app.py

from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch
import gradio as gr
import os
import numpy as np
from scipy.io.wavfile import read

os.system('git clone https://github.com/hmartiro/riffusion-inference.git riffusion')

repo_id = "riffusion/riffusion-model-v1"
pipe = DiffusionPipeline.from_pretrained(repo_id)

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
if torch.cuda.is_available():
  pipe = pipe.to("cuda")

def infer(prompt, steps):

    from riffusion.riffusion import audio

    mel_spectr = pipe(prompt, num_inference_steps=steps).images[0]
    wav_bytes, duration_s = audio.wav_bytes_from_spectrogram_image(mel_spectr)

    return read(wav_bytes)

with gr.Blocks() as app:
    gr.Markdown("## Riffusion Demo")
    gr.Markdown("Generate audio clips from text prompts using the [Riffusion model](https://huggingface.co./riffusion/riffusion-model-v1).")
    with gr.Group(elem_id='gr-interface'):
        prompt = gr.Textbox(lines=1, label="Prompt")
        steps = gr.Slider(minimum=1, maximum=100, value=25, label="Steps")
        audio = gr.Audio(label="Audio")

        btn_generate = gr.Button(value="Generate")

    inputs = [prompt, steps]
    outputs = [audio]

    prompt.submit(infer, inputs, outputs)
    btn_generate.click(infer, inputs, outputs)

    examples = gr.Examples(
        examples=[["rap battle freestyle"], ["techno club banger"], ["acoustic folk ballad"], ["blues guitar riff"], ["jazzy trumpet solo"], ["classical symphony orchestra"], ["rock and roll power chord"], ["soulful R&B love song"], ["reggae dub beat"], ["country western twangy guitar"], ["all 25 steps"]],
        inputs=[prompt])

app.launch(debug=True, share=True)