app.py

import gradio as gr
from transformers import AutoProcessor, BarkModel
import scipy
import numpy as np

# Load the processor and model
processor = AutoProcessor.from_pretrained("suno/bark")
model = BarkModel.from_pretrained("suno/bark")

def generate_audio(text):
    # Your preset may vary
    voice_preset = "v2/en_speaker_6"
    inputs = processor(text, voice_preset=voice_preset)
    audio_array = model.generate(**inputs)
    # Move the tensor to CPU and convert to numpy array
    audio_array = audio_array.cpu().numpy().squeeze()
    sample_rate = model.generation_config.sample_rate

    # Saving the audio file temporarily
    output_file = '/tmp/bark_out.wav'
    scipy.io.wavfile.write(output_file, rate=sample_rate, data=audio_array)

    # Return the path to the saved audio file
    return output_file

# Define the Gradio interface
iface = gr.Interface(
    fn=generate_audio,
    inputs="text",
    outputs="audio",
    examples=[["Hello, my dog is cute"]],
    allow_flagging="never"
)

# Launch the interface
iface.launch()