Create app.py

app.py

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+import gradio as gr
+from transformers import BarkModel, AutoProcessor
+import torch
+from scipy.io.wavfile import write as write_wav
+import os
+
+device = "cpu"
+model = BarkModel.from_pretrained("suno/bark-small").to(device)
+processor = AutoProcessor.from_pretrained("suno/bark")
+
+def generate_audio(text):
+  file_name = "output_file_name.wav"
+  inputs = processor(text)
+  audio_array = model.generate(**inputs)
+  audio_array = audio_array.cpu().numpy().squeeze()
+  sample_rate = model.generation_config.sample_rate
+  write_wav(file_name,sample_rate,audio_array)
+  return file_name
+
+
+iface = gr.Interface(fn=generate_audio,inputs="text",outputs="audio")
+iface.launch()