Create app.py

app.py

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+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')
+os.system('cd riffusion-inference/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)