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Update app.py
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app.py
CHANGED
@@ -14,6 +14,8 @@ from PIL import Image
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from torchaudio.functional import resample
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from os.path import join
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from DenseAV.denseav.train import LitAVAligner
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from DenseAV.denseav.plotting import plot_attention_video, plot_2head_attention_video, plot_feature_video
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from DenseAV.denseav.shared import norm, crop_to_divisor, blur_dim
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@@ -81,14 +83,14 @@ def process_video(video, model_option):
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original_frames, audio, info = torchvision.io.read_video(video, end_pts=10, pts_unit='sec')
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sample_rate = 16000
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print("---"*20)
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print(type(info["video_fps"]))
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print("---"*20)
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if info["audio_fps"] != sample_rate:
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audio = resample(audio, info["audio_fps"], sample_rate)
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audio = audio[0].unsqueeze(0)
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img_transform = T.Compose([
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T.Resize(load_size, Image.BILINEAR),
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lambda x: crop_to_divisor(x, 8),
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from torchaudio.functional import resample
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from os.path import join
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from fractions import Fraction
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from DenseAV.denseav.train import LitAVAligner
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from DenseAV.denseav.plotting import plot_attention_video, plot_2head_attention_video, plot_feature_video
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from DenseAV.denseav.shared import norm, crop_to_divisor, blur_dim
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original_frames, audio, info = torchvision.io.read_video(video, end_pts=10, pts_unit='sec')
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sample_rate = 16000
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if info["audio_fps"] != sample_rate:
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audio = resample(audio, info["audio_fps"], sample_rate)
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audio = audio[0].unsqueeze(0)
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info["video_fps"] = Fraction(info["video_fps"]).limit_denominator(1000)
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print(info["video_fps"].numerator)
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img_transform = T.Compose([
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T.Resize(load_size, Image.BILINEAR),
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lambda x: crop_to_divisor(x, 8),
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