Update app.py

app.py

@@ -4,7 +4,9 @@ import gradio as gr
 # Load pre-trained models
 stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")
 nlp_model = pipeline("text-generation", model="sshleifer/tiny-gpt2")
-tts_model = pipeline("text-to-speech", model="coqui/XTTS-v2")
+
+# Attempt to load Kokoro-82M for TTS (this might require adjustments depending on model support)
+tts_model = pipeline("text-to-speech", model="hexgrad/Kokoro-82M")
 
 # Define a function to handle the workflow
 def conversation(audio):
@@ -19,10 +21,10 @@ def conversation(audio):
         # Default response generation (using GPT-2 model)
         response = nlp_model(text, max_length=50)[0]["generated_text"]
     
-    # Step 3: Convert response text to speech using XTTS-v2
-    audio_response = tts_model(response)
+    # Step 3: Convert response text to speech using Kokoro-82M
+    tts_audio = tts_model(response)
     
-    return text, response, audio_response
+    return text, response, tts_audio
 
 # Create Gradio Interface
 interface = gr.Interface(