0DPwKyBHUGPgZKBCgEJAczB1AEIQNMA1QD1v8u+zT+ogIcAkcGIgrrA2oAyQNIA8//mvy5+r38Cf5x/XX+FP9CAkMHJQPG+oX5gfrk+eX6qv3LACYBa/+n/8P/+/+ZALz7sfeu+9398fzz/mT/+v/kAbH97vqr/gr8vfZA+rD8a/iE+Mb/1gO3/vD53fya/Az3yfj+/A37kvsQ/yL+Ff2g/p7/7/+R/s784fx2/C79GP/g/Uj/+QNVAUj9oQCoAsEACACEACECLwBY/MsC0AnfAnH9CgL2AhD/S/4AARADHQExAP8CuQFu/o4A0wKBAF79n/3z//j+Tf/VBI8E6gBoA8MBOP4EBNgE1/2t/ugBzQGVAucAugG6BYYBW/7UAeP9s/pNAHb/7Ps1/kD8vfrx/u38pPorARoCrPrv+Xr/8/8+/Pj9jwJN/9z6h/4V/wD7LAFpCbkCJvl/++H/Df+oAO4B3Pvg+SEAJv7a9+P+7gW0/sH6iP84/Tn5Z/7oAJP8uvwr/wH+jP6CAbkCBwHq/H78RADY/oX73fyv/SAABQR+ABT+6QHZAID/ZQDo+8394wS2/838iQND/yv53wCxBRYB0/wA/HkBfwayAdD9JgHbAW//Zf4x/hcBvQML/6r7CQHcAVj7f/17BMwCEf/v/FT7hgI4B7n9n/wIBtUBS/tp/5z/eP9PAu37BftyBcsAivhcBUsKUfoM+YAE/f/y+WgALwGs/dwBKAKR/BD+lgMtBbgBKvww/dMBVQDH/4ICtP5U+jb/0wTV/4/4y/7mBKf7bPurBdv+CftyBXz/FvoABXT+l/cACN4EQ/XYAxQMyvid+HcHbgLg/eMEwf4J+BwDkwbQ/ukBEQTg/cz+jQAu/loAlAAV/Sf9UQBuBLMA6vonBGsGRflg/r8GP/sy/IEGc/8s/UcGpgWOAHP/GAHuBKMDY/2L/GAETwid+wv0DwKXBsL9lwH+Abz6KgMGCCz9qf08BckBnv5oAKz+b/76A98DCv4AApwGBf5Y/EYEDwD1+50CfQHR/J8BVwTk/8z/Xga/BNL4kfpXB1oFsAATAgj5Z/mjCjsG0/e/AZoIHf0S+9kA6wBnAo0CmP7KAFsFCARBAtgAmfx//GgCsAGT+Qf9iAhRAwD78AZQDMj8UvdyAfMHBARW+Jb5HgrcB6r5p/32AgYAZwLbAUP/CAINACQAYQRK/Fj6gQkbBnb3IAQcDXX2h/JOCjcIbfdD+x8C/wQlA5/4/QSxEjT2KPHeFNQLt+/f+/kGUQSLA7X8+P84A2j7ywbBBuTs4fnFEGv8i/faCa4ANfoUCdgDE/NJ/bEK0/ob9iINvAaL83gGggp98ij+UQ8U/ev0WgFmBfgBiACcBKcEPPwX/QUC6/wF+0EBAAX7/Xn46wdzCj725AJiDZrtZfWHFIL81vHCCh0EKfs3Acf9UwbyBGf03AOlByXvgv59EkT+Z/f3BO8FF/3Z+J8DygVc95z+jQgq/gcAEwDe+zYLgACF7ZkINwuB7qr+SA3Y+1D7oAEpBRQFk/ac/7EKk/QHAMoR5+9r9sgVrvUw8VQYogOU6OYAURA3BNDyNvtCFbj7VOO/EdITkOEd/I8ekvqx7roG1ghGALf3qft/Bt75mfyTD/r44e//DZIDMfFWBqcH6/he/PQAAwbU+7HuQAooD13sP/+zFVryIvI8Eh0JPvcv9iYBjA0d+9jy0Au3BeP4lwTy9/X36BAy/Grw8A+iAbXm7gM9Em71kO/eByYJNfGN+B4Omfpc9UoSPQFR6DQHlRIj9JHy3ApjDIP2X/TcCq0E0fMxCPsIs+wT/nMS/vUq8xANpP8a9NkFPvzt9sYSsP2J3/APIRtc4sLynxcc+1r31QVC+aQCAgP39UMLRwHA7NgNFAMP6EASTwdw3twQtBY83hgEHSDv5EfwwR+b97LlrRPQBSPkhwXUEiLyXvewC0EDSPlb+O0FewrJ7pT4HhlG+GXneg7MBWjzdwkFAKjy4AZNA8T1wP4SBuAD2/n6+kYIzfz8928KMv5P9MQGb/6t+C8JEfxw9uUKmf+w9U8JIQJj8pUE/QiT8wf85wxW92j0nxMcAo/muwhZEI3uHgDZDgXzq//LDU/sWfneHB/29uK4Fc8M4d+VBbYcbeiZ8bQbYPUb7aEcmvyh4JgYJBDb3z8GmBVo6NP6xBk3+pfuQgh3C136JvV8BG4G3vWJAIsIjfVyAVoMUPWt/nMI8/NvBNMJsOrIAdwVJu+e8qwSRgTm9BX+HAZPBMb52/w3B5f/nfuQAYQCfwGy+yT/6Acm/L/6LgXj/FsBuwOr89IEMQpx8oUJNQyC5loEnxeV6yf3zROA/bn4MQHUADoIwP2/9+cFGAE1//0Dj/n/ADMI2vvIABQBDfqnBuABV/mGBAb+DwBFCXv03v7FEvjxP/YQF5H7QvGNDKgB1fixBpwChP3m/y0CQgMG+4sARgjR9x79ngz5/Pf8vQOS9/MF5Al98s4DUAsv8B8CpBCq98f9bAWF+8IIFwDV8TgPowjY6b0GBBGh7X38/xar/KzwDghzCUr6WPxkA9oBdv8c/oMCmQib/DX3oQw9B2/udQIvEer1iPnBDPD7Ufs0DKr/ufm+BZ8C/f6S/ur8EgdyAa3zjAgMDVzvCf77Fzr5ResEDtcOHfAL+pwU3QBQ6xwJexI677L3vBPl/k70ZQYLBSYCCQFr+hUFaQNt96QHYQV18xUGogj59SIEOQf4+EcDYwO1+VoBawEyASMEQvooAF8JIfw3+84CAQJfBEH7hfvWDXP/r/RMC5wDXfeTBaD9K/1iClH3Hvv7EID5P/beDv/+bPeQBsb+SP/0Ayj6NQUzBpvzjARGDKvyAf4MD2v5ePnECn//QPpGBdT/YfooBd0Goflp+KMIJAkj9gb7aw00/7bzUQgxBmfzMALdCT/35PxkCgwAavp6AZsGUgJI9g3+swuZ+/H2ZAkNAyX6XwIDAMP/IwIU/MYCMQQU+rYB4wFG+uIEmwBE+isI5/5E+D8J1f4h+jALGP26+DMJKfwA/aEKE/gV+0MMOfoJ+cYJwf9e+SsBSgVgAXP0wAC9EVj0a/FJFnD/+ubxDnwQXey4+uoNsf6B+BkAIgUoAgn5ugD+CeL5qPjwCv3/N/akBvj/vPaECiIEefLHBTAHLfROAkAHFPhHAqQDq/ZfBCYGhvSJ/8cIrvpP+5sBMP6QACn/0fztBQQB8/V2BEQJ6PTh+mQMPPyt9MQHeANe9zQE+QQs99H/nAgN+xv4VAbIA3T2Zv02B+L9gvsFBAX/2vvBA9L/c/m1AB8Dh/ue+5oDjgKo+ZX9rgfLAMn3AAAnB+j9yPXIAa8K5vdr894NAAjD7DP/whCJ9XP05w6Z/tDvLQtbBkbtbQWRDXrvDP+0DrLwr/qWEL3zdPZxEj37GPDFCQMF1fan/wYESAA1/UP9FAEw/hH+3wPZ/Iv6lgXJ/2T3BAMCBEP4Bv89Bif75vuMBjv+svqJBeH92/d1BboBi/h1AlkCifvv/2AAvwC/AGz6HQCLA2f6t/+vAnz6xQJgA5b32wFkBQv4Nf9OBbP60/3RBCz+gPurAUAC2vuP/okES/uP+8gIt/wj9csJcQQd86MC+AcI+X79HARH/mT+XQFMAJD9Uv6UA5j/pPoGA2oDDfwP/8r/mv/+Alv9Qf7nAzT7VP7RBwz7j/qdByf+FfqHBAYBP/7ZAAj+HgHXAOz7jwL6AkT7Nf+3AosA3P8j/tcALgF0/FMBxgF2/EsDwwDq+WMFPANk+E0C4gJz+1wC8gBU/voCovwuAEcHe/l5/CMKWf0C+q4FcAC//ZkB+/4fAZMA9v32Arr/NP3qA/L/0f1gAnb97wCdBcP60v4fB+T7L/6BBrf8ef7uBdf9Zv6bA0T+if9PAo3+JAEtAvv9ZQFlA4f9Hv5jBAYC0PsPANQEX/6+/WYEHAAl/VsDtQGQ/l4BSwBJAWMCPvz5/+YGZP03+3UGEAJy+iUDHwUP/FT+XAVMAYP8ugBoAxP/jv4oA2wBOP0ZAS4EJv+x/dgCjAKB/df/OgV5AJz7IAP9BKL7TP7wBcD/D/0WA5ABk//CAV0A6wCGAVD+4QD3Ajb+Rv9ZAx8BLf+FAD8CYAFt/rEAVgPO/3v/mQEoAfgBKv/4/bsEGQIX/IsCjgKS/dMCwQHc/XsDUgF2/fAC5ABp/i4Dw/+i/pEELgC6/Q8EZwHk/R4CWQHp/iYBsgECAXoAIgA6Aj8Bef4qAj0D0P3Q/98DJ//T/u8DkgDn/X8CeQFs/rMBUAJv////lQGDAcj/X/9zAqUBYP76AKwB6v50ATkCbP//ACQBVv/1AK0ANwCSAab/SwBnAgv/1//aAir/1f/6Ag//K/86Aoj/EAAyAoP/2/+3AW0AcAB0AP7/sgDc/zYARgGC/4gArQGH/iMAngKH/uH+mgL7/4r++gC8AIT/tf/vAAkBvP4aAHcCdv5n/nkD+f/F/GUCBwLX/Mn/7AJy/9P9XwB8AYn/Uv6hAPwBNP9b/kwBEgH3/br+NwH//3/+DACjAEv/tP/QAAoAVv/h/yUA9v+N/1T/BQDy/wH/8/+BAMX+J/+8AIj///4IAIT/U/+//z//mf+d/wz/QwD3/33+AADf/xz+KgAOAFX9FABOAW/9Rv9FAnP+9P1uAY7//v0tAI//2v4eAP/+9/6bAPX+9P46AdP+q/34AMr/b/3r/xoAI/6i/ywAef4d/0AANP9m/pb/WAC3/kb+RwCk/+T9zf91ACP+DP+fAHn+Q/5wACX/K/5RAH3/5v1cADIAe/2u/wsB1f10/vQAB/9Q/k4AfP87/oj/0/+p/hT/DgBM/6P+iv+q/+D+R//S/0f/8f5W/4f/CP8d//7/X/+K/tn/1v9R/l//FQCR/hr/KAD+/gj/AAA8/xD/4P9J/97+tP9x/8b+of/T//D+l//5/9L+d/9fALz+n/6PAOj/fP6D/xMA4v7d/gAAkP/E/tD/yP87/lP/lgDK/tH+ugCd/5T+9P/k/xz/oP+H/zj/sf+n/4f/xv+R/4f/1f+v/5T/qv95/3//xP+5/43/lv+q/3//af+9/77/b/+t/8T/bP+q/+H/of/H/7z/X/+m/9n/of/I/9z/qP+R/4//2v/s/5D/4P8BAFP/of8uAJX/kf8UAL3/p//7/8r/rv+7/5z/2/8tAPb/s//e/wMAyv+8//3/AwAAAPH/qv/j/xwAq//S/1IA4v+o/ywATAAFANH//P8zAK7/iP9GABgAuf9/AH4Auf8GAE8A+f8hAEwADQAAAA0AIQA8ACMAKwBPABcA9f87AFgALQAxAGUAZQA+AEoAZQBPAEQASAA6AEgARQAcAF8ArQBPACcAfgBCABUAtwCoAPr/UQCjAP7/EgDJAIMAKgCEAHwAQQBqAGgAaQCKAEAAPwC2AIoATQCrAKQAfAC3AIQAYQDFAJ4AYQCtAHkAQADHAL8ARACMANMAmQCbALIAlgCpALsAcQBFAJcAzACJAHkAqQCOAJUA1QCrAIcAvgCwAJ0AuACLAJQA3wCbAGkAqgCLAIEA4gC+AHUAowCpALQA5QCSAGcAvgCFAEEAxADfAG8AoADdAH4AbwDMAMoApwC2ALAArgC9AJwAoQDZAKwAegCyALMAkgDSAMoAcgCfAMcAfACfAPIArQCCAKcAdwB5AMQAhABpAOIAuABRAK4AwgBcAGgAcgB1ALwAegBKANEAogAoAJ0AlQAQAIAArAAmAHcA6gCJAE8AdACSAKkAcgA2AGIAaAA7AFcAewB8AJQAoACfAKgAhQBRAF8AXgARABYAaAA+AAsAhgC9AFMAcQDeAJAAGwBAAGEAHQARAGAAdgBdAHkAdgBFAF4AWQAYAEAARwDc/xcAiwAuABwAiQAnAMb/LwAvAM7/7//8/8r/6P8NAP//8f/7/x4ACQDI//z/LQDb/+D/JgDi/8n/FQDd/5T/3f/6/87/5v/1/7z/t//i/7j/jf/T/9L/Zv+U/+H/cP95/wIAh/8s/+3/3P8u/5//6v9U/2P/xP+E/2P/mf+6/9T/0v/A/63/e/+R/9z/r/9z/5f/lv+P/8X/r/9q/5T/2f+w/2n/h/+z/27/WP/S/8L/Kf9h/9n/j/9z/7b/i/9//7H/fv9t/5D/Xv9g/5//mf+K/3//qv/+/6f/b/8HAN3/Mv+D/7//XP9N/5f/zP95/yv/z//b/xn/gf/L/yf/ff/W/2X/h/+H/03/e/8X/xf/q/8M//v+3v9E//H+0v+F//X+N/9O/0P/5f7x/sL/Qv/A/tj/of/t/s7/o/8C/6//dP8O/6r/Wv8N/6b/m/+I/6f/Yv+q/8z/Lf9J/8f/pf9Y/z//gv9r/+X+Rv+k/0r/wf8BAFj/n//C//3+aP8HAF7/9v5R/5X/dP9P/77/wv87/7b/4P8m/73/CgDs/lz/IwAe/xL/7P9i/xv/qP+A/0r/hv+e/6z/kf93/8n/vv8j/wf/fv+8/4//kv8AANH/Iv+C/xoAgP9U//D/f/8i/wMAEABS/6f/DgCo/63/AQDF/4D/pP+s/4D/nf/Y/73/oP+K/zX/KP9I/0H/uv/7/0H/OP+g/wb/N/9WAOP/QP8OAB4AW/+Y/wMA//9GAG8ABwCU/4b/CAB9ACwA2/8aADUAFwAXAP7/zv+1/8H/8f/V/7P/8f+L//H+qf/8/wL/Sf9hAOr/Ov/T/2kA2/8N/6f/aQBG/7/+EQDM/8L+wP8JAAv/2//JAAIACADYAIAAIQB9AHUAUgCyAMUAaQB/AMAAbwBdAAAB7ABCAM8AjgG9ACEA2QAcAdEA8QDHAGYAxQA5AfkAdABIAIkAogBhAFkAoQAXAYUBMgGnANkA7gC8AB4BNwHPAPcA1AAnAMAAxwFlAQgBdgEnAZMA3wAHAcwA+QD8AIoAXACHAOYAHwGSACMApADOAFYAqgBuAV4B8gCwADIAz/8QAFcA/v+W/wAAowAxAKn/cABzAAj/Q/9CAE3/D/9/AEYAgf9mAMwAbQDZAOYAfADwAFsBPQF/AaoBWAF1AdoBsAGYASsCeAITAvMBcQKWAtkBYAH4AfgB5QD/ALwBEAH1ABkCrAG/AF0BZAHsALYBpgGIAL4AtwCx/0MAOAGXAKEAdwH2ADkAcACRADMAl/9S/73/xv+k/48AMQGGAPb/wP9y/2j/Uv8T/wz/2v7b/qf/RgCGABYBXgENAZUANwC8AIEBtQDh/5kArwBaAKYBNAIEAf0AgwECARUBVwF6AOH/xv9o/2j/kP+W/xsAlAB8AF4AUgBfADwAZv+y/tL+Mv+H/6L/2f/mAMIBmgF6ATgBQwCu/37/3P4+/hj+UP7L/jv/if9s/6r+UP5q/nz9Nvwn/Jf8+vzC/V3+of5V/xgAIADV/7L/Rf+c/ur+GQC2ADYBngKzA8oD4AP+A8cDFwMLAtcBQQKrAS8B2AEjAgEC6gEvAW4Ao//0/f78N/3E/C78q/xn/Yb9Kf0n/Wj97PyQ/KL8+Psv/OT9lv73/jEATwC3/8L/kv9O/z7/Fv9B/xD/wf7B/7UA5QBjASYBBwAX/8T9BP11/Vr9Pv2d/U79sf0L/6L/UAD+AGsACQA3ABkAgQBzATMCggJiAvgCxwOPAzEEVQViBJIDYQQ/BIgDsgMpBLcEkwSfA1ED7wLVAXQBcgEHAb4AXAACAA0A1/97/xf/Jf45/XT8bvsE+xT7o/pt+tn6/frB+jz6HfkP+J/3G/ea9sj2zva79tT3afgi97n2h/fX9nT1DPW+9E30XfQO9Rb2r/Yu9zz4ofgi+Cj4tviT+fr6Uvxx/Zj+uP9TAUIDpgTUBSgHcQjrCVkLYgxpDX8Osw/tEDoR7xBNEcoR3BE9EqISfhJZEhoSVRGNENsP6A4wDtwNGQ3GC7IKNQrLCfkI5QfcBucFMQWSBHoDMgKAAf4Azv8M/jz8aPp5+MH2kPWO9BbzAPH/7untWu0I7O7pR+h257LmreX55J7k/eN145XjiuMV45vjZ+Ui53/oFuq766ztsvDZ8+H1v/cV+hH84f0iAMkCwgVYCP8JlQs+DWUOqg9AEV4S2BKTEpMRmRDuDzoP6w1mC0cInQV6AxcCWQH6//39U/xn+jT4HPfr9qb2lvae9mb2XPeK+lD+/wAdA6AFbAgcC/0NnBH6FYEaWx4jIagjBSftKjguKjDBMNcwDjHCMLcvli4BLYIqnCe7IyweZBhHE/INVQizAqn8mvYV8eHryOZb4YfbUNbU0WfNpsk8x+nFX8X/xMnEtsU6x1TIC8qMzHnOe9Co0x7X5drN3xDlQ+qi7yT0cPd1+l/9ZADIA94GvAnwDMgPyhFLE4MUqhVSFvwVKRUdFBsTrRIHEtEQ6Q+vDsAMQQvpCV0IcAd0BtQEeAM9AqUAAv+T/RH9jv39/cz+WgBlAWAClQQLBwUJ/gqxDCAODhDEEnoW6RqMHtIgCCPjJdsoZysbLbotpi1ULf0smyzzK1Irxio3KYwm2yObINsbSRZ/EMMKmwXGAEv80/ij9b3x7+3N6lrn1uLK3QDZ6NQ50lvRNtHp0PTQMtEb0WvRh9KJ0/LT5dNg08TSOtOh1WzZLt3L3xPhLeJd5PzmyOj96cfq3+pL6/PsQ+8p8gb2/flL/SAAZQJmBC0HOwqdC/4KNgrQCqIMMg+HEgYW0BiuGh0b+xntGMgYKxgPFvcSihBSEJERcRKYEmgSYhK6EqAROQ5TC4QKXAp6CkwLywyWDycT2RXlF6YZGhqTGXIZWRo7HB8emB+dIfwjlSVHJuYlNSSFIRMeYhraFpkTahEHEBcOBAwfCikHvwI0/T73CvJx7XzpHOeP5T/koePB4o/htODU3hTcGtqx2EvYmdl32/XdL+GJ4xLlROZY5sLlw+QQ4x3ir+Id5H3mD+l+6gfr8eoz6lTpL+hf5lzkr+Ll4VrijONP5cfnEOpl6+3ryOuG62jsrO5m8QX0k/Zq+TT96QE6BkcJuQvzDZMPHxHcEhAUhxU4GPkaIh3rHswfYiBHIV4h7yDYIGQgLCAXIfYhkiLzI/wlLyiYKf4o5yZ5JQcmZicxKFIpvSr5KgQrLyuBKdEmuiQ5ItkeyxpTFjYT6BHUEP8OkQtWBogA8frH9bbwE+ss5orjVOJ74a7gx9/y3sHdqtsK2cbWdNZK2C3apNur3dHfCOKc5Prm5eiL6bfou+hc6oPsKe8R8RPxn/Cg7zftEOvP6U3oqOYl5VrjSuGY397eRt6q3DzaItcW1BvTTtQR1onXs9hk2o/cIN4M4DLjxuY+6/zvrvOX+Gv/iwU5C80QlBSVF+gaux0kITAmqCv/L4gyqTSRN8A5wjp8O6A7wTsGPPE6sjlPOmw7VDvpOcY3uTUwM8kv/SwHK5gojiWpIjwgAB4MHLcaaxgJFAMQhQ2KCvAG2wP0AE7+Hfyc+eb2ufQA80jx9u4e7CrqN+nU55TmWuZ75fjjNeQd5S7k7eJI4/rjteMq43LjIuTJ45/iGuKV4pbjWuRX5Bzk1OMG42fieuJO4hrhFt9S3cHcCN2J3djdyt1c3mDfEt8L3q3dS90y3QPes96T357hNeQ452Pqj+yV7ojxQvRL9pj4mvt5/zEE8Ai2DP4PVxQqGWkckh7sIBgjdSUcKNEprSsoLzUy8zMaNqc39zY8Nv82FTdrNe0z0DMPNHEzCjK+MAowRC8LLRMpKSUDI6chFR+OG70YlRbhE+wQgw4vDJoJIgfEBC4C6v5u+2f5W/hw9lT0Z/IK8PbuKe9C7iXth+z66oHpZuh75sblm+ZB5qnlceYs5yHnT+Zl5frl4OY45uLkseMX4+zjpOUk5yfnPuUh5LPkP+SK47LkjuWq5LXjGOQE5unniugD6Q3p4eeL51Lon+is6fLr9u1P8LnyUPOt8xv2u/hK+TH5G/ur/sEB3gSWCKsKwAvPDosRAxFPEFES5RUTGUwa4Rr7He4hMSNHI60jzCIAIqEj1ST2Iqoh9yM8JjMldyOrIzYkXCOSIRUfIRzQGlobmBqlGB8YgxeHFfkTahJQEK8OdgzVCTEINwYNBP4CPwK9AV4Af/0V/N76FPjf9/X3UfTI8q3zCPKV8d7yUfFP79nug+6z7Sbrwuor72vvS+qG6nHudO1G6gbqC+vU6mLqpOof6q3pHesz7DjsX+w26xLqxOpW6nnpyep763XrcO0p7znvBvB+8fbxEvIV88vz/fJP8yb2OPik+KP5j/tG/oQAOAAdAPcC0QQABB0FPQhOCbgJBAzBDTkOqQ/eEBwQsg+dEb8TfhPHEkQU5RVWFs4WsBU7FFIW3RZTEoYRoBW2FFsR0BOGFSYRgQ/wERgQBA0ODocNsAqCC5sNTQzCCfsIFgmvB0oFgwMrAtsCdgSAApwAkwHv/wv+Bf/b/Iz5R/ke+Br49Plv97f0dvaw9rP0+fN+8qPvVe9e8gTyte3b78n0QvGp7qzyovH67Tvwa/Iy8RnwofAD80z0+vON9HzzyfIR9qH1/vGs9N321vOp9Xr49/Qt9vz7K/n69Df5y/sz+Fn3B/wy/3j7z/gn/roAR/4YARsDrABxAikDdwE0BZEGNAVsCVUJ/AQbCGMLjwrLCtgJ7AklCo4GPwkyDwUMagtZDwcKOwduDVMLSAbfCh8LGwRwBRcK8gdqB0UJDAf2BcUEDAGBBDgIPQMBBGkGQv8NAn4LnwOC/PQDNgQZ/hH/0P/C/vYA2QEC/zf8VP2M/7X8KPq1+z76Nfmx+/f42fb0+z/7s/TK9RT7LPvR9pn0lPcy+av3pPfN9uf3GfuL9oT1kv0q+UT0JP9d/THzlPqy/Rj3wPkt+g/5N/7V+0769v6e+8T7Kf8s+eD6j/+X+lP+UQHK+jwAGgSX/m8CYQAC+zoEaQLa+iIE8gNP/kkHsQa6ALMFtwMGARcGbgM5Ad4E9wP+BBkI0Aa3BAMEkAjRCHr+8wLEDGH/z/vQCxQIaf+FBt0GewLoAygEPAUWA6n+GAScBmUA/wLZB8UCVf8WBFUGeP73+moFMgc6/Vj/4QS1AAv/WwDRANgAx/zM/qEEovyV+DYErQMO+Hr6GgMTAWb6rvtzAfP/Mfwx/qv/Bv6F+w37VAAXAkn7mfjh/XgDXQF++R78JwPA/oz9L/8s+fn9wQPD+lT9SgMP+4v+ZQQb/VD+5/07+4MGxAAH9XEFXAbT9xsFRAdx9gAAmQj9/K/9MQTaASL/Y/6VAh4Ey/4oAnQF4v3O/UkEcANx/9//kAYcBcX7sgWUDDv4l/pzEKUBS/QWChgNafs8/2cIxwKO/W4BEAZWAAf7/QVGCgz94f4eCZ7+Z/oYCAMDa/fHArkIFf8TAMAD7QH0BLz/k/jZBmALVPb695wKlf+W9vMJCQgj9SkBtgqh+Fj62wldAAr5mwMTA5X6vv0TBQwC5fodARkGgfmJ+qYI0P5R9+UG2wWR+fUAYQij/+L22/2KCaP/P/TSAZYFjPoyAlwG1/nm+oAE2gQn/037SwJhBE/7TwEiBRb5wAEACl34xPueCkT/KfxMBa4AcAHJA7H6lf4kCBEBDPqe/rIFYQTG+eb65QcYBFb4e/47BjP/5PuyArECVP4Z/6r9TgFwCmj+s/OKCNYJFfJmAWsRHvMl8rUSvgDS6t4MgRFU6kn4nhf9/Y3xjgl2/5T0twnf/yjwSAvJCy/vqv81EOb2IvaUC3r/IPM8AykH4/jE+D4G9whd+w335wRMBb35I/8uA5H81ALnBLb4XftnBMb/Xvpy/CAEFgQ5+Lv+5AuA/Hf22wn9BL/yjfvICWwB4fOp/y4Qhvs568gIWRNn9JbyeQtpA2TxjgPNDvzzyfQAEp//7ek7B28Mw/R7/6wFf/ZeAIkI2fcC+Y4MTAhs8ET34hRpAzjnNQpTFYzlVfcZICTyguXWHqII1dy2CGsW1OwW+REO5fn19lsDEQWH/2T1hgKVDPjx8flQE3Pzvu/xE/z7Ju7TETcD3u0TCqUHSvG+/hAH4v0e/MD9FwSRBKL5Z/7qBS761P2YCtP1uu0dESsPxej4+WYYtvsg6zQIiApA8ZX1Wwu8AqvwDgTTDsHxjPjwEZ71c++gE1ADEuw5BL4HW/y4/yD69AOeCmPudPpYGP/0fus4FsIDluaBCLYQCfS++BwGUAJJ/2P4OvjPCVkJz/DO9a4RiwHS7LAMowgx5P4Lbxkh3U39ViaW4QLuAjAa81/YmBtUDgLtGQNTAG78CgXI+DYAyAbf9+gAAgJo93IHygJA9GcFuAR9+T4Bxv2C/nEDjfgjAUwIg/bs/BQKogCk+3L6RAM4B6PvOP2oF6DzdvBcF3z+2PDIDcL8sPQtCuX7I/wLC172pfp1Dx38c/YpBwQAvvnqARoCRfy+/CYEAALq+hgChgKR/JUF7ADk9XsFYwh39ZL5GQk3BU74Mf6ODLb6K/HIEjMJIePAA8sa7fDr8nMT4f5Q758KrA1m8nr2Ew1MAvn2XwR3AOn7ogf1/af7ZQkk+7L5IApr/Un9RgYR+ukEKgUQ8UcKpQmL6pYNMQ9M4V8IOhpi55cA1xed6SH5kRp095DuBQ+tCiHy8foZEVwAUvAoC9EJyPHHAgcHVvq+BEn7Ef6IFPLxkO8bIWf3Xd6TJpMM9czmENYqyN1q6twmVwUn4PAH5BgW8Rj0bhaDAE/trguuCBPyhgVjCbD0vwF8CgL3rf5UDjj5n/OTDzgI9O2pAXQSf/dr8lIN4wp483j3AQxUBvXzpf8lDbH8QPoGCSIBwfuKBRQAX/w5BAD+Zfx7CFYAOPcPBm4EJ/iiBmcGwfNeBJAMnvMy/SUP//vl9xEFOQUfAqT3Q/6sEMb49vMKFhz9fuvtF+gG3uNLCzIQR+5QAAQNTfsQ/6cEBfwc/Q8I0gXL8sT57BK7AP7thwqWD1bzyvjpDaAE8fQUASIM+/mh9+kK0AH2+TwH1/4z+8AHyPtx/CYN8/nq9RQRKAGp67MHpBHb9JD1Aw/0BOnvbgXBEF7wn/jJGlL6sOW0FJ0Ot+SPBBIWKe04+xYY3/X18JERmwRm9tED9//l/g8F5Pml/ucKH/yt+AgGHgFh/x0FHvus+5EJIQBM9zwHjATV9NgE4gn08Hz+VRW191/uJQ8kCJ7wZP/mCqP96PgUBvEI9fXW+AEQRf+A7U8JFAwU8oj7wAmxAZv9Rf0bAi8Eo/mjAFUIOfeQ/AMLuvpu+04IRvvH/bAH6fg5/CYIdv0G/sUBBPvdAxMD7PaLAjcEqPgPA6UCS/jDAowCbvwyA9/6SPv+C6z97fPbCFEFi/VX/ZgF+gO8+VD3hQthBjjuvQDwDXH2KfsDCWH65PqoBb//jv2i/fv+/wU0/Pf26wipA/vybAJGCJf2C/2yB5/6avuLB2j9pfd4BYIEI/k5/cUD7gAy/Hn8xAM8AnX3Xf5CCFr9dPkeAtYA7P7Z/uv7+wBDAjH8BQAHAaf8JAEg/4j7cASUANP2lwHOBgv72fpLBUgDv/ch/IYJR/+j9PgDKgYG+jf/oAAR/QIEe/5i+VQEeQB9+vICdv8n/W8DOfsL/KkHL/2b9wEF8wIN+o39FgGyAez/9PuA/sUCHQAM/CT+UwIH/wj9ogKy/9P7hwIJAEb8CgPB/ZP6jgWY/7b58wSr/yb66QQu/v35/wc//wX1gwWdBe33e//rBNr7KfzaA2wC8vrI/PQEXAD6+TkC0gKE+cAA1wek+jf45AcABBP1AACCCtb5a/d5CUYDyPVAACEGzf0P/VYAUQFdAH787/+yBFH9VfsPBAACsvpY/wsFDP9E+g4C/QS9++z8BwXe/+r7YwE8ASj/KQDC/n//1AFJAKT+BP/eAB8BiP4QACYB//1YAR4Clfu4ALYEqvvo/oYFiPyx/DsFyf9q/IMC/QB3/YEBNQIT/Sb/awTO/gL8LwRTAen6DgNfAzn7ngBeA3z9IQDZAaL+hACHAC3/WAEOAHH/mwFh/0v/gAIOABH+kAEPAmH+B//MAg4AYv3uAsgBqPsoArMEnfvJ/mAFUP/Q/Y8BIgDyAAIBOP5kAUsCBv5WAMYCUf+D/8ABcQCf/2sAKgHGAEz/nwA7Anr/Hv9OAvoA9f7CAOkAcAAoAZ//uv8UApcAVP8gASsBywBKAEv/3gEJAir+0AAyA3/+ov9hA67/Iv/DAv0AJf/iAH4BsgDx/6IAZAHy/3QACAIdAOD/1gGjAAMAbQEJAVcAkQDjAO8AeABTAUcBL/8GAboCUf8JAMcC4P8FAIECy/8DAM4CTwBy/9gBDAEQALEA7gBSAZUALgB7AcgAFgCjAS8BOwAVAZsAdwCyAe0AFwAkAT4BLQA2AIUBUAF5/7AAXwKd/4//IQPaAEP+8wEzAuP+ogA0AtP/AwDKAaAA0/84ASsB2/+GAHsBcAAxACMBigAUAAUBEAFmAAsANgBUAf0AZv+VAKIBwf8PAJEB9P+1/5UBowCW/7QApgBFAJoA9f8/AFABDwBf/zQBHQFI/wYAZgEdAGD/+wDKADT/fwBlAUv/ff9hAWIAkP9CABIAegCbAE7////8ALH/0f+0AMr/0v92ANL/yP9NAEYA+P92/wwAxgCS/27/uwDf/zf/iQBFACj/3/91AKP/Y/9MADkAM//C/2wAU/9v/4IAv/9H//3/3/+2/7j/bf/u/w4AOP94/xYAt/9x/5X/1P/a/1b/cf8JAKr/Sv+l/7H/jf9v/1n/xP+2/yz/pf/V/xL/h/8ZACn/Jf8AAG7/Df+//5b/N/+I/3L/Vv+R/2H/Wv+c/3b/YP9l/1H/lf+I/xX/Zf+2/0X/Pf+S/2v/O/9Y/3v/dP9S/1v/bv9T/1T/af9d/1T/WP9k/2D/Rv9g/3X/R/9X/3//U/9E/1r/V/9i/1H/R/9t/0T/N/+Z/2b/Av97/6j/C/8Z/6X/aP8B/13/oP84/x7/ev9m/yb/Wv98/0T/Nf9r/3v/Qf85/3T/W/80/2v/a/9C/2v/WP8q/3j/g/8m/0v/if9W/0f/Yf9U/1n/X/9O/1z/af9T/0z/Yf9w/2P/TP9X/27/af9d/1n/aP90/1X/VP+O/3z/P/9k/4j/ZP9o/4T/cP9h/3P/fP90/2n/bP99/4X/dP9a/3D/l/91/1f/kf+Y/1f/cv+q/3n/YP+g/53/Zf+K/7D/cf9w/7z/m/9j/6r/w/9z/37/y/+u/3L/lP+6/5r/lv+7/6j/kf+w/7D/nv+y/7P/qv+2/7D/sf/B/6b/ov/Q/8n/qf+//9n/wv+r/8z/6//E/7T/5//r/83/4P/r/93/4f/n/+P/3//s/wIA6v/U/wgADADS//n/JgDi/9D/FwASAOH/8v8SAAAA7f8DABUAAAD0/wYADwD///P/CQAhABEADAAfAA8AAQAbABUACAAsAC8ADAAdADkAHwAPAC4APwAsACUAMwAxACwAPABBADgANAAxAD8AVwBLADIAOgBJAEMAOABEAFgATQA6AE0AYQBZAFAATwBVAFcATQBaAGkAUgBYAH8AcQBUAF8AZQBcAGMAcABwAGAAYgB5AHYAbAB1AGYAWwB2AHoAZgB5AIcAbABkAIYAlAB4AGIAcACEAI0AggBsAHgAlgCKAHYAgACOAJoAnQB9AGcAggCaAIoAfgCQAKAAlwCQAJEAjACZAKcAlQCQAK0ArwCXAJ4AugC/ALQAqwCfAJkAsQDLAL8AogCfALgAywC9AKsAuQDAAK4AqgCnAKkAxgDLALAAwQDjAMQAjACAAJ8A0QDsAMYAhQCKANcA+ADFAJAAiACkAMsAwgCUAJkAugCkAIkAnQCvAK0ArACdAJUAsgC+AJQAeACcAMsAygCeAH8AlgC8AL0AuwDIAK8AfwCKAMAAyAChAIcAhgCTAKUAnwCOAJsAmABnAF0AjQCcAHQAWAB6AK8AlwBMAD4AVgBJAEwAgQCTAFMAEwA/AKIArgBaAAIA/v9mALAAWwD7/zIAngCgADsABgBCAGAALgArAFYAXQBfAF0ASABJAFcASAAYANv/zP8AADEAOgARANL/3/8qACUAv/9n/4v///8AAKz/z/8RAOL/uP/S/+D/1P+u/5X/sv/F/8b/1f+5/6D/zP/d/8X/0v/U/53/cP+g/w8ADQCM/1n/cv96/7v/9f+0/33/sf8AABsA2f+U/6j/qP+C/4D/TP/6/iX/hv+W/1n/Dv8N/03/mv/8/+L/Af+b/h3/Yv8X/7r+i/6r/t/+/f4k/w7//f56/7L/Kv/d/vj+6v7O/t/+Hv9C/xL/Df85/xL/HP+E/2z/Hf9M/6H/3f8CABgANQACAKv/zv+v/+j+0f6Q/+f/qv9p/3f/sv+u/7z/AQDY/47/iv8q/+3+rf9kACsAjP9J/5v/uv9p/5n/2P9R/xr/dv9A/9r+EP9e/wv/Hv6K/cj98/35/Tf+0/05/cb9kv5l/t39tf30/R3+M/4E/8P/O/8N/xoAeQDd/5T/tf/d/7f/Z/+r/zYAsQBKARQBVwDxALIBpABU/2L/FgAtAK7/PwCdAYAB6wBzAR8Bmv/Z/tj+Jv96/wr/of4l/2gA8AGwAaL/TP8/AE7/rP0o/SL9cP0e/kj/IAAF/33+FgHEAg0B8v42/jn/6ACGAAf/pv76/iUA+wA9AMMABgK2AK7/sgA9AAn+5/yc/gsBV/+J/C3/EwMpA9YBQQBa/w4Aj/82/uz9bv3r/fz+hf5IAGoDawJkAQcDxgG4/fD6IvtG/cj8SvvE/dP+OPym/Cv/QwBLAI3+wf6RAXgAyf6wAD8BWAKcA43+k/sNARcEYwGj/8gBXwXzA7wBdAbyBjv/gP09AakC4QHQ/RP8h/8EAMwAhgKL/ZD9fgTaACL8YAL+BCQBbgCdARcAJPy+/uoGUAR1/RP/qv4i/6MEFABB/LED8wDN+fv/4ACY95v5NAQ9BB38+fzr/9D3e/y+DYcBNO/qAuoPIvxO+r4K8wQG+rAB6AX9/Z7/dgikAxT7wP/0BOID9wV/BHL7M/nB/vgBFgTgBLkAaf6w/3/+4ACfBU//GvuDBAYFdP65AyIEC/7jAdT+a/pWC/IQLP8m/nsIewbSBMQEawNwB2wEzv60BuYF3/ht/+8Ky/2570T3qwEm/hz2oPnjAq/99fCZ9PIBHwNm+2L6VwFFBiADGf2l/SMG/Qy4CD///f8YC3sQeQn/ARsGxQ+zD3sFR/9TBKcMYAvjANX7hP/P//D6cPnp+5f85/e18YnyQfmg/Sj+Oft/9XT00vraAFcCdQGUACwChAZ7CQgH2wFBA6kMwhCNCPsAHwYcEOsPpwamArYGfwdHAS38Nf7wA2MDtfny8mL40gFFAo/5T/Th+eEA3/+d+xj7Tf4lAosDegJAAfgAFANNB1cJ9AazAbr90gBtCOIJ5ANo/7gAvQMtA34AvQDBARL/nfv5+uT8qQCMA8wCzv88/bz8n/0//TH8QfxZ/Oj8IP4V/YL71v1wAbQAcvtq9zv5d/yp+7v6GP3o/sP9Rfsd+nz7X/zi+uP55fr2/N7+0f8rAi0GFgaLASX/EwAzAOn9SPzH/vsB/QAx/wkArgAAAEr/ef5i/YH7GPon/A4AzgH+AIz/lAAXBIAEWwHlAMwDpAXoBMkDKgXxB3MJXAsLDfYJpgRyA/oFYAcbBHf+r/zJ/pf/r/37+hL64vq++L7zCPK/83T02fMm9E72JfgY+AH6pv7k/9X8tPq4+xL/sgJaBYAIywu0DXUP4BAgEEQNvgjkBJoE3QQqAnv/UwBGA3sD2v4j+mX42vWY8e3uPO717unxtPbT+of6CvZw85f1gvjs96Dz1/AX9T/9/gEwAh0BLgBU/c728u/V7PrsTe8L89/0HvOZ8L7wEvS79iv0Je3Z5mnm3+xC9ST7OgBjBjQL1QtiCIsEPwQFB2oJ+gjdBjMIVQ8wFxIanxfgEr0OEQtjBtABM/8t//UBtQXIBy4I7AfJB5QIUwkKCA4EDv8J/VX/agI1BcUJ/w6WEkQUthTZFb4YORu7GhwXwBNxFDMX0xi9G6ohSSdTKbAngSXTJHsiXxw8FSgPLwoFBRn+FPgK9gT1sPDV5+fbbNAax0i/LLkOtFGvR62Irtmwb7Nntva5g71Gvjm8Q7wywrzNU9uD5p7uVPZl/zYJsBBPE2MT7hOmE6wRhxEHFmodFyTRJiUkGx8cHJ8acxe/EsgOAgzfCS0JsArADZkR4RO2ERcOMw1JDH4J6QdxB0wG1AVXB6gKnA3wDHcJEAe5B5QJWgfnAFz+gALIB1sLtg0XECgUuxdwGGYZJR7GJkIvAjG2LGwqpyy4L5QxVzIcMlovISmUI44ifyNMIvoc9hM7C3AF7wCc/M33KPGs6nfmBuIo3I3X+dSp0j3PysoLyPjIHcr0xxDF88Q5x47KGM4w0QrVwtqf4GblE+qx7j7yjPTT9Wb24fb698n5uvpy+c/2NPMS7pnoKeTA4OvdKNoK1J3M9cbyxmfN+9Sx2HPZ2toQ4artu/tdBhgPrhhpIzsvyzsFSD5SoVjfW+VeP2L5YwNj/F6EV+pNfkQ7PMc0zSySIgsWowgZ+6XtweAn1dTLwsRiv8a8R74UxFTOutuL6BzywPnUAxUTuSQ8NN9AMUypVkdfuWRLZ6FoD2n3Zk5g8FSbR5068izAHGQK6/db5s/UtMPytXasFaRlmdCMJINigaWHnZHamsmhlajVssnB29Mz5r319gCjCWAT3B/lLNA2ETykPIk3NyxBHg0UkBCED10JyvoZ5xPUL8VGuhKxladYna6TyY3FjrqWCaPosJa+48ql1F7c1+WZ9BEHaxiWJnM0mEX1WNtponXgfP9/zX32dY1rv2KkW2RSw0RlNUgpNCJsHK0SBQO07y/dBs9axgXDMMQCyKfMltEC2IrhTO2L91L9gP+WAiwLpRmRKm46aEfmUH5WelftU1ZNqUUHP3A7tTqGOdMzhiiwGvYNEAKO89ngE83gvau2jbbGuj/BG8gazenOS85Szb/NytBf16jgUOnR7jXzhvuYCWIY/x/oHBUSawUr+yz08u+H7j7vj+/W7CjnCuKa4OXg4twU0E28mqnCoAOl1rI5w0nQd9je3QLjOuh+60LrgemP6rfwWvrnBJ4QZx8vMJw90UKuQNU8xTvFPRxBBEXhSRtP/FGOT+FGlTm1KlscIw93BCr//wBsCH4QvROGD+8DP/JX3UHKDb/XvwzMN99T9OIHLBg+JAQrfytkJVsaOA+hCrYR7iSePulVN2QjZyJf3U2BNQgaJAHn7l3kUeER5R3uy/h1/of5T+oB1qzCqrRFrrmw7LuIzXniaPijDGobsyGoH5QYkw+9BUf8V/ZR9xD/tAhYDg4NdQT79FbgHsootv+l15jejuaKco/Bmy6sZ7ySyWnSNtaO1VTTjNNU2fnky/MyAzATwyS+NrBEG0rPRlo+xjOZJ5cZ3QsPAy8CNAgFEm8cjiRLKCYnMCPpH3EekRy3GDoVYhbvHaUpjjUUPqJAZDxOMwQpOB8ZFLoFkPV855zewttX3iflX+0J8xz02PHk7hTtae2p8dn7oQvbHS4vTD42S7NUYFhiVV9NAEOxN5sqCBvJCrT89/JB7g3szeei3kPRy8OkuuO2Sbc2uoy9i8BkxSbPtt6k8B7/1gfMDCAQGxOHFpUZIRpmFmMPSAliBpQDZfyw74ffhM77ve+u+KI8mneTKo9LkBuZbaemtLS7S76YwkrNWN2g7Hb2lfwiBOkPDR7hKv00ITzHPn48AjcRMCMpXyOWH+cePSAVIOMdoxxOHvQhZCQfIw4fwxoyGUgdbibuLy81OTVtMx8zUTL7LA4jDBgID78HKwDC+MTzefED8KXtr+oi6Ubqze138vj2LPsoAEQIXxazKWo8DEjhSSxFYkDZPbE6CTRDKdMbKw48ApL5vvMJ7YXiI9VmyAHAbbwnu0q7bb3VwOfERcru0ZfcD+ln9Cj9OAS7CsAQZBXFF/EXjBZNFGcShhHvDwAL3QGD9NDia87Su5Gwk67osMmwoKxlp3qkjqWQqh2zsr01xvrJo8tx0JHbZusb+1oHJA8BEoAR1xGlFncfBSflJ8IiTB1lHIYg/CXVKE0oNSaHJUwoKC3kMN8xZDEHM4Q4XD6aP8I6ETJ9KWkjEB8fG2UXZxR/EhAQRwrkALf2+e4V66bpI+iz5UjkJei69IMIJx2MK8cwYDC1MJY1gD56Rw9Mv0oRRTU+IzlANRQukR/OCZfxwdxrzTzCybmGs+SuF6t5qKmpvbDYu6XGqc5R1dzdaen89RUBEAokEmIaMiI2KOor5i3NLrkthCjnHX4OPPxN6STXB8cFu9K0IbR/thK5CbuYvWrBLsVNxhDDCb3cuGS7F8e42iTyxQj1Gj0m6ykgJxsgAxesDJ4BPvhu9A75ZAUyFIoeZiDFGl0SfQtfBqUA2flB9er3cQQ/GLwtSUBhTZxT+lG8SMM6gixPIT4aoxaKFRMXjRujIQImjCTsGpEKi/ce5nXZRtOj1D/e8+5DA6AW+SSDLA4ubiueJrohmh4BH68kDzDjPjJMzFFSS3o4yRya/T3g3si7udyynrKlttq84sMRypXMMsl4wM21u62zqzaxir6Z0jfqEgFjE9YeJiMaInUeeBqyFkUSqgzTBh0C2P4b/ET42/HL6LzeRdam0UzRZ9OG1TfWbdV31DjV4dhT31TnXu/B9sj95gTaC10RHBTjE4wRSA42C0oJNQn7CqMNuw8NECsOAAsbCPcF0gMrAen+FP8BA/0JSBKeGjsioSihLSgx5jIAMrMtPychIh8hhyOwJTUkGR8PGSkUYxBXDJcG9P6B9jzvpeui7HTwJPUn+tT/kgbCDRoUSBmtHQ0ikCcPLuYzrjd1OAk2KTEsKj8hHRcDDP3/wfPr54jcNdHwxXy827aKtCezvLBTrZKqSalrqfOr77HrujbFDM8e2CLhnekN8NXzLPZp+YH+BgRMCK0KSAu9CqMJeAhWB/MEuv82+A3x0+zW6zTsYOxZ7Nvszu568t/2R/pt+8v6mvqS/IwAmwU2CzkREhdrG2cdlx0xHekczRzTHDIdKh6rHych8yHRId8gWx+3HWUcXxsZGjQYYBbyFW4X9RneGxccUxs9G20c2B3eHXcb5hZ8EbQMKwlXBnwD3AC4/80A5QKzA3MCpQBOABoC9wTHB+wKbw+pFVMdlCULLW4y+zS1NC0yZC3xJXocnxKKCWYBoPnD8f/paeK+2rvSpMmuvnGy7qZ7nl2azZlmmwKf66SirBO11LzawiTHO8oMzfbQydZd3kHn1PAX+v0BpgeJCqQKQgjNA/H9sfdj8mPvle+o8u32SfqE+8/6afnE+IP5Afv/+yf8ufyy/0UG3Q9bGlAjIymbK8Mr6SqAKUcneyRxItgiNSYaKy4vDTELMVkwpy96Lq8rriYtIPgZ1RVrFDYVPhe7GU8ckR7JH4sfJR5gHOYakRlWFywTPg0zBycD7AGTAqwDVwRmBAQEfANoA3YEcwZlCM8JSwvXDeIRNRdfHcIjXCkHLQgujixOKXQkuh1cFTIMNQP++m/zH+zV5G/d+NWezkbHdL/HtpOtHqXLnlGb2ppEnU+ih6nrsSm6OcGgxqfKBs4V0d/TmNan2ZXdkeIj6LTttPJj9kn4cfgq9wb1l/JO8Nbuze5l8HDzYPeR+5r/JAP6BTwIRgqEDG4PYhONGKke5yRqKr8u+DFDNFM1mTQFMkousyqIKFgo7CmPLDsvJDH3MaoxVzANLr0qpSZNIj4eAhsEGYAYoRkwHFQf4yHgIuIhLh9EG5AWYBEODDEHRwNhAG3+Xf0n/av9T/4f/rD8cPpb+Ib3Vvhj+iL9KABTA7wGKAr9DL0OLw92Dg8NUwtbCSMHhQRTAWv9s/hR85/t4Oce4jncK9Y20KzKycWdwe69qro0uOy28rYGuJq5ZbuEvQjAyMKDxQDIQcp1zOTO9NHS1UTa3d4+4zvnxurC7SzwL/Lz87b12ved+gL+qAHpBHEHYAn7CokMOw49EMASyhUtGbgcRSDBIxUn/SkpLGMtkC3bLL0r4irkKtsraS0uL+sweDKyM1s0RTRzM/wxHTA4Ln8s6ypiKdAnWCYqJTAkESNsIRIfDRx1GHoUYxBnDLgIhwXYAo8Aef5G/Mr5Gfdb9M3xsO8e7hrtpuzE7IDt0O5v8AzyevOp9I31IfZq9mj2FfZz9Zf0q/O+8p/xE/Aa7t/rmelL57vkweFy3hLb6tcW1Y7SWNBxztTMmsv0yvvKj8tXzB3N8c31zifQVNFY0mvT1NSr1gPZ5ds8387iSuaV6cXs0O+W8hT1ZPfV+Z/8nv+gApAFZwhFCzkOJBH7E8UWfxkxHN8egSEXJJUm8ihEK4otmC9RMbgy6jMHNQw27Da1N3E4DTl+OcA5zjmpOVA5vTjrN8k2SzWGM5gxkS9pLfgqFijLJEghyB1zGjYX5hNzENYMGwlRBXQBhf2W+dL1bPJ57+jsn+p96H/mxuRk41finOEq4QDhKOGQ4SPi2uKi41vk5uQ55W/lmuWo5YvlSeXs5H7k9+Nk4+HiYuK54dbg09/T3t/d79wR3F7b4dqR2mHaUdpr2rHaItvK26fcot2r3svfFOGF4gXkk+VE5y3pSOtz7ZLvqPG+8+D1IPiP+ij91v96AgUFgAf6CWYMvQ4YEZcTOBbhGHMb4B02II0i6iRFJ5EpsiuPLR4vazCDMXIyQzMKNNQ0lTUtNn02gjZaNhk2uzUqNUU0AzN+Mc0v/i0SLPwpryc2JaEiASBbHZkaqReSFGIRFw6uCjcHxwNnABn96vnk9gz0VPGh7vfrfulK51nlm+P64X3gMd8W3jrdsNxz3GrcdNyA3JbctdzE3L7ctNzC3PrcT92q3Qzeb97P3jLfot8b4JDg7OAp4VXhhuHH4STipuJM4wrk0+Sg5WjmNecW6BrpR+qQ6+PsQu6370Xx6vKh9GT2Kvjg+Yn7Rf0o/y0BVQOhBQgIdAq/DOAO/BAlE0QVPRcMGcQagBw+HvIfnCE9I8wkRCaeJ8oovClqKtMqBisZKxorEisGK/kq6yrMKoEq9CkrKTkoJSfeJVkknyLJIO0eDx0zG2QZmRfBFdITxBGKDxkNZgp5B3MEbAF1/pn73PhD9tXzh/FI7xHt2+qg6GTmOuQv4k3gnd4k3dPbotqb2cPYGdiI1wfXp9Z71nLWgdah1tHWINeT1xTYmdgo2b7Za9pG21XcmN373lfgoOHo4kXkwOVP59/ocOoI7KrtVO8A8bfyjfSJ9qb4xfrI/Kr+eABBAgcEwQVqBw0JsApRDOsNcw/qEFoSxxMyFZAWzRf0GB0aQxtjHHYdcR5LH/wfhiD7IGYhziE1IpEi2yIRIywjMiMzIy4jFyPcImwi0SEbIVsgpR/yHi8eXR17HIUbfxpjGS0Y6haeFToUuBIVEVcPfw2JC4cJkgebBY4DYgEa/8D8Y/oG+Lj1iPNv8VjvMu0C69XoquZ85Fzia+DA3lbdCdzF2pfZjNil19XWC9ZK1abULdTh07rTttPN0/zTQ9Sf1AbVd9UA1rbWr9fi2DHaltss3Q7fOuGB477l7ucR6jjseu7f8GvzIvb++P77If9DAkIFDgikChgNew/JEf0THRYoGCMaFBzvHZUf4CDCIVsi0CI0I4gjzCMKJE0kjiS5JMkkviSQJDwktSPtIushzyC9H88eGh6lHWcdSh0mHc0cJhw7Gy0aIxk1GGAXjRauFcUU3BMGE1ASrRH1EAsQ5g6LDQ0McAq5CAgHfQUNBI4C1gDf/sX8qfqJ+Fn2D/Sq8T/v+ewJ64TpROj45mbljuOK4W7fUt1e27vZhdi31zbX7dbO1r/Ws9aq1p/WjtZr1iLWt9VV1TDVZtUB1gLXYNgC2sPbhN083/XgtuKE5HTmougF64HtF/Dk8gj2gfk4/RkBEgX8CJ4M1w+1ElYV1RdXGgEd1x+0IlIljCd+KUsr6iwzLv8uPi/3LisuAC3FK7Mq0SkWKXMo2ScnJyMmqSTSIs0gvx7BHOMaLxmjFz4WDBUaFGATxxJDEsoRTBGwEOgP+A76DRINUwyxCw4LUgqICcsIIghjB2MGGwWlAxsCegCw/sX83foB+Sj3VvWM877x1u+/7ZHrgumf59TlEeRg4uzgvN+o3prdnNyu273au9m22NXXI9eR1jDWGNZE1o3Wz9YS12vX0dcm2F3YeNiG2J/Y8tiy2eHaWdwK3v7fROLO5HPnEeqi7CXvpvFI9Cj3Wfrj/b8B4QUyCo4OvRKWFg0aMx0pIAIjtyVGKLwqKy2gLwcyODQMNl43FThKOAw4YDdbNgY1ejPkMVMwty4SLVwriimdJ4YlPSPZIHIeFhzCGWIX/RSmEnMQgg7nDKgLywopCoAJpwiUB1wGIgXoA6cCbAE6AAv/0f2H/D37BPrP+Ib3CPY19Bry1++Z7ZTryukO6EDmWeRz4q7gCN913Qbcytq/2dXY/NdA17bWTNbl1YzVYtV41bnV/tU71nTWqtbu1lDX1td42CXZ19mX2mbbMtzr3JLdPN4C3/TfFuFg4snjZ+VM53LpzetT7gTx5fPq9u/56vz7/0MDtgYsCpcNEBG+FKgYkBw0II0jrCadKWwsAy9JMVAzLDXgNnY47TkmO/s7XTxQPOk7LzsgOsc4OzeMNbozxzHCL7wtqCtSKYomZyM8IFIdxxqHGFAW2hMWEUIOwAvHCSgIoQYkBbMDOgKTAKj+mvy6+ir53Pe+9qr1dfQR84Dx2e837orst+rF6NnmFuV/4+3hTeC43kjdCtz72v7ZAtkW2EfXmtYh1uXV3tX/1UDWr9Zd1z7YJtnv2YraA9t32wPcyNzb3SDfZOCH4Ybib+NZ5D/lAeaM5unmTef059vo1+nn6jfs8u0Z8FXySvT09XT37fiR+nD8gf7UAHYDfwYOCu8NtREYFQoYqBoVHUgfTiFrI9IlnijjK3Uv5DK9NbM33zioOUk6njpoOpg5gziYN/42mjYyNns1QjRrMrwvGSy8JwcjYR46Gs4WBRSUEScPjQzNCfUG/QPeAKv9jfq691n1k/Nz8rfxCfEz8Bzv0+2B7E/rb+rx6ZHpBelX6LvnR+fP5hTmD+Xd437i4uAN30vdJtwP3BDdxt5x4GDhaeHV4CPgw9/U3z7g8eDx4VDjJOVp5wzq6eyp7/TxmPNg9Ev0xfNz89rzFPW99ln4qvmp+mb77/tP/JX8z/zu/OD8pPxb/Dz8Yvzj/O39hf9qAUUDywQNBmEHywgJChwLQAzADdAPTxL2FLkXgBoTHXwf7yFuJLYmciiXKY4qrSviLPAtwy5wL+sv8i9dLzsusizfKqoo1SVIIg4eSRlkFOoPIAzkCMIFSgJ//qb67/Zq8wTwmews6f7ljuNO4hfiTeJ/4oriiuKd4p/iZeL/4ZDhb+EN4nHjPeUB52joaOnt6bHpu+hs5ybmPuW25FfkFuQH5GbklOV553npHutO7FDtcO6L74DwlfEy85b1mfjL+/f+CQLlBKIHWQrXDMIOtw/GD4oPfA+xDxcQjhAQEZcR5xHvEckRSxExEGgOMgwfCqMI5gcGCPkIbgrdC80MOg2HDfINcQ7kDlkPFRA0EZsSWRSlFn8ZaBywHiYgJyEXIiEjKSTXJBYlESUDJTMljSWLJdQkayOCIVgf6hzkGRYWohHgDDwI4gOn/3L7b/fB81Lw6Oxh6dLlbeJF31Dcodl41w3WXNUT1dfUm9Sd1B3VFdYg18zX9tfB15DX6dcN2cXakNz+3fPeqd8+4IHgSuCu397eFN6w3R3ebt9N4SjjmeTc5Y/n6+ms7H/vI/K19JT30Po9/rwBEgUcCB8Lbg42EoEW6xq5HmYh4iKTIw8kjiTlJPUk3STtJGMlCiZdJvYlwyTuIqwgHx56GxoZcxfWFjMXFBjyGHIZeBkXGXAYkRd7FkEVLBTIE6oU5xbvGfQcQB93ILUgQiBXHzIeAh3gGxgb+Bp+G2McLR06HRMclRnjFWURngzmB3oDcv/J+5f45PWH8z/xo+5B6wLnOeJo3R3Z1tXR0/fS89Jq0xXUn9TL1GjUR9OB0Y7P/81czfvNrs/a0dvTU9U81sPWCdf91lnWDNV7017SadLI09vV39eB2draL9yQ3bneld9t4MHhEeSA57/rZvAe9aH5zf1yAWcE5QZoCUgMow9LExgXAxvUHjAi/CQ+J+8oAipnKjQq2SnSKUwqCSuPK3wrwiqwKZYodic6JvckuSN4Ii0h5B/fHkAexx0jHS4cARvwGR4ZZRjHF1oXMxd3F/8Xcxi3GMIYmBhdGBMYtRdcF+wWTBasFUQVEhXLFAcUsRLaEGoOSgukB+0DqQDp/Vj7vvj+9RfzM/Bg7X/qbech5OXgKN4G3GnaPtla2KzXKteJ1p3VlNSm0wHT0tIq093Tb9Rw1OTTMtPH0s7SG9OA0//TmtQ41bHV59UA1k7W/dYF2DjZc9rK23Ddod+H4v3li+nQ7Njv/fJ59ib6nv2oAK8DSweAC98P+xN6F1oa6RxLH6EhCyRLJhsogSmZKnQrGSyJLMEspywwLIAroSqjKdMoRSikJ5Um7ST0Ij8h8R+kHisdqRs3Gt0YnRd7FpYV0hTEE1US8RDuD1QPBw/PDqwOwQ7cDrMORA6wDSANjQznC2oLOwv3Ci8K7gipB74G5AWUBLQCbADz/Zr7nfkK+Kn2AfX88uHw0+7O7MDqw+gy5x3mH+UJ5P/iTOIk4j3iAuIy4f3f796a3gjfy99m4JrgjOBq4CLgqN8k39fe5N4s34nfHuAC4RviMOP541/kpeQh5Rvmu+fe6UTs5e6z8Y70c/dW+ij9CAAPAykGQgk1DO4OnBFmFD0XBBqUHMoenCDmIagiPiPxI7ckTSVaJe0kXSTEIyEjfiLEIeYgxR8uHmYc7Bq3GXEY5RYgFYUTTRJIEVkQdg9uDi0N1wulCt4JZQmyCKgHowbTBUoFDwXlBKEESQS+Aw4DiQIhApgBBAF7APH/XP+y/vb9R/2e/NL7zvq5+bz4uveX9m31TvRL81/yUPE28H7vLe//7sbuV+7A7TTtquw07ALs+evp68/rxOsE7KfsYu3k7RXuDO4B7hnuWe647g/vW+/H71jw+fCq8WXyHvO78zf0u/Rf9TL2U/e2+Db6z/te/dT+XwALAtkDyQWdB08JCwvDDHcONxDDESITiBTmFTwXlRi5GZAaFhs3GysbHxvyGqgaVxrwGWAZnRjFF/AW5hV1FLMSvhC3DqUMhgqZCPQGVQWqAxEClQAz/7L9+/tX+t74X/fZ9WX0LPNZ8tTxevFB8f/wn/A28MfvW+/37oLuIO4O7krux+5s7/DvLfA88Cnw/+/Q77LvrO+w77Tv1O838OLwq/FZ8t3yTfOs8+rzFfRf9N70ifVt9ov3vfj/+Uj7ZfxJ/fz9hP4W/9f/qQB7AVgCVAN5BJQFbgYVB4gHtAfEB+wHLgiKCPgIawn3CaEKRwvSC0MMmwzXDPYMCQ06DawNUw4aDwIQ/RDdEXwS5RJGE6IT0xPxEzYUpBQhFYkVvxXdFfAV0RWBFRgVeRSYE5QSeBFPECoP9A2UDA8LbQmYB4IFQAP8AM/+u/yy+qT4n/a/9P7yPfF879PtLexd6n3o1+aJ5YXksePs4kfi5uGS4f7gTuDR343fX99I32Dfrt8g4KDgEOFu4eLhe+IT46bjWuQ/5U3mcueW6MDpDOt67Pjtde/Z8DDyqvNU9fn2iPgi+t37uf2h/2IB8wKDBB8GtQdBCb4KMAywDSgPahB4EX0ShBN7FF0VKhbqFq4Xaxj/GIMZFhqpGisbkxvjG0EcsBz9HCkdXR2fHdgd9h0MHjMeSB4sHgIe3x27HZYdYx0UHbYcOhyNG8gaDhpSGXgYehdVFhoV6xPHEoQRCBBfDqMM7Qo1CV0HYgViA3MBkv+c/XX7OPkp90z1YvNg8W3vke3G6wjqVOi55jvlxuNs4jrhA+C33oXdj9zd217b9NqU2jra79nC2afZnNm52Qvah9og28zbmNyS3Z7eod+f4LXhBuOK5B7mv+d36T7rB+3E7oDwYPJg9FT2N/gm+i78Nf4eAO0BvQOVBVMH2AhKCtgLZw3ODhQQUxGYEswTyhSeFW0WLRfPF2UYBxm0GV8a8hpiG8obOhyaHPEcUh2fHccd6R0FHhseOR5IHjceHB7zHa8dZR0XHbIcMBybGwAbWxqcGcMY3xf8FgwW8hS3E4oSdhFeECgPyw1nDBULvAlKCNIGXgXtA4QCHgGr/yn+o/wc+6L5O/jS9l316/OF8jLx9O+17m/tPOwm6xLq7+jQ59vmGuZ25dHkH+R94wfjr+Js4kDiHOIH4hPiNeJm4q3iC+OQ4zDkxeRf5SHm+ube59Lo2On86j/sg+3F7hTwcPHR8jf0o/UU94b4BPqP+xv9ov4VAHABxwItBJMF4wYcCE4JgAqVC4wMhg2FDnUPRxAAEbURahIJE5ATDRR3FMoUDxVDFWkVkxXEFe0VARb9FeMVwRWWFVIVARW2FGMU/hOJE/4SbxLxEWMRsBDxDy0PXg6IDaQMvAvkCgkKGAkgCDkHXQZyBW0EWgNQAlQBVgBS/1f+Yv1z/I77o/q7+ej4FvhI95n26PUc9V70tfMV84nyBfKA8RXxv/Bh8Afwv++D71bvMu8A78Xuoe6X7qTuxe7n7gzvSu+G77Lv9e9K8Jbw7fBQ8bPxJfKc8hDzo/NX9An1qvVI9v72zPeW+FP5Efre+r/7ofxy/T/+IP8UAO0AiwEtAgED0QN9BBYFqAVABtAGSQfRB2MI1Qg3CZMJ0wkSCk8KYApqCoUKggpsCmcKYQpWCkwKIAriCbgJgAk7CQwJ1giCCDgI/AexB1sHCge/BmgGBQa2BW4FEQWwBFoE/QORAzID+gK3AjsC0QGVATQBqQA1AOD/i/8W/4P+Ff7D/UD9r/xh/DH82/tv+wv7pvpR+hT6v/le+TT5HfnX+J34lPh9+E/4Q/hL+C/4/Pfr9/f3DPgh+CH4HvhO+I34n/i1+PT4KflR+Xb5ffmX+fP5Rvpy+rj6IvuC+9f7QfzG/DP9hP39/X/+4f5V/83/KACaABYBngFXAtsCJAOaAwwEcgTyBDAFbgUEBl4GjAbgBusG/gZ8B6EHbwePB7gHmAdeBzUHKAfzBq0GrAZ3BtwFdgU0Bc0EYgToA3UDMAPXAmgCBgKcAUIB4QBdABwADAC2/1n/L//m/oT+U/5C/hr+4f20/Y39X/0G/Zb8mfzo/Lj8Z/yA/GH8EvxH/Kb8n/xF/PL7DvxR/FH8Y/xu/Gb88vxo/RD9Jv3p/S3+Jf5F/kH+Rv6F/u7+c/+r/6r/8/9GAE0AMwBQANkADQGBAIMAQwFyAWEBpwFxAQ8BiAH1AXYBVAEiAkYCowHuAb4CvgK3AhwDHQP5AjoDgQONA3sDhwOhA6oDCwRLBNYDGwT5BEwEgANdBJoEugPiA2UEKwQcBC8EAAT7A80DRAMuA3IDJQObArcC7gJ+AvMBmAFUAaIBzAGxAKn/BQCVADQAQ/+a/rz+P/8w/yP+RP39/aL+Tv0t/Jj8z/y5/Hv8fPuC+5n8EfzU+g/7v/vJ+x37Zvrj+oT76/q0+m/7kPtB+1b7gvu1+8H7Hfu0+qj7ofxO/OP7F/yJ/Gj9nf3R/HD9r/7S/UD9yv6I/yv/tv9fACoAAACaAEkBFgH7AOEBQgIAArcCdQPzAmoCrgIuA0cD/QJ3A40ENwRtA4MEUwVpBMUE4AX0BOAEpAakBeADfwZjCBwFugPqBrEHygURBooGpgUbBpQGvQXsBSIGOAVOBX0FogSgBNIEwgMGA7sDWQQ8A0UCSwNJA18CawN6An3/YQCBAfT/2wBzAUD/6v8bARn/aP72/qT+E/90/gL9J/53/8X+z/3//Hf91f6v/Zn7cvyF/n3+vvxN/Gn9AP2A/DD+cv1r+uf7U/7L+9D6bf2R/P76Hf0W/Tf7w/yy/ab8ev3K/Mr6Pf1x/yL9ifz3/WD9Hv6yALP/xfzs/ScBsAB3/mP/CgHf/3/+bP81AdQB9QBqAMYAkADAAAwCBQLJAEEAhQArAooD0wGWADYCwwLnArgDzwCo/qwDSgY6AaH/SAN7A2cBsQFJA2AE9gITAP8AhgRIBDwBrgB4ArACBAECAfICFQMVAagAkQKlAtz/BwBkA6QC0/8iAUMCIwHoAaEBg/8fAfMCMABj/tr/xwDiANb/cf0L/0AEkQJG+1L8dQL5AHH++P/v/Oj7DAN9Apn6Dvw8AP/9zf3x/3T+Af3q/scAHv4L+8X/aANA/BP5vABJAy7+wvv7/Jn/dQCk/E/64f1pAT4A+/v3+Tr+8QIIABz6nfr7/ykC0f6g+ZT4T/5XAhD90Ph4/jsC4fyU+tP/5AAI/KL8eAHN/zD8nP7v/879R/9CAHn91P56AZP+Wv7wAcL+7Ppv//MBrP6O/sT+CfwY/vYCrwE8/P35ff7iA7UAGPsi/RgAsf8EAUkAMfze/TUD7AF0/Vz+kQA6//H/kQHN/T791AIAAaX75P4XAhz/w/zT/eMBSAI0+yv6SAEWAmz+B/2D/Kv/fQKb/1L+W/5U/QYCngOO+1r86wQ/ACD6MAOHBV747fgyB2UFkPum/TUAMwBeA87/Dv1jBHUCjvn5/eoDXQDn/ZD/TQLnAHz8cQGjBCf68fsUCdkBr/YM/n0D0wBd/1n8RP+IBZz9k/exA5QHCPsz+RkCZwGq/NX/sAJb/yz9m/68AEEBR/04++wBUgXk/t770f5DAaUEpgM4+8v5swFHA8j++v56AH/+Dv4DAe8DLwE7+HL7ogtMBL/vkv5FEef9m/SPBJUBCPzEBk0B9/YsA6MJSPyE9+8Cawg8//L6TgIOAaz7sANiBUf4LPuxCbYA9fMIBYAOoPdN80UIzQax+zH/Jf9X/xQC+voC/+II8/z8+Q4Im/7E9ckL1Q6X8ObwRxIyDdHtGfsPEU7+8/gVBe35+wArE1D5/uqxB9QOcv90+Vf5aANbCBD+2/zv/o7/YAZk/tj5QQneADf1KAnJCCb28/0kBjcD9wGs+4n/pQb5/S//jwO7+tQDww1f+WLwGAbvENP9n/A2BKEMo/jF/NsKbf+A/Nb/6ftSB6wGNfZg/joGTgIQBL74kfrbD2YB7PJaA34AbALeECn2JepfELYUevQ98d8E4Qn2/agB1Qrf8uPyLx5xCaDZQgavKPvs5eCSG1sQ4+TkBDcZguot7yogzAZn5AkIpw/v71sB6hCf9gn6YQcR+00JTA4b6Yv2oRvR/a/vfQ43A+/1OwkPASX6iQpoAWj4MgL8/woDPQXz91UB9guF+Xr4wwc1BnwASvmN/JMOEgMj8AQE/Quo+7kB8AIP9pMCpQoV/aj/0f7m80YKeRY38Tfqtg31DDT5Uf+uA8L+3/7s/toC/gKd+28BtQOo9ub+2Q8MA/TxpPlvDb0PKfUZ7LcLeRUA99bsBwX1Eu7/H+0T/YMPwgWG+3r5gfhjBSYNdf9+9tD4+gHuDzgFMu32+DgQeAYr99T7of91/9MJHQhW7070ZBV4B5frpAbcEcnwiPcZDk/9gPr7CP3+efmy/vL+0gr4BrDte/k/ESkCc/ZX/qT/jgVUBun2Y/qCCbEB/PbQ/owIjgJb9Nv53wsfBsz3Zv0WBFn/NPko/BMMcQrZ73ryxQ0hC/P7Bvj09ckH5hOW8UDmNxFPFufxnPIdB3cEX/sr/P0EHQNR8Uz76xYnApbl9ADKFHP9nvZL/+L6GwKHCLn4Q/wTC+j4gfNODS0G1fNHAo8ArvWOCsgJKvFF+p0CqPxrDfgGcON+8zIdwhFb7eDpmwFnD0AIrf139Cz0gQfuD5z/Tvab91b9BwtbCPz0IfTcBREMZ/mM8CUKzwoN7NX9ERbk9J3uFw/mA/zylwS2BZP2OPl3CYAPVfUf5SkJyRmF+ObyugJJ/KkF1Q067NDvTSFMCBnSeP8QLV31Vdq7DagTT/Dm+wgNnfWL8NkOmA5B7iPyoQ6qBe3xef+UCygAsPfN+p8DUgrnADz0o/sZB7ED4QBt/j7yEvz/FmoF0eSU+QESPAMr+937MPfuBuoObfFV6+ASjhco6VTlqRL0EYfz0/ubANn1sghJDXfyY/YMCUUDffxp/kACAgIv+U793wjWAfz4hvkJA0gSx/674coA9B2N/dnsywNwB2f5y/k4BsEGUPmB/E8GVPpc+NsKYwpo+fvyqf/YDtcArPHQBeoJlvKI+lYNHQP09436dwFFBl8AB/zO/gYAOQYlAWDwIQDhEtv61fHdBeb/4fzVD0T+l+QHB+MgYfIZ35YTpxit6TX2GxD8+Pb8Ow+o9PH05hWfA5LovADdErL8aPI5DCcOAecd7WoegxbZ7LDrkf1FDgQUn/kS69gALwmQAXcA1/jB/KgRTwIP4uT9YSPo/R7gQwqIESrtHARqFuHpAO/vGpQGRfAjAxYAF/vKB+wAQACyBAXvSfyhIk4AC9hvBB0g7vdW76UKWgEK9LkJTg0W8wLynQkeESb+1ug0+ScaZAz37vP0vv3ZBfkV2gFx5b/8CBgLAzTpf//zG4v9EOZYCBMKSPjfEjkCRdzAB1Mdx/Ye/o8DDeldAgYfrwEG7IP7OAu7AcL0vgiqEwPz+egBC7kWc/u88N8DmQHU+yMU8wGz37UJ1hjb7TECSRDH64AC8REn7REAoBO+9nD+KgfX9ZACUAnL/if/afbYBGUZOPIl5XcTihLJ+o8A2fTV7zANOxdqBQvvxufoBbIdqwjW8drwMP/ME+oHYO0M/u8PDPzL+QwJ0P6O+EUFUQZjALP93Pre/yMGQwbfAWn24PzlEX4BRO3aBQMKP/NeB9kVyO+A5Y8Oih6jADfpKvQyBv0Mmw3u+w3rQQBbFMcFhPha9OT31A9uEq706u88ABkF6wtuC9r2AO9T/mYLeguxAtH48/YDAKQInAKS/KcEOAHb8ssA5RbfA23mMfWgFM4PJfk89uj5I/1oDZ4Q7PeB7DH9HwyJCXD8CvksA4wBp/mjAzsJTvx/+bcAkf81A34KPf1f7y0B4RDM/jb1UQVjBIv3X/7TBpED2f04+MgAOQyP/LDzTQcPBqL2YwPTCfn2LvZkBhUIIgBj+qj6/QPlCOz9yfP//LEMDAlc9nrxlwDcDMYH1vkD9OH/+A1vAr/t8fqJEloEp/DS/JQH/wGv/xz88PxYCVYCZu8o/P0ObgMK+gQAf/yg+v0D2wR7AG/+rPg8/NMF6P+//pEJjfwg7D8B/hJkAOvzyv4SAkz8HgKSCCv8UPKpAd4KOPpV+MoKMAJP7mz/CxIx/97yE//HADoAOwd0/Lj0yQVOBhr28f8zDEv8qvIHAUsI5v3s+poE0gF+9rf9hwcZ/v35uwOoAcn4kP7RBUP+2fkwAwcEwPm9+gwCTgPxAjL8t/Z2AiAIQfzF+ggBewCpAkv/FvcWAr8KGPqp9R0GKAQt+Fz/cgUe/IT8XAXg/XL23gTFDOj63u/h/d0JfQQP/qn8tvlT/f8GOwR4/ID/rP6T+UUBpQUL/t7/bQIx+UH7nwRw/2D/OQiY/djxbP8UCRsBr/5o/on5Ff5qA+D+lQBkBT799fbE/SwDtgNxA3j9Zvhp/nQEfwDY/XgCQwGt+Tv+Bgh7/wH28QC2BYL9bwIcBO31YvqvCYsB1PmaArf/I/iOAl0JuP2/90r/0gCD/R4EiAhj/eTz4/m2BIoICQVL/iH3f/dPA0gIy//Q/iIDKP3j9zz8BAaWDKMBsPK59Sr/3wm/EKz9Tuv++fYI5wbCA5j8O/kg/2/+nABpCSEC3fbr/CMDQv90AMgGTgAB9HL6pwdmB14EQP1Z8zH9bAk1AwIASP7G+GICkAht/ST79f69/3gG0ARx+jr7bv9VAUkE7gGv/9X9PPrVAv4ICv7i+1D/C/pwAm8MsAAP+bX7ufy+BowKkvzb98L8jgCbCvUHgPX+9uQE/QNRA5kG0/yr9IX+jQgbBd//Rv/j/IX9twRHA1/7vADzByUAL/rD/aX+EALHCZYF8/nd+Jj/MQRiBMoAXP8CAoMB6f1a/D/+0wQaCJsAzvtX/wD/iP/DBawD+Pzn/qoBHgHCATH/hf2HA7EFRP9F/Ib/xAIMA7gA2P+nADD/2/8iBJoCEv7n/2YBVv9ZAcsCV//a/6wCPQAK/0UCCgL//igA0QLeAJP+IQGFAlMAXQGBAt39JfyUAYQEKgNtAjH/sfqY/lUG9QSb/7H+bf08/jcFqQaD/7b75v3yAQEEQgGo/1YBbQA1APgBb//k/fEBEAQlAhH/af14AL8ChwAmAZICFf9S/mMBXQCE/1IDxAPh/i39RwACAg4BXwCKAGYBCwI2ABj+U/+tAR4CuwAY/0EAmgLNAHf+pQDEAbX/EwBVAe7/af/nAJoBHAHB/yz/qgDyALf/rQCEAZf/qf40ABoCsgIGAIf8x/0wAg8EMQKW/k/8pv1aAVcExgM1/2T7Of0YAp8DJAEb/63+w/6uAGcCTADl/hkB2QBo/iD/OwACAB4BzwAa/1sA1QBE/uH+hQGIAAD/tP8MAOj/HADM/yn/Lv+RANABEQDS/UX//gBd/5L+wABXAWv/7/4mAL0AHwCE/u794v9IAc4AMwC7/kT+ggAvAEj+ewAnATn+Sv/zAKz+VP+UAf3+Z/2FAIIBBf+P/jIAwf+Q/ggA3QDM/qv+2AAsAPX9FP9iAdz/h/2S/3cBT/9n/nv/E//D/+wAIP9L/s3/tf9w/zkAjv+k/gb/Uf+G/+P/4v/I/1T/yf5a/woAb/+j/r7+j//GAAcBG/8T/aD99/+6AWEBHv+B/bD9I/6p/6sCoQJU/of7ufxW/zQCIgMl/0f71/1QATEAkf9wAF/+E/2Q/4kAnf9BAAYA7P2R/Sz/jQD9APX/Lf69/bD+qP9oAK8Ay/94/u79Pv5z/zUBfgEs/7X8Nv28/w0B7gA3ADn+Bv3A/gkA0f/2ANMA0/1u/TX/qP4p/5QBpADB/kT/Df7H/Pb/DQIBAAD/vP5j/dP+8AElAQP+dv3u/oL/yv/LACwAEf4r/l//Kf8RAMYBWACu/Xf9N/6v/n0ATALpACz+YP1n/Zr+fwL8A7b/cfuO+xH/KwNdA3r/t/zQ/If+9ADRAeUAyv/x/dz8UP9hAv8BPv8b/WT90f8FAhMCxf/v/O38CwB/AhQC3P/U/eP9sP8WASABm/88/rT/LwGf/+n+6v91//3/vAFNADj+2P6n/0cA/wBUAPT/EADp/sj+GAA2AMEAjgLuAbT+k/z0/F//DwMOBYwCvP0O+0L8mABpBAoELwFa/iT8W/1BAeYCyQLjAT/+Fvz8/g4ChwJxARP/J/4TAK8BUgHk/27/yAC2AG7/jwDnAO7+8/8vAjoB0P/y/rX+NQHyAq4BoQAc/679hf97AdEBNAKQAOP+WQC7ADAAjAGZAHX+SQBSApgBrwCa/+f+GQBLASUBvAC/AKQAJABdAGgALf8SAJgC6wFqAHEAN//X/qAALQG1AX8CwgBo/5b/l/4P/6kBLwJzAYEB7wBPAGwAwP/S/rf/ZgGqAYUB0wGnAL/+pf69/2cBLQPhAvYAQf9u/rj/tAH2AYEBbABZ/8UAFQLlAMb/M/9EAEgDjQL1/qr+Wv/I/2AC+gJ/AMn/YwCTAPwA1QBYAB4AgP/5/lf/CAHnAosCQgGCACz/Pf+iANP/P/+2AGgBVgLEAvH/+f3T/uf/EwJaA1oBWwBlAMr+Av/kAP4AVgHyAcwAawDlAKv/ZP5w/or/cwJhBDMCE/8G/rP+6QARAhcAsf6g/7kAQALHAhEAF/4q/ygArgCeANf+m/4kAbUC4AKUAmUArf1E/Y/+3P87AZ0CqgJ7AX0Akf/q/h7/kP4p/rIAQAN9AkUAVf4w/lwAVgGvANcAwf+o/a7+NAHRAVoBngAkAG4AFQBd/47/NP9d/qT/5wEiAhwBhADT/xr/R/8IAKwAggCT/2//+v+y/8j/2QAGAUYAKP/z/bT+kgCnACkAOQD4/5EAOgFk/1r9DP4SAH8B0AEjAcr/H/63/Yj/XgEEATL/lv6JAGwCsQGM/0L96fu1/XEBQQPkAcP+GP3+/hgBNwBZ/mH9Gf5oAW8EkgPb/9/75frg/kUCWgBd/tD/JQGdAd0BqP/Z++/68P48BJ8EPgDZ/Jz7jvt5/uoCQQSeAa7+kf7d/sD81/vu/U7/NAD+AhQFrgK4/EX5LPv0/awA2gR8BToBBf30+RT64/4FAmABhgEpArUBCQAH/L746Pn5/cUCSAbbBeYANvkv9Zv6TQNyBUgCsf+1/YD7pPyMALMAiv73/xIB1f4h/ln/yv/W/qn8Yv5yBF4Exf1q+mP67PvFAf8GwgP8+uL2x/kb/Ev/FgnvC7L+wfR2+BD9qf9aA+MBkPzq+6L+Sf+g/lgAigIE/wD73gAnB7oA0ffm94/8ugODC34H//YC7//5WwgnDS0LggC97nrsP/+9C4YLugtQAdvtkO+TADkH2Av2DLn6H+2C9az8fgFmDT8MMv1M9qD1+PaGACIImAQL/2r9IvxG+k382QL0BUoCFf59/fH/YQKvAn0Ckv9b+b35CgNvCuUIYf+O9lr3/v2xAyQGlQKX/LT6VfyN/3QCBAPzAb7+uvv2+xz8vf1EAQAAfgENCfgFf/zx+xn9jv4eBJIEygJxA9T+vvuB/8r/0f+NBLcGGwbmAcv47PVs++MAsgVQB+0D6wFqABP8Fvqy/B4BYgT8A/8BagBH/iH9CP2h/XkB5ATzA5cC9P/P+mL6if7KAdIF2QgiB7gD2v46+Xr4ivqC/F8B6gRPBEIFgAWoAc/+0Pyf+1b/3QFn/yX/AwBq/sL+j/+K/pAAqwN8AwQDXgLa/9T9H/wW+1P+CASgBxwIJgV7ACf+//3C/Sn+k/8OAS4DIQXDA5b/t/zt/K7/LQPZA2QBbf99/qb9XP66/4f/mf9YAa4ClgKtAasA6QA9Av8C+QJ9AWn9Vfqv/HMCowcoCi4IhQMDAG/93fuy/Mz9n/9ZBEoHhQUtAlf9Fvmu+ir/5QH8A6gEYAMXArT/EP03/Qf+oP52AXIEfQU2Bh4GQwThAf//sP9cAEL/L/2E/WEAKgTNB2kJXQf9Alj/Gf35+qj5SfrO++n9bQEHBewF3gLp/eL6ZPpZ+tz6MPwT/Sf+EABdAb8BVwGj/9T9wPxJ/Pb9GAGgAhMD2APuA4wDkgLv/wz+I/9AAdQCuwOeAy0DzwI1AlECSgOqA/ACfwFGAKwAAAJMAv8BhgLbAjQBev4V/YP9gf40/6//ZQAGAWEAg/6t/Kf7+/ts/UX+tv2g/L77cvsI/GX9Ef8KALP/LP+v/3gAPABI/zP/7wDBAxsG1wb9BZ4EmwPWAu4BmwFPA3kGfQhTCJgGQgMG/z38U/yw/rMBjQMcA4UATv0t+zH64fnP+mX8rfzt+w38yPyJ/Lj76vtA/af+af8W/6f9h/yF/SYA7QGOATAAf/8PAHIByAJiA/sCzwGdACcAtQC3AckBWwDM/k/+Xv4v/gz+9v4vAYEDdgRtA3IAePwS+cr3q/mj/ZUALQEhAfIBTAMoBJEDHQG//R/7xPlL+QT6Evwg/v/+8v6l/nn+h/5Q/sT80/mW9//3e/qb/XcAKAKAAhMCBwGK/wn+tvw7/Ir9mgBcBDoHOAjsBzQHRgYtBYsD9QD8/fj7UPw//9oCOwUOBvgEwQG7/Xv6rfhm+Dv5ovpx/F/+xv8tAOn/7P85AGn/5vw7+i35L/oL/Q8BogRuBm4GFwUgA8wBTAEAAOv8pvmm+DH66Pzo/wgC+AFqAI/+PvwJ+tz4Cvg/90D3Wviy+r/9zf8WAAn/A/1P+4r7RP3k/tb/JgCZ/53+Yv78/qb/fQDGATYDtgSkBRIF8QPoA9cErQWaBRAE2wH4ACICQAQCBuAFoQLv/Qr7pPqL+8v87vwP+3z4wfYw9ib2bvXh893ylfM59sv53Pw4/+sARQG7AJwAGAEmAgwETwZqCEYKXgt9C9cLug1oEIQRxg/4C7kHYwR7Aq8ByQHAAvwDxwTGBHoDOQAl+8z1bvJj8qX1F/u6AJAEqAVcBNQBK//t/Fz7ovoX+yv9oQCPBM4HFgnlBxoFIgIyAIj///5R/ST6IfYE80LybfMQ9bj1M/R38MbrnOfd5IXjUeOO5KXnRuwm8Ub0N/RA8RbtAurm6VLtWfM7+pYABwbCCjgPdBNnFvkWnBUwFMIUOhilHfYioSaKKIcpAyq2KUQoUCXBIGcbqBaME0USTBLVEjUTKROjEi4RMg7GCbkERQCe/QP90P18/8MBVwTbBh8JOQsNDc0NjQwwCbQE8gCC/3cArAK1BHcFcASfAST9dveJ8TzsEugv5STjNuHB3oLb+dcu1c7TwNMw1APUztI30X7QrdHK1P7Ye93K4azlNOl67Jvv+fLD9rP6d/7gAeQEeQeHCWEL2g1yEeAVJhrpHGsdBhy6GagXiRYsFtEVJRXYFEIWTRqJICYnxCuxLJQpQSMOG1ASPwoXBDUBsQJLCKQP7RScFDIN4f+b75DfPdIdyd7ElMWOyifS0NmZ3pre9Nmh0rbLQMgtyvvR+t6g7/wB5BMeIxMujDQIOCU7G0CpRxFRjFpRYk5nEmmtZ41jJF0DVepLSULoN+4rXh0EDBX5oOZ11iDJ272Cs2OpeZ9GloCOzIjShR+G0Yl+kGmZ66PSrz+9Jczl217refnYBQIRDBz0J880vEF3TbZWklyrXuZchVdQT1FFhzqpL84kbRmNDCr9EuuE1/HEDbafrNaokanGrByw27F0saqvRq5ar4i0o757zQDgjPQ1CTYcDSzBN2Q/DUQ+Rz5KiU3tUCJUzFYqWB1Xf1LcScQ9My8pH6MOqv5u8ATly9xy1xvUVtGmzUnIo8Ezu9G24rVFuWzBG84U3iTvuv7jCu4SeRc0Gi4dBSJfKesyvj2zSFRSr1iYWZJT/0ZqNpIl8heLD2oMAg25DrEO5QraAlz3y+lz26LNRMKXuwm7bsAFyjPVe98D59XqHOvM6AnlDeFF3lneuuKN61D3ngMWDvYUhhcyFnQSRQ4cC4kJkQkBC2sN/g+oEckRiRB4DgAMQAlCBhsDwf8W/EL46vT28sjywfPJ9BT1P/Qv8hPvheu06Orntunt7Q70RvtfAtcHXwq8Ce0GgQMcAf0AnQOSCK0OiBRDGYcc9R32HP8YKxKgCUYBMPvX+HX64P4KBOkHWwlOCDsFqAAH+xn1XPCm7kfxN/jIAWULuxKJFu0WvBSLELcK9gO0/ff5ZPpC/48HPxGRGfkdwhyaFfoJg/wc8G7nNuTU5kbuQ/gDAh4J7gsAChwEBfw29Pbufe3c7xL1ffvFAfcGOwrwCrIIvgNc/Tr3nPIm8Orv3PEH9g780wJ5CM4KbwiyAZn4PfCt65jsrvLU+wkFqQsoDicMYQaF/tn2qvG/8MT08vz+BlwPchKoDnkF2/pk8+/xePbX/ukH5w5lElUShA8VCwcGhgFm/4ABWwghEtUaVh7QGs0RPwcJ/7f6aPlb+Z/5fvpP/G/+h/9p/tf6vPWI8IHsRupy6fLoFeg655/ng+oV8A73Yv04Aa8BHv/i+tL2ivTw9CX4t/3tBK8MWBMUF80W7RJKDQkIYgSFAlICzQMHB3kLjw83ERQPXwkhAhv8IPlc+br71P7kAbgEBwdhCEYIWAb0Anb/mv2Q/gUCGgbVCI0JyghOB08FpwKT/wz9ZPxO/hoCrQU7BqMBFviT7Grj7t/I4qvpwvDu9Dv1+PJW8G7uyOyl6nDo1eeF6tjwsvl3A90MCBUsG2geNR7yGiUWMhJnEZ8UhhpQIIQjOyPbHwwaFBJPCCD+sfV78AbuLezT6L3jxN5D3DTdbeCe40jlwOVO5qfnY+ns6tLs4fCh+MID6w84Gi4hTCXqJ9sp2CrxKcYmMiLtHfkblx3vIdUlQSWBHcUOyvtK6NbXX83NyhbQfdpV5R3sTOz85XDb9c/sxhDD1cVSz57e5fEmBlgXrCGNI7AeNRdxERYQ3BMCHO4mnzLOPOxCiUIuOlIq7RXFAbPyjev16/vwEvet+3P9Sfvy837nn9jdy0LFYsZ1zt7b2ewL/4IPRBsBINQcKBNXB+7/sALuEDImtTp1R+ZIFD9uLMAUyvyh6Unf8t4M5v/uY/PF7sbgVc2SusWtKqnRq4Czub7OzE3cI+oU84n2nPcE+8cDjRF7IVYwJjzcRMVL1VHrVe9UxExWP/ExCSmvJBIhUBp1Dw4CMfRN57vbp9DgxGe4Xq2upkulU6cMqlWsR68MtT6/Ks6I4Ifz4wOrD2kXpR0XJSgvujuZSRVXYmLIaQ1srGjJX6ZSFESsN8gvgivlJgke+g/Q/prtMt7W0GvFIbxQtWyxubDFskm2z7kVvZPB0sh30mbcxuQC7Kr0OAHwEWQkkDQAP7dCfUGOPoI8KTz1POg9BT41PFg3+i7GIzwX9gq4/+30Z+mw3A3QEsZiwCK+77wLu8+45bcBuR+7t7w1vey9d8GryWHWfOUE9D0AgAr0EwAdjCTzKAwqfSlXKckqpS3vMKozuDRAM2gv5SkpIzEbzhFKB2T8jfFX51PfMdvb2sbbotpF1l/QjMtkyR/KNc3U0fvWBtxV4ffnnfD++v4FOBDlF/cahBidEgsOkw/hGJcn2ja/QexEST+GMuAiWxWHDTYMow+9FHwYpBieFNsN9AYSAqH/J/7z+7/41/U79Uv4lv7gBXgLvw0bDQsLFwhoA5b8afWN8bXzFfv+A4MKWAwoCb4BSfeV6yrhy9oo2/vjA/TVBW0RdxD2Au3v8+Ca3HbjDfF1/5UKDxFjE5oSLw8OCbcAbPja8yn2j/8vDOYVHhhFEqAHK/2L9l/0BPVp9tr3bfoM/zsEcAaYAgb5Hu6b59To5/CD+7oDswaWBLn/tvro9sL04fTs92L9MAPjBlEH6AS8AAj8m/iq+AP9lQOhCLYJaAc+BEYCmgHkACX/1/yn+zT9cAE2Bu4IigjMBTYC0f4F/I36ofvn/4kGJA2UEIkOLQf//WP41fkfAaAJRg9eEY8R4hDFDscKNwaNA1EEEQj2DMgQvBF4D5oL1AjfCN0KNAwoC04IrwXiBPcFAQgOCkELgAq7BhYA9fgL9c72nP3KBY0KuAiGAHj1oeyN6fProPCo9MH3UPv6/qD/tfrU8WfqSel07pn1cfqL+/X5qvdJ9rT2tPhr+nT5avW+8I/uCfDM8573Nfor+0n60/cr9UD0M/ah+jAAhAWQCVALBApDBk8C1gAXAxgIjQ2cEcsTUBTGEj0O0Abo/if6fvpq/lQCmQNKAmMAdP/6/q/8TfYJ7BLiw94s5h32wwZmEPYQ/gsVBv4AD/ya94D2BfyaCHgY3CSGJ5EefQ5pABb8GgKrC1AQowwrBBv90voM/Bb9cvvD95D0j/P882nzofCJ7d/tIPQ2/7wKuhGTEXsLnQNk/gL+FwLUCFgQSRdKHLodQRqSEXYFHPov9Pn1uv2nBhYM2QsoBr78PfLP6TPmB+g87fHyY/dt+rz8cP4y/0P/N/9N/7r/yACsAmUFjAjDC+UOBREjEK0KdAHf96/xt/Bs9Gr6Kv83/8X4Ku214C/YPtZl2onhwOcs6jHo7uMq4f7i+emt80r8cAGqA5UFngn1D8IW+BvCHvofbiFjJLMowCxVLmUskCc+IagagBRID3ULjgj6BFD/avet7ivnMOKo31bebtz/2O3UdtLR027ZUOGb6IDtGvBP8kH2q/zoBMkNMBZ/HW8jySeJKuAr8ivwKmIpzCfwJdYi1h1xF4YQ9Qj5//j1Je1t59vjAN9+1qrLcsInvky/AcTJyXrOl9BC0LHPFNKW2drlivQcA8IPbhkdIDIl3ipJMks6hkCwQ35EhURkRGhDnkCwO/A0Pi2PJQQeUBX4CZ78RPCc50biN90V1qXNgMZmwlfBN8LAw13Fg8efy+/Sqdzy5VPsDfD980T7AAf1FVcl0DGpOPc4YzSzLuIrGC7ANAg9aEPzRPY/YjQlJDITdQYkAZICmAYVCN8DN/rK7cjhftjK0m7Qs9Cu0nnVUNiX2gvcitzY22HavtkH3H7iQexC9jb9mf9b/i38z/tD/nYCfgZYCYwL/A0+EJ4Qvg0pCHQC8f+OAgAJ5A7oD04LNQSS/gD8j/tM/Mv+SQPRB3IJAAdHAoP+kv02/2QC7QWMCKUJnAlCCdQIjQciBSkDbwN0BZQGkwQDAOf7xfrS/E0AmQKcARj9Dvey8hzy5PRr+Sn+mQFeAgUA8PtZ+dL6x/+EBY4J0QrXCRgISQe9CBAMEw/7D9UO7AxKC8wJLAgvB50H3QhYCfUH7AQcAQz9X/my97351P4bA68Cgv1o95r0HfaE+Tn8rP2C/nz/8gDBAo4EsQVcBbkDHAL8AQ4EvQeOCzgO2A7ODHIIVAN0/wH+lv7N/5YAlQBx/3P8SPf48KLr8Ojw6G/qNOyC7aztAOx/6ITkXuKP41bnUut17WPtkuwW7fPvQfSk9xr4HvaY9Cf2lfoy/4gBkwE1AdgB/wIoA5QBBf9U/TD+iAFVBTwHhQZoBH0CJwHl/+L+Lv9yAeMExwfyCFoInAbUBFEEpQX4B4AJPQk7CEkIzgl9C6YLxgnhBmwEfAN7BJMGCQi6BwAGJQSpAqMAxv3w+7P9YAOBCSkLbAbY/WH2YPTV+NcAogdJCUgF9v5A+nH4Y/jR+Bj6QP1cAY0DwAGK/OD2F/SX9Wr6PgBxBEsFAgN4/zP98f1SAToFvAelCBQJBQp0C+wMQw5jD88PMw9FDjwOVw9kEBYQqw5oDZEMDwsPCDkEMwEEAPb/UP/H/Cr4rfJV7mrsfOyW7K3qvuYW4/nhfON95XPlxOJB32bdnd6+4qDo0+7h84H2Wvan9PDzgvbD/A4FwAyWEe8S8xG5EEkRgBR5GU0eDSGSIFcdTRmDFtQVURYZFisU8xArDe8IEgQC/8L6CPjK9lb2ovW88yfwRuvB5rTkAear6YLtoO/W72PvfO+t8DvzXfck/cwDhQm7DHcNRA1BDswRbBcTHZ4gEiEsH8occxt0Gywcoxz+G+MZhRZiEg0OCwqBBhADMP+V+o31HPFC7tbsfOu16BDk1N4s23zap9w94FTjw+Sf5ObjyOP95PTnFu3s86P6FP9nAD4AbwEsBVsKSA/aEr8UCRX0E30SJxJmEyAV0BXGFFkSBQ8JCwkHKAQTAysDzgLXALv92/o2+eT4NPkr+Vb4U/dK9/74TPwMALQCgQPQArQBqgG0A6QHWAwkEIARURD3DQwMTAtCCzgLKQstC9IKcwmzBuMC1P5F+/T4Vvi8+Kr4F/c89Hfxvu+q7trt/+2l7/jxYfNc80zzrvQQ99X4jvlj+mT8Lf+CAbIC8gLNAtICdgOSBBwFCQS9ARMAhABkAlkDeQEJ/Sv4BPVL9Cj1/vXL9eb0GPSW8/vyxvEm8DXv4+8/8qn1CflZ+y78zfso+2j7Lf1wAJQEhggtC8kLlgo3CYoJCQxYD1QRFxGdD2EOQA43D34QFxEiEGMNJApPCKAIOApzCz4L3wkOCCIGTQS8Ar8B5gFKAxgF9wXeBCICa/81/rL+0/9yAFUA2/84/2b+TP0c/HD7wPvM/KD9D/3T+iD4tfZZ91v5P/vg+wX7Nvlb90f2X/aI9yv5hvpJ+7n7J/x//I38Zvxh/Nn83/0m/1sAXQHvAdEBQwHcAAgBpAH1AV8BPQBj/zP/kP8AAOb/9f5Y/Yf7F/pf+Sz5Cfm1+DL4gfd59gL1jPPL8v3ypPP483fzcPLF8cfx+fH48dzxE/Ie87z04fXr9Sz1jvQm9Uv3Jvpa/Ov8B/wq+/X76P7gAvUFNAcnB9AG6wbQB5cJLwwaD5URERNOE6USBRIuEnwTqRVyF8wXFhdOFh0WZRZYFosVVxQtE2gSCBJ1EUgQnw7SDGgLhgqPCQQIEQY1BOoCMwKiAdwAwf9S/tr8xftH+yH72PpD+rb5r/lS+gr7Fvte+mj51fgg+Tf6e/tg/Lv8uPyR/Fz8Kvwp/Hf8Kv0U/rL+x/6Z/mH+H/7A/Tf9zfwK/Q3+N/+B/1L+Lvx0+jD6P/t7/NL8HPz6+iL65vka+kf6A/pe+eD4BfnT+dv6jfun+0L7r/p9+jX7w/yD/sP/QwB2AOcAeQHLAQUCoQLhA6gFZQdSCBwIHgciBvUFuQamB/IHlQcaBxUHewdxB1gGjwQBA30CIAP8A/EDngKYAA3/rP7+/ir/yf70/Uj9Nf1d/VP9HP3x/Bn9hf3D/bP9vf1X/ob/wQBuAVQBpgArAKsAIwIHBIAFuQXUBLMDBgM7A2EE+AVhB6oH+wUBA7IAmgChAuQEYQXhA5ABm/91/u397P2R/m7/sP/o/lz91Pvc+n/66fok/Hv9Av51/Yv8WvwP/cX94v3f/YX+3P8ZAZoBcgEcAeAAtwDJAHQBvAL7A0EEawNqAvIBugFyAToBTgHIASgCvAGRACf/6f1X/Zr9Nv5x/sb9afw1+676k/qL+oX6hvp9+n36nfqh+jf6bvmm+IH4i/lh+9j8DP0Q/OL6m/pH+yz8DP0Q/tP+pP6Q/ZD8ffw6/S/+Jv8ZAJ4ADABG/oX8h/xz/rsA3QFLAcX/w/7K/mf/TAAsAfQB3AJHA4ECPAHPAAYCXgQPBjQGqgU2Be4EuASRBCIFuQYrCG8IrQeUBuEFsgWTBawFKwaBBnEGVAY1Bq8FWQSEAlYBkgHQAgcEUgR+A/0BUwAE/3r+1P7X/+AAQQHlAEEAjf+W/pj9of0o//0AXgHi/zX+Wv76/xgB5wABACD/r/60/jT/IwCgAOj/rf7k/cT9Fv41/s79af0r/aX88/ty+0T7fvvJ+4f71fpk+nn62/ov++361vnG+BX55/r2/Lr9hfw2+tH4jPm/+8b9W/6D/W/8BPwV/Hf8Z/2Z/kz/Vf/Z/uD97vzl/BH+0f/mAIIAV//A/vf+Kv8R/x7/tf/GAKsBkgGmAO///v+cADgBYQEqAS8B4gHCAs4C+wELAX0A1wAPAukCxAI5AsEBoQHkAQECtwEtAcMACAGNAYsBbwGAATwBkwC1/0//bgAvAmoC6wBC/7D+TP9NAOoA6wCWABYAbv9S/xQAUwBm/7f+Qf99ABcB1v+v/QT9J/6L/ykA2//m/gD+0f1I/tr+6v4K/vT8Ff0R/l3+7/13/UD9jf3i/Wv9k/w9/Kr8lv0q/rL9hPyV+677wvzR/fv9av3W/GX8+fs2/Fz9Uf5X/tL9Qf0g/av9Vv5+/lH+R/5d/rL+qf+EABwAFf/S/q//GwHIAQQBOgDVACEClwLPAdkApQAwAXkC5wM4BKYDDgN3Aj4COQKHAUMB6gJDBRYGaQRsAdb/vADnAq0EdQRVAsIAEgFzAtID8QNAAi4Av/9WAQ8DFwM3AnYB5gCcAScDuAJiALn+zf6tAB4D4wPjAiwBa//l/pn/WgBbAR0CmwE+AWUBHQBg/tn+AAHPAi8DrwF1/6/+kP+jAHwBMwIrAlQBVQCH/6b/QgH0Av4CkQEHAGP/of9fAGIBugEvATcBqQEgAWAA7/8z/27/DQH7AZ0BuQC1/7P/1QCDAT8BdwCl/9f/2gBFAQABeQDq/zIAIgF6AbIB6QGfAEH/VQCTAX8AQ/+H/ygBCAR8BQMDq/7L+4v8rQCABGgFQQPS/p/8w/+fAy0EAAIZ/tf7N/59AgYFiQRdATD+Hf1N/qgAjwHlAIgA4f8QAFECpgIqADT+3Pw8/asASQNcA9sCDAGr/Vb7iPzSAAYEEASVAtP/l/0+/v7+Zf/0AW4DywHx/2D+F/6IACACAgHm/7P/0P/z/xUAnQBmAG3/OQDsAeUBjwAt/kD8Y/6qAqwDDQHS/cb89/55AXkBOgBD/+D+iP80AOv/1v83AIoASgH7AIf+RP19/lT/IABBAmEDTQIqALn9pvwS/j8AfgHPAYwBoQB3/+//0QFhAfj9hfsB/f4BsAVmA9z+Hf6V/5//cf73/dH/VgIkAuP/xf3I/Jv+9wGtAoUA3f1b/LD9VADeAFEAwgAsAegAfABI/0r8uvmF/HkDJgeVBecANvq+96r9+ANlBJEBsv0L/B8A4QSoArD6c/bN/D4H5gniAuf4TvWW/N8EhQQWAcH+bPvh+zwBHgMIAXv/EPxX+Bv8tQQ+B6QCO/5U+3T4Ifr7AEwEhwH2/0kCygOW/0r2I/L4+9oKnA5oBGr20/Dv9oUA/gfLDCwIo/pz88X1Hfl0ALAJ5wbU/VP+MQIF/wD6wPku/5MEWQA6+NX7hgcTC9QCdPcv8x75swHyBNkC2f+F/s79Q/4xAYEBhfwV+a37FwK1Bi4DP/vh+Bz9IgMSBSEAlfuT+k76FgFyCqkFnvpL+Oj4/vtmBecJSgQ8/PP3lPo6/0EBbgQbA8r6o/sqA8//Svvl//kAS/5hADsBbv+D/9389/jH+wUCFgZ9BlMAJfl++lj/AQAYAGsBiwCJ/hf/7AFYAXb7YPrwAmIIMQOh+x744foTBFYJcAPD+/b4avlo/ywGbQQSABH+fvrj/JEFPQMT+qz7hAFOAjEDtQJS/Wv6sf2RAK4BfQX0BRb+e/rC/Tf8Q/0gBosGpwAAAfH++vkp/XYBNwBKAJYCFANiAc3+YP1I/VL+iAE2BMICcf4y+2r8mwKnCH4FbPql99P/HQR2BPoDZfsc914C7ghwA07/SPpz9Xf8iAlsDxYIZ/fz8En8CQcqCNUEb/3a98D99wcvB939cvtn/5b/2wFmBzYCafmz/YEE/wEH/hz9KP7nAkYIxQaD/PrzW/kkBkULYgYa/OHzO/iCBmcObQhw/ez3RflQAOAI1Qbi+9T54f+JA8IIiArC/unzhPci/wcDwwfdCrsD8vnj/PcBffsZ+8UEhAIj/goIugkG/Zb5kftf+JT+2guNDJMEsP2/9WfzBP2WCekP8QmW+bDy/PwVBgMHIwaK/sDz9vgECksNsQL6/Ez6qvjSBOwPNwOp88L00vrrBY0T8w2j+n7xzvOa+0UG2gyiC5gDR/qF9kz3xvw8Bg8I9QKDBDoHt//f9k74o/60AKwDXgmZA6b5JwFyCaoAYft//cn56fzKCCYJCQDs+hH7o/8PBiAJ2QQW+unyN/Xm/94PGBJ5AA/3U/nU97gAeQ2eA3P20Pg//ikHoQ2xBTL9y/ik9g4A8QNW+wwCYw3xBYf/gv1p9Pn4CwtuCdf3XvcnB/AK6QGY/oD8Vfk2AdYG8v5v/AEBhAD3AJgCef4c+8ABXQ6SC/3zPefp8YYD/BlvIQsBBONb7RX+OgMCCoQFCPxgBygNlPYZ60T85QptC58EPPou98r7o/+IBXgKmwc6/ejtM++CB7cP1gVQCMwAe+zD9Q4IjQLcAE8JLwhB/2fx2+3M/s8J3gwmE04BquO76+IGNA0CCAAD8/0f/Gv4ZPVW/yQNDwt5+4Dznf3JCZUFGPi/9PP/5QrNB6T8iPV+9U4BvRMTEaH2dehI8TH9LgrXF3ASaPd14+ntVQdkECsJzf2s7yj1NRKLFEH2seoE9DP7eghwFXoOkPkK6WHtvAOuEp8O+PzN60H0rwvOCNb3S/7vCN8AVf01AYn3rvINA+EMlgbQ/3z3gPO2/J0GJQtoBd7zVvM5BNAEHACGAtD3mvAEAjUSaQ0y/Mvxufbh+kb+6AgABjP8gQOmBB34K/kOAFP/BwGAA/cA/Puv+Qb+6wKoB9cIPvfX680A5gvE/lgAvQG88bP3Kw50C2IAFALN95bn8vbEERkQMgSp/Mrvs/WqCgsGNf/LB1X5PuyYAe0LqAXyBq724+gTAf0QewYKA5j66uz4+LULYQv7A+v6SfOJ9+MDOA3WCCL4TfDL+GoE4gsSCxUAkvOi8dj+wQozBH76XPylAMEE4wWW/SL4K/rR+5gElw1cA2L3qPsm/RT6bgZfEK3+3+6s+wYFFfxSAFYN7AQr9tX4uP/A/jsA4QZ2AxH3RvpnCKUDXPjH/18EBfpd+7QG/wST/ib+cf1R/kMAa/+EAksFUAF0/0z7fvSA/j4NQAumBOT6Hu5v9GcEJwoaDWAFjvMk9LcAvwUrCN8BI/aP+fwC6gZ6CSb/oe6K9hcNMBCnAt/5E/Q+8BcCsBrcDjjzU/Jp9zb6UBEBGtj6R+gC9yACQQfWELgHq+7x7QMFYQ+jClID0vSx7Uf8ewlCDDoOWgL774Xyn/2qA6sMJQmj95D3lgNHAx0C9AbVAFr0IfeEBK8IIQZVA0n5fPXIBcEPhQVA+oTxA/B/AWwSlRIHC7P7g+wa7wr79AWaENsPwwIp+Ebyd/Ks/5YP6BC5BV37hfSy8Gb4SQjdDkkKhwOq/T77SPvn/WUFCgSq9hT4xQmpDn4F/v0Q+HX1bfy8Bz0OEAtd/lXxaO4s9+AIgBkHGCQG8/NE5TXlVP6qEyUUDRSaCVbuZOvJAAEGc/9V/uj/PAVdCNIEyf+s9zH4dQe/C5oBE/e469H25RnKHp8Cl/AR6l7w2QrdGP4Kivrc9BL4hv6mAzoJlgdQ/+UATQE89bL2gAY6CRwIjgx4/+TqevPxCP8Gx//qBxUHzPy0AI/+AfBM/YcUHQYH9Mz+lgKj+pP/tgOzAwoNMQx+9zPmFOcH/4ceNSFKCkH49OxO6qX40wZOC6QOGwti/xz3PvPM9MH/ZwzdEYsKo/f360TzVgIJD4ESwgbh9ULsVe+bBFcYVg5q+mn20/UK/CYOKQo49dD52AR6/Hn+sgq7A676kwFgAm32oPRLA64NnggdAE36svVB++EISwrr/Jb1n/zCAqECfwUJA4b54P6YCGP/BvfG+av5GABvDfgOkAXA9kftU/hsBjoHQAUz/sn2YwHrC20A6vGv9DoDpA0eDJMCsfWq7gX19//OCpcTZgy/+LTrBe73AKYRpgyEAaf5sPHh+BwIugmoB28DgvWq8Ij5AAOBDasPvv7D7MrvOASkEPcIHv5H947xZ/gaBhIJpgiLBkH5N/H4+koDIgHkAToEMv53+84B9AD5/XcEZQJA93T3svzWAiENJglf+0X5yfZI9csGrBCVAJH1xfmg/Oz/KQZKBf77F/cfANUKHQc/+5/xou/1/DEOaBEMDO3+F+ll5hv/1xJsElUF7/Nd7VH3Zwc0EAgGG/aK9tv7GgAfDVQKJPJL7or9YgWJD9YQG/e66Iz4KgNiA/EHGARM+o/9wAN7/3v5z/Zf+dYENQ3pCIX/+vJ46yv8KBNNDzv/UvjR8STz1QYHE3UHP/gw9j/8Gf7N/nUH0QeX+kT6fP/T9jf7rw3TCbb7zfq3+Sr37vxYB+sO+gWO8Xju1/xmCPALaQW1+lT31fkJAfoGxgCJ+oL+4gJ4BdkDGfwK+Dn2K/tXDxcSqPh58VX93PuM/9wOUAgQ9KfyW/3XBIgL1Qnt+anwk/gkAgUIWQfy+0r6cATmADT9NAWv/Z3zev8nBVEBygiQBJPy/PWfBhYKeQOH++z3uvlr/kMJEA1z/EDyk/6CB2IEw/9K9/zzAQK6D2UN7QNi92DpD+04BwkapRIH//3tMOoR/GEPOgy5Av0BGPye9fv8EwLy/usEagmv//D8EAKb+qn1VgKHClMGxARlAQH1LvHD/igJJAh2Cu4I9fcK7wT37vulA4sRtg56AFX4uPJn9lkDZQfoBuME0fn9+agHBgW++gz9AAB+A1sK4wKa9Lv2eQFUBgAIAAe1/0j5kP3OA0b/OvuRAX0FLQPnANT7hftmB1AMWQFE+K71o/WPAHsQ7Q8UARX4oveH+LH/RAzGCwn8vvPh+nkFHg16DDD/U/Jo8938RgYgDa8MnwJV91j3BwG5BG0Ae/8YAHX/PwRvBp39RvkcAG0FqAYwBFb7yPYg/M4Bpwb/Cd4DSPsJ+/b+DAQLB47/1/WX+bcF/Ap3Bar+6P60/w39N/5q/yz9lgL5CT0EA/wx/FX9Bf81Aw4DxgAJAw0E//5f+lr6rP5kCA0PRQfh+czzB/VTAFkOjQoD/qb9hf48+r/+3gUmA0wADQRQBBD9jvjT+mX91QLLC3MJqPwq91/6IACUBqMHSwI4+3z2DPsNBf0HXgVLAvb9X/ym/i8AVQD7/8P/6v6e/ZgBqge1BoYDVf8P9SDyIv7PCOMKpAgzAfH3PfQy+SAFVQ6oC5j/LPM18Pb5IQeVDSsMiAUS/PPzavOm+50FOgufCTIASPgs+90BUgO1ARD/mPu4/JwDNQebAlf82/nv+moAuAeDCCwBbfjY83j3owPrDt0NpgCv85/y7fuRBeMJBwfG/UL30fqXAskGGwWk/sn6P/3Q//QBfgT2AB38av7gAJoAEAGO/d/6FAFBBT0Cwf9p+4r3+P0iBmYFJALC/3H72PjN++UBJwZ6BS4BkPxE+qL7Pv9pAvoCDwHz/4r/4vwS/PoA/gRuAgv9x/pX/eAAYAENAPL+4f5jAdkDAgFa+zb6k/0+ADgCBQTXAE37LP15A3YEBwFX/J73vPgXARoJkgl6AdX4m/eC+qb+IwXQBpYA+vvt++j8EQC7AnUAfP0z/u8ApAJqAKL7OPpq/f0B2QXEBR4A2PkA+O/62ACaBWwF/wE//rz6nPl5/HMAoQLoApsB1P6k+876ef2IAD4CYQOfAev8+/qg/CX+PQCJAlMBjf6P/pn/3v4T/oT+m/73/kUBIwLl/nf8/f3n//MACAL4ALL9lvtW/Mv/ZAPOAmf/Tv6p/h3+xP7I/9f+b/9vAXT/Tv1jACkCSv8H/q/+L/7P/icAZv/o/rQAxQE0AHP+QP7q/ar97f9GAmYBgP98/h/+M/+/ACYBvwA8/4/9Hv7q/xIBTAExAE7/y/+T/8r+/f4P/07/LgBu/x/+Gv9zAHIAtgCWACT+dPvt+9b+RwFAAmEBvv4o/YH+ZQCLACT/k/3s/fn/HQEjAdEAcf9n/lT/NgAkAB0AkP9f/6oA0wCz/xkAiQAAAAQBDAK/AHv/uP6h/dD+7QH+Av8BzAA1//j9gf7u/98AZwFMAbr/AP6k/igB+wLJAr4AVv5Z/RL+FQAsAkEC9QClAC8BpQGpAX8AUv/j//4ASgEeAY8AHQCiAMEBaQKzAVsA2f/z/n/95v45AtYCOgGE/4T9Vv35/6MB8QASAHH/A/99/yQALAC3/x3/m/9sAV8CvAHyABsAdP9bAMcByQEeAcYAnQADAcUB8QGFAdkANAABABEAMQCAAJcAZQBUABsA0/8eAJIAggAXAIL/7/7E/kv/jQD0AVQCKwFj/2b+rP7d/6cBzQLsATQAp/8GAJ4ARQEhARoAwf+1AK8BggGjADIAVQCcAI8AqP/b/vH/4AE5Ah0BBwCY/+L/YQB5AGMAhADdAEABewGyAcQBPwGuALgAwgCPALwAXwErArkCjALUAUcBQQGOAaABLgGZAHwACwHNAVcCmQIZAsoA0P+U/7T/QQDhAB4BYgFpAa4A+//p/1AACAFHAaIAEgAoAGoAiQCPAJgAqgDAALoAFgD//qf+T/8sAAEBTwFBAHX+aP25/R3/swBBAVgA5v4h/mr+Wv9AAHUA5P8z/xX/nP9WAOoALQHaABMAk/+9/1kA4wDiAJcAkgC5AOkALwEuAdcAggADAH//qP9qACMBgwFOAa4AaQDEAAsBtwAaALr/w/9EAA4BawENAW4A8f+7//3/ZQB2AEEAAADq/1UA9wAOAZEAAAC8////kAAAAUYBZgFOATgBWgGIAY4BYwESAb8AnwDXACQBPAE6ATABFQEWARUBvAAuAKH/Gv/g/hb/hv8EAEUACACL/wn/i/5S/nT+nP6f/n3+Kf7v/U/+K//k//z/RP8D/gL97Py5/dX+l/+p/z7/u/5v/on+9f5k/5z/gf8R/4r+UP6j/oz/sgBmASABDgD6/of+w/5V/93/DwDm/5r/Uf8Q/+z+7f7y/ub+yP6P/jz+7f3F/dz9Lf6G/qn+iv5B/uH9gv1S/XH99P3N/pD/xP9v/+D+af5X/r/+bP8bAIEAagACAK7/rP8BAIMA8gAhAfsAigD3/4j/hP/r/2sAnABJAJH/0/5m/lv+jv7I/tP+oP5L/vT9uf3E/RL+Yf6L/nv+G/6R/TD9HP1u/R7+yv4Y//H+bf7y/fX9fv4o/4r/e/8v//n+Bf9S/7j/BAAWAOj/nv9p/1j/Yf9//63/3P/u/9H/oP+G/4L/bf80//D+wv6+/uH+E/9I/5X/4P/o/63/ZP9G/3b/6/90AOUAGAH6AJIAJAAiALYAcQG1AUABXACy/73/bQBQAdwBvAEKATQAtf/Q/0sAqwCfACkAi/8R/+n+Ev9t/7v/w/96//L+UP7T/aH9pP2s/Y79Nf3I/KL84/xJ/Xn9NP2F/Mr7XPtG+2L7g/uK+4b7lPuq+7L7pPty+xT7v/q/+if7yvtb/Jn8hPxa/EX8Svxm/Ib8kfyZ/Mz8RP3p/X3+4P4h/1f/kP/S/yUAowBnAXMCtwMGBRcGvwYkB5sHWAhQCWAKdgulDAcOfg++EI4R3hHVEbwRvRG8EYURBBF1EDMQTRB3ED4QSA+SDXULZAnCB64G6AUMBfUDzQLMAfYAEQDa/k79wvuU+uz5wfnf+er5rvk2+bL4Yvhf+G74TPjd9zL3jvY69j/2VvYq9oj1h/Ry85TyAvKh8T7xr/D+72jvG+8W7zjvZu+k7//vcvD18KDxk/LT80314/aR+Fv6NPwe/iwAWgKGBI0GVAjfCVQLxgw5Dq0P/BDsEW4SmRKHElcSCRKCEbMQmw9IDtYMcQs3CikJIgj0Bn8F1QM+AvwACgA8/3L+t/0w/fr8F/10/en9Of4//vX9k/2D/fn90P7D/4gA+wAzAVsBewGHAXIBOQHmAJ4AjgDDABcBWAFtAWEBSgEUAbAAPgD+/ykAxwCZAVkC4gIqA1QDjQPbAzUEhQSoBKAEiQRoBDgEBgTkA9UDvANmA88CIQKFAQIBcACo/7v+4/1T/SX9Of1G/R79u/w5/Mn7dfsa+6H6EfqX+X354fmY+lX7z/vu+9L7nPtW+wL7qfpi+lX6oPoy+9b7UfyH/Gr8CPyC+/36pvqj+vH6cfsP/Kf8EP1H/VP9QP0i/fr8yvyq/LH86fxl/SX+Ev/5/6AA+QAjAUYBdgGeAaoBwQEkAukC5gPEBE0FiwWYBYwFgQWPBcgFIgZlBmoGQwYmBj0GcgaABj8GwgU4BdQEtgToBFYFxgUGBgIGugVKBdcEggRwBLEEFgViBXgFXgU2BRwFBwXOBFEEmwPXAjkC7QHxARICHgL0AYoB6wAiAEb/fP7i/Xv9RP0z/Ub9cv2I/Wv9Gf2i/Cj8yfuM+3L7ePuN+7P7/fty/Pr8Vf1J/eP8bvw5/Gz87fx9/eb9BP7Y/Yz9Tv0u/Rr99/zG/Jv8gvx8/H38dvxk/FP8Rfw1/Bb84fus+5v7wPsO/GP8n/zO/Az9af3i/V/+xv4K/y7/Rv97/+X/egAaAaMBAgIxAjECHgIjAlcCpwLvAg4DCwMJAyEDTAN2A5UDmgN0AxQDhALtAYQBWwFnAY8BtAHCAbABdAElAewA2ADjAO0AxABpABAA9/82AKUA7ADhAJcAOwDy/73/m/+T/5T/gv9V/w7/wP58/jb+5v2c/VP9/vyX/CX8xvuM+2D7IvvF+ln6/vnM+cP50vng+dr5vPmY+ZX5xvkc+m/6ofq8+uD6KPuf+y/8rPz+/C39T/2Q/QL+if4A/1b/kP/B/+7/GQBGAG4AkACyAMUA0QDqAA4BNwFqAZUBtwHgAQICFgIoAjkCSQJZAmECZAJ8Aq8C5wIJAxYDKwNQA20DaAM+AwED1gLOAuMCDwM+A1IDPgMNA9ICnwJ1Ak0CIQLzAccBpgGUAY8BgwFiASYBzwBmAP7/lf8o/8X+bP4g/uT9qP1S/dz8QPyH+9r6WPoB+r35dfkn+eT4sviO+Hf4YvhM+Dn4Kvgo+EP4evjG+Cf5kvn4+Uv6gvqt+uj6Qvu3+zn8uvw3/bP9Nf66/iz/f//E/w8AcgDwAHgBBgKXAh8DoQMwBM8EbAX1BWUGzwZFB8QHUgj2CKgJQgqoCtwK9QoPCy8LQws9CxwL2Qp4Cg0KpAk5CcEINQiOB9QGDwZIBYUEywMdA3YC0AEwAaAAJAC0/0b/0v5Y/uD9fP03/RL9B/0R/Tb9eP26/eP99v0A/gj+Cf76/fT9Gf5a/pv+4f4y/4H/u//J/7P/nv+W/5f/pv/F//P/MgCFAOIAMQFjAXgBcwFYATEBFQEcAUkBggGvAcIBvgG2AasBjQFeAR8BvgBFANP/df8z//7+vP5m/v39eP3V/CP8e/vr+mr68/mR+VH5LvkU+ej4r/iK+IT4jvid+K74zPgF+Vr50flx+jL7//vE/HD9Bv6b/jj/1/98AC4B6AGiAlYDAgS1BHEFJgbIBlQHxQcYCFMIhgjMCDMJtAk2CqYK+wo0C1cLagt3C4ALfgtyC2cLZgttC3ILaAtHCxQL0gqCCigKyAldCecIZQjPBywHlAYOBpEFEQWGBPADTgOpAggCaQHKAB8AY/+k/vb9Xf3R/E780vtZ+9T6O/qd+Qf5fPjy91r3wPY/9tv1hfU39ev0o/Ra9Av0vvOA81HzLPML8/Dy5vLr8vfyD/Mz82PzpfPp8yD0T/R69Kn07vRR9dT1dPYc97r3TvjX+Fj52/lw+hz7yftj/PL8iP0z/uj+jv8jALgATgHZAVwC5gJ9AxUEiwTXBBMFWwW1BRsGiAb9BnMH2QclCGAImAjVCBsJZQmpCeIJCAoeCjYKWgqLCsoKBgsuCzkLKgsOC/IK3ArPCsgKugqgCnsKQgrzCZ0JRgnoCH0ICgiUByEHqgYnBpkFBwVvBNIDNAOXAgQCfQHwAFcAwf8q/4/+8v1V/cH8Ofy0+zb7w/pY+vX5lPkq+b34TvjV9173+Pae9kf28vWj9V31H/Xi9Kb0aPQi9NXzi/NL8xnz9PLZ8snyw/LK8tvy9PIP8yvzSvNn84nzv/MU9Ir0GfWt9T/20PZk9/j3jfgs+dj5kPpO+xH83fy2/Zr+gf9iADgB/wG3AmADCwTFBIoFVwYhB9oHhAglCbwJSArCCiQLewvICwYMPwx1DKQM0AzsDOsM2Qy1DHwMNwzsC5oLQAvYCmoK9wl1CeoIVwi6BxkHbgaxBe8EMARzA8ACDgJWAaUA+v9F/4r+y/0H/U/8qvsK+3D66flz+Qn5qfhN+PT3qfds9yr36/bD9rT2t/bL9u72JPdt9733CPhR+J/49fhQ+bX5LPqv+jX7uvs6/LX8Lv2g/RD+fv7m/kr/pP/w/zcAhgDfADcBfAGyAeQBBwIZAioCNwJKAmMCcAJxAnMCaQJTAkICOAIrAhYC+QHZAboBlgFyAU0BKwEQAfUA0wCwAIsAYAA0AAoA5v/H/6//l/93/1X/Pf8x/zH/PP9C/zn/Kv8e/xf/Gv8g/y//S/9m/3b/g/+U/6z/xf/W/+P/8f/9/wgAGwA5AF0AfwCbALUAzADdAOcA8wAJASABKQEuAUQBZAGEAZ8BuQHUAe8BAAIMAh4CMQJBAk4CXgJ7Ap4CvQLaAvUCCwMbAycDLwMzAzIDLgMpAykDLAMzA0IDWwNtA2UDTQMyAw8D5gLHArUCqAKXAn0CWwI5AhYC6QG6AY4BXgEmAe4AugCOAGoARgAaAOj/tP99/z7/A//S/qj+hv5p/kj+Kf4M/ub9vf2Z/Xn9aP1l/WP9Z/1z/YD9j/2j/b795P0V/kj+dv6i/s3+/f43/3j/wP8JAEsAgACrANsAGAFeAaUB5wEiAk4CbAKCApsCuwLZAu8CAQMSAygDQgNYA2oDewOHA40DkgOaA6kDuQPDA8oD0gPYA+MD8wMEBBEEEAT6A94DxwOyA54DjQN+A3IDXwNDAyYDDQP2AtMCpAJ1AkkCHQL2AdsBzgHMAckBuwGpAZkBigF+AXcBdgF3AXIBYgFXAVwBawF2AXsBfAF8AXUBYgFBASIBDAH2ANsAvACfAIUAZAAzAPj/tv9n/wj/nf40/tn9hv0x/db8cfwA/In7E/ud+i36xvlg+fb4gfgM+KP3Sff69rL2aPYe9t/1qvV49Uz1KfUQ9QH19/Tz9AL1JfVV9Y/1zvUT9mb2x/Y097H3OvjG+Ff58vmW+kH7+vvF/Jv9df5S/zQAFwHzAcoCogN+BF0FPQYZB+0HuwiDCUYKBgvCC3oMJw25DTIOnw7/DlYPqg/xDywQXxCBEIsQfxBeEC8Q9w+tD1UP9Q6DDv0Nbg3TDCoMegvFCgYKPglmCH8HigaLBYwEjwOZAqsBuwDB/8D+vv23/Kj7mPqV+ab4w/fo9hT2RvV49Knz3/If8mvxxvAy8KzvLu+97l7uDO7F7YbtTu0q7R3tGe0e7TbtW+2K7cftD+5i7sbuOO+n7w3wefDx8HfxB/Kd8kDz+PO39HH1Kfbh9pn3Tfj4+Kb5Zfot+/v70vyx/Zf+gP9hAD4BEwLaApYDTAQGBdIFqwaFB2AIOgkMCtQKiQsoDL8MRQ20DSEOlg4TD5kPFRB6ENMQHBFIEVsRYRFeEU8RMREDEdMQpRB0ED0QARCyD0APsQ4VDnkN1wwiDGULuwoVCloJkwjIB/cGJQZPBXAEmQPEAtsB6AD8/xP/Of51/bj8+/s7+2z6k/nE+Aj4W/e99ij2kfXx9Ff0z/NU8+jyj/JA8vXxqvFW8Qbxz/Ct8JPwiPCM8JrwqvC98N/wEPFM8ZLx2fEg8nLyzPIs86LzKvS49Ej12PVo9vn2gfcH+Jv4OPnd+Yz6Ovvl+5H8O/3n/Zf+QP/l/4MAHAGzAUkC5gKSAz4E4AR/BRkGqwY2B7kHNAiqCB8JkAn4CVoKwAooC4QL1AscDFgMhgynDLoMxQzRDNkM0Ay/DKoMggxQDCEM7QuwC2ULBQucCi4Ktgk9CccITgjWB1MHwgYyBqAFBgVwBNkDPwOqAhICdgHgAEgAq/8R/3v+8P1s/eT8W/zX+1P7z/pS+t75dfkL+Z34NvjX93/3L/fm9qv2ffZJ9g/23vW29Zf1g/Vy9Wv1cvV/9ZH1q/XO9f31MvZl9pv22vYc92P3q/f591b4vfgn+Z35Ifqj+hb7evvf+1L80fxW/eH9dP4M/5f/DgCIAA8BoAE0AsECRgPHA0IEtgQvBbEFNQawBhoHdAfGBx0IegjNCBcJXwmgCdUJAAojCkQKZQp1CngKdwpwCmYKVwo4Cg8K5AmvCWwJJAnbCJYIUggBCJ8HOQfSBmQG7QV1BfsEdwTtA2QD2wJPAsUBPgG1ACcAlf///m7+4/1c/d/8Zvzp+277+vqJ+iT60PmC+Tz5/vi/+H34P/gL+Ob3z/fE98P3zffg9/z3HfhB+Gf4jvi++Pj4O/mG+d75Qfqo+gv7bPvX+0f8s/wg/Zf9EP6G/vv+c//q/2EA3gBbAdMBQgKoAgoDbwPQAykEfgTWBDIFhgXNBQ0GSQZ6Bp8GuwbXBvkGGwc4B1AHWwdYB1MHRgcyByEHEAf0BtMGsAaIBlwGLQb+BdQFpQVrBS0F7wSsBGQEGwTXA58DbQM5AwADyAKPAlACCgLFAY8BYQEwAfwAxwCRAF4AMwAOAOz/xf+Z/2j/Mv/6/sr+o/6F/nD+ZP5W/kL+J/4K/uz9zv2w/Zb9hv2B/X79fP15/XX9cf1q/WL9W/1T/U39Sv1H/UL9Pv0+/Ub9Vv1j/XH9gf2I/Yn9jf2T/Z39rP2+/df99P0M/iP+P/5f/nz+kP6m/sX+5P4A/yD/R/9x/5f/sv/J/+b/AwAlAEsAcACUALYA0ADtABABKgFAAVQBYwFwAXoBfgGKAZ8BrQGzAbwBxAHCAbkBrAGlAaMBmwGLAXwBbgFdAU0BQAEvAR4BDwH3ANgAuACYAH4AagBPADIAIgAUAP3/4v/G/6r/k/93/1X/N/8f/wz//P7n/tL+wP6y/qT+kf56/mb+Uv4//jX+Lv4j/hr+Ev4H/gH+AP7+/fv9+P30/fL98P3z/fj9+/0A/gf+Df4X/in+PP5J/lL+W/5n/nX+iP6c/qv+uP7G/tT+6f4I/yT/N/9G/1P/XP9o/3n/jv+j/7f/yf/Z/+r/AwAeAC4AOgBEAEcASQBQAFwAaAB0AH8AjACZAKAAnACWAJAAigCAAHMAaQBjAFkATABBADUAKwAfAAoA8//a/7r/mf9//2j/Vv9I/zb/If8J/+7+0v64/p/+iv5x/lT+Of4h/gn+9/3n/dX9w/2u/ZX9fv1s/WT9Xf1L/Tj9Kv0Z/QT99Pzx/Pv8Cf0R/Q39Af33/PT88vz3/Ar9HP0m/S/9Of1G/V39ev2U/av9vf3K/dT95v0B/iH+Rf5u/pn+vf7d/v3+GP8y/1H/c/+X/77/4/8KADkAaQCWAMMA5gD+ABcBLwFKAW4BlwHAAeYBBgIjAjsCTgJnAoQClQKcAp4CnAKdAqUCqwK0AsMCyQLAArECnQKGAnMCYgJLAjECGQIBAuQByAGxAZcBdQFQASsBCwHwAM0AowB7AFIAIQDz/9D/t/+j/47/cf9K/yH/+f7Q/qH+bf5E/i3+Gv4G/vj97f3b/cL9pP2I/Xn9cP1k/Vn9Tf1C/T39O/0+/Uz9Yv11/YP9i/2L/Yn9j/2e/az9wf3f/f39Gf44/lT+cf6W/rf+0f7q/gH/HP84/1H/dP+n/9j//v8cADkAVQBqAHoAlQC5AN8ABAEpAVABcgGGAZUBqQHAAdcB6wH+ARICJAIzAkMCWQJ3ApICngKnAq4CqQKjAqcCrgK9AtUC5QLsAugC1QLAArMCqAKhAp4ClwKNAoMCegJrAlgCRwI6AioCFAL7AeIBzgG8AakBnAGUAY0BgAFvAVoBPQEdAQAB7QDjAN8A1gDEALQArACjAJcAjQB8AGUAUQA+AC4AJwApADEAOwA9ADgANAAsACAAFAAJAAUABwAHAAgADgAaACYAKwApACkAKgAqACoAKAAnADAAPQBHAFEAVwBaAF8AZABkAGEAXABfAGgAcgB8AIMAiQCRAJUAlACWAJkAmgCeAJ0AnACnALEAtQC9AMUAxQDCAMYAzgDUANcA2QDcAOIA6QDrAPMABAEQARMBFwEcAR8BHwEaARsBJwE2AUABRgFGAUIBQAFCAUUBRQFDAT4BNwEvASgBJQEiARsBEwESARIBCQH+APQA6gDeANAAwgC7ALgAsACiAJIAhAB4AGsAXABPAEUANwAiABAAAAD0/+7/6f/h/9n/zf+5/6z/qP+h/5T/iv+H/4r/jf+M/43/jf+I/4P/gP9//37/gv+L/47/jv+T/5z/pP+r/7D/tf+8/8L/xf/G/8b/x//H/8v/1f/j//L/AwALAAQA/f/4//X/+P8AAAcACwANAAsABgAAAP7/AAAAAAAAAAD7//T/7v/m/+H/4P/c/9b/0P/G/7//wv/D/7z/tP+p/5r/i/+A/3//hP+G/4T/fv93/3L/cf9s/2f/ZP9b/1D/Sv9H/0X/S/9T/1X/Uv9O/0f/P/85/zX/NP8y/zD/M/8z/zD/LP8o/yf/Lv81/zf/NP8w/zD/Mf8t/y3/NP87/z7/QP9B/0P/R/9N/1H/Uf9R/1D/UP9V/13/Xv9a/1f/WP9b/1n/Vv9Z/1//Xv9a/1b/Vv9a/1//X/9d/1v/Wv9b/1n/U/9L/0b/Rv9C/0D/SP9Q/07/SP9C/zr/Nv8y/y7/MP80/zX/M/8x/zL/Ov8+/zr/M/8r/yP/Iv8i/yD/If8q/zL/Mv8w/y7/KP8k/yP/IP8c/x3/H/8i/yX/Jv8k/yL/I/8k/yP/IP8g/yD/Hv8b/xv/HP8g/yX/Kf8n/yP/I/8m/yf/J/8n/yf/KP8k/yD/Jv8s/yr/LP8y/zT/NP82/zr/Pf9C/0v/S/9B/z//Q/9E/0n/UP9R/07/SP8+/zz/Rv9U/17/Y/9l/2L/Vv9O/1D/VP9a/2H/Yv9k/2r/c/9//4X/gf95/3X/cP9r/2n/bP94/4P/hv+H/4v/jv+N/4j/gP98/3z/e/93/3X/eP+A/4v/lP+c/6D/n/+X/5D/i/+L/47/lf+a/5z/pv+z/7f/t/+6/7r/t/+0/67/rv+2/7n/vf/H/8//zv/M/8//z//I/8T/xv/L/9D/1P/V/9j/4v/r/+//8f/y//D/7f/p/+f/6f/o/+f/7f/5/wAAAAD9//n/9f/2//r///8GAAoABgACAAQABQACAP3/AAAQACIAJgAhAB0AHAAaABQAEQAXACMAMAA4AD4AQwBHAEsAUgBbAGQAaABpAGcAZQBnAHIAgACOAJ4AqgCuAK8AqwCjAKEApQCqALUAwgDIAMgAzQDQANEA2ADhAOYA4gDYANEA1ADbAOQA8wD/AP8A9wDuAOsA7wD0APYA+gD/AAIBAAH8AAABCAEOARIBEwENAQQBAAEAAQQBBwEKAQ8BFgEZARcBFAESAQ0BCAEIAQwBEgEYARkBGAEYARQBDwESARgBGQEYARYBFQEZARkBDwEIAQgBBQEAAf4A/gD/AP8A+gDzAO4A7QDrAOUA3gDYANAAxwDGAMUAvgC7ALwAugC1ALEAqACeAJsAnACcAJ0AoQCnAKkApACfAJ8AowClAKYApwCqAK8AtQC3ALIArwC0ALwAwgDEAMYAxQDDAL8AvgDCAM0A1wDXANUA1ADSANAA0QDRANEA1QDaANsA3QDhAOEA3wDbANcA0QDJAMAAuACwAKkApQCjAKMApACfAJcAkQCIAHoAbgBnAF4AVABRAFQAWABXAFIASwBAADMAKwApACkAKAApAC0AKwAkACIAJgAmACQAIQAbABsAHAAXABUAGAAYAB0AIwAkACgAMgA1ADMAMQAuAC4AMAAxADMAOABAAEIAQwBHAEgARgBGAEIAPQA+AEAAQwBGAEUARQBHAEQAQgBCADoANAA1ADQANAA3ADgAOAA1ADEAMAAqACEAHgAZABIAFAAWABgAGgAYABUAFAALAAUAAQD5//H/6f/e/9n/2//c/9//4f/d/9j/0v/F/7n/rf+m/6f/rP+w/7X/uP+1/7D/q/+k/5z/mP+d/6L/of+c/5r/nP+g/6L/o/+j/6L/ov+g/5n/lP+W/5n/nv+j/6H/nv+h/6L/oP+i/6T/of+e/53/m/+d/5//nP+Y/5z/of+g/57/oP+d/5b/kf+L/4b/iv+S/5f/mP+U/43/if+E/37/fP+A/4f/kP+V/5L/jP+H/4b/h/+E/37/eP92/3P/b/9s/2//d/9//4H/e/9z/27/bf9q/2T/Yf9f/17/X/9j/2j/af9k/17/W/9W/1f/W/9a/1b/Vv9V/1X/Vf9Y/17/YP9b/1b/V/9Z/1j/Wf9f/1//XP9a/1b/Uf9Y/2P/ZP9k/2b/Yf9Y/1H/S/9M/1H/Uv9R/0//S/9E/z3/OP87/0H/Q/8//zb/K/8l/yf/Lf8x/zD/L/8w/yz/Jf8k/yP/HP8X/xj/Hv8j/yL/H/8g/yH/Hv8c/xr/Ev8J/wX/BP8J/xD/Ev8V/xr/HP8Z/xj/F/8W/xr/IP8g/xr/Fv8Y/x//KP8v/y//L/8x/y7/Jf8i/yn/Mv84/zn/OP80/zX/Of84/zj/Pv9D/0P/Qf8//zz/Pf87/zT/Mv82/zz/Qv9A/zz/Qv9K/07/Tv9L/0r/T/9Q/0//VP9e/2X/av9w/3j/e/93/3f/gv+K/4v/h/+G/4//m/+i/6n/sf+y/6//rv+v/7T/uv+8/7v/u//B/8X/yP/R/9n/2P/R/8z/zf/R/8//zv/a/+T/4v/f/+L/5//s/+7/7P/o/+f/5v/m/+j/7f/y//b/+f/5//b/+f8AAAEAAAABAAIABAAIABAAFgAYABwAJQAvADUANwA7AEQASQBKAFEAXABkAGYAaABrAHEAeACAAIcAjACRAJMAlACYAJ4AowCpAK4AsgC2ALsAvgC+ALcAsgCxAK8ArgCwALAAsQC4ALsAvADAAL8AtQCrAKoArACtALAAtgCwAKkArgCxAK4AsQC1ALQAtACzAK0AqwCxALMAsQCxALUAuQC6ALoAtQCvALEAtwC3ALcAugDCAMkAywDLAM0AzgDPANMA1QDWANwA4ADiAOMA5gDoAOoA6wDsAOwA6ADjAOIA5gDqAO0A9AD4APcA+AD3AO4A6wDqAOUA5wDuAPQAAAEHAQEB/QD5AO8A6QDoAOcA6ADtAO8A6wDmAOYA5gDhAOAA4gDcANMAzQDKAMwAzgDMAMoAygDIAMUAwwC/AL4AwQDEAMMAvgC4ALQAsgCzALcAugC7AL0AuwC0AK0AqQCpAK0AsgCvAKwAqgCoAKYApQCdAJoAogCnAKUAogCfAJ0AmgCVAJMAkwCVAJQAkQCPAI4AjACLAIkAigCPAI4AiQCHAIIAgQCCAHoAcwB5AIEAgwCBAHkAcABtAGsAaABmAGgAbgBvAG4AbgBqAGUAZwBoAGgAaABjAGAAXgBZAFcAVwBYAF4AZQBhAFkAUgBKAEQAQwBDAEUASwBRAFQATgBEAD0AOAA3ADsAQwBLAEsARgBDAEEAPwBBAEUARQBGAEYAQgA/AEAAQgBFAEUAQgA+ADgAMgAwADEAMAAwADEAMQAuACkAJgAmACQAIwAiABkADQAIAAMAAQAHAAwADQAKAAAA8v/r/+n/5P/g/9v/1v/S/87/yv/H/8b/xv/B/7j/tP+0/7D/q/+n/6D/nf+i/6P/nv+b/5r/mv+W/5D/if+G/4f/hv+B/37/f/9+/3//g/+D/3//ff98/3n/d/90/27/bv9w/3H/c/92/3X/b/9r/2z/bf9t/2z/a/9p/2f/ZP9h/2T/Zv9k/13/Wf9b/1v/UP9J/0v/T/9Q/1D/Tv9N/03/Sf9A/zH/J/8l/yT/IP8e/xv/Gv8d/x//G/8Y/xT/D/8G//3++v79/gD//v77/vz+//4A/wH/Av8F/wj/BP///gH/Af///gP/DP8V/xn/Fv8V/xv/Gf8T/xL/Fv8b/xv/Ff8T/xr/HP8X/xb/GP8W/xP/Ev8V/xb/FP8U/xn/Hf8d/xz/H/8i/yL/I/8k/yL/H/8g/yL/Jf8n/yf/Kf8o/yj/Mf85/zX/MP8w/zX/O/87/zr/Qf9F/0P/Rf9N/1L/U/9Q/1D/Vf9X/1r/X/9h/2T/aP9o/2v/c/96/3v/dv9w/3L/ff+C/3//gP+E/4T/hP+G/4f/if+M/4n/g/+C/4L/gP+B/4L/f/98/3z/ef91/3L/d/9//4D/fv98/3z/gf+J/4r/hf+C/4b/j/+U/5X/m/+g/6D/pv+z/7n/vf/E/8r/zP/P/9f/3f/g/+n/8//0//X/+P/3//r/AAABAAUADAAPABQAGwAjACoALAArACsAKwAuADEAMwA4AD0AQABDAEUARgBKAE4ATQBNAEwASQBIAEsAUwBWAFMAUwBUAFYAXQBhAFsAVQBUAFUAWgBcAFwAZQBtAGsAagBoAGUAaQBwAHQAeQB/AH8AgAB/AHsAegCCAIwAkACQAI4AigCMAJYAngCgAKUAqACmAKMApACnAK4AtwC+AL8AvgC9AMAAwwDBAMQAzADMAMkAzQDOAM4A0ADPANAA2gDgANwA1QDNAMkAzADPAM4A0QDWANkA1QDOAMkAxADAAMIAxADDAMYAxwDDAL0AuQC2ALQAtAC4AL0AvgC9ALwAugC4ALQArwCvALEAsgCyALEArwCuAK4ArwCzALcAuQC3ALIArwCtAKoApwCqALAAtAC3ALoAugC4ALkAuQC6AL0AwgDFAMQAvAC1ALYAvADAAMUAzQDOAMoAxQC8ALQAtQC6ALsAvQDDAMYAxwDLAMsAwwC+AL8AvQC5ALUAsQCwALcAvgC+ALgAsACpAKQAoQCdAJsAnACcAJcAkACOAJIAkwCRAJEAjgCKAIcAgwCCAIcAjQCOAIwAiwCMAIgAgwCFAIoAiQCIAIcAhACGAIoAiQCHAIIAfwCEAIUAggCDAIIAgQCEAIQAgQCDAIUAhQCGAIQAggCBAHsAeQB4AHUAdABwAGoAbABvAG0AbQBoAGEAYgBhAF0AWwBXAFQAUgBLAEYASABFAEEAQwBDAEIARABCADsAOAA4ADIALgAyADUAMgAxAC8AKwAoACgAKAAoACUAIAAgACMAIAAWABAAEQARAAsAAgD9//z/+v/1//H/8f/v/+r/5P/g/9v/2P/W/9T/0v/O/8f/w//B/7//uv+0/7L/tf+z/6z/pf+g/5//of+h/57/nf+Z/5P/kv+Q/4n/hv+F/3//ev95/3j/df9x/23/a/9q/2b/Yf9f/2D/Yf9k/2T/X/9Z/1f/U/9O/07/UP9O/0r/TP9P/0r/Rv9H/0b/Qv89/zb/Nf86/zn/M/8u/yn/I/8h/yH/Jf8h/xb/Ev8X/xL/Cf8J/wv/CP8B//v+/P78/vz+/v78/vT+9v78/vn+9/78/v/+/v78/vf+9v76/vn+8/70/vr+Af8D//7++/7+/v/+/f78/vz+//4F/wn/Cv8J/wf/CP8M/xD/Ef8T/xL/Ef8S/xb/F/8Y/xn/Gv8e/x//HP8b/yD/If8e/yH/Jv8m/yb/J/8p/zD/Nv81/zT/Mf8u/zL/N/84/zr/PP88/z3/P/9B/0P/Rf9K/07/Tf9K/0r/UP9Y/1n/WP9c/1//Zf9u/2//bv9v/27/cP91/3f/ev9//4H/g/+J/47/kv+R/5D/j/+N/43/kf+R/5D/kv+U/5f/nP+a/5n/mv+X/5P/lv+b/5v/mf+X/5f/mP+a/5v/m/+c/5v/lP+O/4//lf+c/6L/p/+q/63/rP+r/63/sv+6/8P/yf/M/83/zf/O/9T/2v/f/+X/7f/0//j/+v/5//v/AAAFAAgACwAOAA8AEQATABMAFQAZABsAHQAgACYAKQAqACwALAArAC4ANAA4ADoAOwA7AD0APwA+AD4AQgBFAEYARgBGAEUAQwBEAEcASwBRAFQAUgBPAE8AUABRAFEAUQBTAFcAWwBgAGQAYwBjAGQAZgBpAGoAbABtAG4AbgBwAHUAewB/AIEAgACAAIEAhACGAIgAjACOAI0AjQCOAJIAlwCaAJ0AoACiAKQApgCoAKwAsACyALMAsgCxALIAtAC4ALsAvgDAAMMAwwDCAMAAwQDFAMgAyADIAMgAxwDGAMYAxwDMANEA0wDRAM4AzQDNAM0AzgDPAM4AzgDOAM4AzADLAMsAzADOANAAzwDQANEA0gDSANMA0gDQAM8A0ADTANUA2ADaANkA2QDaANoA2wDcAN0A3QDeAN8A4ADhAOIA5ADlAOYA5wDlAOIA3wDbANgA2gDdAOAA4QDgAN8A3gDdAN0A3QDaANoA2QDWANUA1gDXANgA2ADYANkA2QDYANcA1QDTANIA0ADPAM8AzwDOAMwAyADFAMMAwAC+ALkAtQC0ALMAsgCzALMAsgCwAK0AqACiAJ4AngCeAJ4AnQCbAJoAmgCZAJUAkgCRAI8AjgCNAIsAiQCKAIoAhwCEAIEAfwB+AHwAewB4AHQAcgBxAG8AbwBvAGwAaQBoAGQAYQBgAGAAYABeAFsAWABWAFYAWABVAE8ATQBLAEoASgBIAEMAQgBDAEEAOwA0ADEANAA0AC8AKAAhAB0AHQAcABwAHAAaABcAEgAKAAMA/f/7////AAD6//f/9P/t/+r/6f/h/93/3v/b/9f/1v/W/9P/0P/Q/9D/yv/G/8f/v/+4/7f/sv+w/7n/v/+3/6z/o/+e/5r/mP+Y/5X/lP+b/5v/jf+E/4P/gf+D/4D/e/97/3f/bv9t/27/Z/9j/2L/Xv9a/1P/TP9H/0P/Q/9A/z3/Qf8+/y//Kf8t/yz/KP8j/x7/I/8q/yP/Fv8P/xL/G/8e/xf/FP8Y/xT/Cv8L/xH/Df8M/xL/Ev8P/xD/Df8O/xH/DP8N/xL/C/8H/w//FP8U/xH/Dv8P/w7/DP8R/xf/Ff8R/wv/Bv8F/wf/Cf8L/w3/D/8O/wr/Bv8C///+/P77/v7+BP8J/wj/Av/5/vj+/f7+/vn+8/7v/vH+8/71/vn+/P74/vX+9v75/vv+/v4B//7++P72/vr+AP8H/xD/Gv8k/yr/Lf8y/zf/\" type=\"audio/wav\" />\n",
-              "                    Your browser does not support the audio element.\n",
-              "                </audio>\n",
-              "              "
-            ],
-            "text/plain": [
-              "<IPython.lib.display.Audio object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            " > Saving output to tests-audios/teste_this_demonstration_.wav\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "error",
-          "ename": "KeyboardInterrupt",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-            "\nDuring handling of the above exception, another exception occurred:\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-14-a0b8edea98d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Synthesize sentence with Speaker: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchoise_speaker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"(this speaker seen in training)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mTEXT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter sentence: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Text: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocoder_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSE_CUDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_griffin_lim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSPEAKER_FILEID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -1176,11 +389,7 @@
       "metadata": {
         "id": "5e5_XnLsx3jg",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 704
-        },
-        "outputId": "3c82a3cd-f4b9-4493-aa4c-275c34d3294d"
+        "colab": {}
       },
       "source": [
         "import IPython\n",
@@ -1200,69 +409,7 @@
         "  ap.save_wav(wav, out_path)"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Synthesize sentence with Speaker:  p244 (this speaker seen in training)\n",
-            "Enter sentence: Test this demonstration \n",
-            " > Text: Test this demonstration \n",
-            " > Run-time: 1.06947922706604\n",
-            " > Real-time factor: 0.8609054588373298\n",
-            " > Time per step: 3.904344461788641e-05\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "\n",
-              "                <audio controls=\"controls\" >\n",
-              "                    <source 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\" type=\"audio/wav\" />\n",
-              "                    Your browser does not support the audio element.\n",
-              "                </audio>\n",
-              "              "
-            ],
-            "text/plain": [
-              "<IPython.lib.display.Audio object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            " > Saving output to tests-audios/Test_this_demonstration_.wav\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "error",
-          "ename": "KeyboardInterrupt",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-            "\nDuring handling of the above exception, another exception occurred:\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-32-390fa572c62e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Synthesize sentence with Speaker: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSpeaker_choise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"(this speaker seen in training)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mTEXT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter sentence: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Text: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocoder_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSE_CUDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_griffin_lim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSPEAKER_FILEID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -1309,11 +456,7 @@
       "metadata": {
         "colab_type": "code",
         "id": "bbs85vzz4vHo",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 704
-        },
-        "outputId": "3c82a3cd-f4b9-4493-aa4c-275c34d3294d"
+        "colab": {}
       },
       "source": [
         "import IPython\n",
@@ -1333,69 +476,7 @@
         "  ap.save_wav(wav, out_path)"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Synthesize sentence with Speaker:  p244 (this speaker seen in training)\n",
-            "Enter sentence: Test this demonstration \n",
-            " > Text: Test this demonstration \n",
-            " > Run-time: 1.06947922706604\n",
-            " > Real-time factor: 0.8609054588373298\n",
-            " > Time per step: 3.904344461788641e-05\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "\n",
-              "                <audio controls=\"controls\" >\n",
-              "                    <source 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+fP+wwELAbn+WP1x/VT+1f8+Au0EbwaZBuAG9Qd6CK0G/gL1/+/+P/+CAOMCwgX0B2sIEAfRBA0C1/5q/BL87/1IAd4EwwfCCWgKWQkPBwkEiABc/ff7YP25AMID9wQQBGoB5v1g+qb3oPZc9+v4l/o+/MP9r/6P/qr9Xvx8+j74gPbb9df2xPmv/dsAZgLgAkQDjwMbA9wBVABX////YwL8BG4GxgaRBp4FMQNe/yn7cPci9b/1jvmi/uYCowWQBvEEeACp+tf1APMg8mHzqvZ1+9QA7gQOBvgDl//L+rL3O/dc+Z390wLdB78LAw05Cz0I9gXPAxsAyPsi+mL8kwB7BNMGFweGBekCcADL/iz9Bvur+ZD6J/1q/z0ARwAaAG3/A/7q+8T5uPg++WX7Af+XAq0EjAXMBQUFtAJr/9n8DPxO/Lr8eP0D/00BUAOUA9sBy/9S/zUAWwA5/wv/0AF6Bq4JZQkBBwUFJgSWA9ICSwJpAgADSgR7BiUIWgcKBOb/fvwo+pz4/PeO+EP6dfzB/X/9c/wF++D4XvZh9MzzVfWb+Gv8w//QAS4CWgEBAE7++fsd+U/31vgV/sMEzgmTC5gKUgi9BVkDYAHK/+X+df+KAcEDOAR2Ar7/a/0G/In7pfsy/Fj9zv6r/4//OP9Y/3f/1P75/T/++f/3ASoDAQRjBQQHCgh/CPsIeQloCQEJnAnLCzAOzw6lDEoIeAPX/1P+2P5YAMABWgKWAb7/Kv7J/dP9UvzL+L71GvYp+oP/2gKCAkD/mvrv9dXyXPJV9Jb3pPqw/O39//6EACsCXwI/AAT9xfr/+v/9lQLlBgAK8AsxDJ4JdgQ0/378kPx6/cj9sP10/Wf8Afok98P1zvbQ+Gj5yPdS9QT0TfSv9N3zQfIP8RrxCfPf91oAbgv5FewccB/1HuAd/R07HyIgvB9QHkIcsRlaF9kVjxNuDcUCDvcj7wXt6u6k8c/yNfHJ7ObmH+LT4H3j4Oi+77D3YABqCJINsw6ODDkIegJL/e77WQDjCGUR2BYGGfIY7xZBEvwJaf6c8Y/m6t9M32HkRuyl8g/0ru+b58ffltsl3BHgPuRU5mrnCuuH88b/4AsIFAIXKhZWFIoUKBgZHqYjEiZfJPcfZBt/GGAX3xbLFfES1QxiA335IfPE8UXzrfRP9Qz3nPvfAckGrQg0CGMGggMdAAX+Pv/nA+AJIA60DnoMMwpXCa4IogWc/t/0K+wk6ETqfvEz+yME5giAB14Bf/oR9qrzRvD/6jTnieir7kb2ZPxK/zL9bfU26+bj6uHj4wTo3O0e9dT8bQRdCxAPWwzeA1v6avTK8/z3KP+hBsoMFBLbFmEZKxesD0wFCvwl9w34G/5iBiENNRCtD6UMiggNBWsDIAOwAs8CcwZIDqgW1xt2HVcc8xcLENcGwv+a/OD8G//TAc4DawTaA5UCYwAS/CL1iO1G6MHmm+j87KnyhfdK+XP3mPOL7zjsaeog64LuM/P39179vAOwCGkJ/AW1AKH7xPfn9ej2i/rQ/mYBSQH9/gL8m/nQ9+H1WPP68NrwcvRD+gn/fAHFAjcD2wFQ/wT+9f6yAGUCUwUCCjcOwQ88D4EOyA32C+wI9QVCBCEEXwXLB0EL5g6rEOcOMQrfBOcAGP/k/98CuAVXBlcFlQSVBGEECgOaAI/9qvp1+fj6JP4DAUoCoQHi/4H+Hv4T/n/9gvyF+/356Pe29un2NPeb9oT15/Qj9cb1JPaS9eTz/fG38GHwn/Gt9I74EPxz/pz/bQC9AWQD1ATFBXEGZwfzCPsKEg1vDjQOAQydCNAFcQRmAx8Bufyi9hDxoe5P8Ov0lfm8+2f7lPo5+2D+3QJyBXAEdAKMA0UIeA20EDASShJzEAcNIwrnCTgMsA7uDlMMYAjYBcYFCgY7BDQAUvvO9kHzRvGF8VLzAPW49XD1mfTT9OL3cv2xAr4EzwOoAqsCswJxAf/+ffwi+2z7Ef1W/7oB7QOpBGkC7/3S+dn3VvgR+4D/wAR2CQcM0wvhCW8HOwSK/5j6PPi0+Vr9jwDoAcQBNgFpAKj+y/vQ+N32fvbg9/36Y/9eBNcI+wq9CUkGyQK8AMEArgLbBTQJPwsrC1IJqga7BOoE3wamCKkIVQcYBgkFcQOZAbz/+Py4+N/zW/Bu78fwh/NU9lb3MfYo9EXyUvHS8WnzsfXP+Av9VwKjB7wLpw6EEOgQ+A+NDokNBg0bDEcK/gcwBTABS/xA+PD2c/ja+h78r/tb+if5VfiO99r26fYa+Nn55fv//lYDGwimDCAQ2xChDWcHUwFz/k3/WAK0BQgI8AiyCJoHGwbLBM4DtgL4AKT+xPx0/Hn9N/42/cr6lvjo9yX5x/tv/tb/mP9U/jP9b/wy+435Zvjd99n3C/kS/DYAMwP2AsH/kvvG+J/5I/7rArgEzQNgAswBCgJEAnMBBP9g+zP4gfcP+jz/uAUFC+ALbQe/AOT7Yfr0+hf8y/1nADgDeQUxB1gISwgRBj4BGvtY9rD1DfowAYIHjgtyDawMlglhBkkEEQJ1/qv6Gvly+hH+lgOkCW8NQg37CcYFDgK3/pb7rPk0+kP9xgGkBg8LwQ1yDc4JVwO/+8T1JPOA82n14/e/+hn9kP1J/Er6CvjV9cjzKfKt8VLyDvRc95b7g/8LAq0BzP4R/Pv6Yvtf/Mn88vyH/R7+D/9YAKcAhABCAfkBzwGBAY4BeQGSAGf/Tf8DAHAAmQAWAWMCcgQTBlMG5QXfBcAFBgRPAW0AVwL2BBAGXAUhBEYDcgLIAQgCtQKNAncBuQBeAcIC5gMlBYcGZAYBBIwBSAEpAqIBfP+r/Y79c/7w/lv/gADyAJH/Gv72/Tn+A/6x/bf9o/3n/Nf7qfrf+Ob2KfZQ91/5Wfp5+Tj4B/i/+Gb5G/kM+Nf2RfVn9NP2Afw8AM8BhQEoAJX+D/7r/hgAUgBP//j99v1uAJcEYQgHCmQICwTr/z7+aP5J/7oAKgKrApsCWQMdBW8G2wVHA+T/cf30/FP+PwEMBfAHXgi1Bo0EAwMHAtcAQ/9C/vj+YAEfBKAFpgXPBO8C2//6/Oj71/zB/hAAUAC7AKYBsAErAOb96/uj+hj6APv4/XsBPgMJAyAC9wBS/6n9efww++/5NPpM/Lv+RwDeAAYBuACe/3r+3P0Z/Vj8CfzO+zr87f3K/+wAXwH5ANH/Pv64/C38Kf0h/7oAuwC4/xf/7f7s/un+sP4L/ykAxQBWARQDAgVEBvcGhQY/BYEEXQQDBA0D2QExAc4An/++/TD8k/t7+/D6TPqL+lj7h/wm/qD+wP0Z/lcAgQK7A34E5gT1BF4F4wayCJEJWAqbC4QLiwm9B9wGmAVNA5wA4/1b+yf6rvo2+776jPos+0D7CPoA+eL53Pv6/F79P/7g//8BMATcBXAGbQWRA1UCZAEaACAAoAJsBdcFdQRbA0ICvwDnAFoCcgHi/qL+3f9R//f9Cf5A/gL9gfs9+lL4k/c/+k3+GQEFA4QEFQXABBkE7wNDBBMExgOJBDEFbgTUA6QEDwWdA64BjgBw/wr+av1V/Rj9tP2R/0QBQgLsAggDuAKkAqMCLgJlAaAA9f/N/6oAIQF2/2z9bf0J/k79bPxD/Z3/dAGLAdEAGwBY/+b+pP4J/rz9Wf6b/0gBsQInA4IDhgTcBDwD9ABs/w3+Av1V/Uv+Ov+hALwBeQH6/8v9U/xE/Dv80Pvs+2H83vzb/Wb/uQDbABIAgP85/xL/Ov9Y/0n/Wv/b/wQB9gHwAcgBzAHJAcQBkwCu/sv+RgCwAPYAKQJyArAAAv/6/kH+fvzl/R0BMwCW/RL/1wGeAd8AtQHjABL9wvow/If9SP0+/lwAPgH2ABcB2AEEAuUAFf9i/QD9Yf6G/9z/7QBdAff/sv86AUMBhP+y/ij/tP7e/H78yv7VADkBmwG6AWYA7/4+/lz9nvxD/eH+YADhACQAHQC1ATECMwEYAb0AN/+E/vT9D/04/gsA0P/7/pv+gP6U/1UBrwLeArMAn/6m/48Ayf5f/b393v6XALYBuQEzApECQwF6/1f+Z/2j/BT8Cfya/VUAJQIWAhwBaQBT/z/9UPy3/BT8cPyE/xcBAgEUA+UDGQDq/Dz98vw4+4f89QDnAnUBPgHwAUkA5/49AJ8ApP71/dP+9f4fANECKAM4Ae7/cP66/Kr8xf3+/2MCeQEO/6X/UAEJAbH/V/6L/QX9nvyd/lwCjAPRArwCGgEY/pL9P/60/WP+6f8K/3f+agEbBDMDIwEJALj+Wf0G/rD/LQCKAEoAHf50/X3/sQAqAWcBn/8Z/gr/UQD5APYAIwC7/zH/3v2P/dr+/wD2AS7/Wfwq/qcAegB5APQAawAo/2f9lP3ZAEkCRQBA/0n/c/4O/yYBlAGAAKf/AP+e/qn/NgJWA30BKwBgAAj/5f3h/6wBfgDA/vr+LABSANMAewJoAmUBIwEf/7D9B/+2/XT8IgHuAh7+L/3L/9v+P/7+/3r/lP7y/wEAB/8CAAQBNgHzALL+h/7QAZMAzP0fAecBuf38/nkBu/6l/g4CjgG9/or+A/8I/W79MAJ5AcD8ev8HAtz9Wf6bAigCpQCC/1j+cv+U/0r/9QCaAPT/+wD7/5P/ggB7AMsC0QI7/bD84wBbAX8Auf/z/+sCCwKg/qD/8/9i/64B3P/W+//9GQGjAHD/Bv9jAWUDzgCO/38B2gA0AI0CXwJV/3L/sAHxAAz/W/+W/5f/EgEDAdf/4wAFARsAMQKXAo3+N/0j/5v/YAABAST/4v/lAmUA7vwHAPoBBP7t/NMAfgFk/gkAvQSiAQv90gJfBvn/tf9/BOoA2/18Ao0EzQBg/af+DAJCAOP8+v+RA1kBaf65/y4Cn/9y/bACEANu+3r8sgHX/iP9Wv9YALACMwOTAhMFPgLc/csBVwKZ/h0B0wFcAIgCMACS/2YFbQH8+k8ABQIZ/T/+/AC+AbACAwJyAuoBrv4DAmkGlABw/GUBXgM5/z/9hv8DAVEA3wCBAeUAkAB1/rj+qwNxAYX8lwHbAuD97gLwBTn+8v7ZBAEADPvh/+EDIAB9/sEEDQXC/Nn9igRgATn9i//yACgAaP4H/mgCqAOS/uv9iwFFAJD8FP0HAIcAg/8pAA4BPQGLARUBuAEtAyMAufzGAHoFbwFk/PwBsge+/5L7mgWOBBr5cP0iBOH8vfocAUQC8/9o/p7/LwFr/Tj+aAPr/Lz5AQVKBJP6mf/UAy797v1dBFYD8/4a/90BOgBW/UABcARkAAP+2f/+/+L9I/4BA0QDM/zC/cQFVgK2+5D+IQFA/Q37+v9WA0v+dv48Bd4AYvttAT8CT/5wAEL/mP1MAUEAhP9qA1oA6Pvv/rIBVACG/aX++QKb/6T7ZQE8AoL95f9NAI38VP4IAfcAKP85/ngDpwOI+wr+lgNP/tX9GwIiAAQA2QBL/6AAR/80/Yf/1fyD+7ICQAOn/qr/0ACRADz/F/7GAc//ivn7/m0COfw//vYBN/8qAKYARQAlAo39b/3KBJn+hvmoAwQCd/lvAJUDzfyF/9QCZv3+/akCHP/3/L0AYv6S/D4DpgKz/Hb/6v/X/P7/yf4I/RkDUP8T+c0BbANJ/GICnATD+i/9KANY/P78hgWE/8D5pwKJA0v8Nf9PAdP+bQAy/nb9pQKv/9r+eQKC+pL8Qwc0+xP3DAivAmn4tQMfAwT5pv9ZBUP+hvrYANACkPo3/g4IHP0n+WsI5wDq85ICQwZF+N/95QXI/CD8UAQcAEL7RAEHAor8nv19/1j/OAGq/5f/7gJ+/mb8IQIaAAj+kgKm/xL89QB3A5v/rvxpAeQCcvnc/bwI8/tm+HEImwAh9wMEsgId+0UB7AC6/pX+NfpbAg4GNvi2/doH1vn++hEJNf56+HQFSgIL+xMCFwIn/kgCiAAA/SQAaf7E/gUE+v2u+2MEEQDT+28DDgBO/Y8Ds/5i/2YFWPnm+scJ1fx19kAHsQAG95UEIQP2+OUCgAYs+/H8oAVb/+z5yQNZA/n4uQLlB5z36PyxCt/8ZPl+BlAAFvsNAqIAZP87ALn+9QI0AKn6PAO6A3z76/8qAuL/1wDS+zH/8QaG/Gv6YATj/sL+iwXp/YH+7QQd/ysA5AGn/VADhQAB+poELAGH9+kFDQeV+EH+bgQJAaj/+Pv/AdIGvvhS+1sKlf+V+GkFhwQg+3H9bQRfA5v6gv1UB+YAlPy2Ajr/If8bA7b8awCnBYf75v8uCPn8Cv6YBpX+hf2PAuz+pP9nADkA3QSv/0D9gwXp/hL7eQZzAO75RAWSAX36/QMFAqf7+AMkBKL8aABGBOj///wpAHIDWwDQ/bIB7gJ+/9X+9gHOA0T/FfyjAvgE7P0o/jwE+gJf/uT+4gJkArn9Hf/BA1sBOv1x/8kDLQKo/akAiAP6/ZL/YgXQ/jP84AO4Aq3+0/9l//cBqwI//RUBMgXO/dP+dwTR/qD+ewR/AK/+IwPeAJH+jwGcAIT+/wBXAf/+yQDbART/owF+Axf92v9iBtH7C/oSCT4ClPaiBDwHFfld/4cHnf6J/YsDKgHk/eX+4QFNAiD+DADYBFz+HPvMBJ8E5vro/lsFzv0//C0FzwF6+/MBWwID/MUAIQQX/57/VgFk/84AbgFS/0EAqwEhAEz+MQCeAvz+4f0SA20AE/yQAT8CGv2I/9wBM/8z/7YA3wBTALP/PACAAAEAGv/t/i0ClQFs/B4A0QPk/R//ZwO1/iz/vQGJ/SAAGQOL/ST/4QIA/jP+TAJj/yT+TQF8AKv+VQDDABT/Rf/B/8T/jQC9/rL+FwMEANn7UgIyAt/7DwHHAhz8Df/LAjj/fP7A/0kBBAFi/aoA2wI0+8L9AQUH/i/8aQNDAPr8FQFCADv+pwCLAAj+Iv+BAVf/Af7QAWYAOfybADMCTP3k/0gCYP17/g4Ccf5+/QsB5f9a/t0AbAA1/ooASQE7/lT/0wBp/tr+FQAV/28AWwB//jUAPAD3/pAANf9F/tsAJ/99/k4Bw/5t/joCIf9h/RABTf+a/YoAPgAF/7v/pf/f/wr/Wf7CAFoA2/2l/18AlP4HANIAs/7S/goAlf8+/3r/x/8MAKD/Wf/g/63/If8JAD0Ad/7q/vwArf90/m8AKwBv/tT/cwDQ/v3+BwCu/8r+uP4JAAoAmP7v/8sAKv4H/9ABIP+q/SMBigB7/Xv/PwHE/jX+ZwA7AKj+Uf+kANP/iv67/wcBD/9A/hwBiwBs/Vj//QB1/vb+mwAa/yv/+P9t/yIArv8g/+cAYf/c/RoBZAC7/YUA3QBZ/tv/jQBN/1z/EP8rAKoA1v0Z//oBEv81/swAwf/8/tX/dP9bACoAdf7m/zAAav71/3wAp/58/woAa/82ALH/SP/LAAkApf6f/0UACQBo/1j/pwCA/xf+8gDzAO/9MwDEAZj+tf6/AAcAXv+Y/yYALgAP/8b/dgD6/sX/bADW/mkA+QBH/un/dwH2/pb/ugAr/6v/BwBf/1cA/P/G//MAvf+b/+MAPv+i/2cBOP8t/2ABwP8j/+4AagCZ/+z/RwBwAIv/rv+xAMj/DAAIAVb/tf9mAaX/m/9HAbb/Zv/kAA4A2f+cACwAeQCXAOL/WgB6AAIAQAAMADIAmQD4/2oA2QDE/2sA3wBl/2AAMAFc/0cAiAGF/77/ZwFmAOn/UgA1AAgBsABd/8AAkQGa/8n/iQGEAF7/wABGAd7/9/9JAdkA7/+EABABkgAcAI8ACAEuAPP/MwGZAKH/NQEZAVT/iwBxAQ4ARgDHAEYAzACkABMAJQHvAKj/lwA/AS0ARgAQAYEA2P+iAFwBbwAVADAB4QDy/6wABwFcAFsAowC7AIsARwDUAOwALQCDAKUA9P+ZAPwAJACWABcBPQBIAAsB5wBoAFAAwwDaACgAYwAbAXgAEQDdAM4AIwCfAA8BSgAtAPwAmgASALoAjgAfAP4A1ADw/7IA/gAIAEoA9QBxAAkApQDzABwABAAOAZYAyv/FAOAA7f9SAKAAPABJAEsAmgC1AOT/SQAGAQ4AEADjACYAFADJADQAJwC0ACcACwCFADcALQCVAF0AJABoAHUALwBIAI8AMgDu/1gAJgC5/1EAggDd/xwAlQAyABoAVgADAOX/PwAdANv/IwA2APz/HQA3ACoAUAAhAOP/PAA8ALj/1/8zAA0A9v8vADoA3f/F/zMADwCn/x4AQQCy//D/OQDO/9r/EwDw/97/rv/U/0YA4/+o/xYA0//C/zgAwP+g/0MAy/97/zoACQCD//n/GQCo/6//8P/U/6X/4P8JALP/sv/6/8j/tP/Y/8P/5P/b/4n/1P/+/5b/r//T/4T/nP/W/8L/s/+q/9v/+P+B/37/AACy/0n/wf/k/2f/jv8IALb/af/e/97/Yf+R/9D/n/+l/7f/tv+t/3//wf/2/2z/fv/0/4L/b//g/6n/nf+4/3j/oP+p/1//p/+w/2//vP+z/3j/tP+c/5L/yf9y/27/yf97/3T/xP99/27/ov94/4n/p/9r/4j/xf+L/3D/r/+e/1j/k/+5/0j/Zv/Q/2L/X//v/3r/M//U/6j/Rf+p/6P/Vv97/37/Y/9r/3P/k/+G/1j/jP+q/3L/cv+I/3z/dv9n/37/nv9u/3n/sP9a/0P/w/+h/0z/pv+p/0b/e/+l/2T/cv+Z/3f/XP9z/6b/nf9n/4H/h/9V/4X/mf9l/5r/o/9X/37/kv9t/6b/o/9t/5f/kP98/7T/kv9z/8H/r/9s/5//0/+i/2b/iv+y/23/bv/T/6r/Zf+n/77/lf+e/7P/s/+Y/6D/yv+O/3L/4f/K/3H/vP/B/33/wf/Q/5b/uv/C/7X/3v+9/6b/3//C/5X/wf/K/6r/rP/M/+3/1P+w/9P/3P+7/8z/1//L/+H/5//h//D/4P/g/wUA8f/K/+T/9//g/87/1//6/wwA///9/wIA//8MABIAAAAAAPz/5P/1/wQA8P8GACUAAgD4/xoAEAD//xwAMgAcAAMAFAApABEAIQBSACQA+v82AD0AGAA4AD0AKgBJAEQAIwAoADYAUABMACgATwBlABUAIwBwAEQAJgBYAFkAQQBEAFIAagBbADoAXgBtACsAIABgAGoAPgBBAGwAbQBVAF4AaQBXAEwAWwBqAFYARwBtAG8ARgBzAJsAVABKAIoAaQBKAIAAhQBtAIwAlABsAGgAjQCIAFwAagCAAFgAYACHAGcAaQCZAIMAcwB/AG0AhACRAGgAjACwAHUAbQCNAHQAbgB6AHoAhQBxAGIAhgCLAHkAgQB8AHsAfABrAHoAhgBtAG4AegB5AHgAXwBnAIgAZQBdAIoAbABnAKgAjABSAG4AhgB9AHkAeACEAIAAaABqAGYAWwB3AJAAhwBmAEIATgBuAGcAagByAFgARgBMAGIAdQBoAHMAkQBVAB4AVwBtAEcAVgBhADoAOgBfAGAATQBaAGoARAAfADYAVwBdAEUAIQAvAEsANQAyAEoANAAmADcANAArABoADgAgABQADABNAFEAGQA8AE8AEAAcAEAAGgAGAAkACgAoAC4ALgBHADAAHQA9AB8A//8sABcA4/8kAE0AEwAfAE8AGgD1/yAAGQD5/ykAOAD9/wQAGwD2/wsAJwD8/xMAOQD7/wAALQDj/9D/LQAeAOz/IQAxAPT/5//8//T/7v/0/+//5v/u//3/8f/Z/+7/DgDp/7n/2P/6/+H/zP/b//X/9v/o/wUAFQDa/9v/DgDa/8X/EgAEAOn/JgAbAPn/HgADANT/7v/c/77/6v8AAAoAKgAeAA0ACwDf/8f/3v/R/7v/vP+u/6f/wP/W/97/5v/p/9X/0P/x/9z/o/+6/8P/hP+Y/9f/t/+W/63/sv+j/5L/nf/C/73/uv/R/7L/lf+Q/2T/d/+2/4//g/+2/57/uf8SAOz/qf+y/6L/m/+1/67/ov+r/9T/8v/E/9D/CQCp/3T/5//Y/5v/6//d/4T/nv+x/73/4/+j/3T/kv9o/2P/rf+M/17/gP9m/zb/S/9l/2n/YP9U/07/Of9R/5D/ev9X/4z/pf+P/6D/pv+F/2f/Wf9k/2f/av+g/7r/kP+I/5j/k/+b/4r/Zv9+/4j/Wv9o/5H/cP9o/5f/bf8u/2z/lf9y/7H/4/+O/6L/EwDv/7z/AgAOANT/0P/f/93/5v/w/+X/1//v//T/wP+w/7n/kf+A/5b/if9+/4j/hf9w/07/Pv8//x//Gv88/zn/Uv+a/5n/gP+y/9j/pv90/53/wf+a/6P/4v/m//b/OgBQAEEANAAwAEMALQD8/wsAJQAVAAcAAgAKAAoA/P8AANX/gP+G/5r/af9g/3v/g/+Q/4z/lf+5/8D/7f8tAAQAvf+8/97/AQD3//n/LwAHAND/RQCXAF8AdgCpAFcA9v/6/xYAAQAZAHUARwDS/ygAnwBlAFQAggAsAM7/7//7/9X/BABjAHEAaQB7AGAASgB6AIwAcABoAGoAfgClALIAtACoAI0AmwCVAF0AbgCPAFMAUwCzAKAALQAvAJMAlwBAAC4AOgAHAPf/DwDi/7P/2//9/+X/xf/e/yUAKQD7/x4ASwAbAPz/GwA1AD0ATABvAJMAhwBtAGoAUgBGAHMAkQCHAIgAigBsAEUASQB3AJgAiwBZABIA9v/6/9L/t//S/7z/kv+r/7j/tP/j/wIA+P/p/77/y/9BAHIAYACjAMcAnQDeAF8BkAGnAasBfgFkAV0BewHoAR0CAgItAkgC7AHAAfIBHQIdAtQBjAGaAY8BVAFBAf8AkQBkACcA2v/4/x4A6v+t/5H/kP+M/2P/af+u/7z/mP+L/53/2P8YAB4A+f+//2b/Dv8B/2D/0f8JADYASQAmAB0AGADH/1j/4/6D/nP+Xv5M/tj+gf97/yD/7v7m/vP+3P6v/qD+yf5C/8P//P9CAJMAmgCcAJgAUgAuAEYAYgCcANAA/ABYAWsBHQEXATgBPgFvAYgBRgEHAQEBGQEjATcBpAEEAt8BswG8AdcBOAJyAi4CFAITAtQB8wFTAk0CNQIaAsMBpQHLAcoBwAG6AaMBrwHIAaIBQQHYAI4AXwA7ADoAPAAKAMP/mv+l/8b/s/9v/yL/vf5K/tL9Of3G/NL8N/2i/b/9hv1i/WX9Sf0q/Rz99/zD/KP8rPzp/E391f1h/qL+mP6F/oj+mv6Z/nH+Uf5g/pj+0/78/jb/gP+F/17/S/8a/9P+tv6k/p/+8v6Q/ykAjwDSABsBTgFYAW8BfAFeAVoBhAG5AQkCdQLjAlYDxAMVBEcETAQZBKAD+QKAAlgCYwKUArkCswKzAp8CSwIEAscBTQHXAIwALgDZ/9v/JgB5AJQAbwA5ABAA9f/f/97/AAAYAPb/uP98/z7/9/6V/i7+1f1p/fj8sPx0/Dr8BPyR+9T6APoc+T34ffe+9gH2X/XL9Ef01/OH83jzjPN28z7zEvPx8tXy4PIR8zPzRvOh81b0JvUT9iz3VviL+dr6VPwL/tv/fQHsAlcEygUnB3sI6glFC2cMkQ3fDkQQzBE+E0QU7RRqFdsVTRaYFqMWYRajFZIUlhPBEvMRIRFWEJMPnQ5sDUkMIAuxCSQIkAbTBAwDnwGfAK3/pP7Q/QH90/ts+tH45fYU9azzefJY8Vfwqu9q703vMO8r7wjvkO7H7bHsZ+s+6p3pe+lX6RTp/egX6WzpF+q16uLqnurz6RvpjeiN6Arp7ekx677sg+6V8O7yePUe+GT6APyS/bL/igI5BpgKTg8BFFsYXhz+H9giLCVkJyYpWyqGK+AsZy4AMFAx3zFYMc0vcS02Kk0mFSKmHSsZ2hSwELAMugiWBDsAbPsb9tzwI+wc6PzkveJl4QHhKOGW4WzileMF5dTm3+gS66rt2vCL9HT4WfwHAE4DKAZ3CAAK5ApgC4QLiguCCy8LawoKCeQGEASHAD78hfe78h7u0unM5Rzi4d4d3NfZxteV1bXTx9LL0qHTT9Xq14jb6N/C5A/qgO/W9D36nf/dBF0KRxCJFvYcBiNHKH4siy/VMcYzNTUXNok2ejYgNpk1qzRPM10xZS5KKkEltx9tGtoV1xEQDl8KxwZMA/P/1Pzt+f32nvPR73DsUepg6VXp9+m36j/rpOvz62TsJu0W7gDvke/W74/wL/KK9F/3NvqA/A3+6f5m/9v/EwDE/+P+iv0r/DX7qvps+jr6svmZ+MX2WfTW8Vrv0Oxo6kbonua05XLls+VF5sPmFudQ51/ngef75+noueqC7dPwtfRN+T/+GwNpB+UK7A25ED0TqxUjGL4asB2xIH8jPCaUKBIq0yrzKokq0inHKG0n5yUOJAEiGSAfHtAbJRkWFv0SIhBEDUwKZAefBCsCBQAM/kX8W/r294H1fvMU8jrxqfAw8Nnvku9778LvJ/B/8LzwifAL8Mfv2u868Obwo/FR8uryWPOt8/fzCPS98ybzZfKA8Xbwi+8b7xXvKe8470/vSu/L7rztmOzp673r0+sW7K/stu0T76HwI/JT8zX0F/U59p33Sfl0+y/+GAHkA6MGWgnEC7MNMA9OEE4RjRIZFMEVcBcOGYga1BvAHEIdkB2eHT0dbhxZG2wa2RlWGe8YsRgkGD0XOhbkFCITJBEQDyoNcAuxCRwIrwY4BfYD/ALpAYgA3/4V/Tr7Ivn69jD12PPj8h3yQfGG8Cnw3O9X76vu9O0e7fProeql6VTpsulK6o3qoOrm6kfrcesS60HqtOnG6Q3qTerN6sbrHe2O7rXvUvDC8HLxBPIf8kvyIfOh9Ff21vc5+bf6Ivx1/cz+JwCpATsDnwTnBRsHTgjuCcoLHw31DcwOtQ+bEJcR4hJMFCkVdRXPFTQWZRa0FhsXJRfxFrcWVBYEFgYW6hVcFW4ULRPLEXsQWg+gDhgOVw2bDOILtApoCW0IRgexBfgDTgLjALH/ef5m/b38K/wt++L5x/iu9yv2r/Sx8+fyLfLG8bLxsPGY8Vzx7/A/8EzvT+697cHtFu6w7pfvWfCd8IrwZvBm8GPwI/D47xTwfvB08ZPybvOT9Nj1RvYd9iT2afby9ur3L/li+oL79/yH/qX/jgBYAbEBDQKqAjsDJwSNBdIGBQhUCUYKxAovC6sLLAyBDHIMUQyuDH0NQA62DtIOxQ7dDtMObA45DnUOrA62DrIOwg6/DnMOcg7gDrsOJw4JDt4Neg1/DVkNoAw/DB8MHQtyCTUIKwegBUMEigNVApEAsv9M/8/9wfsz+qf4AvfC9an04POr81Hz+vIK85Hy0vGa8RDxY/B28Hfwb/Ag8ebxxfIl9FD1Hvax9hf3wPcB+JP36/cY+Qf6qfoK+4r7Rfwj/Hv7hvu2+2P7tvoa+kP60PoS+4D7KPzK/Eb9//zS/Nr9tP65/r/+k/7I/hwASgH7AU8D4gSVBZUF/AX9BqgHPQgiCS8J2gitCZgKygqgCwYNVQ1TDH4LrAsCDCkMUQzkC7sLUQykC58KgQvaC3gKkgnzCGkIrwhTCH4HTQeqBvIFPQVgA4EC9AKFAZH/Kv/F/rP+Bv9V/vX91P1H/Fb7ufsM+zP6wfq/+n35Vvmg+jr7/fob+0n7APsa+oL5yvqt+4T6E/t+/N768Pk6+/j6o/q0+jz5RPkv+uz4zvjF+ej4HvlC+mz5q/g7+Tr6l/o4+YX54/v0+sX59PvD+5v6S/1H/nv8lv1r/wn/JP9aAPUAaABSABwC/wL8Ab4CmQRrBK8D4AMtBDIEPAQBBYYFRAWVBpAHiwWUBcYHIQZ5BCoGlAUWBKUFRQbDBcwGtAZ5BVUFOAUGBTQEegJdAwwFwAK/AU8FigXoAaQCTwXvAmcAmALiAn3/xf8/AiABMABJAYkAtP80AHz/If9J/5f9W/0E/zv+F/2Y/YT9pf0L/Tz7xPyz/ZT5xvm//YD6kPft+z38fvg5+lP8kvpS+vX6Dvra+dr5Dfl4+Zr6HPpR+Yv64ftS+xz73PuX+1j7rvsU+2774/3a/jz9u/ym/ij/F/12/bX/Df7J/LEAEwIr/x8A2AJfAUIAVwJmAvf/1/+wARkCggHTAXIDxAT2ApcAIgJFA04BtQHmAk0BogEFAxsCdwNjBRQDrAFtAu0BuwIQA7sAdALBBRICOP9DBFAGIwH7/wUEFQNX/7oBvgSrAXcAsAOAAvr+sgC1AlUA6f6nAHoBk//a/VL/CAH+/tb8Rv7P/2j+MvwU/d3/Yf6x+73+7v9d+4X7OP6w+zn7+f35/PD7o/xc/OP9bf63+9X8O/91/Fv7J/7q/dL84v6L//D93v4WAIf9qfxz/4r+Rfzx/oMAoP6I/vL+8P9eAmMAQf3Y//IAH/4l/wIBHP8r/9AB7gH7ABwBRgA2ANsCkwKL/kX/8wIKAXj/2AMpBOb+AgAeBf4CDv/GAWgEOALVANkBkQL5Ac8AOALUA20AJf+0A1EDjf/nAQMD+/6x/w4DhwAO/uEBKAT6/xH+AQJUA6QALQDrAK7/3v5/AJsBsP9Y/1gCggFr/ov/iv+t/pYC0gFT+739CgT4AIX8A/9gAd7/Ov8b/9X8tP1cASn/w/xbAI7/rfzfAPMAjPvh/q8Drv63+lT/igJm/uH89AHyADT8bAHWBHH9Pv26BJkCpvxO/1sDdAAc/lMCoAIt/RIArASf/eH6dQNJBLj+JQC1AZr/kQCdAScAHQCBAO8AzgFFAGcAZQM8Aa7+0wHyAXQATAKYACUAVQTxAWb+oAFbAw4DsQDM/NsBqAVj/oX+4gN6AF8AfANh//D98wH2ATP+Tv4tBH0DkfxEAK4Em//CAEYDsP7G/icALwCPAk3//P4XBFf/Wv4vBLT/QwAeBHP77/3kBQ38H/sbBcIAB/7jArX/Sv3fASQDpv06+6UC5gNz+9T9xQNcAEf/RwDZ/wYAcf1aAJIDvvwmALMFRPsd/qoHef4t/mcDzftLAEgEH/vpAdAEyPmOAqQF1vfdAa0JqPgD+18I6v7c+TgG0wV1+Q3+rgkF/1/1/ASWCDT4h/vIBxMBe/oRAoQG0/77+3gDWf9/+t0HIQPO8ocEUQtm85r9Eg97+kb3TQh6AUT6qv/VAo4C2PkM/RcL6/t49JkNyAW98cAEpQgC+fz/uwDl/CMGDP1s99cJGASY82IB8Qnm+8r4SQQLBmH6g/pZBw8CbPhlAmAECPym/7r/a/2eA939pfnOBnwCpvZ2A1QGCfmQ/9kDbvo0ATYEV/gt/0YG2vrd/nYGovoF/LQF7fua+n4GPgBq+MgCjQZI+4D5cAXMBCD3a/zpCTv+8vQjBuUIvfWT+eEKoACS83ADoQnR9vr5+ArU/yv2/wNEBTX7xf1BAWsACgC5/BH/+wPY/Qv8CAQsACL5sgCLA736t/3bBWT9QfiqBEEFpfjG/OMFNP9j+vD/OwK2/+79nf/CAhUAvPtQ/mwBNgCf/7j/3v53/1UBrgDt/d3+DAHc/dL8WQH8/1H85/8ZAZv9cP/zAZz/FP5b/s//wwGa/vT7FAJ8A2z6WvucBc4Bpvhp/w4Gpf3z+pgDqQGw+kkBpgSt+gz8TwZ4AB/5GgELBM/88fyYAVMASf2+/scCpQFH/Iz96wIzAcn84v5uAoX/zPxNASUCQ/xd/qMDOf9q/GMABwH1/1v/4/04AEkCjv9X/pj+dP+PAmIAUfzvAGYDQP4T//0Bzf3w/WYDZgDw+/4APQMh/gz/rAI//539PwEFAY/+Nf9lADcAIv/C/mkAOQHL/zL/sAA9AYX+8f1xAkoCV/0w/1AC7f6l/gwCRAAT/nMA1gEZAAb/DQCpAPn/OwCKAGv/X//AAOwAa/8h/4UBBAFs/df/AQOJ/mb+swNdALL8ogE4Aiz+7f8FAuv/WP+TAFwAg//v/1oBRgFf/2D/xAFEAQL+iP/6AlX/AP3UAm4CIfwRAIcEYP7A/QIEuQAC/HIBrAMh/rv+2wJ3/+L9ZgO1ATn8rQG7BKP8Pv3cBP8Af/yEAZwCuf5c/68BIwFP/yIATgLt/ub8oQN5A4z7K/+oBAD/Ev63AlAAuP4/AR0Aq//yAWQAJP7aALoCF/9l/gIDUgEe/W4BDgNb/tv/0QGj/+cAZQCw/v4B7QBC/qsByADo/iMCFADY/rgCEADz/kwC1v4p/7UDS/90/vYCk/8H/9kCs/83/7cC4v9P/p4BhwGM/z0AVAFkAM7//ABrAO//kQEJADL/KQIpADn+dwKoAfr9ewABAi8Aff8BAMYB9QCw/sgARAGY/28BUgDZ/mMCoQDw/UECLQHI/SQCVAL0/UAAOQKT/7n/dwFjAFD/xgBZAbX/VgCXAb3/vP9nAQ4Atv8HATQA6P+VAF0AbwALADsAdwHm/8n+QAFFAV7/BwCIAE0AhQD4/zUAyADZ//n/vQAVAO7/GQAbANQAOwCU/9AAJgAT/+oA3ABm/38AOQCO/0AB8/+n/rwB0QDn/foAmAEc/u//5AFZ/zT/rABNAPH/ev8aAFgBXv+X/oYBuwD//TEA5AEW/0f+EgFdAav+B/+fAT8ACP4PAFABdv8g/xAAOgAjAIL/ov+ZALb/Jv9hAND/KP9UALf/Mf+dAMv/Av+bANf/6v6zAP3/gP45AIAAEv+c/yQAvP9f/0//VgAfAIn+xf/HAL/+Kv/8AGX/fP5QAIkACv/Z/gkAPAAB/x//VgDA/9r+sP9DAFv/rf65/4cACf+Z/moA2f8+/sP/ggC2/vj+YwBj/4L+1/9aABL/6f73/8H/9v5J/8H/hP8o/1P/2f+P/wb/u//1//L+N/8DAFv/Ff+1/4b/N/90/4D/kf96/wn/cv8DADf/3P7b/8v/Cv+C/6r/Gv+g/9X/zf4o/04Af/+Q/p//NwAm//P+5/+7/wn/dv/S/2n/Vf+I/4//hv9k/4H/rf9a/1b/vf+V/1X/cf+J/6//i/9Q/7f/uf80/5j/4/9E/2v/+/94/x//xP/y/0r/Rf/7/8n/D/+T/zwAjv8k/83/8v9y/3X/2//Q/3j/lv/h/57/ff/h/8b/bv/A/+3/pP+0/8b/m//T/+//lf+j/+3/w/+q/9P/zf/C/83/yP/S/9f/u//C/+n/5f+//9L//f/H/5//BgALAJP/2/88ALP/mP81AAYAmf/+/yYAvf/W/yoA8//F/wcAEwDh//j/EgDt//H/FgAFAPL//f/+/w0AFwD2//b/HwAVAAIAGwAZAAAAGQAuAAcAAAAzACwA+P8QAD0AIQARADAALAAZACoAMgAnACkALwArACsANQA3AC0ALwA7ADcANQA9ADsAPQBDADgAOwBOAEYAPQBIAEkARQBOAE8ATABLAEwAVABPAEoAXgBeAEUAVwBoAEwAUABpAFYAUABsAGQAUwBjAGsAXwBiAG4AaQBhAGsAcQBhAGYAfABuAF8AeQCCAGoAbAB8AHUAcAB8AHoAbQB4AIUAdQBxAIAAewB1AIQAfwByAIIAhgB1AHsAigCAAHUAfgCFAH4AegCEAIcAfAB/AIUAgwCGAIYAegCBAIoAgACCAIcAegB+AIoAfAB7AIsAfQBzAIgAhgB2AIAAhQB1AHgAhgB6AHAAfwCAAHEAdAB9AHUAcQB3AHUAcgBxAG4AbwBvAG4AcQBrAGQAawBuAGUAZwBoAGAAYwBpAGIAWgBfAGUAXwBXAFsAXQBVAFkAXgBQAE0AVwBVAE8AUQBQAEwATQBMAEgARgBGAEUAQgBBAEEAPwA+AD4AOgA4ADoAOgAzAC8ANwA3ACkAKQA0ACkAIgAxAC0AGwAlACwAHAAdACgAIQAYAB0AHwAYABcAGgAWABMAGQAWAAoADwAZAAwAAwAMAA0ABAABAAUABQAAAAAAAQD8//v/AAD6//T/+v/6//X/8//y//P/9P/z//L/6//r//L/6v/j/+3/6f/e/+P/5v/g/+D/3v/d/9//2//a/9v/1f/V/9r/1//V/9f/0//T/9f/1v/U/9n/1v/M/8//2f/R/8r/1f/T/8r/0P/Q/8f/yv/Q/8r/xP/D/8L/v/+//8b/wf+3/7//xP+4/7r/wf+6/7f/t/+y/7b/uf+y/7L/t/+1/7T/tP+z/7P/uP+7/7T/rv+0/7f/tP+2/7T/rP+v/7b/tP+w/67/qv+p/6v/rv+v/6r/qP+p/6r/rf+s/6b/ov+j/6f/qP+k/6b/pv+i/6f/qv+f/5//qv+i/5j/m/+d/53/oP+k/6b/nv+W/57/n/+Y/57/of+X/5f/nf+Z/5n/n/+d/5j/mv+Z/4//hv+M/5b/lf+M/4n/jP+N/4//mP+c/5L/j/+W/5b/mP+i/6X/m/+d/6z/sv+s/63/sf+p/6P/qf+s/6z/sf+y/6z/rv+3/7f/sv+2/7f/rv+v/7f/tv+7/77/s/+p/6r/q/+z/7n/sv+q/67/uf+//7f/q/+v/7X/rv+p/7b/v/+z/63/t/+//8H/yP/J/7n/tP/S/+f/zf+5/9L/4P/K/7//0//n/+b/4P/m/+n/5P/o/+v/4//n//n//v/w/+X/7v/3/+r/4v/w//r/+/8DAAIA9v/6/xUALQAnABYAEgAUABwANABFADUAIQAnADMAKAAhAC8AMwAlACkAPAAyABsAJQA7ACoADgAXAC4ALwApADQASgBSAEMANAA4AEgAWABcAFYAVgBjAGcAWQBSAFsAZQBtAHsAggCBAIoAjQB7AGwAawBsAG4AdQB1AGkAWwBRAFAAUwBWAFUASQAyACQANABOAFQATQBHADwAMwAvACcAKAA5AD0AMAAuAEYAYQBkAFsAWgBbAF4AcQB9AGkAVwBwAJsAnAB4AHYAnQCuAKYArAC9AMUAwQCxAKEAkwCIAIYAiACNAIgAWQAhADQAdQB9AEoANABbAH0AXwA9AF4AgQBxAGkAhgCbAI4AbwBqAJ4A2QDLAIUAZgCSAMEAuQCdAKYAxAC1AHUATwBVAEYAJwAfAAEAyf/A/9z/6f/l/8v/rf++/9f/yP+6/8X/2v8DACgALwBFAGEAMwDz/xYAZwBjADQAPABqAIoAhQBhAF8AgQBoADAALQAuAC0AWQBgAB4AAwANAP3/+f////P/8//W/3P/Uf/A/ywAJADm/83/+v82ACAAxv+j/73/q/+V/8T/6P/o//X/4v+9/+H/9/+t/3j/oP/5/y4Av/8H/zT/FgBOAKH/9v7j/jz/fP92/4z/3P/m/4D/N/9j/63/1//g/6r/W/8+/xr/zP63/vj+TP9d/+b+Wv6F/hf/Of/4/tf+Dv9r/57/xf8KADgASQBnAHIAUwA+AGEAtADfANMADwFtARoBQADY/zAAmAAzADj/rP7K/u7+z/6y/sr+uf4j/o79ev24/S3+oP7G/gb/Uf8C/6r+6f4e/wv//v78/mb/EQAVABgAOQElAkwB3f+0/5cA1gCe/0X+rf4KANf/lf4C/5EAoQBg/4v+s/5a/xn/4f0L/pT/1/8a/wT/BP8w/yYAcQCG/wr/eP8/ANkADgBk/pX+PgAbAIb+Mv6K/jP+Dv5T/gH/uACDASAAk/9eAPj/gf/f/xz/hf5T/wr/iv4ZAEgBTAEEAqUBxf+m/3sAAQD5/5YAPAB1AGgBIwD7/X3+IwBFAGb//P1S/U3/pwBZ/hj9Sf84AP3+Gv6U/Y3+qAAbAMf+tAAtAiAAUf7x/mwAFwHF/9D9e/5sAawCHgEJAOsAfAGQAAL/Wv4YAKQBGQCA/7EBfQHr/pj+5P8RAG/+oPxL/Tj/XQCtAGT/RQAeBK0A0/md/cgBm/1C/7UD/P6a/HYAi//G/c//nP8b/fn7ef18/0r+GP6N/677fPoKAaH/ZviY/DIEsAJJ/wAAkQJxBEsDPgB9/2cCZAS3AK79sQKmBz8EBAB8AqEFLgLx/T8AGgKl/aT6wfy9/hP/BP8+/l38Gfvz/YoCAQK+/dX8IgFcBi8H/gQwBWsGggUUBIMDBgQfBOIB7gCJAkwB1P6jAL0CwP8A+xn64/t9+3T6r/t5+r324vjz/34CUAAMARIF7ARFAQwD4QcXB6kFZgljCjsGSAc5DnkOqAUlATYGYQjhAGb6G/xN/xH/x/3g+0n53flP/av8YPfN9Zj5o/oE9+L1hvlw/QgAlQKbA/gBiQF/BbAJhgnMB7sHiweuBt4GfQcDCJEJaQozB3MCxgJJB2UHvgF3/vb/DgAQ/T/8Df8gAAb9Vvoh+/f8iP7PAGUCPwHk/on+xwAQA1oDXwJDAWgAbwAxAaUBlgEcASQAC//b/Rf9gP7AAXwD1QH4/nD+ZAD3AMH+cP2G/ln+Cfsc+PH4nvsc/Fv6dPmx+sv8ef7s/6wBAgNhA5QDpwM0A0QDowSPBpwHnQYCBQQG2wgvCeUFWAK2AQADUQJf/4D+mAC+AI38t/j5+T79Bvy19u/zMPZe+Gr2evTO9578b/xn+Rv6gf/EBA0GUwVzBkYIHAjOB50JSAweDtwOAA/VDtENcwyADGcNbAzsCDIFewOfA/oDtQPWAo4BKgBx/j/8MvpW+LH2Nvak9t32QfeQ+CT7Df9iAgcD4wEaAZsBMgOrBOYExgPeAccAygEsA+wBJ/4c+2L69vgS9Jvu+exQ7ibuC+u16BjqIezv6lvoB+h86bbpGegM6BXstvGT9db35/kF/K793f6uADgDtAT2BOYFfAhhDEIQPBK5EUUP4gunCYgJDwqLCisMmw84E+wTpBHDEM8TfRcwF7sS5Q5pDyQSrRMqFMEUBhVkEzwPAwumCO8FGwHA+2n3VPTd8R7vK+1W7b/tCeyb6JblQuUJ6ILrrO1l7yfyVvVN9234/PqW/44DEgSoAh4Eigm/DusQ9xD+DwYOiAqsBXwBAv8j/Mb3zPNQ8SLvFOyW6K3lMeME4GjbQtZ/077U3dfM2t/dbOFT5Qrpd+uq7fbxAvhM/ooEzQoeEdkWVhuwH+8jyiUkJRkkwiNkJFUl7iWoJ5kqXSshKI8ipxx1F80SGQ7tCR8HtgU4BXgE1AIxAb7/mP3Y+uX36vQK8yvz4fTh98v7Ff9nAOr/nf52/cz8jvzG/Ib96P7nAKECHQPeAnYCBgF0/hX8K/pm+PL3lfnp+xf9UfwV+tz3nfYF9n/18/Qg9Bry5u6e7L3sw+1A7bbq3+eQ5i/ne+kz7A3tFOw56yPq8+fm5Qfla+Wk5gDoiuo77+nzu/YU+QL9QwIJBlQHqAlmD70WHB4VJSEq4SsrK8Qq/iv4LHYsiCuZKiIpySaNI38ghR5lHB4YRxH7CTEFagOPAskAbP7u/a8AJAT3BGADkgLSBKsI5wqFCjAJIwkAC2INRw+HENIP1wt5BQ7/S/q+9g7z1u6G6R3jMt6m3AndUd3g3D/cwNvw2hPbut5s5QjsXvCJ8hr1tvoTA6YLURGpEsMRChE+EGcOHwsuBsX/LPjn7xDofOF53KnYq9Scz2vK7MY9xkDHhMfMxjvHV8o80ILYYuLr7Or3KgTuEZYfGyvqM3g6Oj9dQhZE+kSkRTVGrkYLR+NGK0WjQPQ4LS+LJGsZLA5HBFT9RPn49lb2afj3/MwBEAWABkIGhwSCAiwCGgWKC0oUhB2AJVMrMy91MRsxjSzVIyAZeQ4VBCb6k/Le7hvtQems4ZPYStB1yBnA/bfQsYyuka6lsna7AMiy1UHidOze8+n45/wwAfgF0woXEG4WXh1mI2MnESn4J8oipxiaCif7nuxK4NnWutC3zbrMI8yKypbH58M9wPS8MrqbuIq5Zb4DyHzWnegK/E8Opx1EKbUxgjiwPqlDfEYZSM1KAU9GUm5S5U/pSzBGnD1SMrMlnxhWC0f/4PZS88Pzd/YZ+hH+zgGjBLEGoQjAChsN9w8sFMsa2SP2Lf02wTx5Pmw9FjtUN/ww1ScsHQMSAQYI+Qrsyd/l063HybuasvytXK2JruOvcLG6s5O2C7qDvwrIctK03CrnbPTtBBwVOSGRKLIrMipSJHsdchomHF0eqBzLFpEPqQgMAWv38et53u3NWLtaqxmjD6QvqxC0krt+vxC/xLw8vUTDSM2d1yTh9usn+ugLyh+cMxdFu1GuV6JXO1TFT8ZLNknCR1pFjj9qNr4skiTnHJMT3gcS+zzvRuYh4rjjIOnv7qXzsPg5ACwKzRO4GmofSiROK9k0sz+ZSdFPnlAVTT9IoEPgPQk1LymTHBMRUAbU+vjtGuBm0SvBaa+knt6S0I6WkkWcV6latynE/c7C2H/i4OuZ82f5bv9zCDQVMyQEM2w/oEfBScxEUDpqLWYgoBNiBkH5YO6G5iTgUdlJ0ULI+r2bsZ+kL5symDebGqIAqwi1Qr9+yBPRcNrC5PnuV/hhAbMLUBjPJlA2U0UcURpXplaYUR1LNEWBP584sC8uJVwa5A/lBQD9DvYK8fDs9eiz5b3k++bz63byqPkDAfsHlw4cFg4ghCyBOUdEmksXUIlSBVNaUWxNo0YXPGUuaSDsFKYMlAbOACH50e223hvORr+7s9GqtaMxn26fF6Y+s1LFB9mq6lj4GAKLCEMMNg5FECkU2hmiIK4oAzINO71AZ0DYOaEuECDrDgj89Oh+2GzNochryErJZchDxYzAzbrjtBGwR61CrE6s3q5ktyHHqNpB7F34av/XA4IHJwvZDq0S3xaHGw0hWyiVMcI69j/XPb40LSh4GzcQ7wbp/0P7cfie9sP1c/Zv+O75GPlF9urzaPRc+If+zgXdDnEaNSeOMr06BUCWQ6tFFEZgRfdDrEGbPu86hjYLMRwqnyGeF/ML1/5N8dDkjtqb0i3M2Mb0wkXBgcKvxu7M3NNz2vPgZ+g48cP6CQSeDN0U7RwVJKgpqy1cMMgxljHXLncodx7CEgYIPP//9uvtceTT26DUtc69yU/FEcEmvUK6zbiTuPO5Fb4TxQLNiNNm2LHd4eTZ7JTzgfip/CgBJQY5C/EPzhO7Fh4ZAxsBHJobfBnHFfMQgQv4Bb0AL/wG+dj3fPgI+lX7KfxD/fj+2QCKAiAE/gViCGkLsg8CFlIepCdqMJA30zziP3JADj/QPJc6UDgnNSkxiy3RKtQn0yJQG2cSDAk4//70F+t34tHbcdek1Z3WxdkQ3rXiTeel64TvCPMC90T85QJOCp4RXRheHg0j2iWWJkUlUiINHgIYtg9eBQT6he9a54vhWt3s2aTWcdNX0G7NAst+yLfE07+2u6+6uL3Kw37LXdS63TjmFe2T8iH3qvrB/Or9rf+eAsYFTwikCoINPBDcEJcOvgq+BusCR//u+8D4bvV08oHxwfMD+MP7l/3e/az9tf03/mz/cAHzA7MGPwrDD6sXtCDrKGIvbjSmOEs8YD+2QeBCsELPQSJBeEB0PgU6cjPYK8YjvRp8EBEGFv099gbxv+xp6TLn9+Xx5aHnoep57Q3vI/D/8vL4AAENCX0PGxTnF+EbQCArJK4lviLTGogPnwPc+DDvY+YW39vZWdaZ01rRaNDP0OvQsM44yXvB1rm1tOKz+7fzv/DJcNSk3n7o3/HY+XD/fwKpA9oDjAPuAtUCOQQKBzQKaAzmDIcLLQgHAwn9KPe08bzspOgk5nXl9uUu54vplO3g8uT3FPsl/P77A/yI/RgBQQYbDAgSIBjyHtEmfi8mOJY/4kTdR9VITUjkRjtF0UO0QktBuz5vOnU0aC3LJbsdNRVaDIQDcfsi9WfxTvAX8bbyYvSq9U32XPaF9pX3vPnA/JIAZQVAC68R0heNHIQejRxsFucMRAHY9NfoW94/1tjQGs7RzVPPV9Fn0nTRK87ayErCy7vwtv60prbiuxjEfc4X2rzldPCN+WIAfgT8BbsF8QSKBAYFbAY3CLAJXwoGCoYIzAWzAUP86fVK7ynpWORo4WHg0OAS4svj6+Vx6F7rs+418n31a/he+9v+GQP+B0ENpBJAGE8euSQcKyExsTbUO1dA2kM1RndHo0fSRl1Fo0O0QSk/cztVNiMwkCk8I2cd8ReqEsENtAnGBuIE2wNdA8ICSAG5/vH7Svpe+uD7Pf4XAV8E1gfFCnYMZAwMCiAF6P1G9W3sV+SH3S/YTtTf0dzQ4NAS0ZTQDs/DzAXK98YhxH/CvsLlxLPICM7O1G7c8eO66qnwvPXw+Uv93/+yAcYCYQMBBLoEFAWRBAoDoQCV/TP60/aj86Xw8e296ynqIumC6EPocOgA6d3pEOvK7Envo/K69kT77v+GBPYIGA3YEEgUjRfZGnUemSJKJ1wskzGiNio7tz7pQJ5B/0BVP+88CjrKNlYz8C/pLGAqJijgJVAjaiA3HcMZGhZYEqQOIQvxBz4FNQP0AX8BwQGPApgDYwSOBOMDXQISABH9avk/9bXwC+yK51zjpd+S3DbaddgI16LVPdQE0xTSb9EB0c7QEtH90ZfT39W62PnbbN/w4qDmoOqv7lnyZ/Xd99P5PfsF/E78Ofy4+8T6ZPnJ9zn2zPR4813yi/Hm8ETwj+/l7mfuEu7y7UHuJu+Y8GfyZvSr9mr5p/wmAJ4D4AbsCcUMew8jEr8UVRcNGhkdeSDmIxIn6ylzLJMuIzANMVIx/TAlMAcv6S3iLOAr2SrYKeUo7Se7JjElUyMrIbAe/BtRGdYWeBQjEgUQbg5wDakMsgtkCr4IywaVBBcCUv9J/AH5ofVZ8jnvPuxm6bbmTOQt4j/gf94A3czb6dpV2gLa+Nk92sXajduY3OPdZd8K4bzig+Rw5n/ok+p97Bjuae+M8IjxSPKw8r/yiPIh8qbxO/Hv8LLwcfAy8A/wFPAz8FTwbfCb8PTwcPEK8tny+/OH9Xv3wvk0/JD+owCJAoAEpwbpCBwLMA0xDzURRhNdFWYXZRljG1UdJh/FICYiOSP7I40kFCWFJccl1CW3JXwlKiXUJJEkVST1I04jUyIOIZcfCB58HAUbmhkbGG8WnRTAEuwQIQ9cDYMLdQkbB3gEsgH1/kX8nvn39kr0nvH+7nTsIuoy6KPmVuUk5P/iAuJB4bvgceBe4HLgnuDQ4BDhd+EH4rTihON25H/lhOZk5x7ozeiC6TPqxeom62jrn+vF6+XrIeyO7B3tru067s7uae/774jwHPHE8YnybfN99Mn1Q/fZ+Ib6SvwW/tH/ZgHmAmQE7AWHBy8J1Qp+DDsOFxALEv8T2hWUFyMZhhrDG+Qc9x36HuAftCCOIXoiayNFJPYkeCXBJcUliCUPJWEkjCObIp0hlyCAH1seKh3cG2IatRjWFsEUchLqD0INkgriBzYFlAL9/2n90vo8+Kv1IPOf8CnuyuuT6Zbn4OV+5GHjZ+KI4c/gQeDX34HfNN/13snes9683u7eS9/P33TgOeEV4vfi0eOd5FvlEOa85lnn5eds6ATpuemM6n/riOyR7Y/uju+Z8LDxzPLo8wX1LvZi96H49flg+9r8WP7O/zkBpAIPBHgF6wZvCP8JlAstDdIOiRBAEuQTchXxFmkY2Rk5G4ccxx39Hi4gWyF+IogjbSQiJZ0l2yXZJZolGyVjJIojpSK9Ic4gzR+0Hn8dHBx3GogYVRbtE2ERuA74Cy8JdAbhA30BPf8V/fr62Pih9lb0BvK773vtSus96W/n8eW35K3jzeIf4qjhT+Hv4H/gC+CY3yLfsd5j3lbeiN7s3onfZ+B24Zvix+Ps5Pnl1+aG5xPolugl6dTpq+qw697sNe6y70rx6vKB9Pj1RPdq+HT5a/pc+1v8cv2f/t3/MAGWAgIEaQXPBjMIlQnzClEMsg0ZD4YQ9RFjE9QUSxbEFzgZmRrmGyodbR61HwEhRCJfIzMkuyQPJTklLCXgJFwkryPsIg0iCSHuH8oemR1PHOEaRxlzF04V4RJKEKMN/ApjCOkFmwN6AX7/rP0A/Fz6rfjv9iD1OPMu8R7vO+2V6xvqzOiw59LmKuac5Rvll+Ty4xrjG+IO4RjgUt/F3n7eh97T3lXfBeDs4A7iT+N75G3lH+ah5gzngeco6BvpTeqr6zjt+u7o8PDy+vTz9sn4Yfqt+638Zv3t/XD+Iv8eAFwBvgItBJEF1wYHCDsJbgqDC2gMKg3nDa8Oiw+TEN4RXBPoFGoW4BdKGaAa4hsdHVoegh9xIBchfSG0IcchwCGvIZohbSH/IEQgTx9GHkEdMBz6GpIZ7xcTFgoU3BGnD4kNiQugCdcHPAbLBGgDAgKmAFL/7/1z/OP6SPmq9wT2Y/Tn8qbxkvCG72DuIO3K61/q6uhv5+blUuTO4nbhVuBx38/ea94v3hjeN96M3g3ft9+H4G/hV+Iu4/7j3+Tk5Rbna+jc6WnrGO0E70jx2/OF9u/43vpU/Gn9Lf69/j//xv9rAD0BPQJ5A/UEnQZOCNQJ9AqbC90L5gv0CzYMxQypDccOBhByERsT/xQFF/MYlBrXG7IcMx2RHfkdeh4OH50fHSCZIA4haSGWIX0hECFVIFIfCR6HHOYaPhmiFxgWmhQcE58RJhC3DlwNAAyJCusILwdxBdUDXgL8AJ3/Lf6x/C37lvnr9zb2dvSq8sjww+6S7Dzq2ueX5Yrjq+Hz32Le/dzJ27naw9nv2FHY+dff1wfYjNh62bLaC9xr3dTeS+DB4S/jruRW5jPoUuq97G3vPPLu9F73hfla+9H83/2Q/gb/Zv/h/6YAvwESA4ME9wVFB0sIDQmiCREKSApUCmsKuApKCzEMig1gD4oRvRO7FWEXqRikGXkaRRsIHK4cNR3DHX0eaB9oIFghHyKcIqkiOCJnIVwgLx/vHa0ciRuIGo0ZjBiUF64WyhXHFIsTGRJ5ELAO3QwqC6YJNAizBh4FfgPIAfL/9f3K+2j5zPb88xbxL+5D62DopOUb48jgod6Q3JTawdgr1+DV5dQr1KjTY9Nk07/TgdSn1SbX5Ni62pXcet5v4Hjij+Sv5uToOuuq7SPwifLC9MH2j/g3+rP78vzv/az+Mv+j/y0A4QDCAcQCtwN0BPgEYwXuBbMGgwc7COMIkwljCmoLxgyHDngQTBLyE4QVBxdiGHoZYhpOG08cRh0SHrMeSx/uH5IgKyGhIcEhbCGyINIfAR8+HncduRwLHF8bsRoBGkoZgxinF7QWmRU0FIQSrxDpDlwNCwzECkkJdQdNBd0CNwBw/Yb6UvfO8yTwkuw66RbmIONw4Aze29u/2bHXzdUw1NbSytEp0fPQHNGc0W/SrtNl1XDXoNnr21HeuODr4tPkneaL6LLq/+xH72LxRfME9b/2dfj4+Rn7y/sn/FD8WPxP/FD8c/zQ/Hf9YP5v/3QATwETAvECAgQoBTwGQQdcCKcJLAv3DBMPbhHFE9AVfhfgGA8aHhsVHAgd+x3MHmAftR/gHwYgNSBRIEQg/x+BH+UeSB7THawdsB2YHTsdlxzjG00buhofGp4ZKhmUGMEXwhbQFe0U1BN6Ev8QQw8MDUIKAAeQAxAAdvzP+CP1VfFm7X3p4+XQ4iHgkd0V28HYrdYB1djTKNPf0uXSPdPv0+/UMtbG16XZuNvo3SLgXeKP5K/mxujJ6ovs8e0V7ybwQ/Fe8mXzVPQt9eT1aPa59vX2N/dv93n3QffW9m72Tva49tz3wPk0/Nv+RwE8A7kE7QUWB18I2wl8CyUN3Q7bEEgTHhYuGQscMR5cH6ofeh8gH7ceUh4VHhIeSh6zHjcfux8nIGAgVSDxHyofHR4RHUcc0RuZG4kbjxuXG5MbkBubG6IbdRviGuEZoRhNF80V+hPTEVoPhAxVCe0FdQL4/kj7Offh8o3ufurp5s3j9eAs3nvbI9lr11nWr9U91fXU3tQS1azVqNbp10DZp9o53O3dvN/D4QvkbOaU6Bzq+ep86+/rg+w17c3tRO7D7mrvV/B68ZTyZfOp80jzgfKp8SrxevGx8qL0GPfa+b78m/8wAm8EUQayB6MIbQlJCpULtQ3JEIwUTxhpG7sdZh9zINkgmSDhH/4eIx6XHbgdoR4GIGUhWCLPItoieCKrIY4gTB8iHj8duByVHNgcXh3mHT0eZx56HmUe/h05HTQcERvPGWsY9hZlFXsTBxEXDt4KegfiAxEAE/z499vz5+9W7D/pc+az4/vgdt5F3Gza7djO1wPXatYQ1iTWm9ZF1wHYrNhE2e/ZwtrO2yvdut434IHhkOJx40HkBeWx5U/m2+ZZ5/DnxOjj6VDr2Owo7h3vyO9V8BnxTvLr89r1Gvii+mH9MwDwAoMF0AekCfsKKQx5DfgOnBBrEnoU1BZCGWcb+hzaHQ0exx1NHdkchhxAHAIcDByVHIIdkB6IHzYgaiASIEwfYh6XHQMdphxzHGscmRzwHF0d3R1KHmYeFR5VHT8cABueGRAYcBbIFP4S+BCkDgwMPgkiBrYCH/9w+7v3HPSf8GHtdurP52DlKeMc4TDfZd3G227abNmz2DrYANgC2CnYXNiZ2O3YXdnu2aPabdsv3Ofctd2r3qbfeOAh4dPhsuKl45jkoOXR5inon+kx6+Xsp+5E8L3xTvMq9V/32flz/BT/oQH5AycGRAhaCmcMUg7vDzMRQBJZE7cUMxaFF5YYYRnSGe0Z1Rm8GaQZaRkWGdYYtRi5GPoYfhktGtYaRRtuG18bMxsSGx0bWhu5GxocbBy/HC8dxx1lHs8e1B5eHoUdehxcGy0a4RhlF7sV6RPfEZEP/gwyCjgHDwS5AFz9F/r79gj0LPFs7tvrcukx5yjlTuOM4d3fSd7x3PHbTdv52sbai9pN2hzaBtob2kfagNrO2jTbvNtr3Cfd692y3mPfAOCl4GvhceK44yPlp+Y66N/ptuu27b7vxPG+8671qfe2+eb7S/7LAD8DjgWjB34JKAu0DDUOqA/0EAUSzRJYE9UTZRQEFZkV7RXhFZYVSRUnFUYVlxUCFmwWsBbOFvMWQBe4F0oY0BhAGa4ZIxqtGlcbFRzQHGIduh30HSkeSR5OHjYe8R17HcccxRuEGgkZWheMFZQTXxH6DnYM5wlTB58E0QEJ/0j8hPm39ubzNfG17lzsNOpE6ITm8uSC4y3iBOH+3wbfJ95n3cbcW9w23FDcitzG3ALdSN2W3fDdX97f3mzf+9+Y4FfhMOIe4zbkfeXb5j/opukd667sT+7678LxoPN/9Vr3Pfkx+0H9X/9yAXcDbAVBB+wIagrECw4NSQ5lD2cQZRFmEl0TLBS+FCUVgBXnFVMWtBYDF0gXmBcTGLwYdxknGr0aLBt0G6Yb0xsOHGIcyBwzHaUdFx53HsUeBh8gH+8eZx6cHbMcvhu7GqgZjRhaF/kVYRSbErgQtA54DP8JaQfZBFUC2P9o/Rn75Pir9nD0Q/Ib8Ojtueuv6d3nO+a65FfjFeL34APgQ9+u3jTex91a3fLcodxz3Hrcu9wi3ZjdHt633mLfHODh4LThmOKH43/kluXc5lXo9Omo61ztAO+Z8DLyzvNs9RD3u/h5+k78OP4tABsC6gObBS0HnQj9CVALkQzaDSsPaRCWEbgSzhPSFK8VYBYIF6oXQhjtGLEZcRojG8MbRhy2HBMdVx2QHcsd/B0kHkYeXx5yHnoeZR43HvAdhR36HFEchhupGtMZ/BgJGOwWrBVUFNoSNhGGD9UNCAwTCv8H3gXOA9UB2f/K/aL7bPk89xH17PLm8PXuDO0263XpzudG5srkYOMg4gTh/98M3yPeZN3k3IncUtxQ3G/cotza3AbdQd2m3S3e0t6R32bgXOFz4qnjBeV55vfnfen76nXs++2I7xzxyfKL9F/2Qvgr+h38Ff4AANUBmQNXBQgHmQghCrgLUA3ZDk0QsREWE2kUixWVFqkXshiPGTka2hqNGzMctRwwHa8dJh6JHswe+B4YHyMfGh/+Hs0ekR5HHukdeR33HGcczxsjG1MaaBlwGG0XRxbxFIsTKRK/EE4P1A1LDLYKBgk0B10FigOuAcb/1P3d+9b5tvem9cjzAPI38GvumezQ6iDpiecZ5s3kj+Na4jPhKeBU36reG96s3VbdDd3a3Mrc4NwU3VzduN0w3r3eYN8f4P/gA+Ij40nkcuWv5gfoc+nt6nHs/+2T7y3x0fKQ9Gr2QfgM+tT7lP1N/xQB6AK2BH8GOwjXCVoL2wxnDvIPaxHKEggUIRUgFhUXAhjjGLEZZhr6GnAb3BtIHK8cCh1QHX0dih12HVMdMh0AHbQcWhzuG20b3xpOGrIZ/xg2GF4XbBZXFSkU8xK6EXAQDw+xDWgMHAuzCTIIngb1BD0DhQHN/w7+VPyc+uH4Kfd29djzXvLx8HrvAe6S7DHr5um16KXntObN5fvkUOTE407j8+Ks4mniJuL24ebh+eE04ovi6eJU48rjT+QD5dblpeaK54DoX+lH6mTrp+zx7TTvefDY8UDzpPQf9qn3K/mr+if8i/3t/mgA8QFuA+METganB/AIMAprC5YMog2cDpoPihBXERsS5xKlE1AU6hRvFekVWhayFvYWNxdxF6EXzhfjF+MX3Re6F4EXWxcyF9gWVhbBFSQVhhTVEygTkhLjEfoQ/Q//DvcN6wzgC8oKkwlCCPIGpwVhBC4D+wG4AHP/LP7g/I77O/oD+ez30Paq9ZL0iPOU8rrx4vAL8Efvke7i7T/trew57NjrcusW693qvuqa6nXqaepv6nXqjurF6gfrTuun6x7ssexL7ejtoO5q7y7w8fC/8afytvPK9ND14/YG+Cf5VvqO+7v85/0M/xwAMgFQAmcDgQSXBZ0GlgeCCGQJSQocC84LZAzpDHQN9Q1KDqAOFw95D7cP/Q9IEIMQsBDJEMsQxBDDEMcQuxCYEHUQWRApEOAPlQ9PD/cOgQ4FDpMNIg2pDCcMjwvsCkgKjwm9CO8HIwdOBnoFpQTWAyMDdAKmAdkADQAp/1f+r/3x/BH8Tvuk+v/5cfnt+F342fda98L2LfbB9Wf1EfW39Fj0FfTf84zzUPNE8xnz3vLS8sbyp/Kt8szy6/Is84XzxPPj8x70lfQN9Vv1wPVb9v72kfco+NP4lvlT+vj6ovtX/AP9q/1c/hX/5v+6AGQBBALSApQDGwSqBF4F9AVOBqQGJQe3BxsIbgjxCHAJogm3CeoJMgp1CpcKiQqJCq4KtgqqCsAK1ArHCqgKbgozChoK8QmtCW8JNQn/CLQIQAj9B98HZwfbBpUGJgaMBSwFyQQ6BNIDhwMNA3YCAQKUAfgAWQD0/6L/N/+0/jP+2/2X/Tj9xfw9/Lv7hftT+876YfpP+jX66vmQ+T75G/kx+R35rfhj+JL4sPhv+FH4lPjf+Of4sfim+Pn4IvkA+Sv5jfmx+cv5FPpZ+oz6yPoZ+3b7z/su/IX8wPwR/Yf95/0z/mv+lv4Q/7n/DwA/AI4A7QB8Ae8B6QEeAtUCJwMRA0EDkgPQAyoEfgShBK4E1AQkBUYFGgUHBSwFUQVNBQoF3QQDBSEFIAUoBQMF5gT/BMcEZwSBBKwEUgTeA8YDrwM3A+wCGQPjAk8CRwJZAtcBUwE0AUMBOQHFAEcALADy/4D/SP8X/9j+uv59/i3+//2v/WT9b/08/ar8gfyK/CX8Bvxd/B/8svv2+wj8ovuk+8/71/vh+6P7kPvu++D7ufsi/HL8cvxA/Bb8p/z6/GP8vvzF/Zz9Sv1t/V39v/0X/tz9Wv7Y/lP+bf4S/+v+0P47/3j/ef9t/4n/1f8qAIIAWAAkANkA+wAkAH4AOgGxAHIA9QARARUBTQF6AWIBDAFfAeMBYwErAcIBrgFNAVcBnAFHAkMCYwGbASgCvQHKAesBTwGYASYCgAEjAUoB7wAVAZoBDgFhALcACgF9AKn/ov9bAH8Ayf9m/5H/rP+j/3X/YP/X/9H/a/6k/aX+Hf9E/iD+mv5i/kH+c/7P/Wn9pf7v/tn8X/xv/n3+Iv30/Wn+Nf1v/RD+1f2B/mv+N/2m/Q/+mf1A/gP+Nv3D/mz/yf2c/Yj+8P4+/wT/0f7G/kj+rP4///D+W/+D/+b+X/+Y/6j/mQCa/6b+sgAcAZL/8/+1ABoBRgFgAM8AYwHL/zoAwwFqAG0AHgIQATQAdgHSAXABHAGHAIQA7wAUAeIArADqANYARABhAPsAkAFlAU0A4QABAk0A1P8nAn8Bfv9WAKgA/f/EAOEAZwAQAYEAKv8SAPEA4f8XAFsBTgAm/4EAYwFSAFn/ff/jAHEBN/8//qkATwEW/4v+DACmAMn/JP+T/9//f/+T/+j/b//n/nr/AAAp/+/+PAAiAAT/gv/g/23/3f/L/63/PQHZAGr+sv92Aq0Aa/7mAIMCVP+D/hcCBwIp/zUAfAHK//n/iAG4AIn/IgCSAbkBCADb/0ABYwDJ/5gBRwGs/6kAXwGoAOsADgEbAQICKQFi/4UAWAJNAbz/TQCNAacBHgGSAOr/dABtAiACTf9e/9cBQQHf/x0BhABM/koAbQJ6/2L+9AE7Ajf/BACdAmYB7f7l/z8COQGo/gYAUwIGAPr+HwNZA4L+9/79Ag4CIv9w/00BdwFV/zz/LAIBAdH98AAYAy3+Iv2fAUYBmf73/7gAiv6n/gMChgKi/c375AEYBNL91/1RBJIBEvyJAKkDo/+o/2gBq/7B/pYBcgCP/5kBFwEl/r7+AAPFAnr9eP5FA/f/Pf0RApABMf5mAtEC3Pwz/3QENQCJ+8z/2gR3AQf8k/+zBDAA0PxxAuIDN/7N/WsCzwIG/+H9RAHcAuj/4P7uAIEAZf/YAPIAGv+w/+gBpgE8/6T+5QBAAvcAKP8V/vH/wAPoADz6Xf6TBicC8vrk/cABOQESAf//b/7k/2gBIAB0/g4A9AKb/4H7tQFsBS/9tftBA6wC9P42/9H9//9kBAgAwvyXALX/M/+7A/3/cfpkAMME8//v/M3+5QGsAmr/5/1e//z/IAINAk78Z/xmAjsCMQAcADL9W/7MA1QBT/wh/kEALQERA4z/1/rs/28Fnf/0+rQAugJv/Sv/EwR0/yv7wQCxA+r9AP3mAzYC3vgN/RAIaQKn+JH99ALuAG0Al/40/NYAVwNR/tj9jwEX/2z8XACeAdf9wf6dAQr/2/1RAWYBSP5f/k4A0P8W/8YAyf/M/HkAfQOu/X/8hwFOAFf+h//n/jkABQDy/JkALQFG+64A7QQY+4b7OQQP/3j7kAMKA0/56Pn6A7AEm/z1/DIAvP08AZgC0fkq/rEHyP05+NAB1wEI/oIAbP7A/VECBQHC/Lj8/ADAA+v9UPuJAc8A9P4qAjr9zvzeBEf/hPtPA1D+4vqSBH8AYPnKACwC//03AFEAff4v/5YATgAs/KL+KQVE/uT6fgOQ/4L7kQNWAEr8zALW/nP78ALkAPf8DwEhAG//cQFA/YP9VQOJAND8zwDNAEf8AwC/A8j8Yv2kBtv/+/dnAkYFs/3i/5T/RPvyAAQD1f0L/1IA5P43ATsAGv0kAZ0CVfwx/rIFPQAY+gACzAOr/JD/6gEu/V3/gAHV/1kCJf9n+5kD2QOT+pD+ogTY/uf7MAGwBBwAF/mu/YsHzQLX+DL9zAQ/ALn7AQIdBEz83fz0BKUB7/qKACUFo/78/KQC5/83/X4F0QPw98b9LwjK/x/6twEoAyb+A/1KABcE4gDJ+zr/DQLu/2oAsf+e/icB4v/P/4wDaP0F+pEFIAXp+M/+JAif/ij4oQLeCckAdvZ8/bkIcgLH+kUBEQLG+/YADgW1/Yr99wMzAb37tv0TBKoFe/xV+MQEvwds+Sv6rQfBA5v5AP++Bf//DvvAAWoGJP5s+u8DwQRX+xb+DwXs/+T99ANUAK76oQKGBpb82vvvBccCIPmS/gsGzP+X/EoD0QF0+9IAVwaU/5z7QQGkAhr+r//GBOX/dvkwAi0IFPyi+sUIowNM9fP9kArFA6359/ssBJ0EsP1a/XcBJwEyA7UDSvsD+4IFtgVR/lP9fP+VAeYBH/4F/2oEEwHy+xoBzwPf/gcAQALf/Gf+aQYVAvL5Xv6nBDQCNv3w/E0CwATN/jD7FADZBC0DKP2Z/G8EowOh+Yv+sgnVALj2CwFtCMn/VfsqAGwC8QBB/zn/iQFsAaT+rP+5AjICOf9p/RIAGASCARH9w/4GAYYChQVFABn3xv1BCnQETfiI+xIEQQOb/4//JgCpALMAZ/+S/yEBBgGt//j+JQBSAXIAcgGvAV38+f1XBoYBLPprAcMEw/4L/tX+CAKkB7f+jvVWATQJTQG5/eP82fsXBIwIv/2X9nsAsAmPASX54f68AVUA0gWUAdj1z/znCC4FyP31+pz8DAPWBeYBPvu1+aUDTgdd/bP9wwKo/BT/xgY5/3/6TAFRAuL/8P9G/2wA0gEbAD79ZP22BF0Hdfst9nkBnghYBHr8BPj6/TcGAwVH/9/6UvwbBK0D1PvE/oAE//40/HUC5gJb/YD90QAeAVIB7ABl/e/8pQGCAw0AtPzx/VECmALD/oL+CQB9/8gAVQHV/RD+ewLUAZX9iP2cAF4BGwBn/zL+Hv96A/UASPo7/+cEjv7A/EkBSwAuAeIANfpv/m4Gkv9++6gA8/4b/vMCqgC9/PX/+AFY/4v+nf/0/fP+5QOa/3/6MwMkAw34bP/eB8f8+PlbAmABm/5T/1X/UgAb/4j+lwCV/jz/4QGW/I/9FgVO/zL6NQEWAQP9nwFPAv/7g/yXAowCZ/1N/YcBUwFi/ZL8mAAPAwP/Qv1wAd7/uvuV/70CrQBd/wP9vPyvAa8BNP17/lwBawBJ///9vvyH/yMDYwGu/VH9T/5V/2QBIgJhAHj9Q/sZ/g0EJAR+/nf6wfvxAaUFxgAY+2n8igAWAj4Bsf9L/e/76P9wBHoBk/ze/AH/zAB/An0Bg/0j+9f+9QPYAWn9p/7V/5/9wv40A7ICkvyP+7IBfAIe/kj/rgG8/6r9w/2OAYEDlv3s/LkCW/5O+xoErANs+yj+IwJW/xD/AQG2AH7+s/2SAZcBEvx9/x4Fbv6X+nABEwPX/7f/Xf44/qEBTgAm/noCOgII+1b8+gOMA9f+Tf4D/0v/rwCLAT4A+/20/lcCXQHL/JX+/wK7APr9nABJATj+4P4bAg0BSv5p/1cBIADx/kwAqwBy/pT+hAL4Avz9k/xGAeMCL/+j/qEBRABZ/QYAugJUAML+GADvANUAgv9E/un/PAK5ATf+ZPzJAdAFDv8w+28BPwIO/twAJwP9/uz8aP8hAgsC5/8NABMAxf04ALoDj/9i/Q0CDwKG/qv/JQH0/wwAaQEfAdj+Wf4vAYUBNP/kAEsCVv6m/fMB5wF6/5sAeAAm/gsAUAM5Ab39qv4vAYwBqQABAA4ATACO/0//dgHaAoYA4P1o/sIAkwJaAo3/Hf0Q/zsDSANY/6L9bv8rAdEBPwFp/zz/8gDBALH/kgALAWMAlAD//+j+nwBkAqMAOv+CABQBDACO/xQASQEPAosAKv7J/tMBnQIvAIX+kv8hAbUBVAF1/xn+iABgA1cB9f1s/pEAqwEBAukA8v7e/q4AYgF9AMAAjgGn/9n99P9HAgkCuACe/ln+hAHDAnwACf81/3wA6QEPAeX/8f8m/6v/NgINAq7/v/5b/0wBhAKKAH3+dv+GAdkBBACw/sH/nAFRApUADP4i//oBsgErAGb/D/+uAPkBZwCm/1gADgAwALEAUQBmAIoAMwCBAHMAzP8uAPoA3wDP/wL/PQD0AYkB3/9c/n3+XAFCA1sB+P5l/mr/fAHHAfz/4P/FAMv/O/+kAP0AFQCBAKsASv/K/1gB/P8s/z8BJAFu/+f/2P8+/+4AmwHg/yH/XP/N//EASAFrAJ3/Nv+U/4oAMwHKAAz/cv6dANoBPwDX/i7/bAAzAYgAj/9O/7n/kwBqAJ//CQALAEn/EgDGACMA+/9z/5j+vP9HAekAlP/B/kb/SQBeABoA9P+f/1v/Rv8AABgBiAAl/6D+m/7Y/6ABFgE5/zn+Wf4qANQB1gAk/2z+RP5z/z0BYwHl/2H+S/5p/1EAxABfAPv+fP5Y/y0AmADf/4z+2/7K/+H/OwAFAMj+2f7F/+j///+X/6T+Jf8vANL/Rf9q/3n/e//A/wAAmv/G/v7+9P/u/43/nP8v/+H+gf/1/9r/lP9E/2z/jP/5/vP+0v87AAUAdf+X/mj+UP9QAIwA4//T/jT+q/7n/2EAyP9l/xX/lP5M/3kAAADy/s3+/f6F/2IAVwBJ/3D+kf5z/0gAlwAQANb+ZP48/7j/wP8rAN//3f7c/kv/Qv+g/zkAKwCR/7z+dv42/y4A5QCcAKT+eP3a/h8ANgCaAEIAwv5w/nH/DQDx/2n/J/+3/yQA1P9k/xP/FP+h/xEADwDm/27//P5F/7j/wf/+/0EAzP9U/17/Nv9a/zoANAAx/yb/8f9LAEIAz/8X//D+Uf/s/5gAhQCr//z+rP7y/iAAPQEWAb3/Yv5P/nP/sQA2AYUAC/9R/hD/XgABAZUAuP8R/xL/+v/gAKAAnP/V/gP/IgDhAKgAOwCM/+z+Xf/y/wAAnQAQAQoA+/4q/8//SQCjAIMAy/9g/9n/RwAHANv/BQA1AGAAPgDA/5X/4v8nADUAMABPAFUA9f+b/6f/DgCvANwAZADr/1T/7P7j/ywB+QA7APH/qv/q/7wAtwDT/2n/2/+NAPEA0AAkAF//mP+ZALkAQwBlAFIA4/8VACoA7P+qAJEBEgHy/zv/Vf9WABcB0QBRAPX/w/9AAAcBAgEmAE7/UP9OAE0BJwEuANL/QgCKAKkArQD//3v/DgDIABYBDQFlANr/CgBEAIAA3AC2AE8AGwAUAH8AywB8AIUAuwA+AOX/IQBrAOYAHgFXAJf/5v+1ACMB0AAVALz/AABwANAAFQHpAEUA9P9OALAAmgA+AEQAwACsABMAFwBCACIAwACPAesAi/8w/wUA/AB8AaQBBgHE/4P/XwDQANQAyQBiACYAiwApAVsBvwAzAEkALQA5APsALgGcACYAAwCRAB4B0wDwAC4BUAD7/3cAIAAhAAoB+wBSAGwA9AAoAXkAlf+x/1IABQGeAe8AzP/q/x8A7f+jAFEBwwAAAKj/x/99ANQAYAAWABgAJgCGANcA7wA1AdwAov8x/+T/rQBxAYQBTQCo/4cAGAG4AFkAEADN/8//CgCSAA8BxgAaANb/3v8qALsA6wB8AOX/jf/C/14A3AD5AJAA9/8LAJMAvwCOAMj/3v56/woBcgHnADcAOv/8/jYAPwHKANb/pP/2//P/zv/y/+T/0f9RAG8Anf9Z/+L/5P/T/1kARwCr/+j/mgC5AFcA8P/a/+3/JQDGALUAr/9//7j/Ff9R/7YADgFpANX/Wf90/wUANAAaABMAFAD0/6b/tv9SAG8AUv8l/tj+uAANAfz/Uf/l/gX/MgCrAP7/j/8b/4H+t/6J/z4APQBe//3+lf+8/2T/Of+7/mz+RP8VAMv/V/+V/18AMAH7AIP/Lv4a/u/+1P88ACgA3f+0/xcAwwAkAQQB/f92/kv+4v8+AaAAZf4B/Rz+RgBdAY8Akf6M/W/+4f96AED/t/y1+4L9gwBIAtkApP00/C/9cf+hAWQBxf5b/LD71f0JAlkEjgI1/tv6HvzEAJEDjQKg/4j9Jf71/0IAa/8w/2QAFQLTAWn/Vf0q/RT/jQFkAW3+1PtH+wL9yAAFBLQD1v9h+6X60P7bAuIBV/3d+QX7iQDOBFIDbP7g+hD8+QFgBskDW/33+KL5f/+OBTgGjgJE/oP7c/xmAJsDygSsA27+APim907+BwZKCdoEffsV9kP4l/6ZBdwH3AKK/af7uvrS/HUBuQKVAXkB8gD7//P+Af3I/OT/PgNABIYCFf9T/Hf8Mf9kAdwB6wFeANf9cP7vAK0CGgRIAir+zP6vAaEAZ/77/Mn7wf2aAtwFDAWKAJD7Lfld+sH/fQVlBaMAAPy0+Rv70f60AfICuAGc/sf97f/+AP3/fv8VAGsANQE1A/0DEAId/1j9jf5FAfABfgFuASMAdv8NAU4Bw/9N/0j/+P5z/2IAngDv/7T/JQBz/2r/+AFrAvX+zvyD/U//2wFMA+kCEgMTA4sA7P3J/gwB+QAaAMQAoAETA2EFFwRe/xn9Cv1z/Ov94AHPAwICZf9J/q/9oP3l/y8BZP7T/Mr+hv9w/4UATQBi/0v/uv4C/0UA7f+i/3cAdwAjAfECOQOjAugBdQD//0cAHwD+ANYBPgGqAbsCTQLnAZUBEQB1/04AXQDq/2sANgHAAOT+1/3Z/pT/D/+L/+cANgEaAXkBpgFgAZAAY//w/oT/zwBdAp8CkwE5AWsBegFBAn0CkwB//iz+8P4FAEMBDQJ/AQoAZf+A/xj//v7V/9//Bf+M/0kBqwFjAHT/WP9x//v/lABaAOL/0P+q/6X/tgBkAskCngHdABcBDAH1AF4BVgHlAOQA1ACfAFMBkAKmAlEBPACOAEEBXwGmARwCmgGzAOcA/QHuAu4CXQEz/13+Bv/H/+//9v8DAHD/2v7A/zcBbAGLAC//Bf5r/uT/tgDYAOIA+QAXAdgAQwB3/zD+Xf34/TD/VAAWAYkAHv9U/l3+vf4u/0f/t/6r/Tb9Bf7X/t/+Nv/y/yAACgAxAIIAEwGzAfAB6gEaAs8CnQPNA8ADAATOAxYDDwOlA6YD0AJoAdb/Jf/g/yABtwEnAZf/tf2g/Bf9jv56/wf/3f3v/Kj8K/0r/uT+Lf+P/9z/sf+2/zkAfACIABQB+gGdAocCcAHU/7r+1v7I/0YA3f9x/0P/1P4h/jX9Lvx5++T6Hvp++X/5dfr1+xf9//3p/v7+/f17/Cb76frj+2f9Zf+EARQDAgTRA4YCxQE1AhADzANNAxwB/v6D/uf/0QKGBQQGdQQjAvL/l/5V/pL+xf4C/2r/CgD8ALsBkgHkAFMAAwBBAJoA2/+B/i/+VP9fAT0D2wOUAz0DdALdAEP/Z/5U/qX+7v41/9b/owDnAIcAPABPADcAqP/y/mj+Gv73/b/9Z/1Z/b/9Jv4n/qH95fwC/W7+FQC1AKX/2Pyy+U/4bvkc/KH+gf+Z/iP9Tfwn/OX7xfqs+H72a/Wg9ar2bPhJ+mD7qPst+/j5yvgA+Jv3NvjS+aL7O/1F/uf+RQC+Aj8FgwYLBt8EtQTUBWgH4ggRCg0LHwxKDUUOpA4nDsQMpQqWCKEH8gewCL8I1wfhBlQGegXCAwEBc/19+rv5cvtq/lsAjf+u/Bb6tvnT+8H+YQARAMr+8f1x/nYALwP9BOUEnwOpAu0CFwTxBMgEBAR8A6EDywN9AlH/rvsl+QL4afdc9qz04PKh8UzxcfEG8ajvM+7e7RvvI/Gj8hHzF/O382L16Pfp+hj+EAFcA8kEUwX3BAYEHQOaAlMCuQG6ABAAEgAlAJj/SP6r/D/70/kI+AP2RfSV85r0G/dJ+jH9H/88ACoBWwJGBEIHKQujDzEUURjVG6ceySCTIjgkniWdJh4nUSeJJ9cnCCigJ+IlbiKyHasYPxSeEGsNLwpuBgACL/1X+OnzHfCe7P/oKOVl4W3ek9xD2+zZoNi+15/Xc9hK2tTcIt+D4ALhMeFA4h7lKelA7f/wYPSl9yL7mP7EAb4EXAeMCYYLOA1bDsIOhQ5LDuQOmxDwEqUUkxR2EqoOBQqtBT0Cmv9j/Vf7mPlZ+HD3qPa/9Vz0evJC8PztguyI7N7t8+818mX0ufZJ+R/8X//AAvgFYAmBDbsS/Rg5HwokzybyJ6soKiqiLHsvCDLnM/M0/TS2M8Mw/yvUJWkfBhoiFvkSGw9/CVgC5Po59G7uCOmM49vdjNh+1BvSOtE/0XfRxdGL0gnUDNYL2PnZwdxS4a7n1+5K9fL5tPwb/g7/UgAHAt4DeQWJBgEH0AaqBVsDAwBD/AX5t/YO9ZvzEvKT8JPvRO+K7x/wkPCT8EjwD/CM8EXyJfXO+Mb8fADFA9oG8gkgDRYQJhLtEqES1hEfEYkQkQ/eDZULIgkjBxUG7gUzBjMGegVGBGUDjgPxBEwHPAp5DcsQCBQ9F68akR6hIjImqigKKu0q8CtDLbUu1i/vLwQuTyniIdUYxw8ZCEECmv3T+KDyZ+rc4KjXONDKyofGZsJHvii7bLr5vKbCOcoX0iHZIt+h5CbqkO9n9MH4fv15A7sKehLBGd8fSyR1JtwlMiJjG68RBAZ1+q7xTe3Q7DHuNu9X7ubqz+TR3KLUT85ny3PM2tBr1xLfLufD7yf5FAMaDB0S8BPXEhQSrhSMGw4lMi5pNHo2VjSqLmImWByvEfYHogA7/Pf5Vfha9jD0vvKz8q3zVPSl8yDyoPEW9CL6xwJ3DC4WqB/vKNcxtDmSP9FCv0OKQ2NDb0OqQtM/czoPM2oquyC8FU0J5Pts7s7hd9YmzCTC2bd1rRKkFZ1UmcGY5ZqbnzenALKSv6HOMd1n6YnydvkeAFQIsxKNHqsq+TW3P0lHEUxtTbJKSkNWNzAoLxiqCQj+evV47x/rV+fp4s3cg9Qvyri++bOhrC+rlbCTu3HJk9et5JbwRPtFBDELShCuFOYZBCHtKUUzCDuiP79AED89Ow01uCsdH6UQ6QI8+Gfxi+376ovoNeZ05Gjjw+JU4lniauMH5ojqRvFv+t0FVxNiIsQxWz/gSIhNt06iTqRO107YToVOrk1US/5FsTyUL+YfPw/t/rHvq+Go1K/I6b1ItIirZ6MOnCSWjpJxkveWZaCdrZW8g8u22TDn3fOd/48K9xQkH3spWTSWP/9JoFHrVJBTI05YRfA5FS1BIIUUDApgABT32O0w5InZ2M3owRa3vK7MqdCo/Kv0spS8Rcep0R3bsOPK6wz0YP2OCHwV5yL9Lnw4Jz/8QnRDAUAYOXswSCiOIQMc1RZIEdYKNANm+rrwuOYY3SPVkdBW0LLTzNhv3vTkK+289hgA9geBDhkVIR0fJ8MyLD8dSydV7Vt+Xopcg1a9TSBEUDvcMxUtwyUEHWYSuwUz94Pn4Nd2yem8jbLGquulFqQwpeyon677tJa6a79hxdPOgty67JP8GQoBFfYd2iU5Lf0zqTm0PQBAvUBlP406TjGvJJMX6AyDBd7/svly8d3mytqbzsPDX7vitT2zYLNmtje8NsRvzRfXq+CD6cLwJva9+nAAzQjtE4IgoSxCNn87NTvCNfUsDiPWGY0SCQ4RDNMKlwfLAHT3Te6A50bjauDp3fnbxdtn3i7kaOyy9a7+2QbLDj8X8x+uJ6UtrTKbOFdAxEh4T2JSDVF1TARGqT5ZNpMslyG8FpUNjQYrABn49ux03yzSA8gpwqu/Xr6dvN26CruEviDFa83W1fPdFeZt7u72P/8FB3MO8RWwHWEl2yulLw0wbC3yKOcjqB6RGNkQhgcL/n32u/Fl7r3p1+E512DMCsTQv5W/9cFExYTIKszC0TPaaOTY7Yn0oPj++5YAWQcLEIsZPyKZKLwroiuhKB0j2hs0FMcNXQk1BroC5/3n97PxQ+wI6PPkqOLO4InfpN/44abmHO3J9G39wAYcELoYOiDNJvAsNDP0Od5A3UavSsJLhUqwR6pDnz6kOIIxsCjkHdURHwYU/Onz8ew15hXfc9elz57InsMzwezA8cERxDPIRM+82L7iSeuG8V72j/tVAuIKRRQFHS8kWSkaLAcs8yiUI5wdaBgLFLkPZgpjA9P6l/FD6QrjCt7x177PScfzwX/Bh8R4yCPMC9Ab1WjbSuJV6WHwX/ea/mcGew7EFRAbdh6vIRsmwCqQLNoo6h/9FHQLbATp/pT5DPSF7u/o5uIw3FbVuM/1zCnOO9Nu2ibh2OV46eXuZfiGBbATiiCsK8s11T7GRRVKj0y7TpZRC1VTWBFaRVhhUc5FKDh0K8MgzRbyC/z/8PMa6U7gy9kQ1bPQLst+xIy+rrvmvHnB9sdVzx3XGN9g5z3wyfnQAxQOOBhyIV8ouSuTK4kpiieQJlkm1iXpI9sfZhmfEOsFFPpP7tbjG9uE02jMRsaNwjLCe8Qvxz3IW8cixvPGj8s31OnfB+3C+akENQ2lE7gYNR1nITwlmShQKw8tSS04K1AmeB75E7cHLfun76zlEt2y1f3PR8zEyRXH1sMpwZPAv8Kwx6XPzNpI6Fv2tgNREMUcPSlJNXNAZ0rdUtpZ3l9cZelpMmwda7pmyV+vVnZLiz7/MBckxBgVD/UFkfuI7onfctE5x7XBhb+svlG+176uwNDDFshkzeLTAtwP5tPxbP6CCiEVJR67Jckr3C9vMWgwPy30KL8kayGoHh0bVxX/DAcDn/gf7gXjEtdVy8bB9bsGutu6P72vwMbEvMi6y3nN2M7y0STZW+X89JwEXRG3Gtohjyc8KxIshCpXKDgnbyfeJ6sm0CHjF/4IN/f25QvYRM7+x13EnMKvwRjA1byouMi1YLaBu/DEqNFi4M7vH/9bDqIdSiwCOQZDI0sBU8FbomUocNZ5/3+Vf3l322m/Wl5NDUOwOz82pjD+J1QaWwgK9Zbj3NVhzAbHA8XSxPjEJcU6xuzIl8z3z77SM9Zi3HPm5fMGA4AR9RwLJPkmLCclJnEk+iEIH2EcuhryGacYChUFDt4DGPhJ7AzheNYczSvG18IAw+DEcsa7xi3GK8bTxzfL3s+E1Y3cteXl8Nb84QfpEKgXWBw/H88gqyFQIuAi9SLQIa0e2xgvEGoFqvnB7UPiy9dYz93JOMcHxo/ExMEUvju7KbsEv4XGT9A/2yLnLPQCAlIP4Br6JBYvPzpARvlRaFzxZMdqJ20hbJhou2N5XjRZ71MxTuhGRT2GMaYklhfECmn+Q/M+6njjNt6b2WrVKtJL0I/Pec/5z63RldUn3JnkKO0/9L35D/+CBZsMXhIpFSsVBxRLE2ATphMGE4UQEwzPBvoBtv3b+D/yNupd4gfcdtc+1BDSONEM0g3UDtbk1kTWbNVZ1l7ahOGo6iD0aPxeAsgFnQdmCSwM2Q9VE3cVrxUIFA8RZg00CRcEev1Y9bzsCOUL37vac9fA1L/ST9GszyvNP8r2yLXLBdMt3YznV/CR94P+cQbSDxYaSCTxLQU3Yz+3RptM7FDdU6FVc1aHVnBVZVJGTQFH+UCkO/U1xi4qJvEcqBNiCmUBn/nZ8/LvV+1z63vpiOY/4rXdUdvk3PXhSuiy7VTxlfMP9Tz2y/dV+sn9XQEQBF4FigUPBTIEEQO3AQgArv1O+k729vJM8c/wmu/761HmDOGa3mvf8+EL5HLkU+Pj4YrhFuNP5lvqhu6d8oL2tfm/+9b83/3O/6wCUwVoBncFOQMIAaP/dv5u/P34hPTz7/rrsujy5YrjkuF04EvgROAX33vcSdpm2w7hZ+kI8fj1APk7/A8BlQcgDwMXfR7IJBQqii+vNaA70j+tQUJC4EKWQ7FDqEKHQPM9iTs5OSM2wTBcKH0eCxYEEeYOLA22CW0Eg/4G+Uv0KfCx7GvqzenG6oPs0+0I7m/t7exE7Xru+O9q8SvzofV/+Mn6hvuZ+u74t/ea92T4Q/l8+en4uvc09pL08/Jk8ejvZu7a7GrrXeoS6qvquOuG7LPseux+7B3tNu6N7zrxifNP9rL40PmI+a34evh6+S77evxb/LL6VfgC9uTz9/FZ8EPvn+7M7VTseurB6HLntubS5gHo9unk6zPtQ+5b8J70JPvUAq8Jxg3RDusOmhEtGWUkYy/QNqY5CzkuNy82qTf9O4FBmUWHRvZDtD4fOJsxhSyJKb0nXyVqISociRb2EDILMAVT/+r5BvW/8KXtcuz87PHt4e1B7J3pA+dF5QjlwuYm6gzuGvGK8sny9fKm86P0d/Xv9Uj22/aj90f4dvg8+OT3YvcW9nrzBvAr7R/st+y87Rjufe1R7C/ri+qU6h/ryutk7C7tku6N8JTyKfQ/9Rv2+va/9xP45feJ92P3evdW94H2OvUi9GvzifK+8P/tLOs/6bzop+mC6z7tzO0d7V7s/Oxy7zTzzfeQ/YgEewvHEPUTORZ5GdIeByb4LSI1SDozPaE+kD99QCBBHEGTQMs/uT4OPXE65DawMuktZigjIpAbZxUCEBILOQZxAef8ufjC9NTwD+3P6ZHnzuZd51joxuhS6KDnmueK6C/qMuxO7mTwW/IF9Eb1EvZh9l72TfZB9ib24vV09QX1pfQY9PTy4vAD7hzrKemk6CXpv+m96QHp/Odf55HneOin6Z3qLOuq64zs++3L76PxOPNW9Mf0fvTH8zHzN/PB8yz08/Mr80bygvGp8HrvLO5c7XPtOe4T75jv8+/E8LPy6vXl+eD9bAGkBOAHjwsbEJ0VrBuYId8mdyuhL00zIzYfOK45SDv/PE4+qD4VPu88cjuVOQo3qTOvL1YrsibWId8cERipE5IPmguZB2QDAf+c+m/29PK18NPv0u+l70/u2utA6cjnLuj46fPrUe3z7SDuLO4R7sHtf+2a7Snu7O5M7/buOO6e7WjtR+2P7Pjq+Ohq5/Lmh+eA6BPp2egC6Cfny+YL56PnU+gd6Qzq7eqC69HrQuxH7bju2e8k8LHvC+++7gbv2e/x8LXxn/G58I7vuO5+7sTuXO8m8ADx4PHy8nP0ffba+Cj7Uf2g/3ICBQZCCrYO8hLTFnUaBx6qIWclLynHLMsvADKSM+M0JTY/N/c3Ljj0N2A3ZDbYNKUy4C/CLJIpjCbOIzghaB74GtEWRhLmDRUK1QYOBLkBtP+u/U37YPgr9VHyO/Dz7lnuGu7O7ULte+yu6w3rceqV6ZDoxOd254/nwuf750boc+gu6FvnPuZh5SDlfOU05s7m6uak5nXmuOZC54PnNufJ5vnmB+hW6RPqGurr6RLqwuqu63nsAO057Uvtku1B7lPvkvCL8eTxnvEJ8cjwZfHe8r/0dvav93/4MPkJ+lT7Qv20/zsCdQRyBqkIfwvvDoYSrhUVGOoZ2RuQHiAi3CXUKJQqiyt9LMEtHC8sMOkwfzHlMecxRzHIL5EtPitjKSsoGidtJc8ibh+yGxsY7hQVElwPiAyICYsGvQMqAbD+Cvw0+YL2T/S/8qTxmfBr7yvu8ezP68/q/elr6Qnpv+iY6KToueiJ6P7nf+eF5+vnGOi35xrn1+Y85wPolOiV6AvoOOeP5nLm1uZ05w/ofei46OHoBekb6R/pHelX6RzqYOvC7PztA+/j74rw1PDo8GDxqfKW9LD2kfjs+ab65/ov+yr8Cv5UAIoCgwRMBugHPAlOCowLUg2ED7wRsRN4FVkXXhlNG+kcFR79HgYgYSHoIlwkiCVeJuAm9iaSJuIlSyUUJSklJyWTJAcjsiBjHsccyhu6GvQYhhbuE2QR6Q6PDG0KnwgQB08FDANMAEv9lfq9+Mr3Rfd49u/08vIK8YXviu4I7s/tu+1o7X3sPesq6pzppukM6pLq+erA6svpvOg86IfoUekV6qvqDev+6mfqj+n/6EHpWOq/6+bsX+0M7Vvs9etN7F3tvu4Y8DvxD/Kc8vbyRPPU88r0AfZC92f4cvmA+oD7aPxr/ar+AAAmAd0BYAJHA+gECwciCbkKwQt5DCMN7w3+DmAQ/xGhEyYVghaZF2kY8hg1GXgZ+hmSGh8buRtLHJ0caxyRG2UaahnMGHIYIRiFF3kWDxVnE8ERaRBjD2MOJw2PC50Jiwe8BYEE8QOVA4wCgAAI/uD7avqV+Qr5lfgY+Fv3WvY09dzzc/Js8RTxO/FC8azwou+57kfuPe5j7nXuIe5W7Y7sW+zv7BLuH+9Y76fume3g7AHt5+0O7yvwHPGn8aPxKvG28PTwIvLj8471Y/Y49r/1x/Wp9in4g/kv+mj6pfoe++/7Af0h/jH/AwBpAIUAtQBSAYUCDgR9BZIGQAeMB70HUgiSCTELhwwkDWAN/A0ND/QPXBCVEBcR/hHZEgoTjhILEhwSyRJsEysT+xHQEGMQZRAcEDkPEQ4xDcsMjAzoC4gKqQjzBgoGFQZdBt8FYQSBAgsBYwABAEb/aP6t/fv8ZPzY+0f76/qc+vP59Pi998b2lvbe9ib3Rffh9ij2mfX39D70yPNw83vzUfQo9TL1n/QC9OTz9fNk85DycfJE87D0sfWE9Qf1LvW59VP2ofZT9gv2f/aK99L4xfnb+Yn5sfl++pP7Xvxp/Az8Mfw0/aj+1P8JAE//y/5x//8AxQLZA4sDoAJZAusCLwTJBcoG6QatBnoGkQb8BpgHUQjNCPsIdQnsCZcJ6Qi3CB0JywkhCsYJOwkcCVoJWwm+CBkIFwhvCGoImwdFBjEF+gSfBWIGXwaPBVQEBANdAnoCUAKeAREB/QBXAWEBNACF/rH9vv3s/dr9Z/2Y/O77Afyg/A/9sPxa+/v5yPlJ+m/6SvpW+u/6svtD+3z56feJ9174u/mJ+pP6Nvqo+Vv5QPnK+Gn46/g7+m37avtr+q75ovk1+hX7hvus+xn8Z/ww/ML76/t6/W7/yv+D/tb80fvO/MX/DQLGAW8AMADWACsB5wBXAC8A/gAYApYCggJUAogCBwMZA+cCGANpA3gDQwPUAq0CkwNbBYoG5wUwBM8CLgKRAg8ErgUzBuwEAQMnAwUFQQWhA6ECAQNKBHkF0QS1ApUBNgKKA4gEHQTJAQ3/YP5nACUDEwSNAvH/Qv5+/sX/nQAwAGr+CP0t/mwA3wCG/8H9z/y//WT/0/8u/+P9iPxY/Ar9Z/6HANoAov57/SL+Df5W/Uf9rf0v/sL+Ov8T/4n+/P7I/7v+vfxg/LH9/P47/y7/xf8sAAoA3f+Q/iz8ZPtk/dYAoQOSA90AZv4K/jT/LADz/1P/uv+hAZ0DZAOEAbgABgHMACEAh/9x/8MAUwPKBXIG1wMb/w38Xv38ATgGAwfqA37/V/5tAaQDdwKAARACFQKdAU4BqgAIAOD/lgDzAtoFSQarAvT8FvpV/aYDQgcPBtYB1f1c/U0APwJdAacASgHvAT0CNgHb/kX+t/9kAIwABgHzAKsAXABu/5P+kP6E/zUAoP5P/QAACwNdAvD//fxl+pv75f8zA3IEwQJM/uX7TP1t/gv/2wErBJIC7/7++0376/2mAZQDJQQ9A2L/yfrL+eX9DwQgBsYBkfwE/A3/igE5AeL/5wAPA6kB8Px3+jL9OAIPBRwEFgGO/ij9p/yi/jUDYgXfAdT8cft4/o4DaAbvA0f+lPok/JABjwUWBSgCPP91/S3+5QCeAscBFABy//D/PQFaAi4B4P6a/l3/g/8RAE4AIwCJAUACIwBt/kv+ef5y/woAev+2/0EAkwBfAoMDZwFJ/gD8afvk/SgBIALoAS4CfQJZASD+iPs9/L7+DQGJAroBdf7H+0P9UwKXBasDMv5V+EH3kv2UBEEGGgSD/4P78PzFAIYBc/79+HP43AHbCqcJmgGS+Pz0QvpMAfUE3AbNBOz9/ffk91T+FAYnB8UAKfkX+DMAfweOBPf9Lfxm/br+FADFAOr/1f7d/wkBigBCAvQD7v4n+ej5V/18AmQILAby/MT4YPzNAQoEBQEM/Br7IAC5BdgCTPnD9q/+RQdFCpwEMfdV8Cf6Wge3COYBNfzq+rH+BwSpA6r8lffO+ysFJAnOBA/9Bvfb+IsCKAeDAQ/9Mf3N+378cQL8BWEDyf4Y/Cb9G/+i/XD7NP3pAWQFFQSo/5f91v23/Rf+oP08/BX/HQRrBD8Bhf3/+jr+YwQHBSAAuPpB+ef95gI7A5QC5gGA/lf7Kfxk/3QBxgFLArEAbPuu+i4BlgRHAjkAqP34+2EAfAPt/dz5uv1uAbQCiAQLBHAAw/sH+Kf6vAErBNsCAwJhAIz/KgAIACv/aPwQ+6j/7wGD/0cCfgWiAQj/zP6g+p34xP7ABU8Ehv3r/HsCOQTVAV3/LfvC9xb85wVfCa8C8PvN+rr8jgIwCMgDTPmu9QX8ZwU7CbgERP2H+g3/+QPnAWv8lfrS/YwCrwNpAisBN/2o+2AB3AOZ/7b8u/uc/aMDBAdxBQj/bvYM+TwErgW3ABP/jP27/mADOwTqAdL9Pvlb+7UBxAWxB5wCjPhi+DoB7AWKBGz/Vvkl+4YGwAzBAhP1RvRX/QsJDREBB7Lxpe63/hMKtQraBTr9lPbS+FoCWQiPBDT+SfrJ+bkBywmVBaD+4fs++Ub+NAfbA+b+cwJl/0j5mP/IBk8EPwB9/PP64gFjBwMCNf3f/gP/PP/4AsoDlAFa/wb7rfsmBQ0Icf/J+ZD8fAOmCLcD+Pmf+Q7/pQDVArkGUQRj/ub8y/sA+mUB/Qt9CDP9A/pK/Kr+TAFVAcYB0gWHBZv/xPsL+lb7xQNDC40ILAAI+aD2NvsdAkgGmwmfCEr+lvWM+OH+3QLSBtsFGf8x+1r7Ff6CBY4KaAXe+6T1jvZQALMKWQyWB57/yPaz9r8AMgeIA4b9AP3MAlEIQwXp+lb03fqICZMOMgNH9oT0KvtXBBcLHArdAY34PfV/+1cF0QeUAfr7xP2yBNcIkwK09bTykv/pDEcN7gL99kn0/f01CIoJbgVd/uL1ZfU5AHQKmAtYBhT89fGY9QsF0wyqBjb8ava9+mIF5goFB0n9xPV0+KT/9wKfBZgFef8B/Y3/Kv/p/6oCcQAD/ST8qfwUAqkHTwXnAAj+ffpO+9r/DAIkA7MBhf0f//8DBALV/Bn84v/kBCEFyf2T9q/5JgWWC/4GaP0K9Sv1Tv+tBuMG7QXi/wz4mPreAF4BYgBG/4f+0QCuA4oEuv9H+IX7egID/0T/5gakAz794v7h+0D43ABzCMcE/P8I/m76Cvg+/REGmwjvBUsCi/kI8oD5sQZHCOIBZ/zV+S7+hAa/BhT+SviU+7gCRgWWASL8dvg5+jICgAjjB/oD1v1T9Tzy4PuOCsIMxgCc97D31PxtCNEOpf/S7XbzKwRjC0cK3wIm9EPsuvrhEesUZwMU9JzvA/SBAakN/QsrAhX7UPiM+XH+2wJOA5wAkv0H/Q8ALQNdAm3/Kf2L+wb9MwJUBKAAFv2X/Db84/xYA8UKsgbz+Q70Gvf0/LQFDwtgBBf7Afz0ALX/H/3u/wsB0PzM/fICJv8v+i0CjQm9AcX4SvxyAkYAuvpU+fn7pAJEDC8Ndv998yj1nfph/7AGYwer/gr6zfwLAYAFXQXG/sf5wvjA+Vz/xgarB60AFvpi/FUDiATg/wT6p/b6+5EGoAm8BDf/G/mO9gb+3QTxAfYAVwPl/A/4BQCYBX4D8wGz/Db35Py4BbYFwf5U+YL8bgPsBRoE9Pu089X6uAjFCpYDiPkQ8iL4BQjgD6wG4/ap80/9lQQkBbsAvfq5+ysCigbGBan8R/Qt+f4CGAkMCtL/MvUA+Z4ACQRuBcD/Nfoq/z4DuwD7/2T/Z/3I//cCNAE4/0T/Gf0i/GEAPQJ9AEsD4gXUACb6hPf6+sgDyQgLBh0A1fme+DX9NwJyCrgNjvzU6vvxgQT+EGMSkAKk70XwEf6EB2gINQNd/jf+cv77/FL+YgKxAgQAWAJPBd7/Mvn99/z63gZCEv0ITfav8Pf1vAG+Du4Lsv1t+VX80/xIAZEGLAKH/Fz+g/9L//4DmAUMAPH8Gv36/rUEWwSg/Pz8VwGW/6sCGAkKA6X3WvVz+2UGdA51CNP5IvNK+ScFNw1PCfv7c/N49QX96QgFEWUInvll9hD7QQFmBtkC5/ut/u8FuwXK/uz59v0gBlQGxP23+Gz+GgblBKz/i/99Adn/g/zp/HcB0gP2AncCsP45+u7+YgVVA6YAzAAb/tH71f2oASoFZgXpAa7+A/3S/UYBOQJ0AHQA8f/3/zEFEQcOAAr7u/rv+sAApQhvCLADw/xq9Pf3QwZbDLUHMP4y88X0hQTvDFAHWf/z+Tj51f5uBGkELADi/Ij/TwTLAwcB1P42+gz7wAX1CdsBSPvN+TX84gQvCv0E9fy+9yr6vgMlBloAxv4f/z3/MQNuAwz+8fv0/GMATQazBRf+mfk2+8QAEwYEBnAB5ftQ+tb/VwRPAj0ASP/M/Zb/yAGZAND/fP/s/iwB7gPBAzgAdvmk9gX+TwjqC/0FQ/qQ8/r31AJfC7sHFvz9+iwCvQAw/NH9nv4i/7QEEwcoAaP7k/vR+7j7hwKCCv0ECfq1+ZH9VP9MBNQGDwEP+3r61v7TBOcE4/74+jz9oAOIBnEBj/uA+Vr6FAFhCAEH6gGJ/bD41PmhAKMDywPVAxoAy/pX+RD9pAPZBkYD0fy8+aX9aAPpApn+j/xc/UEA5QJoA6YC7/6e+mf8agCYANwAOgJpAOf8Iv36ATkECADA/a//X/65/JD+Jf50/xoHvQcQ/oT6X/76/Wn8sf6m/lL9qAJmCQgGDfwr+NL6k/yPAO8H2wVs+sr2VvzjADYF5AjjBGL79vU9+QQBqgQeAlT+7Pu7/eUF1QprAj31mPHA+N4E9Q1SChb8PvIo9VoAVwqWCh8Bhvf49av7DgK+BNcDvQAX/mz9L/2w/i4ChAEm/eT8awB9AuYBmv/d/ZH+NwHFAlH/G/tJ/VwAkf9JAAQBTf/CAF4Cuf6a+1P7EvzeAGwGxwTT/Rb5IPpy/9wESwZJAWP52veK/sUESAVxAmb+xfoX/DQCJATC/sH6Df3qAXsFMAVfAPj58/Yw+/ACsAYzBuIBJPmi9Vb9ZgYKCT4EwvmM9KX6TQKrBSsGzwE4/D393gAk/z38zv7tATUAc/5p/4IA5wL/A2D++fiS+1gBagUDBZb9svdJ/DoErgY5BHr+vvhw+cT/RgR8BPsBtf3M++T+jAG/AIf/of7K/7QDBAOe/NH53PyEAgkI6wWh+5f1zPllArEIQAhbAX/62vg6+93+pwMzB3cEs/0U+n77YQBWBa0FtwEn/Oj3o/q8An0HNweDArn5QvaV/WYGKAijAgf7ePnq/g0E1QSuAfX8t/ut/74DZAOa/9n7l/vZAEMHWQYc/rf3WflNAdoHegaKAIP8I/vA/IgBRAXgBC4BrvyP+wX/6gL/AwsC8v6k/jsBWAHZ/Uf8Vf/dAzAGWAQp/9z6nvpD/skDfQYqA6390foJ/KcB3QZEBf//Nf2A/Cb9LAB8AxkE6wFj/0n++/0l/04CRgOSANL+//4k/z4AiAEdAbwADQGKAPD/GwBRAO3/5f4J/4UBZAOcApgAGf6f/Hb+tgEtA8ECPwF2/z7+zP0X/+UBJQO7Abv/tP4p/1kArgB9APYAQAFjAEX/+/5a/0UAGAJhAxQCd/82/r7+RQDCAeABjwBZ/5H/uwBvAY8BRAESAAT/Pf/Y/3oAOQFGAQ0BAwFPAG//M/90/1gA2QBRANUA1gGjADj/0P9QAPH/IgDCAMEA2v89/x0ANwFtAaIBhQFsAGD/tv6P/q//8QArAWABsgFPAYwAev97/j3/KwGBATIAkP///zAAIQDZAPsBKQIyAab/+v15/Tr/kwH7Ae0AagByAJAACQF+AEX+Vf0b/yIBDQLvAX4A/v4i/3YAcAERAfX/nv/N/7D/7/+fABABTwFQAc8A1P+5/qP+x/+0ACgBigERAdb/O/+H/wYAaQCdAC0AM/8d/wQAIQCl/+P/kQCEARQCcQCt/db8TP6PAC8CMQI4ASYADv+n/jz/1v9CAKcAqwCFAEEAf/+//rD+nf9uAWIC5gBz/ib9mP3G/zgCngIUAUL/7/3B/fT+aADxAIkA/v/k/8n/Qv+3/pP+Dv8lABQB9wDF/3H+Nv4i/ysAzQDZABEA5f4k/lX+gv/AABYBugAhAFr/2v7u/ir/lv+BABIBZABR/wP/a//7/zUA0f8//xD/SP+U/4f/T/+z/0cA9P8f/7T+m/69/if/Xf9I/4P/8P+3/wX/7f6G/+//wv8v/77+8v6k/3QA9wCDAGv/5/4q/8r/mADcABcAIv/s/nz/EgAIAJX/If/Y/h3/xP/h/1n/yv6F/qP+If+4/9//Nf9c/j3+yP6t/3QAMQAH/07+ov6U/4cAzwAlADz/Gf+x//f/v/+q/7H/xv8jAFsAIgD5/+z/m/8t//f+8v4E/0n/zP9EAHEASwDP/zD/7f42/6X/v/+W/43/xv8jAGsAXgArAB0ANgBWABsAXv/s/mD/NQDJALYA+f9D/x3/UP+E/3X/LP8D//b+1f7B/rz+y/4j/43/lv8o/4b+If45/qX+IP9m/0H/1P6U/tL+a//+/1gAWgD3/5n/of/q/2IAAwFYARUBbwDZ/+j/tgB6AXEBugD8/77/EwCqAAwB6ABUAL//lv/z/3UAqgB+ABAAtv/W/z8AeQCWAMMA2ADNAKoAeQBuAKQA8wAgAfsAsACgAMkAAQE4AUMBBQGjAEgAIQBEAIIApgCTADQAvP+D/6j/+v8qABEAwv9c//7+2/4I/1X/gv9u/xv/rv5e/lP+n/4o/4f/Z//h/kj+7/0s/vj+0P8gALH/2f5c/qj+d/8tAGAAIADX/9L/JwCtAAsBEAHUAJMAgACdANIAFgFAATsBQAFVAUQBIAEMAe8A0QDKAMUAugC6AMIAzQDhAPwADAEBAdIAgQA+AEUAjwDbAPwA4wCrAH0AWgBJAGsAmwCRAFEAAADF/9D/GABYAGwASwD+/7T/mP+x//j/NQA2ABEA6P/M/9n/CgA/AGwAhABvAEQAPwCHAAQBcAGeAaIBmwGWAZsBrAG+Ac0B5QECAiACUwKTAr8C1QLqAu8CxQJzAikCHAJWAqwC4wLoAscCjwJSAiIC7QGbATcB1QCBAEIAEwDw/9//0v+l/z//nv7k/Uv97PzK/Nn8+fwD/e38sPxS/AH87vsL/B389vuj+2j7evvO+zz8oPzl/AT9B/3//Ar9Pf2U/fn9Xv6z/uT+7/7y/hj/b//h/00ArwAHAUkBdgGwAQsCgAL1AkoDfQOoA98DKQSJBPsEcgXUBQgGDQb6BesF/AU1BooG4gYaByUHEQfuBtIGywbIBqoGbQYcBscFeAUsBdsEhQQnBLwDNwOOAssBBAFRAMn/af8Z/7b+KP50/bn8Cvx8+x373vqm+mH6Cfqu+W35TPk6+SX5CPnl+Lj4gvhZ+Fb4fPi0+N747Pjh+Mj4rPia+KH4y/gS+Vb5gfmW+aL5uvnv+UT6pfoA+037j/vP+x/8i/wJ/YX98P1G/or+yP4Q/27/3P9NALcADwFMAXcBpwHpATkCjALYAiEDcgPJAyAEagSbBK8EvAToBEwF3gV5BvEGMQc+Bz4HUAeDB8oHDQgxCC8IGAgICBcIQAhfCEMI0wcaB0QGhgUUBQEFNwV9BYgFLgWFBMUDHgOmAlgCIwLxAakBTQEBAeYAAAEuATgB7QA7ADv/Kv5W/ej83fz4/N78UfxQ+xD64vjy9yr3XPZa9Rj0tfJu8YDwDfD47+jvjO/O7uTtNO0J7W3tL+4D77jvWfAn8V7yA/TU9X/32Pj0+Qz7Uvza/Zb/ZgEpA8EEIQZKB0EIBgmQCdgJ5AnGCYsJRgkQCfkI+AjvCLoISQitBwAHXgbaBXMFJgX9BAIFNwWKBdsFFgZFBnsGxgYlB4MHzAf9ByQITAh6CKsI1gjyCO4IughOCMEHOgfPBnUGBAZWBWEEQAMfAiQBYgDc/4H/Ov/v/pj+Pf7z/cX9o/16/T396/yS/E/8N/xX/Kb8/fwx/TH9+/yj/E38D/zg+6X7UPvz+rL6lfqD+lH65flK+aP4Dvid91P3JPf29r32g/Zi9m32lPav9qr2lfaY9tf2VPf096H4XPku+hz7Hvwc/QT+2f6r/4oAdwFfAi0D1wNtBAwFtwVSBrsG5wbyBv8GEgcfBxgH/QblBtkGzAa2BowGRQbgBVkFwARLBB8EOwSFBNIEAAUCBdoEoAR2BGoEdgSEBIEEewSKBLkEAwVSBYkFmAV4BSoFugQ8BMcDdwNOAz0DMgMZA+oCpgJOAu4BmQFZASIB2QBtAO//gv9M/1P/bf9d/wb/cf7I/Tf90vyY/HD8LPy0+xH7bvr6+cf5uvmo+W/5BPl6+Or3cfcs9xj3Hfca9/j2u/aD9mz2hvbP9if3dPej97L3vvfy91z48/ii+VL69/qM+xL8mPws/dH9iP5L/wYAtwBgAQcCvQKOA2wEPQXsBWcGvAYMB2oH2gdUCMoIOwmaCdkJ/wkdCjQKQQo4ChAK3gm0CZUJhAl9CXQJXAkjCcgIZwgSCMAHagcPB7EGWgYHBrIFXwUNBa4EPwTGA08D5wKOAjcC2AF0AQgBjwAQAJn/MP/T/nT+Df6r/Vv9FP3I/HL8E/yu+0j75fqL+kL6CPrY+bH5kvly+Uf5C/nC+Hr4OvgK+PD35/fk9+P35ffw9wT4GPgn+DX4SPhd+HX4j/iz+O34O/mX+ff5TfqZ+uX6N/uO++/7WvzO/EL9sP0d/pL+E/+e/ywAuQA7Aa0BDgJpAsgCMwOuAy8EqgQeBZEFAgZwBt0GUgfEByIIaAijCN0IGglgCbAJCwplCqIKvwrFCrsKqwqeCo8KegpWChwK2gmjCXsJXQk9CQEJlwj7Bz8HhQbnBWkFBgWrBDwErQMMA2wC4AFrAfIAWQCa/7v+3f0o/av8XPwg/M77SPuQ+sH5APlo+Pb3jfcS94T26/Vh9QH11fTQ9Nj0xPR79BX0t/N783PznvPo8zX0bPSK9Kv07vRc9ef1dPbv9lH3mffd90P45Pi3+Zb6Xfv5+2v8x/ws/bL9Vf78/o7/AABaALMAIQGuAU4C3wI9A2IDaQN5A6sDAQRtBNoELAVdBYIFtgUDBl0GrAbjBgkHLwdpB8YHPwi8CCgJdAmsCeUJLAp9CsYK8Ar1CuMKzArCCsgKzwq8CoAKGQqaCSMJwwhwCBQImAfwBiQGUAWMBOcDWAPLAjACfAG1APH/Q/+v/iv+of38/Dn8aPuf+vP5a/n6+Ir4CPhr97z2CvZi9cj0PPSz8yLzivL28XXxD/HI8J7wivCB8HbwavBj8GjwgvC08APxavHi8WryA/Ot82T0KfUB9ur22vfI+LH5lPpy+038Kf0M/vT+1/+uAHoBOwL1AqgDVwQABZUFEQZ2Bs0GIQduB68H5wcYCEQIcgiiCNAI+ggbCS4JNAk3CUUJZAmJCa8J1Qn9CSoKXgqWCs4K+woTCxULBAvuCucK+AobCz4LTgs/Cw8LxApuCh0KzwmCCSoJvQg+CLYHNQfJBmYG/wWNBfwERAR1A6YC7AFJAa4AEgB0/8r+GP5j/az89/tG+4/60PkL+UD4ePe89gz2Z/XP9Dv0p/MQ83Ly1fFF8c7wcvAq8O7vv++h75LvmO+z7+DvGvBc8KXw+/Bl8ejxh/I28+7zq/Rw9T72Fff59+b41vnD+q37mPyI/X7+ef91AGsBVwI8AxkE6wSzBWwGGQfEB2gIAgmWCRsKjQrwCkcLlQvhCyoMbAykDMkM3AzsDP4MFQ0xDUgNVQ1VDUYNKg0ODfcM5wzTDLUMiwxVDBkM3gulC24LMwvqCosKHAqmCSwJtAg/CMoHUwfOBjYGlgX1BFIEsgMVA3YC0QEjAWwAtf8F/1/+v/0e/XL8v/sJ+1L6o/kE+XH44/dN96z2CPZp9dT0UfTf83fzEfOo8kDy5PGZ8V/xOPEg8RPxCfEC8Q3xLvFj8a7xCvJv8tbyQfO18zv00/R79TL28/a39334RPkS+uv6y/uw/Jj9fP5b/zQACwHlAcUCpgOCBFUFFwbMBnoHIgjECGIJ+gmECgELbwvOCykMgAzMDAwNPg1iDXcNfw2BDX8New1yDV0NNw0EDcoMhww/DPULpwtSC+4KgAoQCqIJMQnACEwI0wdRB8YGNwaqBR4FkgQCBGsD1AI/AqsBHQGQAAIAdP/g/kX+p/0K/XH84/tZ+836RfrD+UH5v/hA+MX3TffU9lf24PVz9Qz1q/RU9Af0w/N/8zrz+fLF8pbybfJQ8kHyPfJA8kzyZvKQ8sXyAfNE84zz2vMz9Jn0CvWK9RT2ovYz9833dfgt+ez5rvpy+zT87/yp/W/+Qf8cAPsA1gGoAnUDPgQEBc8FmwZfBxkIwwhgCfgJkAoqC8ULVQzUDEENmw3jDSMOXQ6QDrgO0A7XDs4OtA6NDl8OKw7rDZwNOg3GDEIMtgsnC5YKBQpuCcoIGgheB50G3wUtBX8EywMLA0MCdwGuAPD/Pf+U/u/9RP2P/Nr7L/uU+gf6gvn++HT43/dD97D2MfbI9XH1IPXJ9G70DvSv817zG/Pn8rzyk/Jl8jnyF/IE8gfyHvI88lbyaPJz8oDymvLL8hfzd/Pd8z/0n/QD9XL17/V49gj3m/cp+LL4Qfni+Zv6Zvs2/AL9xf19/jP/7f+xAIMBWgIrA/QDtwR6BUMGFAfpB7oIfgkwCtIKawsADJgMNQ3ODVgO0w4+D5wP8Q8+EIEQtxDYEN8Q1xDIELEQkRBkECQQzg9hD+IOWw7PDTsNmgzoCyMLVAqCCa8I3wcNBzQGUQVhBG8DgAKYAbsA5/8R/zf+Wv2B/LP79PpE+p/5/vhd+L73J/ee9if2vfVY9fP0jvQr9M/zg/NM8yPzAfPi8sDyoPKJ8nvyd/KA8o/ynvKv8sHy2vL+8inzW/OW89PzF/Rn9Lv0FPV09dL1MfaX9gf3iPca+LP4T/nt+YX6IvvM+3/8Pf3+/bT+Xv8CAKsAZAEzAhED8APABHoFKQbXBo8HVgglCe4JpAo9C8ELRQzYDH0NLQ7VDmQPzQ8VEFEQlhDvEFERoxHSEdURsBF8EU8RNhEmEQYRvBBBEJwP5A44DqINFw18DLcLyQrACa0Ipge6Bt8FAgUNBPwC4QHMAMb/1/77/R79OPxF+0z6YPmK+Mn3HveD9uv1VfXC9Dn0wfNW8/DylPJA8vTxs/GG8W7xZvFl8WnxcfF/8ZPxsvHd8Q7yP/Jy8q/y+vJS87nzK/Sh9Bf1ifX29Wn25PZj9+L3Zfjy+I/5OPro+pz7UPz5/Jj9O/7m/pj/SQD0AJcBOALhApsDYwQsBesFkwYdB5gHGAipCEsJ8AmHCg4LhAvtC10M2AxTDb4NEA5HDm0Okg7BDvwOOQ9pD30PdQ9aDzsPGg/xDrUOYg78DYQNBA2LDBUMkQv3CkQKfwmvCNsHCwc7Bl0FaARnA2cCcgGIAKj/yf7i/e788/sE+yP6UvmP+NT3FPdO9ov12vRA9LTzN/PL8mjyBfKp8VjxFfHe8LHwkfCA8Hjwe/CN8Knwz/AB8ULxj/Hm8T3ykvLn8kDzp/Mh9K30RPXd9XL2A/eW9y74z/h6+Sv61Ppw+wn8p/xP/Qb+x/6K/0cA9QCSASoCxAJiAwcErARGBdkFYwbnBm8HAgiaCCsJpwkIClcKnwrpCj8LoQsEDFgMlAy3DM8M7AwPDTENSQ1ODTwNEw3iDLwMpwyXDHoMRAzrC3gL+gp9CgwKpQk4CbQIFghjB6gG9gVSBbUEEwRbA4gCpAG/AOf/If9q/rf9+Pwm/Er7c/qu+QH5YvjF9yL3dPbA9RX1gvQL9KnzTPPu8oryJvLQ8ZLxb/Fg8VfxSvE+8TjxP/Fb8Y7x1vEo8nfywvIT827z1/NQ9Nb0ZvX79Y72JffC92P4Cvm3+Wf6GfvP+4X8Pv34/a/+ZP8YAM4AgAEwAt8CigMqBMEEUQXkBXoGEgeoBzcItgggCXsJ0AkoCoMK3QouC3ILpAvHC+YLBgwpDEkMWgxUDD0MGgz1C9cLwQuoC4ILSQv/CqsKUwr+CaoJUgnvCH0I/Qd7B/0GgwYQBpsFHAWQBPUDUAOuAhMCfQHrAFUAt/8X/3j+2v1H/bv8Lvye+wz7dvrk+V/54vht+Pv3ivce97r2X/YT9tL1lPVW9Rj12/Sq9Iz0f/R/9IX0i/ST9KL0vfTs9Cz1dfXA9Qn2TvaY9vL2Xvfa91/45Phi+dn5TvrN+l/7Avyp/Ez95/15/gX/kv8nAMcAaAEDApQCGQOXAxYElgQaBaIFJQaaBgEHWAemB/IHPQiKCNgIHQlXCYcJrAnLCekJCAokCjIKMAojCg0K8AnUCbgJngmECV8JLQn1CLcIcwgvCOYHmwdOB/YGlAYwBswFawUQBbQEWAT1A4gDEgOYAiACrQFCAdoAcAAAAI//HP+r/kP+3/17/RX9rvxE/Nr7dvsZ+8b6ePoq+t/5m/lX+Rf54Piu+H/4VPgs+Aj47PfU98T3wPfF98/33/fy9wz4LvhV+IT4u/j5+Dz5hvnV+Sz6ifrq+k77t/sj/JL8BP12/ej9Wv7N/kL/vP84ALcANAGpARYCgALoAlADtgMcBHwE0wQhBWkFsAX4BTsGeAawBt0G/gYVByoHPgdRB2AHZgdiB1YHQwcuBxoHCAf0BtgGsQZ8BjsG+QW9BYUFTwUWBdYEjQQ9BPADqgNqAycD2wKDAiACuAFWAQUBwQCCADwA6/+Q/zH/1P6C/jv+9v2w/WT9FP3K/Ir8Uvwk/Pj7yfuU+1r7Ifvu+sb6pPqG+mz6VvpA+i36HvoV+g76Bvr++fj58Pnp+e/5Avob+jr6X/qG+qj6xvro+hf7UPuM+8r7CfxE/IH8x/wX/XH9zf0l/nX+v/4G/1H/pP8AAGEAvQAPAV0BpgHuATYCgALLAhMDVAOOA8ID8QMdBEoEdASdBMIE4wT8BA4FGwUmBTEFOwU/BTwFMAUdBQEF4QTHBLIEnwSGBGUEOQQFBM8DmANiAykD7QKtAmcCJALqAbcBgwFKAQkBvwBxACUA4P+f/1z/FP/L/oX+Rv4T/ur9wv2R/VT9Df3E/Ib8WPw5/Bz8/PvZ+7X7kvt4+2r7ZPta+0b7KPsM+/f68Pr4+gf7Ffsf+yb7L/s++077XPtr+3j7hvue+8L77vsb/EP8ZPx//J/8yfz+/Df9bf2a/cX98f0l/mf+sP72/jH/X/+G/7P/6/8tAHcAvADzACABSgF6AbgB+wE4AmcChgKbArcC4QIaA1kDjAOnA6wDqQOyA84D9AMXBCgEHgQGBPID7gMABB4ELQQeBPADsQN6A1oDUQNSA0cDHwPfApYCWQI3AikCHQL9Ab4BZAEDAbUAhABqAFcAPAAIAL//bv8o//r+3v7G/qX+cf4p/tz9nP10/WT9Zf1m/Vb9M/0F/df8uPyu/Lb8w/zG/Lv8p/ya/Jz8r/zN/O38A/0K/QP9Af0N/Sr9U/2A/ab9xP3b/e79CP4q/lD+d/6f/sb+6f4K/yz/Uf93/5//x//t/w4AKQA/AFcAcwCVAL4A5gAEAR8BNgFLAWIBfAGVAagBtgHCAdAB4AH0AQkCHAIrAjcCQgJPAlgCXAJdAlsCWQJZAl4CZAJqAm8CbgJnAmACXgJcAlUCSwI8AicCFAIEAvwB+gHzAeYB1gG/AaYBjgF2AV8BSwE3ASEBDAH4AOMAzwC3AJwAfwBkAEoAMQAbAAYA7//X/8P/sP+g/5D/ff9n/03/NP8i/xj/Ff8T/xD/C/8H/wP//f77/vn+9P7u/uz+7/76/gX/EP8b/yX/Lv8+/1P/Zf92/4T/iv+Q/5z/sf/L/+T/+f8MABwALABCAFwAdACIAJUAnQCjAKsAtwDLAOMA9wAFAREBHAEkAS0BOQFDAUsBUAFTAVIBUQFVAVsBYgFoAWoBaAFkAV4BVwFSAU0BRgE+ATMBKAEdARMBDAEIAQUB/gDxAOAAywC3AKQAlQCKAIMAegBtAF0ASwA8ADMAKgAeAA8A/f/p/9X/xv+8/7b/r/+k/5f/jP+E/3v/cv9o/1n/R/83/yz/KP8o/yn/Kf8m/yH/Hf8d/x7/Hf8b/xf/E/8R/xL/GP8i/y7/Of9E/07/Wv9l/2z/cf94/4L/jf+e/7b/zv/f/+n/8f/6/wAACAAWACUALgAzADkAQgBOAFwAagB2AH0AfQB7AHkAdwB5AH4AhQCNAJAAkACQAI8AjACJAIcAhwCFAIEAfQB8AHsAegB5AHMAbABmAF4AVABMAEQAPAAzACoAIgAYAA4ABwD///T/6P/e/9L/xf+4/67/qf+k/5//lP+G/3f/a/9i/13/XP9Z/1P/Rv85/zD/K/8n/yb/If8Y/w7/BP/+/v/+BP8K/wr/Av/2/un+3v7a/uD+6P7w/vX+9/73/vj++/4B/wj/Dv8S/xP/Ef8R/xb/IP8u/z3/S/9Y/13/XP9Z/1j/Wv9f/2j/c/+A/4v/lf+e/6n/s/+4/7z/wP/D/8T/xf/F/8f/zf/Z/+j/9f/9/wAA///4//H/8f/0//n//f////z/+f/3//n//P/+//3/+v/2//T/9P/2//f/9//z/+z/5v/j/+X/6f/p/+b/3//V/87/zf/P/9H/zv/D/7T/qP+j/6r/t/++/7v/r/+b/4n/g/+I/5H/lv+R/4T/dP9s/3L/gv+S/5b/if9x/1v/Tv9T/2T/dP97/3b/Z/9b/1z/aP94/4X/hf92/2b/Xv9i/3H/hv+V/5n/lf+O/4v/kP+d/6r/tf+2/7D/rf+w/7f/wf/N/9n/4P/g/93/3f/g/+b/7//2//z/AAAAAAAAAQAFAAsAEQAVABgAFwAWABcAGgAgACQAIgAgAB8AHAAaABsAHAAeAB8AHgAgACIAIgAjACMAIgAjACIAHwAdABsAGgAcACAAJgArACoAJgAkACYAKgAwADUANQAzADMAOABCAE4AWABdAF8AXABZAF0AZABsAHEAcQBuAG4AdACAAI0AlwCXAI8AgwB6AHsAhwCTAJkAmgCVAIwAiACIAIwAkQCOAIIAcwBmAGAAYwBqAHIAdgByAGgAXQBVAFEAUQBRAE8ASgBEAEAAQQBEAEkAUgBaAF4AWwBTAEkAQQBAAEYAUABcAGMAaABsAHEAewCKAJcAmgCWAI0AhgCGAJEAowC0AL4AwAC/AL4AwQDIAM8A0QDOAMcAwAC9AMEAyQDPANQA0wDMAMMAugCwAKgAoQCeAJ0AmgCSAIoAgAByAGYAXwBbAFcAUQBIAD8ANAAsAC0AMQA0ADUALwAiABIAAQD5//v/AQAKABIAFAARAA8ADwATABsAIgAjABwAEgAKAAoAEwAkADgARQBJAEYAQQA+AD4AQwBKAFAAUgBSAFMAVABXAF0AZQBoAGgAZQBgAFsAWQBZAF0AYgBjAGIAYABeAFwAWgBYAFcAVABOAEgARQBCAEEAPwA4AC4AJAAcABQAEQARABIADwAJAAEA+f/y/+7/6v/m/+H/3f/b/9v/3v/i/+X/5P/g/9v/2P/X/9f/2f/d/9//3//g/+H/4f/f/9//4P/f/9//3//d/9r/2f/Z/9v/3//j/+X/4//e/9n/1//Z/9z/3v/i/+T/4v/e/9n/1v/W/9n/3f/g/93/2P/S/8z/yf/O/9X/2P/Z/9T/zP/G/8L/xf/L/87/zf/M/8f/wP++/8D/xP/J/8v/yf/C/7r/tv+1/7n/wP/G/8f/xP+//7r/uP+6/73/vf+5/7L/q/+m/6f/rv+2/7r/uf+y/6f/nv+Z/5j/mv+b/5r/l/+T/4//jf+P/5D/j/+O/4n/hP+C/4P/gv+B/4L/g/+F/4b/h/+H/4P/ff94/3b/d/98/4H/g/+E/4P/gP9//37/gP+C/4T/hf+F/4P/hf+K/43/kP+U/5j/mf+Y/5f/mP+a/5z/nf+f/6H/o/+n/6z/rf+s/6n/pv+l/6X/qP+s/63/qv+m/6H/n/+j/6n/rP+s/6j/ov+g/5//of+l/6j/p/+k/5//nf+e/6L/pv+m/6L/n/+b/5n/nP+g/6T/pv+k/6P/o/+l/6z/tP+1/7L/rP+j/5//pf+v/7n/v/+//7r/tv+0/7b/vP/B/8D/t/+s/6b/qP+z/7//yf/K/8T/u/+2/7b/uv/A/8T/w/+//73/wP/G/8//1v/Z/9n/1//V/9X/1//b/97/4f/l/+f/6v/v//L/9P/2//f/+f/8////AQADAAMAAwAHAA0AEQASABEAEAAPABEAFwAgACcAJwAkACMAJQAoAC0ANAA3ADcANgA4AD4ARgBLAEwASABBADwAOwBAAEgATwBRAFAAUABUAFoAXwBkAGQAXQBUAE8AUQBYAF8AZQBoAGcAYgBfAF0AXgBhAGIAYQBgAF0AWwBdAGAAZQBqAGwAawBoAGQAYABeAGEAZQBnAGcAZABjAGQAaABsAG8AbQBnAGAAWwBYAFoAXwBkAGYAZwBmAGYAaQBuAHIAcwByAG0AZwBiAGIAZgBsAHIAdgB2AHUAdQB3AHsAfgB/AH0AeQBwAGkAaABrAG4AcwB1AHUAcgBtAGYAYABdAFkAVgBVAFMAUQBPAE4AUABTAFQAUgBPAEsARwBDAD8APgBAAEMARgBJAEoASQBHAEQAQQBCAEgAUABTAFIAUwBRAFAAUwBYAFwAXgBbAFYAUwBVAFwAZQBsAHAAcgBxAG8AbwBwAHEAcwByAHAAbwBuAG8AcgB2AHgAdwB1AG8AZwBgAF0AWwBbAFsAWQBVAFEATgBPAE8ATQBKAEMAPAA2ADAAKwArACoAJwAnACYAIgAgAB8AHQAaABUADwALAAoACgALAA0ADwAQAA8ADgAMAAoACwANAA4AEQAUABcAGgAcAB4AIQAiACIAIwAkACUAJwAqAC0ALQAuADAAMQAyADUANwA1ADIAMAAvAC0ALgAxADEALQAqACcAIgAeABwAGQAUABAADAAIAAQAAAD7//T/7//q/+f/5//k/+D/2//U/8//y//I/8f/xv/D/7//u/+4/7b/tv+2/7f/tv+z/7D/rP+n/6T/pP+n/6r/rP+t/6v/p/+l/6b/p/+q/6z/qv+m/6P/pP+o/6v/rP+u/67/rP+r/6r/qP+n/6T/of+d/5z/nP+d/57/nP+a/5n/mf+Y/5X/kv+P/4z/if+H/4f/iv+M/43/jv+M/4n/h/+D/4H/f/9+/37/fv99/37/f/+A/4L/g/+D/4H/fP92/3L/cv93/33/g/+I/4j/hf+B/4D/gP+C/4X/iP+I/4f/hf+F/4f/jP+R/5X/lf+T/47/iv+H/4f/if+M/4//kf+R/5D/jf+L/4r/jP+P/5H/kf+O/4z/iv+K/43/j/+Q/4//i/+G/4T/hv+K/5D/lP+U/5L/j/+M/43/jv+Q/5H/kv+R/5L/lf+Z/5//pv+q/6v/qP+l/6P/pP+n/63/tP+6/77/wP/B/8L/xf/I/8v/zf/M/8r/yP/H/8n/z//W/9r/2f/V/9L/0P/S/9b/2f/a/9j/1f/R/9D/0v/X/9z/3P/a/9f/1v/a/97/4v/m/+j/6P/n/+j/7f/0//j/+f/5//j/9//5//7/AQADAAMAAgAEAAYACgAOABIAEwATABIAEgAUABcAGAAWABQAEwAUABcAGwAeACAAHwAdAB0AHgAhACQAJwApACgAJgAmACcAKAAqACwALQAtACwALAAuADEANAA3ADcANwA3ADYANAAzADMAMwA2ADsAQQBFAEYARABDAEIAQwBGAEgASgBKAEcARgBJAE4AUgBWAFgAVgBTAFAATwBQAFMAVQBWAFcAWABaAFwAXgBeAFsAWABUAFEATwBQAFIAUwBSAFAATwBPAFEAUwBVAFYAVQBTAFQAVgBYAFwAYABiAGIAYABeAF4AYQBkAGkAawBqAGkAaQBqAGwAbwBzAHMAcQBtAGoAagBtAHMAdwB4AHcAdABxAG8AbgBuAG4AbABnAGUAZABlAGgAawBrAGoAaQBlAGMAYgBiAGEAYQBgAF4AXABaAFwAYABhAGEAYABeAFwAWwBZAFkAWgBcAF4AXwBfAGAAXwBeAF0AXgBgAGEAYgBiAGMAZABlAGgAagBrAGwAawBqAGkAaABnAGYAZwBpAG0AbgBtAGsAaABlAGIAYgBiAGAAXgBbAFgAWABZAFsAXABdAFkAUwBPAEwASgBIAEQAQQA+ADwAOwA6ADgANQAxAC0AKwAoACcAJQAhAB0AGQAWABQAEgASABQAFQAWABcAFgAVABQAEQAPABAAEgARABEAEgATABcAGwAeAB8AHgAaABYAEwASABQAGAAaABsAGwAaABsAHAAaABgAGAAWABIADwANAAwADAAMAAwACwAIAAMA///6//b/9P/z/+//6v/n/+P/3v/b/9r/1v/S/9D/y//F/8D/u/+4/7X/sv+w/7D/sP+u/6v/qP+l/6L/oP+g/6D/n/+e/5z/mv+Y/5f/l/+X/5j/mP+X/5X/kv+R/5D/kP+R/5L/lP+U/5P/kv+S/5P/lf+V/5X/lP+S/5H/kv+R/5H/kv+S/5H/kf+Q/4//jf+M/4v/if+H/4b/hv+F/4X/hP+C/4H/gf+C/4H/f/99/3r/ev95/3n/ef96/3n/d/91/3b/ef97/3z/e/96/3n/eP95/3v/ff+A/4D/gP9//4D/gv+G/4r/jv+P/43/jP+M/4z/jv+R/5X/mP+Z/5f/lv+X/5n/nf+g/6H/oP+e/5z/nP+e/6L/pf+m/6X/o/+h/6H/o/+k/6b/pv+k/6T/pP+l/6f/qv+r/6v/qv+p/6n/q/+s/67/sf+0/7b/t/+3/7f/t/+3/7n/vf/A/8P/xv/J/8r/y//O/9L/1f/W/9X/1P/T/9L/1f/Z/9z/3f/d/9z/2v/Z/9r/3P/d/9z/2//c/93/4P/j/+X/5//m/+T/4//i/+H/4v/i/+P/5f/p/+z/7v/x//L/8v/y//L/8//0//b/9v/3//j/+f/7//3///8AAAEAAQABAAAAAQACAAUACAAKAAsACwAKAAsADAANAA4ADgAPABAAEwAVABcAGAAaABwAHAAcABwAHAAcABwAHQAfACAAIAAiACMAJAAlACUAJAAkACMAIgAiACQAJwAqACwALQAsACoAKgArAC0AMAAzADQANgA2ADcAOgA+AEIARQBGAEQAQQBAAEIARQBIAEsATABNAE8AUABSAFQAVQBUAFMAUwBSAFMAVQBXAFgAWQBaAFsAWwBcAFsAWwBaAFkAWgBbAFsAXABeAF8AYABiAGMAZABmAGcAaQBrAG0AbQBtAGwAbQBuAG4AcABxAHIAcgByAHIAcwB0AHYAeAB5AHcAdgB1AHQAdAB2AHgAegB8AHwAegB6AHoAewB9AH8AgACCAIEAfgB9AH0AfQCAAIMAhACDAIEAfwB+AH0AfQB9AHsAeAB2AHUAdQB2AHcAdwB3AHcAdwB3AHgAdwB0AHEAbQBpAGgAZwBoAGsAawBpAGcAZwBnAGYAZABiAGAAXwBeAF4AYQBkAGYAZQBlAGQAYwBjAGIAYgBiAGMAZABkAGMAYwBkAGMAYwBkAGIAXgBcAFoAWgBaAFoAWABXAFMATwBNAEsASABFAEIAPgA6ADUAMQAvAC0AKQAlACMAIgAgABwAFwASAA4ADAAJAAYABQAEAAEAAAD8//v/+//6//n/+P/1//D/7v/s/+r/6//s/+7/8P/w/+7/7f/t/+3/7v/v/+//7//u/+z/7P/t/+//8P/x//H/8P/v/+3/7f/s/+v/6f/m/+T/5P/i/+L/4v/i/+H/3//c/9n/1f/S/9D/z//N/8r/x//E/8H/v/+9/7v/uf+1/7L/r/+s/6v/qv+n/6X/o/+g/57/nv+c/5v/mf+W/5T/kv+Q/5D/j/+N/4z/i/+K/4r/iv+N/5D/kP+O/47/jP+M/4z/jP+M/43/jv+N/4v/i/+M/43/j/+Q/4//jv+M/4n/iP+J/4v/jf+M/4r/h/+F/4P/g/+D/4H/gP9+/33/fP98/3z/ff99/3v/ef94/3f/d/94/3j/eP94/3j/ef95/3f/d/94/3f/d/93/3j/ef95/3v/ff9//4D/gv+D/4L/gf+C/4T/h/+J/4z/jv+Q/5D/kP+R/5T/lv+Y/5n/mf+Y/5j/mf+a/5z/nf+e/5//nv+e/57/nv+g/6L/pP+m/6f/p/+o/6n/q/+t/67/r/+w/7H/sv+0/7f/uf+8/73/vv+//8D/wv/E/8b/yP/J/8r/zP/M/83/z//P/9D/0f/S/9T/1//Y/9j/2v/b/9z/3v/f/+D/4P/g/+H/4f/i/+T/5v/n/+j/6f/q/+3/7//x//H/8f/x//P/9f/3//r/+//9////AAAAAAEAAwAEAAYABwAJAAgACAAKAAsADQAPABEAEgARABEAEgATABQAFgAYABoAHQAeAB8AHwAfAB8AIQAjACUAJwAoACgAKAAqACwALwAwADAAMAAuAC0ALgAuAC8AMQAxADEAMwAzADMANAA0ADMAMwA0ADUANgA4ADoAPQA+AD8APgA/AD8APwA/AEAAQQBCAEMARQBHAEkASgBKAEoASgBKAEsASwBKAEoASwBNAE4ATwBPAE8ATgBPAE8AUQBTAFMAUgBRAFIAUwBWAFgAWgBZAFcAVgBWAFgAWwBdAF8AYQBjAGMAZABnAGkAagBrAGsAbABuAG8AbwBwAHAAcQByAHQAdgB3AHcAdgB2AHUAdQB1AHYAdQBzAHIAcwBzAHIAcgByAHMAcgBwAG8AbgBtAG0AbQBsAGwAbQBtAG0AbABrAGoAagBpAGkAaQBrAGwAbABrAGwAbABtAG0AbQBuAG8AbQBtAG4AcAByAHQAdAB0AHMAcQBxAHAAcQByAHIAcgByAHIAcwB1AHcAeQB5AHUAcwByAHAAcABvAG4AbQBsAGkAZwBnAGYAZgBlAGMAYABbAFcAVABUAFMAUgBRAFAATgBMAEoASQBIAEYAQgA/ADwAOgA4ADYAMwAwAC4AKwApACkAKQAoACYAJQAhAB0AGwAbABsAGgAXABUAFAASABAAEQAQAA8ADgALAAgABgAEAAMAAgACAAMAAwAAAP7//P/6//n/9//1//L/7//r/+j/5v/j/+L/3//b/9n/1//U/9L/0P/N/8v/yf/I/8X/wf++/7z/uv+4/7b/tf+z/7H/r/+t/6v/qv+q/6v/qf+m/6X/pf+m/6b/p/+m/6X/o/+i/6H/of+h/6L/ov+j/6T/pP+k/6X/pf+l/6X/pf+l/6T/o/+i/6D/oP+f/5//n/+e/5z/mv+X/5X/k/+S/5H/j/+O/4z/iv+I/4f/hf+E/4L/gP9+/33/ff99/37/fv99/3z/e/97/3v/ev97/3r/ef95/3n/ef94/3n/ev97/3z/fP98/3z/ff99/33/f/9//4D/gf+C/4L/g/+E/4b/h/+I/4j/if+J/4r/iv+M/4z/jf+N/47/jv+O/4//kP+R/5L/k/+U/5X/lf+V/5X/lv+W/5b/lv+W/5b/lv+W/5b/l/+X/5f/l/+X/5f/l/+Y/5n/mf+a/5v/m/+d/57/nv+f/6D/oP+i/6P/pP+m/6j/qf+q/6v/rf+u/6//sP+x/7H/sv+z/7T/tf+3/7j/uf+7/7z/vf++/7//wP/B/8L/xP/G/8j/yf/K/8z/zP/O/8//0P/R/9P/1f/X/9n/2//c/97/3//h/+P/4//k/+b/5//o/+r/7P/t/+//8P/y//P/9f/2//f/9//5//r//P/9//7///8AAAEAAgADAAQABAAFAAYABwAJAAoACwAMAA4AEAAQABIAEgATABUAFwAZABoAHAAdAB4AIAAhACMAJAAmACcAKQAqACsALAAuAC8AMQAzADUANwA4ADkAOgA7AD0APwBAAEEAQwBEAEYASABKAEsATQBOAE8AUABRAFMAVABVAFYAVwBYAFoAWwBcAF0AXgBfAGAAYQBiAGMAZABlAGYAZwBoAGkAagBqAGsAbABtAG4AbwBwAHAAcQBxAHIAcgBzAHQAdAB1AHUAdgB3AHgAeQB5AHoAegB7AHwAfAB9AH0AfgB/AIAAgACBAIEAgQCCAIMAhACEAIUAhQCGAIYAhwCIAIgAiQCJAIkAiQCJAIkAigCKAIoAiwCLAIsAiwCKAIoAiQCJAIkAiQCJAIkAiACHAIcAhgCFAIUAhACEAIQAgwCCAIEAgACAAIAAgACAAIAAfwB+AH0AfAB7AHoAeQB5AHgAeAB3AHYAdQB1AHUAdgB3AHYAdAByAHAAbwBvAHAAcABwAG8AbgBtAGwAawBrAGoAaQBmAGQAYwBiAGEAYABgAF4AXQBcAFoAWABWAFUAUwBQAE8ATwBNAEsASgBIAEYAQwA/AD4APQA7ADkAOAA2ADQAMQAvAC8ALgAtACwAKAAkACIAIQAfAB4AHgAfAB8AGwAYABYAFAATABMAEQAQAA0ACgAHAAYABgAGAAcABgADAAAA/v/8//v/+//5//b/8v/v/+3/7f/t/+v/6v/n/+T/4f/f/93/2v/X/9X/0v/O/83/zP/L/8n/xf/D/8P/wf+9/7r/uP+0/7L/sf+w/6//rv+s/6r/qP+n/6X/pP+l/6P/of+f/57/nf+c/5z/nP+d/5z/m/+a/5n/mP+Y/5j/l/+X/5f/lv+V/5T/k/+S/5L/kv+Q/5D/kP+Q/5D/j/+O/47/jP+K/4r/iP+G/4X/hf+D/4H/gf+A/4D/gP9//33/e/95/3j/eP93/3f/dv91/3X/dP9z/3H/bv9r/2r/\" type=\"audio/wav\" />\n",
-              "                    Your browser does not support the audio element.\n",
-              "                </audio>\n",
-              "              "
-            ],
-            "text/plain": [
-              "<IPython.lib.display.Audio object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            " > Saving output to tests-audios/Test_this_demonstration_.wav\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "error",
-          "ename": "KeyboardInterrupt",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-            "\nDuring handling of the above exception, another exception occurred:\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-32-390fa572c62e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Synthesize sentence with Speaker: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSpeaker_choise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"(this speaker seen in training)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mTEXT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter sentence: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Text: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocoder_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSE_CUDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_griffin_lim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSPEAKER_FILEID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -1423,31 +504,14 @@
       "metadata": {
         "id": "r0IEFZ0B5vQg",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 121
-        },
-        "outputId": "93ee404d-3fdc-48ac-f867-9ef7f7ecaa0c"
+        "colab": {}
       },
       "source": [
         "!wget -c -q --show-progress -O ./SpeakerEncoder-checkpoint.zip  https://github.com/Edresson/TTS/releases/download/v1.0.0/GE2E-SpeakerEncoder-iter25k.zip\n",
         "!unzip ./SpeakerEncoder-checkpoint.zip"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "./SpeakerEncoder-ch 100%[===================>]  25.35M  25.1MB/s    in 1.0s    \n",
-            "Archive:  ./SpeakerEncoder-checkpoint.zip\n",
-            "   creating: GE2E-SpeakerEncoder/\n",
-            "  inflating: GE2E-SpeakerEncoder/best_model.pth.tar  \n",
-            "  inflating: GE2E-SpeakerEncoder/config.json  \n",
-            "  inflating: GE2E-SpeakerEncoder/README.MD  \n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -1470,11 +534,7 @@
       "metadata": {
         "id": "tOwkfQqT6-Qo",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 451
-        },
-        "outputId": "091c9020-302a-429e-bc7e-7fd2bb6f68f9"
+        "colab": {}
       },
       "source": [
         "from TTS.utils.audio import AudioProcessor\n",
@@ -1489,39 +549,7 @@
         "    se_model.cuda()"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            " > Setting up Audio Processor...\n",
-            " | > sample_rate:16000\n",
-            " | > num_mels:40\n",
-            " | > min_level_db:-100\n",
-            " | > frame_shift_ms:12.5\n",
-            " | > frame_length_ms:50\n",
-            " | > ref_level_db:20\n",
-            " | > fft_size:1024\n",
-            " | > power:None\n",
-            " | > preemphasis:0.98\n",
-            " | > griffin_lim_iters:None\n",
-            " | > signal_norm:True\n",
-            " | > symmetric_norm:True\n",
-            " | > mel_fmin:0\n",
-            " | > mel_fmax:8000.0\n",
-            " | > spec_gain:20.0\n",
-            " | > stft_pad_mode:reflect\n",
-            " | > max_norm:4.0\n",
-            " | > clip_norm:True\n",
-            " | > do_trim_silence:False\n",
-            " | > trim_db:60\n",
-            " | > do_sound_norm:False\n",
-            " | > stats_path:None\n",
-            " | > hop_length:200\n",
-            " | > win_length:800\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -1542,66 +570,14 @@
       "metadata": {
         "id": "_FWwHPjJ8NXl",
         "colab_type": "code",
-        "colab": {
-          "resources": {
-            "http://localhost:8080/nbextensions/google.colab/files.js": {
-              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7CgpmdW5jdGlvbiBfdXBsb2FkRmlsZXMoaW5wdXRJZCwgb3V0cHV0SWQpIHsKICBjb25zdCBzdGVwcyA9IHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCk7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICAvLyBDYWNoZSBzdGVwcyBvbiB0aGUgb3V0cHV0RWxlbWVudCB0byBtYWtlIGl0IGF2YWlsYWJsZSBmb3IgdGhlIG5leHQgY2FsbAogIC8vIHRvIHVwbG9hZEZpbGVzQ29udGludWUgZnJvbSBQeXRob24uCiAgb3V0cHV0RWxlbWVudC5zdGVwcyA9IHN0ZXBzOwoKICByZXR1cm4gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpOwp9CgovLyBUaGlzIGlzIHJvdWdobHkgYW4gYXN5bmMgZ2VuZXJhdG9yIChub3Qgc3VwcG9ydGVkIGluIHRoZSBicm93c2VyIHlldCksCi8vIHdoZXJlIHRoZXJlIGFyZSBtdWx0aXBsZSBhc3luY2hyb25vdXMgc3RlcHMgYW5kIHRoZSBQeXRob24gc2lkZSBpcyBnb2luZwovLyB0byBwb2xsIGZvciBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcC4KLy8gVGhpcyB1c2VzIGEgUHJvbWlzZSB0byBibG9jayB0aGUgcHl0aG9uIHNpZGUgb24gY29tcGxldGlvbiBvZiBlYWNoIHN0ZXAsCi8vIHRoZW4gcGFzc2VzIHRoZSByZXN1bHQgb2YgdGhlIHByZXZpb3VzIHN0ZXAgYXMgdGhlIGlucHV0IHRvIHRoZSBuZXh0IHN0ZXAuCmZ1bmN0aW9uIF91cGxvYWRGaWxlc0NvbnRpbnVlKG91dHB1dElkKSB7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICBjb25zdCBzdGVwcyA9IG91dHB1dEVsZW1lbnQuc3RlcHM7CgogIGNvbnN0IG5leHQgPSBzdGVwcy5uZXh0KG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSk7CiAgcmV0dXJuIFByb21pc2UucmVzb2x2ZShuZXh0LnZhbHVlLnByb21pc2UpLnRoZW4oKHZhbHVlKSA9PiB7CiAgICAvLyBDYWNoZSB0aGUgbGFzdCBwcm9taXNlIHZhbHVlIHRvIG1ha2UgaXQgYXZhaWxhYmxlIHRvIHRoZSBuZXh0CiAgICAvLyBzdGVwIG9mIHRoZSBnZW5lcmF0b3IuCiAgICBvdXRwdXRFbGVtZW50Lmxhc3RQcm9taXNlVmFsdWUgPSB2YWx1ZTsKICAgIHJldHVybiBuZXh0LnZhbHVlLnJlc3BvbnNlOwogIH0pOwp9CgovKioKICogR2VuZXJhdG9yIGZ1bmN0aW9uIHdoaWNoIGlzIGNhbGxlZCBiZXR3ZWVuIGVhY2ggYXN5bmMgc3RlcCBvZiB0aGUgdXBsb2FkCiAqIHByb2Nlc3MuCiAqIEBwYXJhbSB7c3RyaW5nfSBpbnB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIGlucHV0IGZpbGUgcGlja2VyIGVsZW1lbnQuCiAqIEBwYXJhbSB7c3RyaW5nfSBvdXRwdXRJZCBFbGVtZW50IElEIG9mIHRoZSBvdXRwdXQgZGlzcGxheS4KICogQHJldHVybiB7IUl0ZXJhYmxlPCFPYmplY3Q+fSBJdGVyYWJsZSBvZiBuZXh0IHN0ZXBzLgogKi8KZnVuY3Rpb24qIHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IGlucHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKGlucHV0SWQpOwogIGlucHV0RWxlbWVudC5kaXNhYmxlZCA9IGZhbHNlOwoKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIG91dHB1dEVsZW1lbnQuaW5uZXJIVE1MID0gJyc7CgogIGNvbnN0IHBpY2tlZFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgaW5wdXRFbGVtZW50LmFkZEV2ZW50TGlzdGVuZXIoJ2NoYW5nZScsIChlKSA9PiB7CiAgICAgIHJlc29sdmUoZS50YXJnZXQuZmlsZXMpOwogICAgfSk7CiAgfSk7CgogIGNvbnN0IGNhbmNlbCA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2J1dHRvbicpOwogIGlucHV0RWxlbWVudC5wYXJlbnRFbGVtZW50LmFwcGVuZENoaWxkKGNhbmNlbCk7CiAgY2FuY2VsLnRleHRDb250ZW50ID0gJ0NhbmNlbCB1cGxvYWQnOwogIGNvbnN0IGNhbmNlbFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgY2FuY2VsLm9uY2xpY2sgPSAoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9OwogIH0pOwoKICAvLyBXYWl0IGZvciB0aGUgdXNlciB0byBwaWNrIHRoZSBmaWxlcy4KICBjb25zdCBmaWxlcyA9IHlpZWxkIHsKICAgIHByb21pc2U6IFByb21pc2UucmFjZShbcGlja2VkUHJvbWlzZSwgY2FuY2VsUHJvbWlzZV0pLAogICAgcmVzcG9uc2U6IHsKICAgICAgYWN0aW9uOiAnc3RhcnRpbmcnLAogICAgfQogIH07CgogIGNhbmNlbC5yZW1vdmUoKTsKCiAgLy8gRGlzYWJsZSB0aGUgaW5wdXQgZWxlbWVudCBzaW5jZSBmdXJ0aGVyIHBpY2tzIGFyZSBub3QgYWxsb3dlZC4KICBpbnB1dEVsZW1lbnQuZGlzYWJsZWQgPSB0cnVlOwoKICBpZiAoIWZpbGVzKSB7CiAgICByZXR1cm4gewogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgICAgfQogICAgfTsKICB9CgogIGZvciAoY29uc3QgZmlsZSBvZiBmaWxlcykgewogICAgY29uc3QgbGkgPSBkb2N1bWVudC5jcmVhdGVFbGVtZW50KCdsaScpOwogICAgbGkuYXBwZW5kKHNwYW4oZmlsZS5uYW1lLCB7Zm9udFdlaWdodDogJ2JvbGQnfSkpOwogICAgbGkuYXBwZW5kKHNwYW4oCiAgICAgICAgYCgke2ZpbGUudHlwZSB8fCAnbi9hJ30pIC0gJHtmaWxlLnNpemV9IGJ5dGVzLCBgICsKICAgICAgICBgbGFzdCBtb2RpZmllZDogJHsKICAgICAgICAgICAgZmlsZS5sYXN0TW9kaWZpZWREYXRlID8gZmlsZS5sYXN0TW9kaWZpZWREYXRlLnRvTG9jYWxlRGF0ZVN0cmluZygpIDoKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgJ24vYSd9IC0gYCkpOwogICAgY29uc3QgcGVyY2VudCA9IHNwYW4oJzAlIGRvbmUnKTsKICAgIGxpLmFwcGVuZENoaWxkKHBlcmNlbnQpOwoKICAgIG91dHB1dEVsZW1lbnQuYXBwZW5kQ2hpbGQobGkpOwoKICAgIGNvbnN0IGZpbGVEYXRhUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICAgIGNvbnN0IHJlYWRlciA9IG5ldyBGaWxlUmVhZGVyKCk7CiAgICAgIHJlYWRlci5vbmxvYWQgPSAoZSkgPT4gewogICAgICAgIHJlc29sdmUoZS50YXJnZXQucmVzdWx0KTsKICAgICAgfTsKICAgICAgcmVhZGVyLnJlYWRBc0FycmF5QnVmZmVyKGZpbGUpOwogICAgfSk7CiAgICAvLyBXYWl0IGZvciB0aGUgZGF0YSB0byBiZSByZWFkeS4KICAgIGxldCBmaWxlRGF0YSA9IHlpZWxkIHsKICAgICAgcHJvbWlzZTogZmlsZURhdGFQcm9taXNlLAogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbnRpbnVlJywKICAgICAgfQogICAgfTsKCiAgICAvLyBVc2UgYSBjaHVua2VkIHNlbmRpbmcgdG8gYXZvaWQgbWVzc2FnZSBzaXplIGxpbWl0cy4gU2VlIGIvNjIxMTU2NjAuCiAgICBsZXQgcG9zaXRpb24gPSAwOwogICAgd2hpbGUgKHBvc2l0aW9uIDwgZmlsZURhdGEuYnl0ZUxlbmd0aCkgewogICAgICBjb25zdCBsZW5ndGggPSBNYXRoLm1pbihmaWxlRGF0YS5ieXRlTGVuZ3RoIC0gcG9zaXRpb24sIE1BWF9QQVlMT0FEX1NJWkUpOwogICAgICBjb25zdCBjaHVuayA9IG5ldyBVaW50OEFycmF5KGZpbGVEYXRhLCBwb3NpdGlvbiwgbGVuZ3RoKTsKICAgICAgcG9zaXRpb24gKz0gbGVuZ3RoOwoKICAgICAgY29uc3QgYmFzZTY0ID0gYnRvYShTdHJpbmcuZnJvbUNoYXJDb2RlLmFwcGx5KG51bGwsIGNodW5rKSk7CiAgICAgIHlpZWxkIHsKICAgICAgICByZXNwb25zZTogewogICAgICAgICAgYWN0aW9uOiAnYXBwZW5kJywKICAgICAgICAgIGZpbGU6IGZpbGUubmFtZSwKICAgICAgICAgIGRhdGE6IGJhc2U2NCwKICAgICAgICB9LAogICAgICB9OwogICAgICBwZXJjZW50LnRleHRDb250ZW50ID0KICAgICAgICAgIGAke01hdGgucm91bmQoKHBvc2l0aW9uIC8gZmlsZURhdGEuYnl0ZUxlbmd0aCkgKiAxMDApfSUgZG9uZWA7CiAgICB9CiAgfQoKICAvLyBBbGwgZG9uZS4KICB5aWVsZCB7CiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICB9CiAgfTsKfQoKc2NvcGUuZ29vZ2xlID0gc2NvcGUuZ29vZ2xlIHx8IHt9OwpzY29wZS5nb29nbGUuY29sYWIgPSBzY29wZS5nb29nbGUuY29sYWIgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYi5fZmlsZXMgPSB7CiAgX3VwbG9hZEZpbGVzLAogIF91cGxvYWRGaWxlc0NvbnRpbnVlLAp9Owp9KShzZWxmKTsK",
-              "ok": true,
-              "headers": [
-                [
-                  "content-type",
-                  "application/javascript"
-                ]
-              ],
-              "status": 200,
-              "status_text": "OK"
-            }
-          },
-          "base_uri": "https://localhost:8080/",
-          "height": 246
-        },
-        "outputId": "cb0ca2ad-73b9-44ce-d0bd-6139552232d8"
+        "colab": {}
       },
       "source": [
         "from google.colab import files\n",
         "file_list = files.upload()"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "\n",
-              "     <input type=\"file\" id=\"files-1890223f-cc4a-4274-a149-c6368625bb38\" name=\"files[]\" multiple disabled\n",
-              "        style=\"border:none\" />\n",
-              "     <output id=\"result-1890223f-cc4a-4274-a149-c6368625bb38\">\n",
-              "      Upload widget is only available when the cell has been executed in the\n",
-              "      current browser session. Please rerun this cell to enable.\n",
-              "      </output>\n",
-              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Saving p238_001.wav to p238_001.wav\n",
-            "Saving p238_002.wav to p238_002.wav\n",
-            "Saving p238_003.wav to p238_003.wav\n",
-            "Saving p238_004.wav to p238_004.wav\n",
-            "Saving p238_005.wav to p238_005.wav\n",
-            "Saving p238_006.wav to p238_006.wav\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -1635,11 +611,7 @@
       "metadata": {
         "colab_type": "code",
         "id": "xmItcGac5WiG",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 872
-        },
-        "outputId": "4940027a-a303-4782-ef47-0c91716665a1"
+        "colab": {}
       },
       "source": [
         "import IPython\n",
@@ -1659,100 +631,7 @@
         "  ap.save_wav(wav, out_path)"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Synthesize sentence with New Speaker using files:  dict_keys(['p238_001.wav', 'p238_002.wav', 'p238_003.wav', 'p238_004.wav', 'p238_005.wav', 'p238_006.wav']) (this speaker not seen in training (new speaker))\n",
-            "Enter sentence: Test this demonstration\n",
-            " > Text: Test this demonstration\n",
-            " > Run-time: 1.1401546001434326\n",
-            " > Real-time factor: 0.8927653497084975\n",
-            " > Time per step: 4.0488326075402176e-05\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "\n",
-              "                <audio controls=\"controls\" >\n",
-              "                    <source 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type=\"audio/wav\" />\n",
-              "                    Your browser does not support the audio element.\n",
-              "                </audio>\n",
-              "              "
-            ],
-            "text/plain": [
-              "<IPython.lib.display.Audio object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            " > Saving output to tests-audios/Test_this_demonstration.wav\n",
-            "Enter sentence: Ask her to bring these things with her from the store.\n",
-            " > Text: Ask her to bring these things with her from the store.\n",
-            " > Run-time: 1.594754934310913\n",
-            " > Real-time factor: 0.6868010717444122\n",
-            " > Time per step: 3.1147492118179796e-05\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "\n",
-              "                <audio controls=\"controls\" >\n",
-              "                    <source 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\" type=\"audio/wav\" />\n",
-              "                    Your browser does not support the audio element.\n",
-              "                </audio>\n",
-              "              "
-            ],
-            "text/plain": [
-              "<IPython.lib.display.Audio object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            " > Saving output to tests-audios/Ask_her_to_bring_these_things_with_her_from_the_store.wav\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "error",
-          "ename": "KeyboardInterrupt",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-            "\nDuring handling of the above exception, another exception occurred:\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-59-d2d06555a22b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Synthesize sentence with New Speaker using files: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfile_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"(this speaker not seen in training (new speaker))\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mTEXT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter sentence: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" > Text: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mwav\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvocoder_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUSE_CUDA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_griffin_lim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSPEAKER_FILEID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspeaker_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ]
+      "outputs": []
     }
   ]
 }
\ No newline at end of file

From 9d28f311689018e81998355f029f520890a19a6f Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 23:24:37 -0300
Subject: [PATCH 38/56] add Colab Notebook from TTS Multi-Speaker with GST

---
 ...MultiSpeaker_jia_et_al_2018_With_GST.ipynb | 834 ++++++++++++++++++
 1 file changed, 834 insertions(+)
 create mode 100755 notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018_With_GST.ipynb

diff --git a/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018_With_GST.ipynb b/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018_With_GST.ipynb
new file mode 100755
index 00000000..e059461e
--- /dev/null
+++ b/notebooks/Demo_Mozilla_TTS_MultiSpeaker_jia_et_al_2018_With_GST.ipynb
@@ -0,0 +1,834 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Demo-Mozilla-TTS-MultiSpeaker-jia-et-al-2018-With-GST.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "yZK6UdwSFnOO",
+        "ENA2OumIVeMA",
+        "dV6cXXlfi72r",
+        "vnV-FigfvsS2",
+        "g_G_HweN04W-",
+        "LEE6mQLh5Who"
+      ],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yZK6UdwSFnOO",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Download and install Mozilla TTS**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yvb0pX3WY6MN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import os \n",
+        "!git clone https://github.com/Edresson/TTS -b dev-gst-embeddings"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iB9nl2UEG3SY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!apt-get install espeak\n",
+        "os.chdir('TTS')\n",
+        "!pip install -r requirements.txt\n",
+        "!python setup.py develop\n",
+        "os.chdir('..')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w6Krn8k1inC_",
+        "colab_type": "text"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "**Download Checkpoint**\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PiYHf3lKhi9z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!wget -c -q --show-progress -O ./TTS-checkpoint.zip https://github.com/Edresson/TTS/releases/download/v1.0.0/Checkpoints-TTS-MultiSpeaker-Jia-et-al-2018-with-GST.zip\n",
+        "!unzip ./TTS-checkpoint.zip\n",
+        "\n",
+        "# Download gst style example\n",
+        "!wget https://github.com/Edresson/TTS/releases/download/v1.0.0/gst-style-example.wav"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MpYNgqrZcJKn",
+        "colab_type": "text"
+      },
+      "source": [
+        "**Utils Functions**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4KZA4b_CbMqx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "%load_ext autoreload\n",
+        "%autoreload 2\n",
+        "import argparse\n",
+        "import json\n",
+        "# pylint: disable=redefined-outer-name, unused-argument\n",
+        "import os\n",
+        "import string\n",
+        "import time\n",
+        "import sys\n",
+        "import numpy as np\n",
+        "\n",
+        "TTS_PATH = \"../content/TTS\"\n",
+        "# add libraries into environment\n",
+        "sys.path.append(TTS_PATH) # set this if TTS is not installed globally\n",
+        "\n",
+        "import torch\n",
+        "\n",
+        "from TTS.tts.utils.generic_utils import setup_model\n",
+        "from TTS.tts.utils.synthesis import synthesis\n",
+        "from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols\n",
+        "from TTS.utils.audio import AudioProcessor\n",
+        "from TTS.utils.io import load_config\n",
+        "from TTS.vocoder.utils.generic_utils import setup_generator\n",
+        "\n",
+        "\n",
+        "def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid, speaker_embedding=None, gst_style=None):\n",
+        "    t_1 = time.time()\n",
+        "    waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_fileid, gst_style, False, CONFIG.enable_eos_bos_chars, use_gl, speaker_embedding=speaker_embedding)\n",
+        "    if CONFIG.model == \"Tacotron\" and not use_gl:\n",
+        "        mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n",
+        "    if not use_gl:\n",
+        "        waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
+        "    if use_cuda and not use_gl:\n",
+        "        waveform = waveform.cpu()\n",
+        "    if not use_gl:\n",
+        "        waveform = waveform.numpy()\n",
+        "    waveform = waveform.squeeze()\n",
+        "    rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
+        "    tps = (time.time() - t_1) / len(waveform)\n",
+        "    print(\" > Run-time: {}\".format(time.time() - t_1))\n",
+        "    print(\" > Real-time factor: {}\".format(rtf))\n",
+        "    print(\" > Time per step: {}\".format(tps))\n",
+        "    return waveform\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ENA2OumIVeMA",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Vars definitions**\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jPD0d_XpVXmY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "TEXT = ''\n",
+        "OUT_PATH = 'tests-audios/'\n",
+        "# create output path\n",
+        "os.makedirs(OUT_PATH, exist_ok=True)\n",
+        "\n",
+        "SPEAKER_FILEID = None # if None use the first embedding from speakers.json\n",
+        "\n",
+        "# model vars \n",
+        "MODEL_PATH = 'best_model.pth.tar'\n",
+        "CONFIG_PATH = 'config.json'\n",
+        "SPEAKER_JSON = 'speakers.json'\n",
+        "\n",
+        "# vocoder vars\n",
+        "VOCODER_PATH = ''\n",
+        "VOCODER_CONFIG_PATH = ''\n",
+        "\n",
+        "USE_CUDA = True"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dV6cXXlfi72r",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Restore  TTS Model**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "x1WgLFauWUPe",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# load the config\n",
+        "C = load_config(CONFIG_PATH)\n",
+        "C.forward_attn_mask = True\n",
+        "\n",
+        "# load the audio processor\n",
+        "ap = AudioProcessor(**C.audio)\n",
+        "\n",
+        "# if the vocabulary was passed, replace the default\n",
+        "if 'characters' in C.keys():\n",
+        "    symbols, phonemes = make_symbols(**C.characters)\n",
+        "\n",
+        "speaker_embedding = None\n",
+        "speaker_embedding_dim = None\n",
+        "num_speakers = 0\n",
+        "# load speakers\n",
+        "if SPEAKER_JSON != '':\n",
+        "    speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
+        "    num_speakers = len(speaker_mapping)\n",
+        "    if C.use_external_speaker_embedding_file:\n",
+        "        if SPEAKER_FILEID is not None:\n",
+        "            speaker_embedding = speaker_mapping[SPEAKER_FILEID]['embedding']\n",
+        "        else: # if speaker_fileid is not specificated use the first sample in speakers.json\n",
+        "            choise_speaker = list(speaker_mapping.keys())[0]\n",
+        "            print(\" Speaker: \",choise_speaker.split('_')[0],'was chosen automatically', \"(this speaker seen in training)\")\n",
+        "            speaker_embedding = speaker_mapping[choise_speaker]['embedding']\n",
+        "        speaker_embedding_dim = len(speaker_embedding)\n",
+        "\n",
+        "# load the model\n",
+        "num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
+        "model = setup_model(num_chars, num_speakers, C, speaker_embedding_dim)\n",
+        "cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))\n",
+        "model.load_state_dict(cp['model'])\n",
+        "model.eval()\n",
+        "\n",
+        "if USE_CUDA:\n",
+        "    model.cuda()\n",
+        "\n",
+        "model.decoder.set_r(cp['r'])\n",
+        "\n",
+        "# load vocoder model\n",
+        "if VOCODER_PATH!= \"\":\n",
+        "    VC = load_config(VOCODER_CONFIG_PATH)\n",
+        "    vocoder_model = setup_generator(VC)\n",
+        "    vocoder_model.load_state_dict(torch.load(VOCODER_PATH, map_location=\"cpu\")[\"model\"])\n",
+        "    vocoder_model.remove_weight_norm()\n",
+        "    if USE_CUDA:\n",
+        "        vocoder_model.cuda()\n",
+        "    vocoder_model.eval()\n",
+        "else:\n",
+        "    vocoder_model = None\n",
+        "    VC = None\n",
+        "\n",
+        "# synthesize voice\n",
+        "use_griffin_lim = VOCODER_PATH== \"\"\n",
+        "\n",
+        "if not C.use_external_speaker_embedding_file:\n",
+        "    if SPEAKER_FILEID.isdigit():\n",
+        "        SPEAKER_FILEID = int(SPEAKER_FILEID)\n",
+        "    else:\n",
+        "        SPEAKER_FILEID = None\n",
+        "else:\n",
+        "    SPEAKER_FILEID = None\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tNvVEoE30qY6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Synthesize sentence with Speaker\n",
+        "\n",
+        "> Stop running the cell to leave!\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2o8fXkVSyXOa",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with Speaker: \",choise_speaker.split('_')[0], \"(this speaker seen in training)\")\n",
+        "gst_style = 'gst-style-example.wav'\n",
+        "while True:\n",
+        "  TEXT = input(\"Enter sentence: \")\n",
+        "  print(\" > Text: {}\".format(TEXT))\n",
+        "  wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "  IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "  # save the results\n",
+        "  file_name = TEXT.replace(\" \", \"_\")\n",
+        "  file_name = file_name.translate(\n",
+        "      str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "  out_path = os.path.join(OUT_PATH, file_name)\n",
+        "  print(\" > Saving output to {}\".format(out_path))\n",
+        "  ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vnV-FigfvsS2",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Select Speaker**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RuCGOnJ_fgDV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "\n",
+        "# VCTK speakers not seen in training (new speakers)\n",
+        "VCTK_test_Speakers = [\"p225\", \"p234\", \"p238\", \"p245\", \"p248\", \"p261\", \"p294\", \"p302\", \"p326\", \"p335\", \"p347\"]\n",
+        "\n",
+        "# VCTK speakers seen in training\n",
+        "VCTK_train_Speakers = ['p244', 'p300', 'p303', 'p273', 'p292', 'p252', 'p254', 'p269', 'p345', 'p274', 'p363', 'p285', 'p351', 'p361', 'p295', 'p266', 'p307', 'p230', 'p339', 'p253', 'p310', 'p241', 'p256', 'p323', 'p237', 'p229', 'p298', 'p336', 'p276', 'p305', 'p255', 'p278', 'p299', 'p265', 'p267', 'p280', 'p260', 'p272', 'p262', 'p334', 'p283', 'p247', 'p246', 'p374', 'p297', 'p249', 'p250', 'p304', 'p240', 'p236', 'p312', 'p286', 'p263', 'p258', 'p313', 'p376', 'p279', 'p340', 'p362', 'p284', 'p231', 'p308', 'p277', 'p275', 'p333', 'p314', 'p330', 'p264', 'p226', 'p288', 'p343', 'p239', 'p232', 'p268', 'p270', 'p329', 'p227', 'p271', 'p228', 'p311', 'p301', 'p293', 'p364', 'p251', 'p317', 'p360', 'p281', 'p243', 'p287', 'p233', 'p259', 'p316', 'p257', 'p282', 'p306', 'p341', 'p318']\n",
+        "\n",
+        "\n",
+        "num_samples_speaker = 2 # In theory the more samples of the speaker the more similar to the real voice it will be!\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hkvv7gRcx4WV",
+        "colab_type": "text"
+      },
+      "source": [
+        "## **Example select  a VCTK seen speaker in training**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BviNMI9UyCYz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# get embedding\n",
+        "Speaker_choise = VCTK_train_Speakers[0] # choise one of training speakers\n",
+        "# load speakers\n",
+        "if SPEAKER_JSON != '':\n",
+        "    speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
+        "    if C.use_external_speaker_embedding_file:\n",
+        "        speaker_embeddings = []\n",
+        "        for key in list(speaker_mapping.keys()):\n",
+        "          if Speaker_choise in key:\n",
+        "            if len(speaker_embeddings) < num_samples_speaker:\n",
+        "              speaker_embeddings.append(speaker_mapping[key]['embedding'])\n",
+        "        # takes the average of the embedings samples of the announcers\n",
+        "        speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()\n",
+        "        "
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5e5_XnLsx3jg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker seen in training)\")\n",
+        "gst_style = 'gst-style-example.wav'\n",
+        "while True:\n",
+        "  TEXT = input(\"Enter sentence: \")\n",
+        "  print(\" > Text: {}\".format(TEXT))\n",
+        "  wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "  IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "  # save the results\n",
+        "  file_name = TEXT.replace(\" \", \"_\")\n",
+        "  file_name = file_name.translate(\n",
+        "      str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "  out_path = os.path.join(OUT_PATH, file_name)\n",
+        "  print(\" > Saving output to {}\".format(out_path))\n",
+        "  ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "QJ6VgT2a4vHW"
+      },
+      "source": [
+        "## **Example select  a VCTK not seen speaker in training (new Speakers)**\n",
+        "\n",
+        "\n",
+        "> Fitting new Speakers :)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "SZS57ZK-4vHa",
+        "colab": {}
+      },
+      "source": [
+        "# get embedding\n",
+        "Speaker_choise = VCTK_test_Speakers[0] # choise one of training speakers\n",
+        "# load speakers\n",
+        "if SPEAKER_JSON != '':\n",
+        "    speaker_mapping = json.load(open(SPEAKER_JSON, 'r'))\n",
+        "    if C.use_external_speaker_embedding_file:\n",
+        "        speaker_embeddings = []\n",
+        "        for key in list(speaker_mapping.keys()):\n",
+        "          if Speaker_choise in key:\n",
+        "            if len(speaker_embeddings) < num_samples_speaker:\n",
+        "              speaker_embeddings.append(speaker_mapping[key]['embedding'])\n",
+        "        # takes the average of the embedings samples of the announcers\n",
+        "        speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()\n",
+        "        "
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "bbs85vzz4vHo",
+        "colab": {}
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker not seen in training (new speaker))\")\n",
+        "gst_style = 'gst-style-example.wav'\n",
+        "while True:\n",
+        "  TEXT = input(\"Enter sentence: \")\n",
+        "  print(\" > Text: {}\".format(TEXT))\n",
+        "  wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "  IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "  # save the results\n",
+        "  file_name = TEXT.replace(\" \", \"_\")\n",
+        "  file_name = file_name.translate(\n",
+        "      str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "  out_path = os.path.join(OUT_PATH, file_name)\n",
+        "  print(\" > Saving output to {}\".format(out_path))\n",
+        "  ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g_G_HweN04W-",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Changing GST tokens manually (without wav reference)**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jyFP5syW2bjt",
+        "colab_type": "text"
+      },
+      "source": [
+        "You can define tokens manually, this way you can increase/decrease the function of a given GST token. For example a token is responsible for the length of the speaker's pauses, if you increase the value of that token you will have longer pauses and if you decrease it you will have shorter pauses."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SpwjDjCM2a3Y",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# set gst tokens, in this model we have 5 tokens\n",
+        "gst_style = {\"0\": 0, \"1\": 0, \"3\": 0, \"4\": 0}"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qWChMbI_0z5X",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker  not seen in training (new speaker))\")\n",
+        "TEXT = input(\"Enter sentence: \")\n",
+        "print(\" > Text: {}\".format(TEXT))\n",
+        "wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "# save the results\n",
+        "file_name = TEXT.replace(\" \", \"_\")\n",
+        "file_name = file_name.translate(\n",
+        "    str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "out_path = os.path.join(OUT_PATH, file_name)\n",
+        "print(\" > Saving output to {}\".format(out_path))\n",
+        "ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uFjUi9xQ3mG3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "gst_style = {\"0\": 0.9, \"1\": 0, \"3\": 0, \"4\": 0}\n",
+        "print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker  not seen in training (new speaker))\")\n",
+        "TEXT = input(\"Enter sentence: \")\n",
+        "print(\" > Text: {}\".format(TEXT))\n",
+        "wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "# save the results\n",
+        "file_name = TEXT.replace(\" \", \"_\")\n",
+        "file_name = file_name.translate(\n",
+        "    str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "out_path = os.path.join(OUT_PATH, file_name)\n",
+        "print(\" > Saving output to {}\".format(out_path))\n",
+        "ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Uw0d6gWg4L27",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "gst_style = {\"0\": -0.9, \"1\": 0, \"3\": 0, \"4\": 0}\n",
+        "print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker  not seen in training (new speaker))\")\n",
+        "TEXT = input(\"Enter sentence: \")\n",
+        "print(\" > Text: {}\".format(TEXT))\n",
+        "wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "# save the results\n",
+        "file_name = TEXT.replace(\" \", \"_\")\n",
+        "file_name = file_name.translate(\n",
+        "    str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "out_path = os.path.join(OUT_PATH, file_name)\n",
+        "print(\" > Saving output to {}\".format(out_path))\n",
+        "ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "V9izw4-54-Tl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "gst_style = {\"0\": 0, \"1\": 0.9, \"3\": 0, \"4\": 0}\n",
+        "print(\"Synthesize sentence with Speaker: \",Speaker_choise.split('_')[0], \"(this speaker  not seen in training (new speaker))\")\n",
+        "TEXT = input(\"Enter sentence: \")\n",
+        "print(\" > Text: {}\".format(TEXT))\n",
+        "wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "# save the results\n",
+        "file_name = TEXT.replace(\" \", \"_\")\n",
+        "file_name = file_name.translate(\n",
+        "    str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "out_path = os.path.join(OUT_PATH, file_name)\n",
+        "print(\" > Saving output to {}\".format(out_path))\n",
+        "ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "LEE6mQLh5Who"
+      },
+      "source": [
+        "# **Example Synthesizing with your own voice :)**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "La70gSB65nrs",
+        "colab_type": "text"
+      },
+      "source": [
+        " Download and load GE2E Speaker Encoder "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "r0IEFZ0B5vQg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!wget -c -q --show-progress -O ./SpeakerEncoder-checkpoint.zip  https://github.com/Edresson/TTS/releases/download/v1.0.0/GE2E-SpeakerEncoder-iter25k.zip\n",
+        "!unzip ./SpeakerEncoder-checkpoint.zip"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jEH8HCTh5mF6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "SE_MODEL_RUN_PATH = \"GE2E-SpeakerEncoder/\"\n",
+        "SE_MODEL_PATH = os.path.join(SE_MODEL_RUN_PATH, \"best_model.pth.tar\")\n",
+        "SE_CONFIG_PATH =os.path.join(SE_MODEL_RUN_PATH, \"config.json\")\n",
+        "USE_CUDA = True"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tOwkfQqT6-Qo",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from TTS.utils.audio import AudioProcessor\n",
+        "from TTS.speaker_encoder.model import SpeakerEncoder\n",
+        "se_config = load_config(SE_CONFIG_PATH)\n",
+        "se_ap = AudioProcessor(**se_config['audio'])\n",
+        "\n",
+        "se_model = SpeakerEncoder(**se_config.model)\n",
+        "se_model.load_state_dict(torch.load(SE_MODEL_PATH)['model'])\n",
+        "se_model.eval()\n",
+        "if USE_CUDA:\n",
+        "    se_model.cuda()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0TLlbUFG8O36",
+        "colab_type": "text"
+      },
+      "source": [
+        "Upload one or more wav audio files in your voice.\n",
+        "\n",
+        "\n",
+        "> We recommend files longer than 3 seconds, the bigger the file the closer to your voice :)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_FWwHPjJ8NXl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# select one or more wav files\n",
+        "from google.colab import files\n",
+        "file_list = files.upload()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WWOf6sgbBbGY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# extract embedding from wav files\n",
+        "speaker_embeddings = []\n",
+        "for name in file_list.keys():\n",
+        "    if '.wav' in name:\n",
+        "      mel_spec = se_ap.melspectrogram(se_ap.load_wav(name, sr=se_ap.sample_rate)).T\n",
+        "      mel_spec = torch.FloatTensor(mel_spec[None, :, :])\n",
+        "      if USE_CUDA:\n",
+        "          mel_spec = mel_spec.cuda()\n",
+        "      embedd = se_model.compute_embedding(mel_spec).cpu().detach().numpy().reshape(-1)\n",
+        "      speaker_embeddings.append(embedd)\n",
+        "    else:\n",
+        "      print(\"You need upload Wav files, others files is not supported !!\")\n",
+        "\n",
+        "# takes the average of the embedings samples of the announcers\n",
+        "speaker_embedding = np.mean(np.array(speaker_embeddings), axis=0).tolist()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AQ7eP31d9yzq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import IPython\n",
+        "from IPython.display import Audio\n",
+        "print(\"Synthesize sentence with New Speaker using files: \",file_list.keys(), \"(this speaker not seen in training (new speaker))\")\n",
+        "gst_style = {\"0\": 0, \"1\": 0.0, \"3\": 0, \"4\": 0}\n",
+        "gst_style = 'gst-style-example.wav'\n",
+        "TEXT = input(\"Enter sentence: \")\n",
+        "print(\" > Text: {}\".format(TEXT))\n",
+        "wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "# save the results\n",
+        "file_name = TEXT.replace(\" \", \"_\")\n",
+        "file_name = file_name.translate(\n",
+        "    str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "out_path = os.path.join(OUT_PATH, file_name)\n",
+        "print(\" > Saving output to {}\".format(out_path))\n",
+        "ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "11i10yE1-LMJ",
+        "colab_type": "text"
+      },
+      "source": [
+        "Uploading your own GST reference wav file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eKohSQG1-KkT",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# select one wav file for GST reference\n",
+        "from google.colab import files\n",
+        "file_list = files.upload()\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "xmItcGac5WiG",
+        "colab": {}
+      },
+      "source": [
+        "print(\"Synthesize sentence with New Speaker using files: \",file_list.keys(), \"(this speaker not seen in training (new speaker))\")\n",
+        "gst_style = list(file_list.keys())[0]\n",
+        "TEXT = input(\"Enter sentence: \")\n",
+        "print(\" > Text: {}\".format(TEXT))\n",
+        "wav = tts(model, vocoder_model, TEXT, C, USE_CUDA, ap, use_griffin_lim, SPEAKER_FILEID, speaker_embedding=speaker_embedding, gst_style=gst_style)\n",
+        "IPython.display.display(Audio(wav, rate=ap.sample_rate))\n",
+        "# save the results\n",
+        "file_name = TEXT.replace(\" \", \"_\")\n",
+        "file_name = file_name.translate(\n",
+        "    str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'\n",
+        "out_path = os.path.join(OUT_PATH, file_name)\n",
+        "print(\" > Saving output to {}\".format(out_path))\n",
+        "ap.save_wav(wav, out_path)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From ad570f3cab816bd5245a15788b890a72dd11b8a5 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 23:29:27 -0300
Subject: [PATCH 39/56] add Jupyter Notebook for Extract Speaker Embedding per
 sample using GE2E

---
 ...- ExtractSpeakerEmbeddings-by-sample.ipynb | 163 ++++++++++++++++++
 1 file changed, 163 insertions(+)
 create mode 100644 notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb

diff --git a/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb b/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb
new file mode 100644
index 00000000..612ec146
--- /dev/null
+++ b/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb	
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a noteboook used to generate the speaker embeddings with the GE2E speaker encoder model for multi-speaker training.\n",
+    "\n",
+    "Before running this script please DON'T FORGET: \n",
+    "- to set file paths.\n",
+    "- to download related model files from TTS.\n",
+    "- download or clone related repos, linked below.\n",
+    "- setup the repositories. ```python setup.py install```\n",
+    "- to checkout right commit versions (given next to the model) of TTS.\n",
+    "- to set the right paths in the cell below.\n",
+    "\n",
+    "Repository:\n",
+    "- TTS: https://github.com/mozilla/TTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import importlib\n",
+    "import random\n",
+    "import librosa\n",
+    "import torch\n",
+    "\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "from TTS.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
+    "\n",
+    "# you may need to change this depending on your system\n",
+    "os.environ['CUDA_VISIBLE_DEVICES']='0'\n",
+    "\n",
+    "\n",
+    "from TTS.speaker_encoder.model import SpeakerEncoder\n",
+    "from TTS.utils.audio import AudioProcessor\n",
+    "from TTS.utils.generic_utils import load_config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should also adjust all the path constants to point at the relevant locations for you locally"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_RUN_PATH = \"../../Mozilla-TTS/checkpoints/libritts_360-half-September-28-2019_10+46AM-8565c50-20200323T115637Z-001/\"\n",
+    "MODEL_PATH = MODEL_RUN_PATH + \"best_model.pth.tar\"\n",
+    "CONFIG_PATH = MODEL_RUN_PATH + \"config.json\"\n",
+    "\n",
+    "\n",
+    "DATASETS_NAME = ['brspeech'] # list the datasets\n",
+    "DATASETS_PATH = ['../../../datasets/BRSpeech-2.0-beta8']\n",
+    "DATASETS_METAFILE = ['TTS_metadata_brspeech2+cv_all_valited_lines.csv']\n",
+    "\n",
+    "USE_CUDA = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Preprocess dataset\n",
+    "meta_data = []\n",
+    "for i in range(len(DATASETS_NAME)):\n",
+    "    preprocessor = importlib.import_module('TTS.datasets.preprocess')\n",
+    "    preprocessor = getattr(preprocessor, DATASETS_NAME[i].lower())\n",
+    "    meta_data += preprocessor(DATASETS_PATH[i],DATASETS_METAFILE[i])\n",
+    "      \n",
+    "meta_data= list(meta_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = load_config(CONFIG_PATH)\n",
+    "ap = AudioProcessor(**c['audio'])\n",
+    "\n",
+    "model = SpeakerEncoder(**c.model)\n",
+    "model.load_state_dict(torch.load(MODEL_PATH)['model'])\n",
+    "model.eval()\n",
+    "if USE_CUDA:\n",
+    "    model.cuda()\n",
+    "\n",
+    "embeddings_dict = {}\n",
+    "len_meta_data= len(meta_data)\n",
+    "\n",
+    "for i in tqdm(range(len_meta_data)):\n",
+    "    _, wav_file, speaker_id = meta_data[i]\n",
+    "    wav_file_name = os.path.basename(wav_file)\n",
+    "    mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T\n",
+    "    mel_spec = torch.FloatTensor(mel_spec[None, :, :])\n",
+    "    if USE_CUDA:\n",
+    "        mel_spec = mel_spec.cuda()\n",
+    "    embedd = model.compute_embedding(mel_spec).cpu().detach().numpy().reshape(-1)\n",
+    "    embeddings_dict[wav_file_name] = [embedd,speaker_id]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create and export speakers.json\n",
+    "speaker_mapping = {sample: {'name': embeddings_dict[sample][1], 'embedding':embeddings_dict[sample][0].reshape(-1).tolist()} for i, sample in enumerate(embeddings_dict.keys())}\n",
+    "save_speaker_mapping(MODEL_RUN_PATH, speaker_mapping)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#test load integrity\n",
+    "speaker_mapping_load = load_speaker_mapping(MODEL_RUN_PATH)\n",
+    "assert speaker_mapping == speaker_mapping_load\n",
+    "print(\"The file speakers.json has been exported to \",MODEL_RUN_PATH, ' with ', len(embeddings_dict.keys()), ' speakers')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 8b9c951da799fe202105221f70b2c2d66697fa58 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Thu, 30 Jul 2020 23:33:57 -0300
Subject: [PATCH 40/56] Ops! Map Notebooks imports for TTS current version

---
 ...peaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb b/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb
index 612ec146..324de2d0 100644
--- a/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb	
+++ b/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb	
@@ -34,15 +34,15 @@
     "\n",
     "import numpy as np\n",
     "from tqdm import tqdm\n",
-    "from TTS.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
+    "from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
     "\n",
     "# you may need to change this depending on your system\n",
     "os.environ['CUDA_VISIBLE_DEVICES']='0'\n",
     "\n",
     "\n",
-    "from TTS.speaker_encoder.model import SpeakerEncoder\n",
+    "from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
     "from TTS.utils.audio import AudioProcessor\n",
-    "from TTS.utils.generic_utils import load_config"
+    "from TTS.utils.io import load_config"
    ]
   },
   {

From f0bcc390d24730e31656ded6f6df88ae765bc8ac Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 00:05:08 -0300
Subject: [PATCH 41/56] Implement Angular Prototypical loss

---
 mozilla_voice_tts/speaker_encoder/loss.py | 42 +++++++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/mozilla_voice_tts/speaker_encoder/loss.py b/mozilla_voice_tts/speaker_encoder/loss.py
index ab290547..6f83be63 100644
--- a/mozilla_voice_tts/speaker_encoder/loss.py
+++ b/mozilla_voice_tts/speaker_encoder/loss.py
@@ -23,6 +23,8 @@ class GE2ELoss(nn.Module):
         self.b = nn.Parameter(torch.tensor(init_b))
         self.loss_method = loss_method
 
+        print('Initialised Generalized End-to-End loss')
+
         assert self.loss_method in ["softmax", "contrast"]
 
         if self.loss_method == "softmax":
@@ -119,3 +121,43 @@ class GE2ELoss(nn.Module):
         cos_sim_matrix = self.w * cos_sim_matrix + self.b
         L = self.embed_loss(dvecs, cos_sim_matrix)
         return L.mean()
+
+# adapted from https://github.com/clovaai/voxceleb_trainer/blob/master/loss/angleproto.py
+class AngleProtoLoss(nn.Module):
+    """ 
+    Implementation of the Angular Prototypical loss defined in https://arxiv.org/abs/2003.11982
+        Accepts an input of size (N, M, D)
+            where N is the number of speakers in the batch,
+            M is the number of utterances per speaker,
+            and D is the dimensionality of the embedding vector
+        Args:
+            - init_w (float): defines the initial value of w 
+            - init_b (float): definies the initial value of b
+    """
+    def __init__(self, init_w=10.0, init_b=-5.0):
+        super(AngleProtoLoss, self).__init__()
+        # pylint: disable=E1102
+        self.w = nn.Parameter(torch.tensor(init_w))
+        # pylint: disable=E1102
+        self.b = nn.Parameter(torch.tensor(init_b))
+        self.criterion = torch.nn.CrossEntropyLoss()
+        self.use_cuda = torch.cuda.is_available()
+
+        print('Initialised Angular Prototypical loss')
+
+    def forward(self, x):
+        """
+        Calculates the AngleProto loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
+        """
+        out_anchor = torch.mean(x[:,1:,:],1)
+        out_positive = x[:,0,:]
+        num_speakers = out_anchor.size()[0]
+
+        cos_sim_matrix  = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1,-1,num_speakers),out_anchor.unsqueeze(-1).expand(-1,-1,num_speakers).transpose(0,2))
+        torch.clamp(self.w, 1e-6)
+        cos_sim_matrix = cos_sim_matrix * self.w + self.b
+        label = torch.from_numpy(np.asarray(range(0,num_speakers)))
+        if self.use_cuda:
+            label = label.cuda()
+        L = self.criterion(cos_sim_matrix, label)
+        return L
\ No newline at end of file

From bc09ca81116e25aa1d7250c6a8418b6f10a59ae8 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 00:24:42 -0300
Subject: [PATCH 42/56] add suport for AngleProto loss

---
 mozilla_voice_tts/bin/train_encoder.py        |  11 +-
 mozilla_voice_tts/speaker_encoder/config.json |   1 +
 .../speaker_encoder/generic_utils.py          |   4 +-
 mozilla_voice_tts/speaker_encoder/losses.py   | 160 ++++++++++++++++++
 4 files changed, 171 insertions(+), 5 deletions(-)
 create mode 100644 mozilla_voice_tts/speaker_encoder/losses.py

diff --git a/mozilla_voice_tts/bin/train_encoder.py b/mozilla_voice_tts/bin/train_encoder.py
index d612ac6e..c7c2e647 100644
--- a/mozilla_voice_tts/bin/train_encoder.py
+++ b/mozilla_voice_tts/bin/train_encoder.py
@@ -100,7 +100,7 @@ def train(model, criterion, optimizer, scheduler, ap, global_step):
         if global_step % c.steps_plot_stats == 0:
             # Plot Training Epoch Stats
             train_stats = {
-                "GE2Eloss": avg_loss,
+                "loss": avg_loss,
                 "lr": current_lr,
                 "grad_norm": grad_norm,
                 "step_time": step_time
@@ -140,7 +140,13 @@ def main(args):  # pylint: disable=redefined-outer-name
                            lstm_dim=384,
                            num_lstm_layers=3)
     optimizer = RAdam(model.parameters(), lr=c.lr)
-    criterion = GE2ELoss(loss_method='softmax')
+
+    if c.loss == "ge2e":
+        criterion = GE2ELoss(loss_method='softmax')
+    elif c.loss == "angleproto":
+        criterion = AngleProtoLoss()
+    else:
+        raise Exception("The %s  not is a loss supported" %c.loss)
 
     if args.restore_path:
         checkpoint = torch.load(args.restore_path)
@@ -186,7 +192,6 @@ def main(args):  # pylint: disable=redefined-outer-name
     _, global_step = train(model, criterion, optimizer, scheduler, ap,
                            global_step)
 
-
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument(
diff --git a/mozilla_voice_tts/speaker_encoder/config.json b/mozilla_voice_tts/speaker_encoder/config.json
index 0d0f8f68..5f72135f 100644
--- a/mozilla_voice_tts/speaker_encoder/config.json
+++ b/mozilla_voice_tts/speaker_encoder/config.json
@@ -21,6 +21,7 @@
         "do_trim_silence": false // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
     },
     "reinit_layers": [],
+    "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
     "grad_clip": 3.0, // upper limit for gradients for clipping.
     "epochs": 1000, // total number of epochs to train.
     "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
diff --git a/mozilla_voice_tts/speaker_encoder/generic_utils.py b/mozilla_voice_tts/speaker_encoder/generic_utils.py
index f649ceb9..bc72c91c 100644
--- a/mozilla_voice_tts/speaker_encoder/generic_utils.py
+++ b/mozilla_voice_tts/speaker_encoder/generic_utils.py
@@ -15,7 +15,7 @@ def save_checkpoint(model, optimizer, model_loss, out_path,
         'optimizer': optimizer.state_dict() if optimizer is not None else None,
         'step': current_step,
         'epoch': epoch,
-        'GE2Eloss': model_loss,
+        'loss': model_loss,
         'date': datetime.date.today().strftime("%B %d, %Y"),
     }
     torch.save(state, checkpoint_path)
@@ -29,7 +29,7 @@ def save_best_model(model, optimizer, model_loss, best_loss, out_path,
             'model': new_state_dict,
             'optimizer': optimizer.state_dict(),
             'step': current_step,
-            'GE2Eloss': model_loss,
+            'loss': model_loss,
             'date': datetime.date.today().strftime("%B %d, %Y"),
         }
         best_loss = model_loss
diff --git a/mozilla_voice_tts/speaker_encoder/losses.py b/mozilla_voice_tts/speaker_encoder/losses.py
new file mode 100644
index 00000000..7feced64
--- /dev/null
+++ b/mozilla_voice_tts/speaker_encoder/losses.py
@@ -0,0 +1,160 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+# adapted from https://github.com/cvqluu/GE2E-Loss
+class GE2ELoss(nn.Module):
+    def __init__(self, init_w=10.0, init_b=-5.0, loss_method="softmax"):
+        """
+        Implementation of the Generalized End-to-End loss defined in https://arxiv.org/abs/1710.10467 [1]
+        Accepts an input of size (N, M, D)
+            where N is the number of speakers in the batch,
+            M is the number of utterances per speaker,
+            and D is the dimensionality of the embedding vector (e.g. d-vector)
+        Args:
+            - init_w (float): defines the initial value of w in Equation (5) of [1]
+            - init_b (float): definies the initial value of b in Equation (5) of [1]
+        """
+        super(GE2ELoss, self).__init__()
+        # pylint: disable=E1102
+        self.w = nn.Parameter(torch.tensor(init_w))
+        # pylint: disable=E1102
+        self.b = nn.Parameter(torch.tensor(init_b))
+        self.loss_method = loss_method
+
+        print('Initialised Generalized End-to-End loss')
+
+        assert self.loss_method in ["softmax", "contrast"]
+
+        if self.loss_method == "softmax":
+            self.embed_loss = self.embed_loss_softmax
+        if self.loss_method == "contrast":
+            self.embed_loss = self.embed_loss_contrast
+
+    # pylint: disable=R0201
+    def calc_new_centroids(self, dvecs, centroids, spkr, utt):
+        """
+        Calculates the new centroids excluding the reference utterance
+        """
+        excl = torch.cat((dvecs[spkr, :utt], dvecs[spkr, utt + 1 :]))
+        excl = torch.mean(excl, 0)
+        new_centroids = []
+        for i, centroid in enumerate(centroids):
+            if i == spkr:
+                new_centroids.append(excl)
+            else:
+                new_centroids.append(centroid)
+        return torch.stack(new_centroids)
+
+    def calc_cosine_sim(self, dvecs, centroids):
+        """
+        Make the cosine similarity matrix with dims (N,M,N)
+        """
+        cos_sim_matrix = []
+        for spkr_idx, speaker in enumerate(dvecs):
+            cs_row = []
+            for utt_idx, utterance in enumerate(speaker):
+                new_centroids = self.calc_new_centroids(
+                    dvecs, centroids, spkr_idx, utt_idx
+                )
+                # vector based cosine similarity for speed
+                cs_row.append(
+                    torch.clamp(
+                        torch.mm(
+                            utterance.unsqueeze(1).transpose(0, 1),
+                            new_centroids.transpose(0, 1),
+                        )
+                        / (torch.norm(utterance) * torch.norm(new_centroids, dim=1)),
+                        1e-6,
+                    )
+                )
+            cs_row = torch.cat(cs_row, dim=0)
+            cos_sim_matrix.append(cs_row)
+        return torch.stack(cos_sim_matrix)
+
+    # pylint: disable=R0201
+    def embed_loss_softmax(self, dvecs, cos_sim_matrix):
+        """
+        Calculates the loss on each embedding $L(e_{ji})$ by taking softmax
+        """
+        N, M, _ = dvecs.shape
+        L = []
+        for j in range(N):
+            L_row = []
+            for i in range(M):
+                L_row.append(-F.log_softmax(cos_sim_matrix[j, i], 0)[j])
+            L_row = torch.stack(L_row)
+            L.append(L_row)
+        return torch.stack(L)
+
+    # pylint: disable=R0201
+    def embed_loss_contrast(self, dvecs, cos_sim_matrix):
+        """
+        Calculates the loss on each embedding $L(e_{ji})$ by contrast loss with closest centroid
+        """
+        N, M, _ = dvecs.shape
+        L = []
+        for j in range(N):
+            L_row = []
+            for i in range(M):
+                centroids_sigmoids = torch.sigmoid(cos_sim_matrix[j, i])
+                excl_centroids_sigmoids = torch.cat(
+                    (centroids_sigmoids[:j], centroids_sigmoids[j + 1 :])
+                )
+                L_row.append(
+                    1.0
+                    - torch.sigmoid(cos_sim_matrix[j, i, j])
+                    + torch.max(excl_centroids_sigmoids)
+                )
+            L_row = torch.stack(L_row)
+            L.append(L_row)
+        return torch.stack(L)
+
+    def forward(self, dvecs):
+        """
+        Calculates the GE2E loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
+        """
+        centroids = torch.mean(dvecs, 1)
+        cos_sim_matrix = self.calc_cosine_sim(dvecs, centroids)
+        torch.clamp(self.w, 1e-6)
+        cos_sim_matrix = self.w * cos_sim_matrix + self.b
+        L = self.embed_loss(dvecs, cos_sim_matrix)
+        return L.mean()
+
+# adapted from https://github.com/clovaai/voxceleb_trainer/blob/master/loss/angleproto.py
+class AngleProtoLoss(nn.Module):
+    """ 
+    Implementation of the Angular Prototypical loss defined in https://arxiv.org/abs/2003.11982
+        Accepts an input of size (N, M, D)
+            where N is the number of speakers in the batch,
+            M is the number of utterances per speaker,
+            and D is the dimensionality of the embedding vector
+        Args:
+            - init_w (float): defines the initial value of w 
+            - init_b (float): definies the initial value of b
+    """
+    def __init__(self, init_w=10.0, init_b=-5.0):
+        super(AngleProtoLoss, self).__init__()
+        # pylint: disable=E1102
+        self.w = nn.Parameter(torch.tensor(init_w))
+        # pylint: disable=E1102
+        self.b = nn.Parameter(torch.tensor(init_b))
+        self.criterion = torch.nn.CrossEntropyLoss()
+
+        print('Initialised Angular Prototypical loss')
+
+    def forward(self, x):
+        """
+        Calculates the AngleProto loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
+        """
+        out_anchor = torch.mean(x[:,1:,:],1)
+        out_positive = x[:,0,:]
+        num_speakers = out_anchor.size()[0]
+
+        cos_sim_matrix  = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1,-1,num_speakers),out_anchor.unsqueeze(-1).expand(-1,-1,num_speakers).transpose(0,2))
+        torch.clamp(self.w, 1e-6)
+        cos_sim_matrix = cos_sim_matrix * self.w + self.b
+        label = torch.from_numpy(np.asarray(range(0,num_speakers))).to(cos_sim_matrix.device)
+        L = self.criterion(cos_sim_matrix, label)
+        return L
\ No newline at end of file

From ac85ccae994b6291cf003cb87d3e5e365ece8aeb Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 00:27:51 -0300
Subject: [PATCH 43/56] update speaker encoder config.json, for compatibility
 with the TTS model

---
 mozilla_voice_tts/speaker_encoder/config.json | 49 ++++++++++---------
 1 file changed, 27 insertions(+), 22 deletions(-)

diff --git a/mozilla_voice_tts/speaker_encoder/config.json b/mozilla_voice_tts/speaker_encoder/config.json
index 5f72135f..f9166066 100644
--- a/mozilla_voice_tts/speaker_encoder/config.json
+++ b/mozilla_voice_tts/speaker_encoder/config.json
@@ -1,24 +1,30 @@
+
 {
-    "run_name": "libritts_360-half",
-    "run_description": "train speaker encoder for libritts 360",
-    "audio": {
+    "run_name": "libritts_100+360-angleproto",
+    "run_description": "train speaker encoder for libritts 100 and 360",
+    "audio":{
         // Audio processing parameters
-        "num_mels": 40, // size of the mel spec frame. 
-        "num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "frame_length_ms": 50, // stft window length in ms.
-        "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
-        "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "min_level_db": -100, // normalization range
-        "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
+        "num_mels": 80,         // size of the mel spec frame. 
+        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "win_length": 1024,     // stft window length in ms.
+        "hop_length": 256,      // stft window hop-lengh in ms.
+        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
+        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "min_level_db": -100,   // normalization range
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
         // Normalization parameters
-        "signal_norm": true, // normalize the spec values in range [0, 1]
+        "signal_norm": true,    // normalize the spec values in range [0, 1]
         "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true, // clip normalized values into the range.
-        "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
-        "do_trim_silence": false // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
+        "do_trim_silence": false,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
     },
     "reinit_layers": [],
     "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
@@ -34,10 +40,9 @@
     "checkpoint": true, // If true, it saves checkpoints per "save_step"
     "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.
+    "output_path": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
     "model": {
-        "input_dim": 40,
+        "input_dim": 80, // input_dim == num_mels
         "proj_dim": 128,
         "lstm_dim": 384,
         "num_lstm_layers": 3
@@ -46,13 +51,13 @@
         [
             {
                 "name": "libri_tts",
-                "path": "/home/erogol/Data/Libri-TTS/train-clean-360/",
+                "path": "../../datasets/LibriTTS/train-clean-360/",
                 "meta_file_train": null,
                 "meta_file_val": null
             },
             {
                 "name": "libri_tts",
-                "path": "/home/erogol/Data/Libri-TTS/train-clean-100/",
+                "path": "../../datasets/LibriTTS/train-clean-100/",
                 "meta_file_train": null,
                 "meta_file_val": null
             }

From 5e11d81e1221afaa435e3130a0e4d1084ea828ec Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 00:39:08 -0300
Subject: [PATCH 44/56] fix broken imports in speaker encoder

---
 .../config.json                               | 60 +++++++++++++++++++
 .../config.json                               | 60 +++++++++++++++++++
 mozilla_voice_tts/bin/train_encoder.py        |  2 +-
 mozilla_voice_tts/speaker_encoder/config.json | 10 +---
 4 files changed, 123 insertions(+), 9 deletions(-)
 create mode 100644 checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+37AM-debug/config.json
 create mode 100644 checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+38AM-debug/config.json

diff --git a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+37AM-debug/config.json b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+37AM-debug/config.json
new file mode 100644
index 00000000..dbea7ef2
--- /dev/null
+++ b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+37AM-debug/config.json
@@ -0,0 +1,60 @@
+
+"github_branch":"* dev-gst-embeddings",
+{
+    "run_name": "libritts_100+360-angleproto",
+    "run_description": "train speaker encoder for libritts 100 and 360",
+    "audio":{
+        // Audio processing parameters
+        "num_mels": 80,         // size of the mel spec frame. 
+        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "win_length": 1024,     // stft window length in ms.
+        "hop_length": 256,      // stft window hop-lengh in ms.
+        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
+        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "min_level_db": -100,   // normalization range
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+        // Normalization parameters
+        "signal_norm": true,    // normalize the spec values in range [0, 1]
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
+        "do_trim_silence": false,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
+    },
+    "reinit_layers": [],
+    "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
+    "grad_clip": 3.0, // upper limit for gradients for clipping.
+    "epochs": 1000, // total number of epochs to train.
+    "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
+    "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
+    "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
+    "steps_plot_stats": 10, // number of steps to plot embeddings.
+    "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "wd": 0.000001, // Weight decay weight.
+    "checkpoint": true, // If true, it saves checkpoints per "save_step"
+    "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": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
+    "model": {
+        "input_dim": 80, // input_dim == num_mels
+        "proj_dim": 128,
+        "lstm_dim": 384,
+        "num_lstm_layers": 3
+    },
+    "datasets": 
+        [
+            {
+                "name": "vctk",
+                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
+                "meta_file_train": null,
+                "meta_file_val": null
+            }
+        ]
+}
\ No newline at end of file
diff --git a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+38AM-debug/config.json b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+38AM-debug/config.json
new file mode 100644
index 00000000..dbea7ef2
--- /dev/null
+++ b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+38AM-debug/config.json
@@ -0,0 +1,60 @@
+
+"github_branch":"* dev-gst-embeddings",
+{
+    "run_name": "libritts_100+360-angleproto",
+    "run_description": "train speaker encoder for libritts 100 and 360",
+    "audio":{
+        // Audio processing parameters
+        "num_mels": 80,         // size of the mel spec frame. 
+        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "win_length": 1024,     // stft window length in ms.
+        "hop_length": 256,      // stft window hop-lengh in ms.
+        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
+        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "min_level_db": -100,   // normalization range
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+        // Normalization parameters
+        "signal_norm": true,    // normalize the spec values in range [0, 1]
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
+        "do_trim_silence": false,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
+    },
+    "reinit_layers": [],
+    "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
+    "grad_clip": 3.0, // upper limit for gradients for clipping.
+    "epochs": 1000, // total number of epochs to train.
+    "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
+    "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
+    "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
+    "steps_plot_stats": 10, // number of steps to plot embeddings.
+    "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "wd": 0.000001, // Weight decay weight.
+    "checkpoint": true, // If true, it saves checkpoints per "save_step"
+    "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": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
+    "model": {
+        "input_dim": 80, // input_dim == num_mels
+        "proj_dim": 128,
+        "lstm_dim": 384,
+        "num_lstm_layers": 3
+    },
+    "datasets": 
+        [
+            {
+                "name": "vctk",
+                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
+                "meta_file_train": null,
+                "meta_file_val": null
+            }
+        ]
+}
\ No newline at end of file
diff --git a/mozilla_voice_tts/bin/train_encoder.py b/mozilla_voice_tts/bin/train_encoder.py
index c7c2e647..1352a02e 100644
--- a/mozilla_voice_tts/bin/train_encoder.py
+++ b/mozilla_voice_tts/bin/train_encoder.py
@@ -13,7 +13,7 @@ from torch.utils.data import DataLoader
 from mozilla_voice_tts.generic_utils import count_parameters
 from mozilla_voice_tts.speaker_encoder.dataset import MyDataset
 from mozilla_voice_tts.speaker_encoder.generic_utils import save_best_model
-from mozilla_voice_tts.speaker_encoder.loss import GE2ELoss
+from mozilla_voice_tts.speaker_encoder.losses import GE2ELoss
 from mozilla_voice_tts.speaker_encoder.model import SpeakerEncoder
 from mozilla_voice_tts.speaker_encoder.visual import plot_embeddings
 from mozilla_voice_tts.tts.datasets.preprocess import load_meta_data
diff --git a/mozilla_voice_tts/speaker_encoder/config.json b/mozilla_voice_tts/speaker_encoder/config.json
index f9166066..281578da 100644
--- a/mozilla_voice_tts/speaker_encoder/config.json
+++ b/mozilla_voice_tts/speaker_encoder/config.json
@@ -50,14 +50,8 @@
     "datasets": 
         [
             {
-                "name": "libri_tts",
-                "path": "../../datasets/LibriTTS/train-clean-360/",
-                "meta_file_train": null,
-                "meta_file_val": null
-            },
-            {
-                "name": "libri_tts",
-                "path": "../../datasets/LibriTTS/train-clean-100/",
+                "name": "vctk",
+                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
                 "meta_file_train": null,
                 "meta_file_val": null
             }

From cb212b804950ca3112bba8d236b0b48b861ec4ab Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 00:54:45 -0300
Subject: [PATCH 45/56] update deprecated functions  call from speaker encoder

---
 .../config.json                               | 60 +++++++++++++++++++
 mozilla_voice_tts/bin/train_encoder.py        |  2 +-
 mozilla_voice_tts/speaker_encoder/config.json |  1 +
 mozilla_voice_tts/speaker_encoder/dataset.py  |  2 +-
 4 files changed, 63 insertions(+), 2 deletions(-)
 create mode 100644 checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json

diff --git a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json
new file mode 100644
index 00000000..dbea7ef2
--- /dev/null
+++ b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json
@@ -0,0 +1,60 @@
+
+"github_branch":"* dev-gst-embeddings",
+{
+    "run_name": "libritts_100+360-angleproto",
+    "run_description": "train speaker encoder for libritts 100 and 360",
+    "audio":{
+        // Audio processing parameters
+        "num_mels": 80,         // size of the mel spec frame. 
+        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "win_length": 1024,     // stft window length in ms.
+        "hop_length": 256,      // stft window hop-lengh in ms.
+        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
+        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "min_level_db": -100,   // normalization range
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+        // Normalization parameters
+        "signal_norm": true,    // normalize the spec values in range [0, 1]
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
+        "do_trim_silence": false,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
+    },
+    "reinit_layers": [],
+    "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
+    "grad_clip": 3.0, // upper limit for gradients for clipping.
+    "epochs": 1000, // total number of epochs to train.
+    "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
+    "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
+    "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
+    "steps_plot_stats": 10, // number of steps to plot embeddings.
+    "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "wd": 0.000001, // Weight decay weight.
+    "checkpoint": true, // If true, it saves checkpoints per "save_step"
+    "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": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
+    "model": {
+        "input_dim": 80, // input_dim == num_mels
+        "proj_dim": 128,
+        "lstm_dim": 384,
+        "num_lstm_layers": 3
+    },
+    "datasets": 
+        [
+            {
+                "name": "vctk",
+                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
+                "meta_file_train": null,
+                "meta_file_val": null
+            }
+        ]
+}
\ No newline at end of file
diff --git a/mozilla_voice_tts/bin/train_encoder.py b/mozilla_voice_tts/bin/train_encoder.py
index 1352a02e..46b7f46f 100644
--- a/mozilla_voice_tts/bin/train_encoder.py
+++ b/mozilla_voice_tts/bin/train_encoder.py
@@ -247,7 +247,7 @@ if __name__ == '__main__':
                      new_fields)
 
     LOG_DIR = OUT_PATH
-    tb_logger = TensorboardLogger(LOG_DIR)
+    tb_logger = TensorboardLogger(LOG_DIR, model_name='Speaker_Encoder')
 
     try:
         main(args)
diff --git a/mozilla_voice_tts/speaker_encoder/config.json b/mozilla_voice_tts/speaker_encoder/config.json
index 281578da..37d976ce 100644
--- a/mozilla_voice_tts/speaker_encoder/config.json
+++ b/mozilla_voice_tts/speaker_encoder/config.json
@@ -36,6 +36,7 @@
     "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
     "steps_plot_stats": 10, // number of steps to plot embeddings.
     "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
     "wd": 0.000001, // Weight decay weight.
     "checkpoint": true, // If true, it saves checkpoints per "save_step"
     "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
diff --git a/mozilla_voice_tts/speaker_encoder/dataset.py b/mozilla_voice_tts/speaker_encoder/dataset.py
index 42c75dd9..d3243c13 100644
--- a/mozilla_voice_tts/speaker_encoder/dataset.py
+++ b/mozilla_voice_tts/speaker_encoder/dataset.py
@@ -31,7 +31,7 @@ class MyDataset(Dataset):
             print(f" | > Num speakers: {len(self.speakers)}")
 
     def load_wav(self, filename):
-        audio = self.ap.load_wav(filename)
+        audio = self.ap.load_wav(filename, sr=self.ap.sample_rate)
         return audio
 
     def load_data(self, idx):

From bd4c6ee42ae984f16b97ecd9d931e7688a8b39a9 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 00:55:42 -0300
Subject: [PATCH 46/56] add speaker encoder parameters in config.json, for more
 easy changes

---
 mozilla_voice_tts/bin/train_encoder.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mozilla_voice_tts/bin/train_encoder.py b/mozilla_voice_tts/bin/train_encoder.py
index 46b7f46f..c89469b9 100644
--- a/mozilla_voice_tts/bin/train_encoder.py
+++ b/mozilla_voice_tts/bin/train_encoder.py
@@ -135,10 +135,10 @@ def main(args):  # pylint: disable=redefined-outer-name
     global meta_data_eval
 
     ap = AudioProcessor(**c.audio)
-    model = SpeakerEncoder(input_dim=40,
-                           proj_dim=128,
-                           lstm_dim=384,
-                           num_lstm_layers=3)
+    model = SpeakerEncoder(input_dim=c.model['input_dim'],
+                           proj_dim=c.model['proj_dim'],
+                           lstm_dim=c.model['lstm_dim'],
+                           num_lstm_layers=c.model['num_lstm_layers'])
     optimizer = RAdam(model.parameters(), lr=c.lr)
 
     if c.loss == "ge2e":

From f37159c13567223392dcbe0ed07fe6e98cca62fa Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 01:03:03 -0300
Subject: [PATCH 47/56] add Jupyter Notebook for Extract Speaker Embedding per
 sample using AngleProto

---
 .../config.json                               |  60 -------
 .../config.json                               |  60 -------
 .../config.json                               |  60 -------
 mozilla_voice_tts/speaker_encoder/losses.py   |   4 +-
 ...- ExtractSpeakerEmbeddings-by-sample.ipynb | 163 ++++++++++++++++++
 ...- ExtractSpeakerEmbeddings-by-sample.ipynb |   6 +-
 6 files changed, 168 insertions(+), 185 deletions(-)
 delete mode 100644 checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+37AM-debug/config.json
 delete mode 100644 checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+38AM-debug/config.json
 delete mode 100644 checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json
 create mode 100644 notebooks/AngleProto-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb

diff --git a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+37AM-debug/config.json b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+37AM-debug/config.json
deleted file mode 100644
index dbea7ef2..00000000
--- a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+37AM-debug/config.json
+++ /dev/null
@@ -1,60 +0,0 @@
-
-"github_branch":"* dev-gst-embeddings",
-{
-    "run_name": "libritts_100+360-angleproto",
-    "run_description": "train speaker encoder for libritts 100 and 360",
-    "audio":{
-        // Audio processing parameters
-        "num_mels": 80,         // size of the mel spec frame. 
-        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "win_length": 1024,     // stft window length in ms.
-        "hop_length": 256,      // stft window hop-lengh in ms.
-        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
-        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "min_level_db": -100,   // normalization range
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-        // Normalization parameters
-        "signal_norm": true,    // normalize the spec values in range [0, 1]
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
-        "do_trim_silence": false,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
-    },
-    "reinit_layers": [],
-    "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
-    "grad_clip": 3.0, // upper limit for gradients for clipping.
-    "epochs": 1000, // total number of epochs to train.
-    "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
-    "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
-    "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
-    "steps_plot_stats": 10, // number of steps to plot embeddings.
-    "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-    "wd": 0.000001, // Weight decay weight.
-    "checkpoint": true, // If true, it saves checkpoints per "save_step"
-    "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": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
-    "model": {
-        "input_dim": 80, // input_dim == num_mels
-        "proj_dim": 128,
-        "lstm_dim": 384,
-        "num_lstm_layers": 3
-    },
-    "datasets": 
-        [
-            {
-                "name": "vctk",
-                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
-                "meta_file_train": null,
-                "meta_file_val": null
-            }
-        ]
-}
\ No newline at end of file
diff --git a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+38AM-debug/config.json b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+38AM-debug/config.json
deleted file mode 100644
index dbea7ef2..00000000
--- a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+38AM-debug/config.json
+++ /dev/null
@@ -1,60 +0,0 @@
-
-"github_branch":"* dev-gst-embeddings",
-{
-    "run_name": "libritts_100+360-angleproto",
-    "run_description": "train speaker encoder for libritts 100 and 360",
-    "audio":{
-        // Audio processing parameters
-        "num_mels": 80,         // size of the mel spec frame. 
-        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "win_length": 1024,     // stft window length in ms.
-        "hop_length": 256,      // stft window hop-lengh in ms.
-        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
-        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "min_level_db": -100,   // normalization range
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-        // Normalization parameters
-        "signal_norm": true,    // normalize the spec values in range [0, 1]
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
-        "do_trim_silence": false,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
-    },
-    "reinit_layers": [],
-    "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
-    "grad_clip": 3.0, // upper limit for gradients for clipping.
-    "epochs": 1000, // total number of epochs to train.
-    "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
-    "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
-    "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
-    "steps_plot_stats": 10, // number of steps to plot embeddings.
-    "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-    "wd": 0.000001, // Weight decay weight.
-    "checkpoint": true, // If true, it saves checkpoints per "save_step"
-    "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": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
-    "model": {
-        "input_dim": 80, // input_dim == num_mels
-        "proj_dim": 128,
-        "lstm_dim": 384,
-        "num_lstm_layers": 3
-    },
-    "datasets": 
-        [
-            {
-                "name": "vctk",
-                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
-                "meta_file_train": null,
-                "meta_file_val": null
-            }
-        ]
-}
\ No newline at end of file
diff --git a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json
deleted file mode 100644
index dbea7ef2..00000000
--- a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json
+++ /dev/null
@@ -1,60 +0,0 @@
-
-"github_branch":"* dev-gst-embeddings",
-{
-    "run_name": "libritts_100+360-angleproto",
-    "run_description": "train speaker encoder for libritts 100 and 360",
-    "audio":{
-        // Audio processing parameters
-        "num_mels": 80,         // size of the mel spec frame. 
-        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "win_length": 1024,     // stft window length in ms.
-        "hop_length": 256,      // stft window hop-lengh in ms.
-        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
-        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "min_level_db": -100,   // normalization range
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-        // Normalization parameters
-        "signal_norm": true,    // normalize the spec values in range [0, 1]
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
-        "do_trim_silence": false,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
-    },
-    "reinit_layers": [],
-    "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
-    "grad_clip": 3.0, // upper limit for gradients for clipping.
-    "epochs": 1000, // total number of epochs to train.
-    "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_decay": false, // if true, Noam learning rate decaying is applied through training.
-    "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
-    "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
-    "steps_plot_stats": 10, // number of steps to plot embeddings.
-    "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-    "wd": 0.000001, // Weight decay weight.
-    "checkpoint": true, // If true, it saves checkpoints per "save_step"
-    "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": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
-    "model": {
-        "input_dim": 80, // input_dim == num_mels
-        "proj_dim": 128,
-        "lstm_dim": 384,
-        "num_lstm_layers": 3
-    },
-    "datasets": 
-        [
-            {
-                "name": "vctk",
-                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
-                "meta_file_train": null,
-                "meta_file_val": null
-            }
-        ]
-}
\ No newline at end of file
diff --git a/mozilla_voice_tts/speaker_encoder/losses.py b/mozilla_voice_tts/speaker_encoder/losses.py
index 7feced64..9065ccfd 100644
--- a/mozilla_voice_tts/speaker_encoder/losses.py
+++ b/mozilla_voice_tts/speaker_encoder/losses.py
@@ -23,7 +23,7 @@ class GE2ELoss(nn.Module):
         self.b = nn.Parameter(torch.tensor(init_b))
         self.loss_method = loss_method
 
-        print('Initialised Generalized End-to-End loss')
+        print(' > Initialised Generalized End-to-End loss')
 
         assert self.loss_method in ["softmax", "contrast"]
 
@@ -142,7 +142,7 @@ class AngleProtoLoss(nn.Module):
         self.b = nn.Parameter(torch.tensor(init_b))
         self.criterion = torch.nn.CrossEntropyLoss()
 
-        print('Initialised Angular Prototypical loss')
+        print(' > Initialised Angular Prototypical loss')
 
     def forward(self, x):
         """
diff --git a/notebooks/AngleProto-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb b/notebooks/AngleProto-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb
new file mode 100644
index 00000000..d660a7f5
--- /dev/null
+++ b/notebooks/AngleProto-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb	
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a noteboook used to generate the speaker embeddings with the AngleProto speaker encoder model for multi-speaker training.\n",
+    "\n",
+    "Before running this script please DON'T FORGET: \n",
+    "- to set file paths.\n",
+    "- to download related model files from TTS.\n",
+    "- download or clone related repos, linked below.\n",
+    "- setup the repositories. ```python setup.py install```\n",
+    "- to checkout right commit versions (given next to the model) of TTS.\n",
+    "- to set the right paths in the cell below.\n",
+    "\n",
+    "Repository:\n",
+    "- TTS: https://github.com/mozilla/TTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import importlib\n",
+    "import random\n",
+    "import librosa\n",
+    "import torch\n",
+    "\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
+    "\n",
+    "# you may need to change this depending on your system\n",
+    "os.environ['CUDA_VISIBLE_DEVICES']='0'\n",
+    "\n",
+    "\n",
+    "from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
+    "from TTS.utils.audio import AudioProcessor\n",
+    "from TTS.utils.io import load_config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should also adjust all the path constants to point at the relevant locations for you locally"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_RUN_PATH = \"../../Mozilla-TTS/checkpoints/libritts_100+360-angleproto-June-06-2020_04+12PM-9c04d1f/\"\n",
+    "MODEL_PATH = MODEL_RUN_PATH + \"best_model.pth.tar\"\n",
+    "CONFIG_PATH = MODEL_RUN_PATH + \"config.json\"\n",
+    "\n",
+    "\n",
+    "DATASETS_NAME = ['vctk'] # list the datasets\n",
+    "DATASETS_PATH = ['../../../datasets/VCTK/']\n",
+    "DATASETS_METAFILE = ['']\n",
+    "\n",
+    "USE_CUDA = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Preprocess dataset\n",
+    "meta_data = []\n",
+    "for i in range(len(DATASETS_NAME)):\n",
+    "    preprocessor = importlib.import_module('TTS.datasets.preprocess')\n",
+    "    preprocessor = getattr(preprocessor, DATASETS_NAME[i].lower())\n",
+    "    meta_data += preprocessor(DATASETS_PATH[i],DATASETS_METAFILE[i])\n",
+    "      \n",
+    "meta_data= list(meta_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = load_config(CONFIG_PATH)\n",
+    "ap = AudioProcessor(**c['audio'])\n",
+    "\n",
+    "model = SpeakerEncoder(**c.model)\n",
+    "model.load_state_dict(torch.load(MODEL_PATH)['model'])\n",
+    "model.eval()\n",
+    "if USE_CUDA:\n",
+    "    model.cuda()\n",
+    "\n",
+    "embeddings_dict = {}\n",
+    "len_meta_data= len(meta_data)\n",
+    "\n",
+    "for i in tqdm(range(len_meta_data)):\n",
+    "    _, wav_file, speaker_id = meta_data[i]\n",
+    "    wav_file_name = os.path.basename(wav_file)\n",
+    "    mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T\n",
+    "    mel_spec = torch.FloatTensor(mel_spec[None, :, :])\n",
+    "    if USE_CUDA:\n",
+    "        mel_spec = mel_spec.cuda()\n",
+    "    embedd = model.compute_embedding(mel_spec).cpu().detach().numpy().reshape(-1)\n",
+    "    embeddings_dict[wav_file_name] = [embedd,speaker_id]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create and export speakers.json\n",
+    "speaker_mapping = {sample: {'name': embeddings_dict[sample][1], 'embedding':embeddings_dict[sample][0].reshape(-1).tolist()} for i, sample in enumerate(embeddings_dict.keys())}\n",
+    "save_speaker_mapping(MODEL_RUN_PATH, speaker_mapping)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#test load integrity\n",
+    "speaker_mapping_load = load_speaker_mapping(MODEL_RUN_PATH)\n",
+    "assert speaker_mapping == speaker_mapping_load\n",
+    "print(\"The file speakers.json has been exported to \",MODEL_RUN_PATH, ' with ', len(embeddings_dict.keys()), ' speakers')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb b/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb
index 324de2d0..2fba4d49 100644
--- a/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb	
+++ b/notebooks/GE2E-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb	
@@ -63,9 +63,9 @@
     "CONFIG_PATH = MODEL_RUN_PATH + \"config.json\"\n",
     "\n",
     "\n",
-    "DATASETS_NAME = ['brspeech'] # list the datasets\n",
-    "DATASETS_PATH = ['../../../datasets/BRSpeech-2.0-beta8']\n",
-    "DATASETS_METAFILE = ['TTS_metadata_brspeech2+cv_all_valited_lines.csv']\n",
+    "DATASETS_NAME = ['vctk'] # list the datasets\n",
+    "DATASETS_PATH = ['../../../datasets/VCTK/']\n",
+    "DATASETS_METAFILE = ['']\n",
     "\n",
     "USE_CUDA = True"
    ]

From f3b8ef4272b5da8270477173355f794583f002a4 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 01:12:17 -0300
Subject: [PATCH 48/56] fix Lint check errors

---
 mozilla_voice_tts/speaker_encoder/losses.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/mozilla_voice_tts/speaker_encoder/losses.py b/mozilla_voice_tts/speaker_encoder/losses.py
index 9065ccfd..f4687949 100644
--- a/mozilla_voice_tts/speaker_encoder/losses.py
+++ b/mozilla_voice_tts/speaker_encoder/losses.py
@@ -124,14 +124,14 @@ class GE2ELoss(nn.Module):
 
 # adapted from https://github.com/clovaai/voxceleb_trainer/blob/master/loss/angleproto.py
 class AngleProtoLoss(nn.Module):
-    """ 
+    """
     Implementation of the Angular Prototypical loss defined in https://arxiv.org/abs/2003.11982
         Accepts an input of size (N, M, D)
             where N is the number of speakers in the batch,
             M is the number of utterances per speaker,
             and D is the dimensionality of the embedding vector
         Args:
-            - init_w (float): defines the initial value of w 
+            - init_w (float): defines the initial value of w
             - init_b (float): definies the initial value of b
     """
     def __init__(self, init_w=10.0, init_b=-5.0):
@@ -148,13 +148,13 @@ class AngleProtoLoss(nn.Module):
         """
         Calculates the AngleProto loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
         """
-        out_anchor = torch.mean(x[:,1:,:],1)
-        out_positive = x[:,0,:]
+        out_anchor = torch.mean(x[:, 1:, :], 1)
+        out_positive = x[:, 0, :]
         num_speakers = out_anchor.size()[0]
 
-        cos_sim_matrix  = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1,-1,num_speakers),out_anchor.unsqueeze(-1).expand(-1,-1,num_speakers).transpose(0,2))
+        cos_sim_matrix  = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1, -1, num_speakers),out_anchor.unsqueeze(-1).expand(-1, -1,num_speakers).transpose(0, 2))
         torch.clamp(self.w, 1e-6)
         cos_sim_matrix = cos_sim_matrix * self.w + self.b
-        label = torch.from_numpy(np.asarray(range(0,num_speakers))).to(cos_sim_matrix.device)
+        label = torch.from_numpy(np.asarray(range(0, num_speakers))).to(cos_sim_matrix.device)
         L = self.criterion(cos_sim_matrix, label)
         return L
\ No newline at end of file

From 3c6c749de2a0fa7c64ffa4d49872bdfa5a5f0ff4 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 01:21:31 -0300
Subject: [PATCH 49/56] add test for AngleProtoLoss

---
 tests/test_encoder.py | 31 ++++++++++++++++++++++++++++++-
 1 file changed, 30 insertions(+), 1 deletion(-)

diff --git a/tests/test_encoder.py b/tests/test_encoder.py
index 711ad195..46266f29 100644
--- a/tests/test_encoder.py
+++ b/tests/test_encoder.py
@@ -4,7 +4,7 @@ import unittest
 import torch as T
 from tests import get_tests_input_path
 
-from mozilla_voice_tts.speaker_encoder.loss import GE2ELoss
+from mozilla_voice_tts.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
 from mozilla_voice_tts.speaker_encoder.model import SpeakerEncoder
 from mozilla_voice_tts.utils.io import load_config
 
@@ -59,6 +59,7 @@ class GE2ELossTests(unittest.TestCase):
         dummy_input = T.ones(4, 5, 64)  # num_speaker x num_utterance x dim
         loss = GE2ELoss(loss_method="softmax")
         output = loss.forward(dummy_input)
+        assert output.item() >= 0.0
         # check speaker loss with orthogonal d-vectors
         dummy_input = T.empty(3, 64)
         dummy_input = T.nn.init.orthogonal(dummy_input)
@@ -73,6 +74,34 @@ class GE2ELossTests(unittest.TestCase):
         output = loss.forward(dummy_input)
         assert output.item() < 0.005
 
+class AngleProtoLossTests(unittest.TestCase):
+    # pylint: disable=R0201
+    def test_in_out(self):
+        # check random input
+        dummy_input = T.rand(4, 5, 64)  # num_speaker x num_utterance x dim
+        loss = AngleProtoLoss()
+        output = loss.forward(dummy_input)
+        assert output.item() >= 0.0
+
+        # check all zeros
+        dummy_input = T.ones(4, 5, 64)  # num_speaker x num_utterance x dim
+        loss = AngleProtoLoss()
+        output = loss.forward(dummy_input)
+        assert output.item() >= 0.0
+
+        # check speaker loss with orthogonal d-vectors
+        dummy_input = T.empty(3, 64)
+        dummy_input = T.nn.init.orthogonal(dummy_input)
+        dummy_input = T.cat(
+            [
+                dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
+                dummy_input[1].repeat(5, 1, 1).transpose(0, 1),
+                dummy_input[2].repeat(5, 1, 1).transpose(0, 1),
+            ]
+        )  # num_speaker x num_utterance x dim
+        loss = AngleProtoLoss()
+        output = loss.forward(dummy_input)
+        assert output.item() < 0.005
 
 # class LoaderTest(unittest.TestCase):
 #     def test_output(self):

From df02e876aeaafd15d479d02ade6bac0bb8aa7a5f Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Fri, 31 Jul 2020 01:58:22 -0300
Subject: [PATCH 50/56] fix Lint check errors

---
 mozilla_voice_tts/speaker_encoder/losses.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mozilla_voice_tts/speaker_encoder/losses.py b/mozilla_voice_tts/speaker_encoder/losses.py
index f4687949..750648e5 100644
--- a/mozilla_voice_tts/speaker_encoder/losses.py
+++ b/mozilla_voice_tts/speaker_encoder/losses.py
@@ -152,7 +152,7 @@ class AngleProtoLoss(nn.Module):
         out_positive = x[:, 0, :]
         num_speakers = out_anchor.size()[0]
 
-        cos_sim_matrix  = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1, -1, num_speakers),out_anchor.unsqueeze(-1).expand(-1, -1,num_speakers).transpose(0, 2))
+        cos_sim_matrix = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1, -1, num_speakers), out_anchor.unsqueeze(-1).expand(-1, -1, num_speakers).transpose(0, 2))
         torch.clamp(self.w, 1e-6)
         cos_sim_matrix = cos_sim_matrix * self.w + self.b
         label = torch.from_numpy(np.asarray(range(0, num_speakers))).to(cos_sim_matrix.device)

From 26beea0e1bf6d444514d6e99c02f30fbda9240b9 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Tue, 4 Aug 2020 07:58:12 -0300
Subject: [PATCH 51/56] do not resample audio in Dataloader

---
 mozilla_voice_tts/tts/datasets/TTSDataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mozilla_voice_tts/tts/datasets/TTSDataset.py b/mozilla_voice_tts/tts/datasets/TTSDataset.py
index 2ef78e11..1ecca75f 100644
--- a/mozilla_voice_tts/tts/datasets/TTSDataset.py
+++ b/mozilla_voice_tts/tts/datasets/TTSDataset.py
@@ -72,7 +72,7 @@ class MyDataset(Dataset):
         self.sort_items()
 
     def load_wav(self, filename):
-        audio = self.ap.load_wav(filename, sr=self.sample_rate)
+        audio = self.ap.load_wav(filename)
         return audio
 
     @staticmethod

From 1d782487f5e6870170bea77758a7dd16d174379b Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Tue, 4 Aug 2020 14:43:31 -0300
Subject: [PATCH 52/56] use tacotron abstract for multispeaker common
 definitions

---
 mozilla_voice_tts/bin/train_tts.py            |  1 +
 mozilla_voice_tts/tts/models/tacotron.py      | 43 +++++++---------
 mozilla_voice_tts/tts/models/tacotron2.py     | 49 ++++++++-----------
 .../tts/models/tacotron_abstract.py           | 15 ++++++
 4 files changed, 53 insertions(+), 55 deletions(-)

diff --git a/mozilla_voice_tts/bin/train_tts.py b/mozilla_voice_tts/bin/train_tts.py
index 1b9bc032..2b6cbfd0 100644
--- a/mozilla_voice_tts/bin/train_tts.py
+++ b/mozilla_voice_tts/bin/train_tts.py
@@ -536,6 +536,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     else:
         num_speakers = 0
         speaker_embedding_dim = None
+        speaker_mapping = None
 
     model = setup_model(num_chars, num_speakers, c, speaker_embedding_dim)
 
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index 3837e63c..ac88133b 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -27,6 +27,8 @@ class Tacotron(TacotronAbstract):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
+                 encoder_in_features=256,
+                 decoder_in_features=256,
                  speaker_embedding_dim=None,
                  gst=False,
                  gst_embedding_dim=256,
@@ -40,39 +42,28 @@ class Tacotron(TacotronAbstract):
                              forward_attn, trans_agent, forward_attn_mask,
                              location_attn, attn_K, separate_stopnet,
                              bidirectional_decoder, double_decoder_consistency,
-                             ddc_r, gst, gst_embedding_dim, gst_num_heads, gst_style_tokens)
+                             ddc_r, encoder_in_features, decoder_in_features, 
+                             speaker_embedding_dim, gst, gst_embedding_dim, 
+                             gst_num_heads, gst_style_tokens)
 
-        # init layer dims
-        decoder_in_features = 256
-        encoder_in_features = 256
-
-        if speaker_embedding_dim is None:
-            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
-            self.embeddings_per_sample = False
-            speaker_embedding_dim = 256
-        else:
-            # if speaker_embedding_dim is not None we need use speaker embedding per sample
-            self.embeddings_per_sample = True
+        # speaker embedding layers
+        if self.num_speakers > 1:
+            if not self.embeddings_per_sample:
+                speaker_embedding_dim = 256
+                self.speaker_embedding = nn.Embedding(self.num_speakers, speaker_embedding_dim)
+                self.speaker_embedding.weight.data.normal_(0, 0.3)
 
         # speaker and gst embeddings is concat in decoder input
-        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
+        if self.num_speakers > 1:
+            self.decoder_in_features += speaker_embedding_dim # add speaker embedding dim
 
         # embedding layer
         self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
-
-        # speaker embedding layers
-        if num_speakers > 1:
-            if not self.embeddings_per_sample:
-                self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
-                self.speaker_embedding.weight.data.normal_(0, 0.3)
+        self.embedding.weight.data.normal_(0, 0.3)
 
         # base model layers
-        self.embedding.weight.data.normal_(0, 0.3)
-        self.encoder = Encoder(encoder_in_features)
-        self.decoder = Decoder(decoder_in_features, decoder_output_dim, r,
+        self.encoder = Encoder(self.encoder_in_features)
+        self.decoder = Decoder(self.decoder_in_features, 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,
@@ -93,7 +84,7 @@ class Tacotron(TacotronAbstract):
         # setup DDC
         if self.double_decoder_consistency:
             self.coarse_decoder = Decoder(
-                decoder_in_features, decoder_output_dim, ddc_r, memory_size,
+                self.decoder_in_features, decoder_output_dim, ddc_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)
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 9aeeb3d2..9fa640b0 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -33,6 +33,8 @@ class Tacotron2(TacotronAbstract):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
+                 encoder_in_features=512,
+                 decoder_in_features=512,
                  speaker_embedding_dim=None,
                  gst=False,
                  gst_embedding_dim=512,
@@ -45,38 +47,27 @@ class Tacotron2(TacotronAbstract):
                              forward_attn, trans_agent, forward_attn_mask,
                              location_attn, attn_K, separate_stopnet,
                              bidirectional_decoder, double_decoder_consistency,
-                             ddc_r, gst, gst_embedding_dim, gst_num_heads, gst_style_tokens)
+                             ddc_r, encoder_in_features, decoder_in_features, 
+                             speaker_embedding_dim, gst, gst_embedding_dim, 
+                             gst_num_heads, gst_style_tokens)
 
-        # init layer dims
-        decoder_in_features = 512
-        encoder_in_features = 512
-
-        if speaker_embedding_dim is None:
-            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
-            self.embeddings_per_sample = False
-            speaker_embedding_dim = 512
-        else:
-            # if speaker_embedding_dim is not None we need use speaker embedding per sample
-            self.embeddings_per_sample = True
+        # speaker embedding layer
+        if self.num_speakers > 1:
+            if not self.embeddings_per_sample:
+                speaker_embedding_dim = 512
+                self.speaker_embedding = nn.Embedding(self.num_speakers, speaker_embedding_dim)
+                self.speaker_embedding.weight.data.normal_(0, 0.3)
 
         # speaker and gst embeddings is concat in decoder input
-        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
-
+        if self.num_speakers > 1:
+            self.decoder_in_features += speaker_embedding_dim # add speaker embedding dim
+            
         # embedding layer
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
 
-        # speaker embedding layer
-        if num_speakers > 1:
-            if not self.embeddings_per_sample:
-                self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
-                self.speaker_embedding.weight.data.normal_(0, 0.3)
-
         # base model layers
-        self.encoder = Encoder(encoder_in_features)
-        self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
+        self.encoder = Encoder(self.encoder_in_features)
+        self.decoder = Decoder(self.decoder_in_features, 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)
@@ -85,16 +76,16 @@ class Tacotron2(TacotronAbstract):
         # global style token layers
         if self.gst:
             self.gst_layer = GST(num_mel=80,
-                                 num_heads=gst_num_heads,
-                                 num_style_tokens=gst_style_tokens,
-                                 embedding_dim=gst_embedding_dim)
+                                 num_heads=self.gst_num_heads,
+                                 num_style_tokens=self.gst_style_tokens,
+                                 embedding_dim=self.gst_embedding_dim)
         # backward pass decoder
         if self.bidirectional_decoder:
             self._init_backward_decoder()
         # setup DDC
         if self.double_decoder_consistency:
             self.coarse_decoder = Decoder(
-                decoder_in_features, self.decoder_output_dim, ddc_r, attn_type,
+                self.decoder_in_features, self.decoder_output_dim, ddc_r, attn_type,
                 attn_win, attn_norm, prenet_type, prenet_dropout, forward_attn,
                 trans_agent, forward_attn_mask, location_attn, attn_K,
                 separate_stopnet)
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index 6f3d32ad..0077f3e4 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -28,6 +28,9 @@ class TacotronAbstract(ABC, nn.Module):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
+                 encoder_in_features=512, 
+                 decoder_in_features=512, 
+                 speaker_embedding_dim=None,
                  gst=False,
                  gst_embedding_dim=512,
                  gst_num_heads=4,
@@ -57,6 +60,9 @@ class TacotronAbstract(ABC, nn.Module):
         self.location_attn = location_attn
         self.attn_K = attn_K
         self.separate_stopnet = separate_stopnet
+        self.encoder_in_features = encoder_in_features
+        self.decoder_in_features = decoder_in_features
+        self.speaker_embedding_dim = speaker_embedding_dim
 
         # layers
         self.embedding = None
@@ -64,8 +70,17 @@ class TacotronAbstract(ABC, nn.Module):
         self.decoder = None
         self.postnet = None
 
+        # multispeaker
+        if self.speaker_embedding_dim is None:
+            # if speaker_embedding_dim is None we need use the nn.Embedding, with default speaker_embedding_dim
+            self.embeddings_per_sample = False
+        else:
+            # if speaker_embedding_dim is not None we need use speaker embedding per sample
+            self.embeddings_per_sample = True
+
         # global style token
         if self.gst:
+            self.decoder_in_features += gst_embedding_dim # add gst embedding dim
             self.gst_layer = None
 
         # model states

From ac032f00f37704f772f49987e23c335b58073024 Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Tue, 4 Aug 2020 15:05:04 -0300
Subject: [PATCH 53/56] add tests for tacotron and tacotron2 Multi-speaker

---
 tests/test_tacotron2_model.py | 53 +++++++++++++++++++++++++++++
 tests/test_tacotron_model.py  | 63 +++++++++++++++++++++++++++++++++++
 2 files changed, 116 insertions(+)

diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index 92ffb9aa..0ff79f6e 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -131,6 +131,59 @@ class TacotronGSTTrainTest(unittest.TestCase):
                 count, param.shape, param, param_ref)
             count += 1
 
+class MultiSpeakeTacotronTrainTest(unittest.TestCase):
+    @staticmethod
+    def test_train_step():
+        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 128, (8, )).long().to(device)
+        input_lengths = torch.sort(input_lengths, descending=True)[0]
+        mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
+        mel_lengths[0] = 30
+        stop_targets = torch.zeros(8, 30, 1).float().to(device)
+        speaker_embeddings = torch.rand(8, 55).to(device)
+
+        for idx in mel_lengths:
+            stop_targets[:, int(idx.item()):, 0] = 1.0
+
+        stop_targets = stop_targets.view(input_dummy.shape[0],
+                                         stop_targets.size(1) // c.r, -1)
+        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
+
+        criterion = MSELossMasked(seq_len_norm=False).to(device)
+        criterion_st = nn.BCEWithLogitsLoss().to(device)
+        model = Tacotron2(num_chars=24, r=c.r, num_speakers=5, speaker_embedding_dim=55).to(device)
+        model.train()
+        model_ref = copy.deepcopy(model)
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            assert (param - param_ref).sum() == 0, param
+            count += 1
+        optimizer = optim.Adam(model.parameters(), lr=c.lr)
+        for i in range(5):
+            mel_out, mel_postnet_out, align, stop_tokens = model.forward(
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_embeddings=speaker_embeddings)
+            assert torch.sigmoid(stop_tokens).data.max() <= 1.0
+            assert torch.sigmoid(stop_tokens).data.min() >= 0.0
+            optimizer.zero_grad()
+            loss = criterion(mel_out, mel_spec, mel_lengths)
+            stop_loss = criterion_st(stop_tokens, stop_targets)
+            loss = loss + criterion(mel_postnet_out, mel_postnet_spec, mel_lengths) + stop_loss
+            loss.backward()
+            optimizer.step()
+        # check parameter changes
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            # ignore pre-higway layer since it works conditional
+            # if count not in [145, 59]:
+            assert (param != param_ref).any(
+            ), "param {} with shape {} not updated!! \n{}\n{}".format(
+                count, param.shape, param, param_ref)
+            count += 1
+
 class TacotronGSTTrainTest(unittest.TestCase):
     @staticmethod
     def test_train_step():
diff --git a/tests/test_tacotron_model.py b/tests/test_tacotron_model.py
index 2b55cbac..0b80243f 100644
--- a/tests/test_tacotron_model.py
+++ b/tests/test_tacotron_model.py
@@ -89,6 +89,69 @@ class TacotronTrainTest(unittest.TestCase):
                 count, param.shape, param, param_ref)
             count += 1
 
+class MultiSpeakeTacotronTrainTest(unittest.TestCase):
+    @staticmethod
+    def test_train_step():
+        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 129, (8, )).long().to(device)
+        input_lengths[-1] = 128
+        mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        linear_spec = torch.rand(8, 30, c.audio['fft_size']).to(device)
+        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
+        stop_targets = torch.zeros(8, 30, 1).float().to(device)
+        speaker_embeddings = torch.rand(8, 55).to(device)
+
+        for idx in mel_lengths:
+            stop_targets[:, int(idx.item()):, 0] = 1.0
+
+        stop_targets = stop_targets.view(input_dummy.shape[0],
+                                         stop_targets.size(1) // c.r, -1)
+        stop_targets = (stop_targets.sum(2) >
+                        0.0).unsqueeze(2).float().squeeze()
+
+        criterion = L1LossMasked(seq_len_norm=False).to(device)
+        criterion_st = nn.BCEWithLogitsLoss().to(device)
+        model = Tacotron(
+            num_chars=32,
+            num_speakers=5,
+            postnet_output_dim=c.audio['fft_size'],
+            decoder_output_dim=c.audio['num_mels'],
+            r=c.r,
+            memory_size=c.memory_size,
+            speaker_embedding_dim=55,
+        ).to(device)  #FIXME: missing num_speakers parameter to Tacotron ctor
+        model.train()
+        print(" > Num parameters for Tacotron model:%s" %
+              (count_parameters(model)))
+        model_ref = copy.deepcopy(model)
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            assert (param - param_ref).sum() == 0, param
+            count += 1
+        optimizer = optim.Adam(model.parameters(), lr=c.lr)
+        for _ in range(5):
+            mel_out, linear_out, align, stop_tokens = model.forward(
+                input_dummy, input_lengths, mel_spec, mel_lengths,
+                speaker_embeddings=speaker_embeddings)
+            optimizer.zero_grad()
+            loss = criterion(mel_out, mel_spec, mel_lengths)
+            stop_loss = criterion_st(stop_tokens, stop_targets)
+            loss = loss + criterion(linear_out, linear_spec,
+                                    mel_lengths) + stop_loss
+            loss.backward()
+            optimizer.step()
+        # check parameter changes
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            # ignore pre-higway layer since it works conditional
+            # if count not in [145, 59]:
+            assert (param != param_ref).any(
+            ), "param {} with shape {} not updated!! \n{}\n{}".format(
+                count, param.shape, param, param_ref)
+            count += 1
+
 class TacotronGSTTrainTest(unittest.TestCase):
     @staticmethod
     def test_train_step():

From 07c961382fcc1882a80e4ef65e3c9a357928d49d Mon Sep 17 00:00:00 2001
From: Edresson <edresson1@gmail.com>
Date: Wed, 5 Aug 2020 08:43:27 -0300
Subject: [PATCH 54/56] add support for CorentinJ Speaker encoder and add
 notebook for extract embeddings

---
 mozilla_voice_tts/speaker_encoder/config.json |    27 +-
 mozilla_voice_tts/speaker_encoder/model.py    |    38 +-
 ...- ExtractSpeakerEmbeddings-by-sample.ipynb |     2 +-
 ...J-ExtractSpeakerEmbeddings-by-sample.ipynb | 25495 ++++++++++++++++
 4 files changed, 25541 insertions(+), 21 deletions(-)
 create mode 100644 notebooks/GE2E-CorentinJ-ExtractSpeakerEmbeddings-by-sample.ipynb

diff --git a/mozilla_voice_tts/speaker_encoder/config.json b/mozilla_voice_tts/speaker_encoder/config.json
index 37d976ce..11da0cf6 100644
--- a/mozilla_voice_tts/speaker_encoder/config.json
+++ b/mozilla_voice_tts/speaker_encoder/config.json
@@ -1,14 +1,14 @@
 
 {
-    "run_name": "libritts_100+360-angleproto",
-    "run_description": "train speaker encoder for libritts 100 and 360",
+    "run_name": "Model compatible to  CorentinJ/Real-Time-Voice-Cloning",
+    "run_description": "train speaker encoder with voxceleb1, voxceleb2 and libriSpeech ",
     "audio":{
         // Audio processing parameters
-        "num_mels": 80,         // size of the mel spec frame. 
-        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "win_length": 1024,     // stft window length in ms.
-        "hop_length": 256,      // stft window hop-lengh in ms.
+        "num_mels": 40,         // size of the mel spec frame. 
+        "fft_size": 400,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "sample_rate": 16000,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "win_length": 400,     // stft window length in ms.
+        "hop_length": 160,      // stft window hop-lengh in ms.
         "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
         "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
         "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
@@ -27,7 +27,7 @@
         "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
     },
     "reinit_layers": [],
-    "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
+    "loss": "ge2e", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
     "grad_clip": 3.0, // upper limit for gradients for clipping.
     "epochs": 1000, // total number of epochs to train.
     "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
@@ -41,12 +41,13 @@
     "checkpoint": true, // If true, it saves checkpoints per "save_step"
     "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": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
+    "output_path": "../../checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
     "model": {
-        "input_dim": 80, // input_dim == num_mels
-        "proj_dim": 128,
-        "lstm_dim": 384,
-        "num_lstm_layers": 3
+        "input_dim": 40,
+        "proj_dim": 256,
+        "lstm_dim": 256,
+        "num_lstm_layers": 3,
+        "use_lstm_with_projection": false
     },
     "datasets": 
         [
diff --git a/mozilla_voice_tts/speaker_encoder/model.py b/mozilla_voice_tts/speaker_encoder/model.py
index ca2abe31..df0527bc 100644
--- a/mozilla_voice_tts/speaker_encoder/model.py
+++ b/mozilla_voice_tts/speaker_encoder/model.py
@@ -16,15 +16,33 @@ class LSTMWithProjection(nn.Module):
         o, (_, _) = self.lstm(x)
         return self.linear(o)
 
+class LSTMWithoutProjection(nn.Module):
+    def __init__(self, input_dim, lstm_dim, proj_dim, num_lstm_layers):
+        super().__init__()
+        self.lstm = nn.LSTM(input_size=input_dim,
+                            hidden_size=lstm_dim,
+                            num_layers=num_lstm_layers,
+                            batch_first=True)
+        self.linear = nn.Linear(lstm_dim, proj_dim, bias=True)
+        self.relu = nn.ReLU()
+    def forward(self, x):
+        _, (hidden, _) = self.lstm(x)
+        return self.relu(self.linear(hidden[-1]))
 
 class SpeakerEncoder(nn.Module):
-    def __init__(self, input_dim, proj_dim=256, lstm_dim=768, num_lstm_layers=3):
+    def __init__(self, input_dim, proj_dim=256, lstm_dim=768, num_lstm_layers=3, use_lstm_with_projection=True):
         super().__init__()
+        self.use_lstm_with_projection = use_lstm_with_projection
         layers = []
-        layers.append(LSTMWithProjection(input_dim, lstm_dim, proj_dim))
-        for _ in range(num_lstm_layers - 1):
-            layers.append(LSTMWithProjection(proj_dim, lstm_dim, proj_dim))
-        self.layers = nn.Sequential(*layers)
+        # choise LSTM layer
+        if use_lstm_with_projection:
+            layers.append(LSTMWithProjection(input_dim, lstm_dim, proj_dim))
+            for _ in range(num_lstm_layers - 1):
+                layers.append(LSTMWithProjection(proj_dim, lstm_dim, proj_dim))
+            self.layers = nn.Sequential(*layers)
+        else:
+            self.layers = LSTMWithoutProjection(input_dim, lstm_dim, proj_dim, num_lstm_layers)
+
         self._init_layers()
 
     def _init_layers(self):
@@ -37,12 +55,18 @@ class SpeakerEncoder(nn.Module):
     def forward(self, x):
         # TODO: implement state passing for lstms
         d = self.layers(x)
-        d = torch.nn.functional.normalize(d[:, -1], p=2, dim=1)
+        if self.use_lstm_with_projection:
+            d = torch.nn.functional.normalize(d[:, -1], p=2, dim=1)
+        else:
+            d = torch.nn.functional.normalize(d, p=2, dim=1)
         return d
 
     def inference(self, x):
         d = self.layers.forward(x)
-        d = torch.nn.functional.normalize(d[:, -1], p=2, dim=1)
+        if self.use_lstm_with_projection:
+            d = torch.nn.functional.normalize(d[:, -1], p=2, dim=1)
+        else:
+            d = torch.nn.functional.normalize(d, p=2, dim=1)
         return d
 
     def compute_embedding(self, x, num_frames=160, overlap=0.5):
diff --git a/notebooks/AngleProto-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb b/notebooks/AngleProto-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb
index d660a7f5..15206130 100644
--- a/notebooks/AngleProto-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb	
+++ b/notebooks/AngleProto-Speaker_Encoder- ExtractSpeakerEmbeddings-by-sample.ipynb	
@@ -79,7 +79,7 @@
     "#Preprocess dataset\n",
     "meta_data = []\n",
     "for i in range(len(DATASETS_NAME)):\n",
-    "    preprocessor = importlib.import_module('TTS.datasets.preprocess')\n",
+    "    preprocessor = importlib.import_module('TTS.tts.datasets.preprocess')\n",
     "    preprocessor = getattr(preprocessor, DATASETS_NAME[i].lower())\n",
     "    meta_data += preprocessor(DATASETS_PATH[i],DATASETS_METAFILE[i])\n",
     "      \n",
diff --git a/notebooks/GE2E-CorentinJ-ExtractSpeakerEmbeddings-by-sample.ipynb b/notebooks/GE2E-CorentinJ-ExtractSpeakerEmbeddings-by-sample.ipynb
new file mode 100644
index 00000000..576a95fe
--- /dev/null
+++ b/notebooks/GE2E-CorentinJ-ExtractSpeakerEmbeddings-by-sample.ipynb
@@ -0,0 +1,25495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a noteboook used to generate the speaker embeddings with the CorentinJ GE2E model trained with Angular Prototypical loss for multi-speaker training.\n",
+    "\n",
+    "Before running this script please DON'T FORGET:\n",
+    "- to set the right paths in the cell below.\n",
+    "\n",
+    "Repositories:\n",
+    "- TTS: https://github.com/mozilla/TTS\n",
+    "- CorentinJ GE2E: https://github.com/Edresson/GE2E-Speaker-Encoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import importlib\n",
+    "import random\n",
+    "import librosa\n",
+    "import torch\n",
+    "\n",
+    "import numpy as np\n",
+    "from TTS.utils.io import load_config\n",
+    "from tqdm import tqdm\n",
+    "from TTS.tts.utils.speakers import save_speaker_mapping, load_speaker_mapping\n",
+    "\n",
+    "# you may need to change this depending on your system\n",
+    "os.environ['CUDA_VISIBLE_DEVICES']='0'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'Real-Time-Voice-Cloning'...\n",
+      "remote: Enumerating objects: 5, done.\u001b[K\n",
+      "remote: Counting objects: 100% (5/5), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
+      "remote: Total 2508 (delta 0), reused 3 (delta 0), pack-reused 2503\u001b[K\n",
+      "Receiving objects: 100% (2508/2508), 360.78 MiB | 17.84 MiB/s, done.\n",
+      "Resolving deltas: 100% (1387/1387), done.\n",
+      "Checking connectivity... done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Clone encoder \n",
+    "!git clone https://github.com/CorentinJ/Real-Time-Voice-Cloning.git\n",
+    "os.chdir('Real-Time-Voice-Cloning/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Install voxceleb_trainer Requeriments\n",
+    "!python -m pip install umap-learn visdom webrtcvad librosa>=0.5.1 matplotlib>=2.0.2 numpy>=1.14.0  scipy>=1.0.0  tqdm sounddevice Unidecode inflect multiprocess numba"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2020-08-05 06:51:05--  https://github.com/Edresson/Real-Time-Voice-Cloning/releases/download/checkpoints/pretrained.zip\n",
+      "Resolving github.com (github.com)... 18.231.5.6\n",
+      "Connecting to github.com (github.com)|18.231.5.6|:443... connected.\n",
+      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
+      "Location: https://github.com/Edresson/GE2E-Speaker-Encoder/releases/download/checkpoints/pretrained.zip [following]\n",
+      "--2020-08-05 06:51:05--  https://github.com/Edresson/GE2E-Speaker-Encoder/releases/download/checkpoints/pretrained.zip\n",
+      "Reusing existing connection to github.com:443.\n",
+      "HTTP request sent, awaiting response... 302 Found\n",
+      "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/263893598/f7f31d80-96df-11ea-8345-261fc35f9849?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200805%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200805T101614Z&X-Amz-Expires=300&X-Amz-Signature=df7724c28668ebd5dfbcc6a9b51f6afb78193c30119f3a1c3eef678188aabd1e&X-Amz-SignedHeaders=host&actor_id=0&repo_id=263893598&response-content-disposition=attachment%3B%20filename%3Dpretrained.zip&response-content-type=application%2Foctet-stream [following]\n",
+      "--2020-08-05 06:51:05--  https://github-production-release-asset-2e65be.s3.amazonaws.com/263893598/f7f31d80-96df-11ea-8345-261fc35f9849?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200805%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200805T101614Z&X-Amz-Expires=300&X-Amz-Signature=df7724c28668ebd5dfbcc6a9b51f6afb78193c30119f3a1c3eef678188aabd1e&X-Amz-SignedHeaders=host&actor_id=0&repo_id=263893598&response-content-disposition=attachment%3B%20filename%3Dpretrained.zip&response-content-type=application%2Foctet-stream\n",
+      "Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.18.24\n",
+      "Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.216.18.24|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 383640573 (366M) [application/octet-stream]\n",
+      "Saving to: ‘pretrained.zip’\n",
+      "\n",
+      "pretrained.zip      100%[===================>] 365,87M  6,62MB/s    in 56s     \n",
+      "\n",
+      "2020-08-05 06:52:03 (6,48 MB/s) - ‘pretrained.zip’ saved [383640573/383640573]\n",
+      "\n",
+      "Archive:  pretrained.zip\n",
+      "   creating: encoder/saved_models/\n",
+      "  inflating: encoder/saved_models/pretrained.pt  \n",
+      "   creating: synthesizer/saved_models/\n",
+      "   creating: synthesizer/saved_models/logs-pretrained/\n",
+      "   creating: synthesizer/saved_models/logs-pretrained/taco_pretrained/\n",
+      " extracting: synthesizer/saved_models/logs-pretrained/taco_pretrained/checkpoint  \n",
+      "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.data-00000-of-00001  \n",
+      "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.index  \n",
+      "  inflating: synthesizer/saved_models/logs-pretrained/taco_pretrained/tacotron_model.ckpt-278000.meta  \n",
+      "   creating: vocoder/saved_models/\n",
+      "   creating: vocoder/saved_models/pretrained/\n",
+      "  inflating: vocoder/saved_models/pretrained/pretrained.pt  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Download encoder Checkpoint\n",
+    "!wget https://github.com/Edresson/Real-Time-Voice-Cloning/releases/download/checkpoints/pretrained.zip\n",
+    "!unzip pretrained.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from encoder import inference as encoder\n",
+    "from encoder.params_model import model_embedding_size as speaker_embedding_size\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preparing the encoder, the synthesizer and the vocoder...\n",
+      "Loaded encoder \"pretrained.pt\" trained to step 1564501\n",
+      "Testing your configuration with small inputs.\n",
+      "\tTesting the encoder...\n",
+      "(256,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Preparing the encoder, the synthesizer and the vocoder...\")\n",
+    "encoder.load_model(Path('encoder/saved_models/pretrained.pt'))\n",
+    "print(\"Testing your configuration with small inputs.\")\n",
+    "# Forward an audio waveform of zeroes that lasts 1 second. Notice how we can get the encoder's\n",
+    "# sampling rate, which may differ.\n",
+    "# If you're unfamiliar with digital audio, know that it is encoded as an array of floats \n",
+    "# (or sometimes integers, but mostly floats in this projects) ranging from -1 to 1.\n",
+    "# The sampling rate is the number of values (samples) recorded per second, it is set to\n",
+    "# 16000 for the encoder. Creating an array of length <sampling_rate> will always correspond \n",
+    "# to an audio of 1 second.\n",
+    "print(\"\\tTesting the encoder...\")\n",
+    "\n",
+    "wav = np.zeros(encoder.sampling_rate)    \n",
+    "embed = encoder.embed_utterance(wav)\n",
+    "print(embed.shape)\n",
+    "\n",
+    "# Embeddings are L2-normalized (this isn't important here, but if you want to make your own \n",
+    "# embeddings it will be).\n",
+    "#embed /= np.linalg.norm(embed) # for random embedding\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SAVE_PATH = '../'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set constants\n",
+    "DATASETS_NAME = ['vctk'] # list the datasets\n",
+    "DATASETS_PATH = ['../../../../../datasets/VCTK-Corpus-removed-silence/']\n",
+    "DATASETS_METAFILE = ['']\n",
+    "USE_CUDA = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  4%|▍         | 1959/44063 [03:20<1:04:23, 10.90it/s]\u001b[A\n",
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
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+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+    {
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+     "output_type": "stream",
+     "text": [
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+    {
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+    {
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
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+     "output_type": "stream",
+     "text": [
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+    {
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
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+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+    },
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+     "output_type": "stream",
+     "text": [
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+    },
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+     "output_type": "stream",
+     "text": [
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+    },
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    }
+   ],
+   "source": [
+    "#Preprocess dataset\n",
+    "meta_data = []\n",
+    "for i in range(len(DATASETS_NAME)):\n",
+    "    preprocessor = importlib.import_module('TTS.tts.datasets.preprocess')\n",
+    "    preprocessor = getattr(preprocessor, DATASETS_NAME[i].lower())\n",
+    "    meta_data += preprocessor(DATASETS_PATH[i],DATASETS_METAFILE[i])\n",
+    "      \n",
+    "meta_data= list(meta_data)\n",
+    "\n",
+    "meta_data = meta_data\n",
+    "embeddings_dict = {}\n",
+    "len_meta_data= len(meta_data)\n",
+    "for i in tqdm(range(len_meta_data)):\n",
+    "    _, wave_file_path, speaker_id = meta_data[i]\n",
+    "    wav_file_name = os.path.basename(wave_file_path)\n",
+    "    # Extract Embedding\n",
+    "    preprocessed_wav = encoder.preprocess_wav(wave_file_path)\n",
+    "    file_embedding = encoder.embed_utterance(preprocessed_wav)\n",
+    "    embeddings_dict[wav_file_name] = [file_embedding.reshape(-1).tolist(), speaker_id]\n",
+    "    del file_embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create and export speakers.json  and aplly a L2_norm in embedding\n",
+    "speaker_mapping = {sample: {'name': embeddings_dict[sample][1], 'embedding':embeddings_dict[sample][0]} for i, sample in enumerate(embeddings_dict.keys())}\n",
+    "save_speaker_mapping(SAVE_PATH, speaker_mapping)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file speakers.json has been exported to  ../  with  44063  samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "#test load integrity\n",
+    "speaker_mapping_load = load_speaker_mapping(SAVE_PATH)\n",
+    "assert speaker_mapping == speaker_mapping_load\n",
+    "print(\"The file speakers.json has been exported to \",SAVE_PATH, ' with ', len(embeddings_dict.keys()), ' samples')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 6a46339a438eae17df7bfa46f423ab23ea58f8d0 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 5 Aug 2020 20:19:23 +0200
Subject: [PATCH 55/56] rebase fixes

---
 mozilla_voice_tts/bin/train_encoder.py        |  15 +-
 mozilla_voice_tts/speaker_encoder/loss.py     | 163 ----------------
 mozilla_voice_tts/speaker_encoder/losses.py   |   2 +-
 mozilla_voice_tts/tts/datasets/preprocess.py  |  32 ++-
 mozilla_voice_tts/tts/models/tacotron.py      |   5 +-
 mozilla_voice_tts/tts/models/tacotron2.py     |  12 +-
 .../tts/models/tacotron_abstract.py           |   4 +-
 synthesize.py                                 | 182 ------------------
 tests/test_tacotron2_model.py                 |  59 +-----
 9 files changed, 41 insertions(+), 433 deletions(-)
 delete mode 100644 mozilla_voice_tts/speaker_encoder/loss.py
 delete mode 100644 synthesize.py

diff --git a/mozilla_voice_tts/bin/train_encoder.py b/mozilla_voice_tts/bin/train_encoder.py
index c89469b9..f9bfea7f 100644
--- a/mozilla_voice_tts/bin/train_encoder.py
+++ b/mozilla_voice_tts/bin/train_encoder.py
@@ -10,21 +10,21 @@ import traceback
 import torch
 from torch.utils.data import DataLoader
 
-from mozilla_voice_tts.generic_utils import count_parameters
 from mozilla_voice_tts.speaker_encoder.dataset import MyDataset
 from mozilla_voice_tts.speaker_encoder.generic_utils import save_best_model
-from mozilla_voice_tts.speaker_encoder.losses import GE2ELoss
+from mozilla_voice_tts.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
 from mozilla_voice_tts.speaker_encoder.model import SpeakerEncoder
 from mozilla_voice_tts.speaker_encoder.visual import plot_embeddings
 from mozilla_voice_tts.tts.datasets.preprocess import load_meta_data
-from mozilla_voice_tts.tts.utils.audio import AudioProcessor
 from mozilla_voice_tts.tts.utils.generic_utils import (
     create_experiment_folder, get_git_branch, remove_experiment_folder,
     set_init_dict)
 from mozilla_voice_tts.tts.utils.io import copy_config_file, load_config
-from mozilla_voice_tts.tts.utils.radam import RAdam
-from mozilla_voice_tts.tts.utils.tensorboard_logger import TensorboardLogger
-from mozilla_voice_tts.tts.utils.training import NoamLR, check_update
+from mozilla_voice_tts.utils.audio import AudioProcessor
+from mozilla_voice_tts.utils.generic_utils import count_parameters
+from mozilla_voice_tts.utils.radam import RAdam
+from mozilla_voice_tts.utils.tensorboard_logger import TensorboardLogger
+from mozilla_voice_tts.utils.training import NoamLR, check_update
 
 torch.backends.cudnn.enabled = True
 torch.backends.cudnn.benchmark = True
@@ -146,7 +146,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     elif c.loss == "angleproto":
         criterion = AngleProtoLoss()
     else:
-        raise Exception("The %s  not is a loss supported" %c.loss)
+        raise Exception("The %s  not is a loss supported" % c.loss)
 
     if args.restore_path:
         checkpoint = torch.load(args.restore_path)
@@ -192,6 +192,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     _, global_step = train(model, criterion, optimizer, scheduler, ap,
                            global_step)
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument(
diff --git a/mozilla_voice_tts/speaker_encoder/loss.py b/mozilla_voice_tts/speaker_encoder/loss.py
deleted file mode 100644
index 6f83be63..00000000
--- a/mozilla_voice_tts/speaker_encoder/loss.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-# adapted from https://github.com/cvqluu/GE2E-Loss
-class GE2ELoss(nn.Module):
-    def __init__(self, init_w=10.0, init_b=-5.0, loss_method="softmax"):
-        """
-        Implementation of the Generalized End-to-End loss defined in https://arxiv.org/abs/1710.10467 [1]
-        Accepts an input of size (N, M, D)
-            where N is the number of speakers in the batch,
-            M is the number of utterances per speaker,
-            and D is the dimensionality of the embedding vector (e.g. d-vector)
-        Args:
-            - init_w (float): defines the initial value of w in Equation (5) of [1]
-            - init_b (float): definies the initial value of b in Equation (5) of [1]
-        """
-        super(GE2ELoss, self).__init__()
-        # pylint: disable=E1102
-        self.w = nn.Parameter(torch.tensor(init_w))
-        # pylint: disable=E1102
-        self.b = nn.Parameter(torch.tensor(init_b))
-        self.loss_method = loss_method
-
-        print('Initialised Generalized End-to-End loss')
-
-        assert self.loss_method in ["softmax", "contrast"]
-
-        if self.loss_method == "softmax":
-            self.embed_loss = self.embed_loss_softmax
-        if self.loss_method == "contrast":
-            self.embed_loss = self.embed_loss_contrast
-
-    # pylint: disable=R0201
-    def calc_new_centroids(self, dvecs, centroids, spkr, utt):
-        """
-        Calculates the new centroids excluding the reference utterance
-        """
-        excl = torch.cat((dvecs[spkr, :utt], dvecs[spkr, utt + 1 :]))
-        excl = torch.mean(excl, 0)
-        new_centroids = []
-        for i, centroid in enumerate(centroids):
-            if i == spkr:
-                new_centroids.append(excl)
-            else:
-                new_centroids.append(centroid)
-        return torch.stack(new_centroids)
-
-    def calc_cosine_sim(self, dvecs, centroids):
-        """
-        Make the cosine similarity matrix with dims (N,M,N)
-        """
-        cos_sim_matrix = []
-        for spkr_idx, speaker in enumerate(dvecs):
-            cs_row = []
-            for utt_idx, utterance in enumerate(speaker):
-                new_centroids = self.calc_new_centroids(
-                    dvecs, centroids, spkr_idx, utt_idx
-                )
-                # vector based cosine similarity for speed
-                cs_row.append(
-                    torch.clamp(
-                        torch.mm(
-                            utterance.unsqueeze(1).transpose(0, 1),
-                            new_centroids.transpose(0, 1),
-                        )
-                        / (torch.norm(utterance) * torch.norm(new_centroids, dim=1)),
-                        1e-6,
-                    )
-                )
-            cs_row = torch.cat(cs_row, dim=0)
-            cos_sim_matrix.append(cs_row)
-        return torch.stack(cos_sim_matrix)
-
-    # pylint: disable=R0201
-    def embed_loss_softmax(self, dvecs, cos_sim_matrix):
-        """
-        Calculates the loss on each embedding $L(e_{ji})$ by taking softmax
-        """
-        N, M, _ = dvecs.shape
-        L = []
-        for j in range(N):
-            L_row = []
-            for i in range(M):
-                L_row.append(-F.log_softmax(cos_sim_matrix[j, i], 0)[j])
-            L_row = torch.stack(L_row)
-            L.append(L_row)
-        return torch.stack(L)
-
-    # pylint: disable=R0201
-    def embed_loss_contrast(self, dvecs, cos_sim_matrix):
-        """
-        Calculates the loss on each embedding $L(e_{ji})$ by contrast loss with closest centroid
-        """
-        N, M, _ = dvecs.shape
-        L = []
-        for j in range(N):
-            L_row = []
-            for i in range(M):
-                centroids_sigmoids = torch.sigmoid(cos_sim_matrix[j, i])
-                excl_centroids_sigmoids = torch.cat(
-                    (centroids_sigmoids[:j], centroids_sigmoids[j + 1 :])
-                )
-                L_row.append(
-                    1.0
-                    - torch.sigmoid(cos_sim_matrix[j, i, j])
-                    + torch.max(excl_centroids_sigmoids)
-                )
-            L_row = torch.stack(L_row)
-            L.append(L_row)
-        return torch.stack(L)
-
-    def forward(self, dvecs):
-        """
-        Calculates the GE2E loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
-        """
-        centroids = torch.mean(dvecs, 1)
-        cos_sim_matrix = self.calc_cosine_sim(dvecs, centroids)
-        torch.clamp(self.w, 1e-6)
-        cos_sim_matrix = self.w * cos_sim_matrix + self.b
-        L = self.embed_loss(dvecs, cos_sim_matrix)
-        return L.mean()
-
-# adapted from https://github.com/clovaai/voxceleb_trainer/blob/master/loss/angleproto.py
-class AngleProtoLoss(nn.Module):
-    """ 
-    Implementation of the Angular Prototypical loss defined in https://arxiv.org/abs/2003.11982
-        Accepts an input of size (N, M, D)
-            where N is the number of speakers in the batch,
-            M is the number of utterances per speaker,
-            and D is the dimensionality of the embedding vector
-        Args:
-            - init_w (float): defines the initial value of w 
-            - init_b (float): definies the initial value of b
-    """
-    def __init__(self, init_w=10.0, init_b=-5.0):
-        super(AngleProtoLoss, self).__init__()
-        # pylint: disable=E1102
-        self.w = nn.Parameter(torch.tensor(init_w))
-        # pylint: disable=E1102
-        self.b = nn.Parameter(torch.tensor(init_b))
-        self.criterion = torch.nn.CrossEntropyLoss()
-        self.use_cuda = torch.cuda.is_available()
-
-        print('Initialised Angular Prototypical loss')
-
-    def forward(self, x):
-        """
-        Calculates the AngleProto loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
-        """
-        out_anchor = torch.mean(x[:,1:,:],1)
-        out_positive = x[:,0,:]
-        num_speakers = out_anchor.size()[0]
-
-        cos_sim_matrix  = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1,-1,num_speakers),out_anchor.unsqueeze(-1).expand(-1,-1,num_speakers).transpose(0,2))
-        torch.clamp(self.w, 1e-6)
-        cos_sim_matrix = cos_sim_matrix * self.w + self.b
-        label = torch.from_numpy(np.asarray(range(0,num_speakers)))
-        if self.use_cuda:
-            label = label.cuda()
-        L = self.criterion(cos_sim_matrix, label)
-        return L
\ No newline at end of file
diff --git a/mozilla_voice_tts/speaker_encoder/losses.py b/mozilla_voice_tts/speaker_encoder/losses.py
index 750648e5..35ff73fa 100644
--- a/mozilla_voice_tts/speaker_encoder/losses.py
+++ b/mozilla_voice_tts/speaker_encoder/losses.py
@@ -157,4 +157,4 @@ class AngleProtoLoss(nn.Module):
         cos_sim_matrix = cos_sim_matrix * self.w + self.b
         label = torch.from_numpy(np.asarray(range(0, num_speakers))).to(cos_sim_matrix.device)
         L = self.criterion(cos_sim_matrix, label)
-        return L
\ No newline at end of file
+        return L
diff --git a/mozilla_voice_tts/tts/datasets/preprocess.py b/mozilla_voice_tts/tts/datasets/preprocess.py
index 7865652a..ece3bcb6 100644
--- a/mozilla_voice_tts/tts/datasets/preprocess.py
+++ b/mozilla_voice_tts/tts/datasets/preprocess.py
@@ -93,9 +93,10 @@ def mozilla_de(root_path, meta_file):
 
 def mailabs(root_path, meta_files=None):
     """Normalizes M-AI-Labs meta data files to TTS format"""
-    speaker_regex = re.compile("by_book/(male|female)/(?P<speaker_name>[^/]+)/")
+    speaker_regex = re.compile(
+        "by_book/(male|female)/(?P<speaker_name>[^/]+)/")
     if meta_files is None:
-        csv_files = glob(root_path+"/**/metadata.csv", recursive=True)
+        csv_files = glob(root_path + "/**/metadata.csv", recursive=True)
     else:
         csv_files = meta_files
     # meta_files = [f.strip() for f in meta_files.split(",")]
@@ -115,12 +116,15 @@ def mailabs(root_path, meta_files=None):
                 if meta_files is None:
                     wav_file = os.path.join(folder, 'wavs', cols[0] + '.wav')
                 else:
-                    wav_file = os.path.join(root_path, folder.replace("metadata.csv", ""), 'wavs', cols[0] + '.wav')
+                    wav_file = os.path.join(root_path,
+                                            folder.replace("metadata.csv", ""),
+                                            'wavs', cols[0] + '.wav')
                 if os.path.isfile(wav_file):
                     text = cols[1].strip()
                     items.append([text, wav_file, speaker_name])
                 else:
-                    raise RuntimeError("> File %s does not exist!"%(wav_file))
+                    raise RuntimeError("> File %s does not exist!" %
+                                       (wav_file))
     return items
 
 
@@ -185,7 +189,8 @@ def libri_tts(root_path, meta_files=None):
                 text = cols[1]
                 items.append([text, wav_file, speaker_name])
     for item in items:
-        assert os.path.exists(item[1]), f" [!] wav files don't exist - {item[1]}"
+        assert os.path.exists(
+            item[1]), f" [!] wav files don't exist - {item[1]}"
     return items
 
 
@@ -197,7 +202,8 @@ def custom_turkish(root_path, meta_file):
     with open(txt_file, 'r', encoding='utf-8') as ttf:
         for line in ttf:
             cols = line.split('|')
-            wav_file = os.path.join(root_path, 'wavs', cols[0].strip() + '.wav')
+            wav_file = os.path.join(root_path, 'wavs',
+                                    cols[0].strip() + '.wav')
             if not os.path.exists(wav_file):
                 skipped_files.append(wav_file)
                 continue
@@ -206,6 +212,7 @@ def custom_turkish(root_path, meta_file):
     print(f" [!] {len(skipped_files)} files skipped. They don't exist...")
     return items
 
+
 # ToDo: add the dataset link when the dataset is released publicly
 def brspeech(root_path, meta_file):
     '''BRSpeech 3.0 beta'''
@@ -223,20 +230,25 @@ def brspeech(root_path, meta_file):
             items.append([text, wav_file, speaker_name])
     return items
 
+
 def vctk(root_path, meta_files=None, wavs_path='wav48'):
     """homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
     test_speakers = meta_files
     items = []
-    meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
+    meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt",
+                      recursive=True)
     for meta_file in meta_files:
-        _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
+        _, speaker_id, txt_file = os.path.relpath(meta_file,
+                                                  root_path).split(os.sep)
         file_id = txt_file.split('.')[0]
-        if isinstance(test_speakers, list): # if is list ignore this speakers ids
+        if isinstance(test_speakers,
+                      list):  # if is list ignore this speakers ids
             if speaker_id in test_speakers:
                 continue
         with open(meta_file) as file_text:
             text = file_text.readlines()[0]
-        wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id+'.wav')
+        wav_file = os.path.join(root_path, wavs_path, speaker_id,
+                                file_id + '.wav')
         items.append([text, wav_file, speaker_id])
 
     return items
\ No newline at end of file
diff --git a/mozilla_voice_tts/tts/models/tacotron.py b/mozilla_voice_tts/tts/models/tacotron.py
index ac88133b..1dcf2fc8 100644
--- a/mozilla_voice_tts/tts/models/tacotron.py
+++ b/mozilla_voice_tts/tts/models/tacotron.py
@@ -6,6 +6,7 @@ from mozilla_voice_tts.tts.layers.gst_layers import GST
 from mozilla_voice_tts.tts.layers.tacotron import Decoder, Encoder, PostCBHG
 from mozilla_voice_tts.tts.models.tacotron_abstract import TacotronAbstract
 
+
 class Tacotron(TacotronAbstract):
     def __init__(self,
                  num_chars,
@@ -42,8 +43,8 @@ class Tacotron(TacotronAbstract):
                              forward_attn, trans_agent, forward_attn_mask,
                              location_attn, attn_K, separate_stopnet,
                              bidirectional_decoder, double_decoder_consistency,
-                             ddc_r, encoder_in_features, decoder_in_features, 
-                             speaker_embedding_dim, gst, gst_embedding_dim, 
+                             ddc_r, encoder_in_features, decoder_in_features,
+                             speaker_embedding_dim, gst, gst_embedding_dim,
                              gst_num_heads, gst_style_tokens)
 
         # speaker embedding layers
diff --git a/mozilla_voice_tts/tts/models/tacotron2.py b/mozilla_voice_tts/tts/models/tacotron2.py
index 9fa640b0..a9ba442c 100644
--- a/mozilla_voice_tts/tts/models/tacotron2.py
+++ b/mozilla_voice_tts/tts/models/tacotron2.py
@@ -1,15 +1,9 @@
 import torch
 from torch import nn
 
-<<<<<<< HEAD:mozilla_voice_tts/tts/models/tacotron2.py
 from mozilla_voice_tts.tts.layers.gst_layers import GST
 from mozilla_voice_tts.tts.layers.tacotron2 import Decoder, Encoder, Postnet
 from mozilla_voice_tts.tts.models.tacotron_abstract import TacotronAbstract
-=======
-from TTS.tts.layers.gst_layers import GST
-from TTS.tts.layers.tacotron2 import Decoder, Encoder, Postnet
-from TTS.tts.models.tacotron_abstract import TacotronAbstract
->>>>>>> bugfix in DDC now DDC work on Tacotron1:TTS/tts/models/tacotron2.py
 
 # TODO: match function arguments with tacotron
 class Tacotron2(TacotronAbstract):
@@ -47,8 +41,8 @@ class Tacotron2(TacotronAbstract):
                              forward_attn, trans_agent, forward_attn_mask,
                              location_attn, attn_K, separate_stopnet,
                              bidirectional_decoder, double_decoder_consistency,
-                             ddc_r, encoder_in_features, decoder_in_features, 
-                             speaker_embedding_dim, gst, gst_embedding_dim, 
+                             ddc_r, encoder_in_features, decoder_in_features,
+                             speaker_embedding_dim, gst, gst_embedding_dim,
                              gst_num_heads, gst_style_tokens)
 
         # speaker embedding layer
@@ -61,7 +55,7 @@ class Tacotron2(TacotronAbstract):
         # speaker and gst embeddings is concat in decoder input
         if self.num_speakers > 1:
             self.decoder_in_features += speaker_embedding_dim # add speaker embedding dim
-            
+
         # embedding layer
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
 
diff --git a/mozilla_voice_tts/tts/models/tacotron_abstract.py b/mozilla_voice_tts/tts/models/tacotron_abstract.py
index 0077f3e4..d98d03b7 100644
--- a/mozilla_voice_tts/tts/models/tacotron_abstract.py
+++ b/mozilla_voice_tts/tts/models/tacotron_abstract.py
@@ -28,8 +28,8 @@ class TacotronAbstract(ABC, nn.Module):
                  bidirectional_decoder=False,
                  double_decoder_consistency=False,
                  ddc_r=None,
-                 encoder_in_features=512, 
-                 decoder_in_features=512, 
+                 encoder_in_features=512,
+                 decoder_in_features=512,
                  speaker_embedding_dim=None,
                  gst=False,
                  gst_embedding_dim=512,
diff --git a/synthesize.py b/synthesize.py
deleted file mode 100644
index bd720123..00000000
--- a/synthesize.py
+++ /dev/null
@@ -1,182 +0,0 @@
-# pylint: disable=redefined-outer-name, unused-argument
-import os
-import time
-import argparse
-import torch
-import json
-import string
-
-from TTS.utils.synthesis import synthesis
-from TTS.utils.generic_utils import setup_model
-from TTS.utils.io import load_config
-from TTS.utils.text.symbols import make_symbols, symbols, phonemes
-from TTS.utils.audio import AudioProcessor
-
-
-def tts(model,
-        vocoder_model,
-        C,
-        VC,
-        text,
-        ap,
-        ap_vocoder,
-        use_cuda,
-        batched_vocoder,
-        speaker_id=None,
-        figures=False):
-    t_1 = time.time()
-    use_vocoder_model = vocoder_model is not None
-    waveform, alignment, _, postnet_output, stop_tokens, _ = synthesis(
-        model, text, C, use_cuda, ap, speaker_id, style_wav=C.gst['gst_style_input'],
-        truncated=False, enable_eos_bos_chars=C.enable_eos_bos_chars,
-        use_griffin_lim=(not use_vocoder_model), do_trim_silence=True)
-
-    if C.model == "Tacotron" and use_vocoder_model:
-        postnet_output = ap.out_linear_to_mel(postnet_output.T).T
-    # correct if there is a scale difference b/w two models
-    if use_vocoder_model:
-        postnet_output = ap._denormalize(postnet_output)
-        postnet_output = ap_vocoder._normalize(postnet_output)
-        vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
-        waveform = vocoder_model.generate(
-            vocoder_input.cuda() if use_cuda else vocoder_input,
-            batched=batched_vocoder,
-            target=8000,
-            overlap=400)
-    print(" >  Run-time: {}".format(time.time() - t_1))
-    return alignment, postnet_output, stop_tokens, waveform
-
-
-if __name__ == "__main__":
-
-    global symbols, phonemes
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('text', type=str, help='Text to generate speech.')
-    parser.add_argument('config_path',
-                        type=str,
-                        help='Path to model config file.')
-    parser.add_argument(
-        'model_path',
-        type=str,
-        help='Path to model file.',
-    )
-    parser.add_argument(
-        'out_path',
-        type=str,
-        help='Path to save final wav file. Wav file will be names as the text given.',
-    )
-    parser.add_argument('--use_cuda',
-                        type=bool,
-                        help='Run model on CUDA.',
-                        default=False)
-    parser.add_argument(
-        '--vocoder_path',
-        type=str,
-        help=
-        'Path to vocoder model file. If it is not defined, model uses GL as vocoder. Please make sure that you installed vocoder library before (WaveRNN).',
-        default="",
-    )
-    parser.add_argument('--vocoder_config_path',
-                        type=str,
-                        help='Path to vocoder model config file.',
-                        default="")
-    parser.add_argument(
-        '--batched_vocoder',
-        type=bool,
-        help="If True, vocoder model uses faster batch processing.",
-        default=True)
-    parser.add_argument('--speakers_json',
-                        type=str,
-                        help="JSON file for multi-speaker model.",
-                        default="")
-    parser.add_argument(
-        '--speaker_id',
-        type=int,
-        help="target speaker_id if the model is multi-speaker.",
-        default=None)
-    args = parser.parse_args()
-
-    if args.vocoder_path != "":
-        assert args.use_cuda, " [!] Enable cuda for vocoder."
-        from WaveRNN.models.wavernn import Model as VocoderModel
-
-    # load the config
-    C = load_config(args.config_path)
-    C.forward_attn_mask = True
-
-    # load the audio processor
-    ap = AudioProcessor(**C.audio)
-
-    # if the vocabulary was passed, replace the default
-    if 'characters' in C.keys():
-        symbols, phonemes = make_symbols(**C.characters)
-
-    # load speakers
-    if args.speakers_json != '':
-        speakers = json.load(open(args.speakers_json, 'r'))
-        num_speakers = len(speakers)
-    else:
-        num_speakers = 0
-
-    # load the model
-    num_chars = len(phonemes) if C.use_phonemes else len(symbols)
-    model = setup_model(num_chars, num_speakers, C)
-    cp = torch.load(args.model_path)
-    model.load_state_dict(cp['model'])
-    model.eval()
-    if args.use_cuda:
-        model.cuda()
-    model.decoder.set_r(cp['r'])
-
-    # load vocoder model
-    if args.vocoder_path != "":
-        VC = load_config(args.vocoder_config_path)
-        ap_vocoder = AudioProcessor(**VC.audio)
-        bits = 10
-        vocoder_model = VocoderModel(rnn_dims=512,
-                                     fc_dims=512,
-                                     mode=VC.mode,
-                                     mulaw=VC.mulaw,
-                                     pad=VC.pad,
-                                     upsample_factors=VC.upsample_factors,
-                                     feat_dims=VC.audio["num_mels"],
-                                     compute_dims=128,
-                                     res_out_dims=128,
-                                     res_blocks=10,
-                                     hop_length=ap.hop_length,
-                                     sample_rate=ap.sample_rate,
-                                     use_aux_net=True,
-                                     use_upsample_net=True)
-
-        check = torch.load(args.vocoder_path)
-        vocoder_model.load_state_dict(check['model'])
-        vocoder_model.eval()
-        if args.use_cuda:
-            vocoder_model.cuda()
-    else:
-        vocoder_model = None
-        VC = None
-        ap_vocoder = None
-
-    # synthesize voice
-    print(" > Text: {}".format(args.text))
-    _, _, _, wav = tts(model,
-                       vocoder_model,
-                       C,
-                       VC,
-                       args.text,
-                       ap,
-                       ap_vocoder,
-                       args.use_cuda,
-                       args.batched_vocoder,
-                       speaker_id=args.speaker_id,
-                       figures=False)
-
-    # save the results
-    file_name = args.text.replace(" ", "_")
-    file_name = file_name.translate(
-        str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
-    out_path = os.path.join(args.out_path, file_name)
-    print(" > Saving output to {}".format(out_path))
-    ap.save_wav(wav, out_path)
diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index 0ff79f6e..28d39de5 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -76,61 +76,6 @@ class TacotronTrainTest(unittest.TestCase):
             count += 1
 
 
-class TacotronGSTTrainTest(unittest.TestCase):
-    def test_train_step(self):
-        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
-        input_lengths = torch.randint(100, 128, (8, )).long().to(device)
-        input_lengths = torch.sort(input_lengths, descending=True)[0]
-        mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
-        mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
-        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
-        mel_lengths[0] = 30
-        stop_targets = torch.zeros(8, 30, 1).float().to(device)
-        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
-
-        for idx in mel_lengths:
-            stop_targets[:, int(idx.item()):, 0] = 1.0
-
-        stop_targets = stop_targets.view(input_dummy.shape[0],
-                                         stop_targets.size(1) // c.r, -1)
-        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
-        criterion = MSELossMasked(seq_len_norm=False).to(device)
-        criterion_st = nn.BCEWithLogitsLoss().to(device)
-        model = Tacotron2(num_chars=24,
-                          gst=True,
-                          r=c.r,
-                          num_speakers=5).to(device)
-        model.train()
-        model_ref = copy.deepcopy(model)
-        count = 0
-        for param, param_ref in zip(model.parameters(),
-                                    model_ref.parameters()):
-            assert (param - param_ref).sum() == 0, param
-            count += 1
-        optimizer = optim.Adam(model.parameters(), lr=c.lr)
-        for i in range(5):
-            mel_out, mel_postnet_out, align, stop_tokens = model.forward(
-                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
-            assert torch.sigmoid(stop_tokens).data.max() <= 1.0
-            assert torch.sigmoid(stop_tokens).data.min() >= 0.0
-            optimizer.zero_grad()
-            loss = criterion(mel_out, mel_spec, mel_lengths)
-            stop_loss = criterion_st(stop_tokens, stop_targets)
-            loss = loss + criterion(mel_postnet_out, mel_postnet_spec, mel_lengths) + stop_loss
-            loss.backward()
-            optimizer.step()
-        # check parameter changes
-        count = 0
-        for param, param_ref in zip(model.parameters(),
-                                    model_ref.parameters()):
-            # ignore pre-higway layer since it works conditional
-            # if count not in [145, 59]:
-            assert (param != param_ref).any(
-            ), "param {} with shape {} not updated!! \n{}\n{}".format(
-                count, param.shape, param, param_ref)
-            count += 1
-
 class MultiSpeakeTacotronTrainTest(unittest.TestCase):
     @staticmethod
     def test_train_step():
@@ -185,8 +130,8 @@ class MultiSpeakeTacotronTrainTest(unittest.TestCase):
             count += 1
 
 class TacotronGSTTrainTest(unittest.TestCase):
-    @staticmethod
-    def test_train_step():
+    #pylint: disable=no-self-use
+    def test_train_step(self):
         # with random gst mel style
         input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
         input_lengths = torch.randint(100, 128, (8, )).long().to(device)

From 037fc91b2b09ff26dc40cad26f2b505ead90d197 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 6 Aug 2020 14:23:17 +0200
Subject: [PATCH 56/56] remove redundant arguments

---
 mozilla_voice_tts/tts/utils/generic_utils.py | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/mozilla_voice_tts/tts/utils/generic_utils.py b/mozilla_voice_tts/tts/utils/generic_utils.py
index fc35840d..f0b718fa 100644
--- a/mozilla_voice_tts/tts/utils/generic_utils.py
+++ b/mozilla_voice_tts/tts/utils/generic_utils.py
@@ -253,18 +253,6 @@ def check_config(c):
     check_argument('use_external_speaker_embedding_file', c, restricted=True, val_type=bool)
     check_argument('external_speaker_embedding_file', c, restricted=True, val_type=str)
     check_argument('use_gst', c, restricted=True, val_type=bool)
-    check_argument('gst_style_input', c, restricted=True, val_type=str)
-    check_argument('gst', c, restricted=True, val_type=dict)
-    check_argument('gst_embedding_dim', c['gst'], restricted=True, val_type=int, min_val=1)
-    check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=1)
-    check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1)
-
-    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)
-
     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)