Make style

TTS/server/server.py

@@ -191,10 +191,10 @@ lock = Lock()
 @app.route("/api/tts", methods=["GET", "POST"])
 def tts():
     with lock:
-        text = request.headers.get('text') or request.values.get("text", "")
-        speaker_idx = request.headers.get('speaker-id') or request.values.get("speaker_id", "")
-        language_idx = request.headers.get('language-id') or request.values.get("language_id", "")
-        style_wav = request.headers.get('style-wav') or request.values.get("style_wav", "")
+        text = request.headers.get("text") or request.values.get("text", "")
+        speaker_idx = request.headers.get("speaker-id") or request.values.get("speaker_id", "")
+        language_idx = request.headers.get("language-id") or request.values.get("language_id", "")
+        style_wav = request.headers.get("style-wav") or request.values.get("style_wav", "")
         style_wav = style_wav_uri_to_dict(style_wav)
 
         print(f" > Model input: {text}")

TTS/tts/layers/bark/hubert/tokenizer.py

@@ -102,7 +102,7 @@ class HubertTokenizer(nn.Module):
             model_zip.close()
 
     @staticmethod
-    def load_from_checkpoint(path, map_location = None):
+    def load_from_checkpoint(path, map_location=None):
         old = True
         with ZipFile(path) as model_zip:
             filesMatch = [file for file in model_zip.namelist() if file.endswith("/.info")]

TTS/tts/layers/bark/inference_funcs.py

@@ -184,7 +184,6 @@ def generate_text_semantic(
     Returns:
         np.ndarray: The generated semantic tokens.
     """
-    print(f"history_prompt in gen: {history_prompt}")
     assert isinstance(text, str)
     text = _normalize_whitespace(text)
     assert len(text.strip()) > 0

TTS/tts/models/vits.py

@@ -1849,17 +1849,17 @@ class Vits(BaseTTS):
         scales = torch.FloatTensor([self.inference_noise_scale, self.length_scale, self.inference_noise_scale_dp])
         dummy_input = (sequences, sequence_lengths, scales)
         input_names = ["input", "input_lengths", "scales"]
-        
+
         if self.num_speakers > 0:
             speaker_id = torch.LongTensor([0])
-            dummy_input += (speaker_id, )
+            dummy_input += (speaker_id,)
             input_names.append("sid")
-            
-        if hasattr(self, 'num_languages') and self.num_languages > 0 and self.embedded_language_dim > 0:
+
+        if hasattr(self, "num_languages") and self.num_languages > 0 and self.embedded_language_dim > 0:
             language_id = torch.LongTensor([0])
-            dummy_input += (language_id, )
-            input_names.append("langid")            
-        
+            dummy_input += (language_id,)
+            input_names.append("langid")
+
         # export to ONNX
         torch.onnx.export(
             model=self,
@@ -1875,7 +1875,7 @@ class Vits(BaseTTS):
                 "output": {0: "batch_size", 1: "time1", 2: "time2"},
             },
         )
-       
+
         # rollback
         self.forward = _forward
         if training:
@@ -1885,7 +1885,7 @@ class Vits(BaseTTS):
 
     def load_onnx(self, model_path: str, cuda=False):
         import onnxruntime as ort
-        
+
         providers = [
             "CPUExecutionProvider"
             if cuda is False
@@ -1913,16 +1913,12 @@ class Vits(BaseTTS):
             [self.inference_noise_scale, self.length_scale, self.inference_noise_scale_dp],
             dtype=np.float32,
         )
-        input_params = {
-            "input": x,
-            "input_lengths": x_lengths,
-            "scales": scales
-        }
+        input_params = {"input": x, "input_lengths": x_lengths, "scales": scales}
         if not speaker_id is None:
             input_params["sid"] = torch.tensor([speaker_id]).cpu().numpy()
         if not language_id is None:
             input_params["langid"] = torch.tensor([language_id]).cpu().numpy()
-            
+
         audio = self.onnx_sess.run(
             ["output"],
             input_params,