Update app.py

app.py

@@ -12,6 +12,7 @@ embeddings_dataset = load_dataset("umarigan/turkish_voice_dataset_embedded", spl
 
 # Define the speech generation function
 def generate_speech(text):
+    # Use a pre-defined speaker embedding from the dataset
     speaker_embedding = torch.tensor(embeddings_dataset[768]["speaker_embeddings"]).unsqueeze(0)
     speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding})
     
@@ -23,12 +24,35 @@ def generate_speech(text):
 
 # Define the Gradio interface
 inputs = [
-    gr.Textbox(label="Enter Text", placeholder="Bir berber bir berbere gel beraber bir berber kuralım demiş"),
-    #gr.Number(label="Speaker ID", value=736, precision=0)
+    gr.Textbox(label="📝 Enter Text", placeholder="Bir berber bir berbere gel beraber bir berber kuralım demiş", lines=3),
 ]
 
-outputs = gr.Audio(label="Generated Speech")
+outputs = gr.Audio(label="🎤 Generated Speech")
 
-gr.Interface(fn=generate_speech, inputs=inputs, outputs=outputs, title="Turkish Text-to-Speech").launch()
+# Additional elements to include information and style
+title = "🎙️ Turkish Text-to-Speech with Fine-Tuned TTS Model"
+description = """
+Welcome to the **Turkish Text-to-Speech** app! 🌟 This model is a fine-tuned version of Microsoft's SpeechT5, trained on a large Turkish dataset with over 20k audio samples. 
+It helps generate natural-sounding speech from text input in **Turkish**! 🇹🇷
 
-gr.Markdown("This model fine-tuned version of microsoft speech tts model over 20k record of Turkish Audio dataset, model: https://huggingface.co/umarigan/speecht5_tts_tr_v1.0", elem_classes="markdown-text") 
+**Use Cases**: 
+- Easily generate **custom speech datasets**.
+- Automate **text-to-speech pipelines** for various applications with low cost and efficiency. 💡
+
+Check out the model on [Hugging Face](https://huggingface.co/umarigan/speecht5_tts_tr_v1.0)
+"""
+
+footer = """
+💻 Connect with me on [X](https://x.com/Umar26338572e) 🐦
+"""
+
+# Create the Gradio app interface
+gr.Interface(
+    fn=generate_speech,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    description=description,
+    article=footer,
+    theme="compact",  # Choose a theme that matches the colorful aesthetic
+).launch()