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import gradio as gr
import os
import shutil
import spaces
import sys
# we will clone the repo and install the dependencies
os.system('git lfs install')
os.system('git clone https://huggingface.co/jadechoghari/qa-mdt')
os.system('pip install -r qa_mdt/requirements.txt')
os.system('pip install xformers==0.0.26.post1')
os.system('pip install torchlibrosa==0.0.9 librosa==0.9.2')
os.system('pip install -q pytorch_lightning==2.1.3 torchlibrosa==0.0.9 librosa==0.9.2 ftfy==6.1.1 braceexpand')
os.system('pip install torch==2.3.0+cu121 torchvision==0.18.0+cu121 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu121')
sys.path.append(os.path.abspath("qa_mdt"))
# only then import the necessary modules from qa_mdt
from qa_mdt.pipeline import MOSDiffusionPipeline
pipe = MOSDiffusionPipeline()
# this runs the pipeline with user input and saves the output as 'awesome.wav'
@spaces.GPU()
def generate_waveform(description):
pipe(description)
generated_file_path = "./awesome.wav"
if os.path.exists(generated_file_path):
return generated_file_path
else:
return "Error: Failed to generate the waveform."
# gradio interface
iface = gr.Interface(
fn=generate_waveform,
inputs=gr.inputs.Textbox(lines=2, placeholder="Enter a music description here..."), # Text input for description
outputs=gr.outputs.File(label="Download Generated WAV file"), # File output for download
title="Flux Music Diffusion Pipeline",
description="Enter a music description, and the model will generate a corresponding audio waveform. Download the output as 'awesome.wav'."
)
# Launch the Gradio app
if __name__ == "__main__":
iface.launch()
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