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| import gradio as gr | |
| import os | |
| import numpy as np | |
| def no_cpu_warning(video_file, dataset, lora, size): | |
| gr.Warning("This interface is running on CPU. Only cached examples are displayed here, no new music can be generated.") | |
| output = "./cached_examples/" | |
| output += "peft" if lora else "audiocraft" | |
| output += "_" + dataset + "_" + size + "_" + video_file[-7:-4] | |
| chosen_video = np.random.choice(['v', 'v1', 'v2']) | |
| output += "_" + chosen_video + ".mp4" | |
| if not os.path.exists(output): | |
| print(output) | |
| raise gr.Error("This combination of video and model has not been cached. Please use the other model or try one of the listed examples instead.") | |
| print("Displaying video: " + output) | |
| return output | |
| interface = gr.Interface(fn=no_cpu_warning, | |
| inputs=[ | |
| gr.Video(value="videos/n_5.mp4", | |
| label="Video Input", | |
| min_length=5, | |
| max_length=20, | |
| sources=['upload'], | |
| show_download_button=True, | |
| include_audio=True), | |
| gr.Radio(["nature", "symmv"], | |
| value="nature", | |
| label="Available Models", | |
| info="Choose one of the available Datasets on which the models has been trained on."), | |
| gr.Radio([False, True], | |
| label="Use the LoRA version of the MusicGen Audio Decoder", | |
| value=False, | |
| info="If set to 'True' the MusicGen Audio Decoder models trained with LoRA " | |
| "(Low Rank Adaptation) are used. If set to 'False', the original " | |
| "MusicGen models are used instead."), | |
| gr.Radio(["small", "medium", "large"], | |
| label="Model Size", | |
| value="large", | |
| info="Choose one of the available model sizes. The larger models are more likely produce " | |
| "results of higher audio quality, but also take more time to generate it."), | |
| ], | |
| outputs=[gr.Video(label="video output")], | |
| examples=[ | |
| [os.path.abspath("./videos/n_1.mp4"), "nature", False, "large"], | |
| [os.path.abspath("./videos/n_2.mp4"), "nature", False, "large"], | |
| [os.path.abspath("./videos/n_3.mp4"), "nature", False, "large"], | |
| [os.path.abspath("./videos/n_4.mp4"), "nature", False, "large"], | |
| [os.path.abspath("./videos/n_5.mp4"), "nature", True, "large"], | |
| [os.path.abspath("./videos/n_6.mp4"), "nature", True, "large"], | |
| [os.path.abspath("./videos/n_7.mp4"), "nature", True, "large"], | |
| [os.path.abspath("./videos/n_8.mp4"), "nature", True, "large"], | |
| [os.path.abspath("./videos/s_1.mp4"), "symmv", False, "large"], | |
| [os.path.abspath("./videos/s_2.mp4"), "symmv", False, "large"], | |
| [os.path.abspath("./videos/s_3.mp4"), "symmv", False, "large"], | |
| [os.path.abspath("./videos/s_4.mp4"), "symmv", False, "large"], | |
| [os.path.abspath("./videos/s_5.mp4"), "symmv", True, "large"], | |
| [os.path.abspath("./videos/s_6.mp4"), "symmv", True, "large"], | |
| [os.path.abspath("./videos/s_7.mp4"), "symmv", True, "large"], | |
| [os.path.abspath("./videos/s_8.mp4"), "symmv", True, "large"], | |
| ], | |
| cache_examples=False, | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch( | |
| share=True | |
| ) | |