Create app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+from gtts import gTTS
+
+# Load the Whisper model for speech-to-text
+pipe = pipeline(model="openai/whisper-small")
+
+# Load the text generation model
+text_pipe = pipeline("text2text-generation", model="google/byt5-small")
+
+def transcribe(audio):
+    # Transcribe the audio to text
+    text = pipe(audio)["text"]
+    
+    # Generate a response from the transcribed text
+    lm_response = text_pipe(text)[0]["generated_text"]
+    
+    # Convert the response text to speech
+    tts = gTTS(lm_response, lang='ko')
+    
+    # Save the generated audio
+    out_audio = "output_audio.mp3"
+    tts.save(out_audio)
+    
+    return out_audio
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath"),
+    outputs=gr.Audio(type="filepath"),
+    title="Whisper Small Glaswegian",
+    description="Realtime demo for Glaswegian speech recognition using a fine-tuned Whisper small model."
+)
+
+# Launch the interface
+iface.launch(share=True)