Create app.py

app.py

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+import torch
+import gradio as gr
+import torchaudio
+from transformers import AutoModel
+import spaces
+
+checkpoint_path = "/content/VoiceRestore"
+model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True)
+
+
+@spaces.GPU()
+def restore_audio(input_audio):
+    # load the audio file
+    output_path = "restored_output.wav"
+    model(input_audio, output_path)
+    return output_path
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center;'>🔊 Voice Restoration with Transformer-based Model</h1>")
+    gr.Markdown(
+        """
+        <p style='text-align: center;'>Upload a degraded audio file or select an example, and the space will restore it using the <b>VoiceRestore</b> model!<br>
+        Based on this <a href='https://github.com/skirdey/voicerestore' target='_blank'>repo</a> by @StanKirdey,<br>
+        and the HF Transformers model by <a href='https://github.com/jadechoghari' target='_blank'>@jadechoghari</a>.
+        </p>
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### 🎧 Select an Example or Upload Your Audio:")
+            input_audio = gr.Audio(label="Upload Degraded Audio", type="filepath")
+            gr.Examples(
+                examples=["example1.wav", "example2.wav", "example3.wav"],
+                inputs=input_audio,
+                label="Sample Degraded Audios"
+            )
+        
+        with gr.Column():
+            gr.Markdown("### 🎶 Restored Audio Output:")
+            output_audio = gr.Audio(label="Restored Audio", type="filepath")
+
+    with gr.Row():
+        restore_btn = gr.Button("✨ Restore Audio")
+
+    # Connect the button to the function
+    restore_btn.click(restore_audio, inputs=input_audio, outputs=output_audio)
+
+# Launch the demo
+demo.launch(debug=True)