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import gradio as gr
from subprocess import call
with gr.Blocks() as ui:
with gr.Row():
video = gr.File(label="Video or Image", info="Filepath of video/image that contains faces to use")
audio = gr.File(label="Audio", info="Filepath of video/audio file to use as raw audio source")
with gr.Column():
checkpoint = gr.Radio(["wav2lip", "wav2lip_gan"], label="Checkpoint", info="Name of saved checkpoint to load weights from")
no_smooth = gr.Checkbox(label="No Smooth", info="Prevent smoothing face detections over a short temporal window")
resize_factor = gr.Slider(minimum=1, maximum=4, step=1, label="Resize Factor", info="Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p")
with gr.Row():
with gr.Column():
pad_top = gr.Slider(minimum=0, maximum=50, step=1, value=0, label="Pad Top", info="Padding above")
pad_bottom = gr.Slider(minimum=0, maximum=50, step=1, value=10, label="Pad Bottom (Often increasing this to 20 allows chin to be included)", info="Padding below lips")
pad_left = gr.Slider(minimum=0, maximum=50, step=1, value=0, label="Pad Left", info="Padding to the left of lips")
pad_right = gr.Slider(minimum=0, maximum=50, step=1, value=0, label="Pad Right", info="Padding to the right of lips")
generate_btn = gr.Button("Generate")
with gr.Column():
result = gr.Video()
def generate(video, audio, checkpoint, no_smooth, resize_factor, pad_top, pad_bottom, pad_left, pad_right):
if video is None or audio is None or checkpoint is None:
return
smooth = "--nosmooth" if no_smooth else ""
cmd = [
"python",
"inference.py",
"--checkpoint_path", f"checkpoints/{checkpoint}.pth",
"--segmentation_path", "checkpoints/face_segmentation.pth",
"--enhance_face", "gfpgan",
"--face", video.name,
"--audio", audio.name,
"--outfile", "results/output.mp4",
]
call(cmd)
return "results/output.mp4"
generate_btn.click(
generate,
[video, audio, checkpoint, pad_top, pad_bottom, pad_left, pad_right, resize_factor],
result)
ui.queue().launch(share=True, debug=True, inline=False)
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