Create app.py

app.py

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+from transformers.utils import logging
+
+logging.set_verbosity_error()
+
+from transformers import pipeline
+
+import soundfile as sf
+import numpy as np
+import tempfile
+
+def launch(input_text):
+    try:
+        # Assuming `narrator` function returns a numpy array with audio data and a sampling rate.
+        narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
+        out = narrator(input_text)
+        audio_data, samplerate = np.array(out["audio"][0]), 22050  # Example: 22050 Hz as common sampling rate
+
+        # Save the audio data to a temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmpfile:
+            sf.write(tmpfile.name, audio_data, samplerate)
+            # Return the path of the temporary audio file
+            return tmpfile.name
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return None
+
+# Create the Gradio interface
+iface = gr.Interface(fn=launch, inputs="text", outputs=gr.Audio())
+
+# Launch the Gradio app
+iface.launch()