Upload folder using huggingface_hub

.gitattributes

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.gitignore

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+/.venv/
+/venv/

README.md

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 ---
-title: Video Background Music Generator
-emoji: 🦀
-colorFrom: gray
-colorTo: purple
+title: video-background-music-generator
+emoji: 💻
+colorFrom: green
+colorTo: gray
 sdk: gradio
 sdk_version: 4.19.2
 app_file: app.py
 pinned: false
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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
+    )

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