Fix bug of importing GST and add more parameters in toolbox

synthesizer/inference.py

@@ -70,7 +70,7 @@ def load(self):
 
     def synthesize_spectrograms(self, texts: List[str],
                                 embeddings: Union[np.ndarray, List[np.ndarray]],
-                                return_alignments=False, style_idx=0):
+                                return_alignments=False, style_idx=0, min_stop_token=5):
         """
         Synthesizes mel spectrograms from texts and speaker embeddings.
 
@@ -125,7 +125,7 @@ def synthesize_spectrograms(self, texts: List[str],
             speaker_embeddings = torch.tensor(speaker_embeds).float().to(self.device)
 
             # Inference
-            _, mels, alignments = self._model.generate(chars, speaker_embeddings, style_idx=style_idx)
+            _, mels, alignments = self._model.generate(chars, speaker_embeddings, style_idx=style_idx, min_stop_token=min_stop_token)
             mels = mels.detach().cpu().numpy()
             for m in mels:
                 # Trim silence from end of each spectrogram

synthesizer/models/tacotron.py

@@ -419,7 +419,7 @@ def forward(self, texts, mels, speaker_embedding):
 
         return mel_outputs, linear, attn_scores, stop_outputs
 
-    def generate(self, x, speaker_embedding=None, steps=200, style_idx=0):
+    def generate(self, x, speaker_embedding=None, steps=200, style_idx=0, min_stop_token=5):
         self.eval()
         device = next(self.parameters()).device  # use same device as parameters
 
@@ -454,9 +454,9 @@ def generate(self, x, speaker_embedding=None, steps=200, style_idx=0):
             scale[:] = 0.3
             speaker_embedding = (gst_embed[style_idx] * scale).astype(np.float32)
             speaker_embedding = torch.from_numpy(np.tile(speaker_embedding, (x.shape[0], 1))).to(device)
-        style_embed = self.gst(speaker_embedding)
-        style_embed = style_embed.expand_as(encoder_seq)
-        encoder_seq = encoder_seq + style_embed
+            style_embed = self.gst(speaker_embedding)
+            style_embed = style_embed.expand_as(encoder_seq)
+            encoder_seq = encoder_seq + style_embed
         encoder_seq_proj = self.encoder_proj(encoder_seq)
 
         # Need a couple of lists for outputs
@@ -472,7 +472,7 @@ def generate(self, x, speaker_embedding=None, steps=200, style_idx=0):
             attn_scores.append(scores)
             stop_outputs.extend([stop_tokens] * self.r)
             # Stop the loop when all stop tokens in batch exceed threshold
-            if (stop_tokens > 0.5).all() and t > 10: break
+            if (stop_tokens * 10 > min_stop_token).all() and t > 10: break
 
         # Concat the mel outputs into sequence
         mel_outputs = torch.cat(mel_outputs, dim=2)

toolbox/__init__.py

@@ -234,7 +234,8 @@ def synthesize(self):
         texts = processed_texts
         embed = self.ui.selected_utterance.embed
         embeds = [embed] * len(texts)
-        specs = self.synthesizer.synthesize_spectrograms(texts, embeds, style_idx=int(self.ui.slider.value()))
+        min_token = int(self.ui.token_slider.value())
+        specs = self.synthesizer.synthesize_spectrograms(texts, embeds, style_idx=int(self.ui.style_slider.value()), min_stop_token=min_token)
         breaks = [spec.shape[1] for spec in specs]
         spec = np.concatenate(specs, axis=1)
 

toolbox/ui.py

@@ -588,18 +588,36 @@ def __init__(self):
         self.seed_textbox = QLineEdit()
         self.seed_textbox.setMaximumWidth(80)
         layout_seed.addWidget(self.seed_textbox, 0, 1)
-        self.slider = QSlider(Qt.Horizontal)
-        self.slider.setTickInterval(1)
-        self.slider.setFocusPolicy(Qt.NoFocus)
-        self.slider.setSingleStep(1)
-        self.slider.setRange(-1, 9)
-        self.slider.setValue(-1)
-        layout_seed.addWidget(QLabel("Style:"), 0, 2)
-        layout_seed.addWidget(self.slider, 0, 3)
         self.trim_silences_checkbox = QCheckBox("Enhance vocoder output")
         self.trim_silences_checkbox.setToolTip("When checked, trims excess silence in vocoder output."
             " This feature requires `webrtcvad` to be installed.")
-        layout_seed.addWidget(self.trim_silences_checkbox, 0, 4, 1, 2)
+        layout_seed.addWidget(self.trim_silences_checkbox, 0, 2, 1, 2)
+        self.style_slider = QSlider(Qt.Horizontal)
+        self.style_slider.setTickInterval(1)
+        self.style_slider.setFocusPolicy(Qt.NoFocus)
+        self.style_slider.setSingleStep(1)
+        self.style_slider.setRange(-1, 9)
+        self.style_value_label = QLabel("-1")
+        self.style_slider.setValue(-1)
+        layout_seed.addWidget(QLabel("Style:"), 1, 0)
+
+        self.style_slider.valueChanged.connect(lambda s: self.style_value_label.setNum(s))
+        layout_seed.addWidget(self.style_value_label, 1, 1)
+        layout_seed.addWidget(self.style_slider, 1, 3)
+
+        self.token_slider = QSlider(Qt.Horizontal)
+        self.token_slider.setTickInterval(1)
+        self.token_slider.setFocusPolicy(Qt.NoFocus)
+        self.token_slider.setSingleStep(1)
+        self.token_slider.setRange(3, 9)
+        self.token_value_label = QLabel("5")
+        self.token_slider.setValue(4)
+        layout_seed.addWidget(QLabel("Accuracy(精度):"), 2, 0)
+
+        self.token_slider.valueChanged.connect(lambda s: self.token_value_label.setNum(s))
+        layout_seed.addWidget(self.token_value_label, 2, 1)
+        layout_seed.addWidget(self.token_slider, 2, 3)
+
         gen_layout.addLayout(layout_seed)
 
         self.loading_bar = QProgressBar()