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Update app.py
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app.py
CHANGED
@@ -14,11 +14,14 @@ controlnet = ControlNetModel.from_pretrained("lauraibnz/midi-audioldm", torch_dt
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pipe = AudioLDMControlNetPipeline.from_pretrained("cvssp/audioldm-m-full", controlnet=controlnet, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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def predict(midi_file, prompt, audio_length_in_s, controlnet_conditioning_scale, num_inference_steps=20):
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midi = PrettyMIDI(midi_file)
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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))
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return (16000, audio.audios.T)
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demo = gr.Interface(fn=predict, inputs=[gr.File(
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demo.launch()
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pipe = AudioLDMControlNetPipeline.from_pretrained("cvssp/audioldm-m-full", controlnet=controlnet, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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def predict(midi_file=None, prompt="", audio_length_in_s=5, controlnet_conditioning_scale=1, num_inference_steps=20):
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if midi_file:
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midi_file = midi_file.name
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else:
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midi_file = "test.mid"
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midi = PrettyMIDI(midi_file)
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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))
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return (16000, audio.audios.T)
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demo = gr.Interface(fn=predict, inputs=[gr.File(file_types=[".mid"]), "text", 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")
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demo.launch()
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