Update app.py

app.py

@@ -1,12 +1,22 @@
 import torchaudio
 from audiocraft.models import AudioGen
 from audiocraft.data.audio import audio_write
+import argparse
 ​
 model = AudioGen.get_pretrained('facebook/audiogen-medium')
-model.set_generation_params(duration=5)  # generate 5 seconds.
-descriptions = ['dog barking', 'sirene of an emergency vehicle', 'footsteps in a corridor']
-wav = model.generate(descriptions)  # generates 3 samples.
+model.set_generation_params(duration=5)  # generate [duration] seconds.
 ​
-for idx, one_wav in enumerate(wav):
-    # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
-    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
+def generate_audio(descriptions):
+  wav = model.generate(descriptions)  # generates samples for all descriptions in array.
+  
+  for idx, one_wav in enumerate(wav):
+      # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
+      audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
+      print(f'Generated {idx}th sample.')
+​
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate audio based on descriptions.")
+    parser.add_argument("descriptions", nargs='+', help="List of descriptions for audio generation")
+    args = parser.parse_args()
+    
+    generate_audio(args.descriptions)