Now compatible with new inference pipeline

app.py

@@ -39,7 +39,8 @@ def main(reference_paths, text_prompt, denoise, avg_style, stabilize):
         }
 
         with torch.no_grad():
-            r = model.generate(text_prompt, speakers, avg_style, stabilize, denoise, 18, "[id_1]") #Should seperate style computation process to style caching.
+            styles = model.get_styles(speakers, denoise, avg_style)
+            r = model.generate(text_prompt, styles, stabilize, 18, "[id_1]")
             r = r / np.abs(r).max()
             
         sf.write("output.wav", r, samplerate=24000)
@@ -68,7 +69,7 @@ def on_file_upload(file_list):
 
 def gen_example(reference_paths, text_prompt):
     output, status = main(reference_paths, text_prompt, 0.6, True, True)
-    return output, eg_voices, status
+    return output, reference_paths, status
 
 
 # Gradio UI
@@ -121,7 +122,7 @@ with gr.Blocks() as demo:
     )
 
     gr.Examples(
-        examples=[[eg_voices[0], eg_texts[0]], [eg_voices, eg_texts[1]]],
+        examples=[[[eg_voices[0]], eg_texts[0]], [eg_voices, eg_texts[1]]],
         inputs=[reference_audios, text_prompt],
         outputs=[synthesized_audio, reference_audios, status],
         fn=gen_example,