app.py

import gradio as gr
from transformers import MusicGenForConditionalGeneration, MusicGenProcessor
import torch
import soundfile as sf  

from transformers import AutoModel, AutoProcessor

# Use AutoModel and AutoProcessor to automatically select the correct model and processor
model_name = "facebook/musicgen-small"
model = AutoModel.from_pretrained(model_name)
processor = AutoProcessor.from_pretrained(model_name)

# Define a function to generate audio from text
def generate_audio(text):
    # Tokenize the input text
    inputs = processor(text, return_tensors="pt")

    # Generate audio (samples)
    with torch.no_grad():
        generated_audio = model.generate(**inputs)

    # Convert tensor to numpy and save as a WAV file
    audio_path = "/tmp/generated_audio.wav"
    audio_data = generated_audio[0].cpu().numpy()  # Access the first sample

    # Save the generated audio
    sf.write(audio_path, audio_data, 16000)  # Assuming a sample rate of 16kHz

    return audio_path

# Set up the Gradio interface
iface = gr.Interface(
    fn=generate_audio, 
    inputs=gr.Textbox(label="Enter Text"), 
    outputs=gr.Audio(type="file", label="Generated Audio"),
    title="Text-to-Audio Chatbot",
    description="Enter a text prompt and get a music clip generated by the MusicGen model."
)

iface.launch()