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import gradio as gr | |
from diffusers import AudioLDMControlNetPipeline, ControlNetModel | |
from pretty_midi import PrettyMIDI | |
import torch | |
if torch.cuda.is_available(): | |
device = "cuda" | |
torch_dtype = torch.float16 | |
else: | |
device = "cpu" | |
torch_dtype = torch.float32 | |
controlnet = ControlNetModel.from_pretrained("lauraibnz/midi-audioldm", torch_dtype=torch_dtype) | |
pipe = AudioLDMControlNetPipeline.from_pretrained("cvssp/audioldm-m-full", controlnet=controlnet, torch_dtype=torch_dtype) | |
pipe = pipe.to(device) | |
def predict(prompt, midi_file=None, audio_length_in_s=5, controlnet_conditioning_scale=1.0, num_inference_steps=20): | |
if midi_file: | |
midi_file = midi_file.name | |
else: | |
midi_file = "test.mid" | |
midi = PrettyMIDI(midi_file) | |
audio = pipe(prompt, midi=midi, audio_length_in_s=audio_length_in_s, num_inference_steps=num_inference_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale)) | |
return (16000, audio.audios.T) | |
demo = gr.Interface(fn=predict, inputs=[gr.Textbox(label="Prompt"), gr.UploadButton("Upload a MIDI File", file_types=[".mid"]), gr.Slider(0, 30, value=5, step=5, label="Duration (seconds)"), gr.Slider(0.0, 1.0, value=1.0, step=0.1, label="Conditioning scale")], outputs="audio") | |
demo.launch() |