app.py

from pyharp import ModelCard, build_endpoint, save_and_return_filepath
from audiotools import AudioSignal
from audioldm import build_model, save_wave, text_to_audio
import gradio as gr

audioldm = build_model(model_name="audioldm-m-full")

def process_fn(input_audio_path, seed, guidance_scale, num_inference_steps, num_candidates, audio_length_in_s):
                waveform = text_to_audio(
                    audioldm,
                    'placeholder', 
                    input_audio_path, 
                    seed = int(seed),
                    duration = audio_length_in_s, 
                    guidance_scale = guidance_scale, 
                    n_candidate_gen_per_text = int(num_candidates), 
                    ddim_steps = int(num_inference_steps)
                    )

                save_wave(waveform, "./", name="output.wav")


card = ModelCard(
    name='AudioLDM Variations',
    description='AudioLDM Variation Generator, operates on region selected in track.',
    author='Team Audio',
    tags=['AudioLDM', 'Variations', 'audio-to-audio']
)

with gr.Blocks() as webapp:
    # Define your Gradio interface
    inputs = [
        gr.Audio(
            label="Audio Input", 
            type="filepath"
        ), 
        gr.Slider(
            label="seed",
            minimum="0",
            maximum="65535",
            value="43534",
            step="1"
        ),
        gr.Slider(
            minimum=0, maximum=10, 
            step=0.1, value=2.5, 
            label="Guidance Scale"
        ),
        gr.Slider(
            minimum=1, maximum=500, 
            step=1, value=200, 
            label="Inference Steps"
        ),
        gr.Slider(
            minimum=1, maximum=10, 
            step=1, value=1, 
            label="Candidates"
        ),
        gr.Slider(
            minimum=2.5, maximum=10.0, 
            step=2.5, value=5, 
            label="Duration"
        ),
    ]

    output = gr.Audio(label="Audio Output", type="filepath")

    ctrls_data, ctrls_button, process_button, cancel_button = build_endpoint(inputs, output, process_fn, card)

# queue the webapp: https://www.gradio.app/guides/setting-up-a-demo-for-maximum-performance
#webapp.queue()
webapp.launch(share=True)