add app.py

app.py

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+import re
+import glob
+import pickle
+import os
+import torch
+import numpy as np
+from utils.audio import load_spectrograms
+from utils.compute_args import compute_args
+from utils.tokenize import tokenize, create_dict, sent_to_ix, cmumosei_2, cmumosei_7, pad_feature
+from model_LA import Model_LA
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+working_dir = "."
+
+# load model
+ckpts_path = os.path.join(working_dir, 'ckpt')
+model_name = "Model_LA_e"
+# Listing sorted checkpoints
+ckpts = sorted(glob.glob(os.path.join(ckpts_path, model_name,'best*')), reverse=True)
+
+# Load original args
+args = torch.load(ckpts[0], map_location=torch.device(device))['args']
+args = compute_args(args)
+pretrained_emb = np.load("train_glove.npy")
+token_to_ix = pickle.load(open("token_to_ix.pkl", "rb")) 
+state_dict = torch.load(ckpts[0], map_location=torch.device(device))['state_dict']
+
+net = Model_LA(args, len(token_to_ix), pretrained_emb).to(device)
+net.load_state_dict(state_dict)
+
+
+
+def inference(video_path, text):
+    
+    # data preprocessing
+    # text
+    def clean(w):
+        return re.sub(
+                r"([.,'!?\"()*#:;])",
+                '',
+                w.lower()
+                ).replace('-', ' ').replace('/', ' ')
+
+    s = [clean(w) for w in text.split() if clean(w) != '']
+
+    # Sound
+    _, mel, mag = load_spectrograms(video_path)
+
+    l_max_len = args.lang_seq_len
+    a_max_len = args.audio_seq_len
+    v_max_len = args.video_seq_len
+    L = sent_to_ix(s, token_to_ix, max_token=l_max_len)
+    A = pad_feature(mel, a_max_len)
+    V = pad_feature(mel, v_max_len)
+    # print shapes
+    print("Processed text shape: ", L.shape)
+    print("Processed audio shape: ", A.shape)
+    print("Processed video shape: ", V.shape)
+    return out
+
+
+title="Emotion Recognition"
+description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector by using frame-by-frame detection. \
+            To use it, simply upload your video, or click one of the examples to load them.\
+            This demo and model represent the work of \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by Lee Sheng Yeh. \
+            The examples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference details is available in \"references.txt.\" \
+            The examples are used under fair use to demo the working of the model only. If any copyright is infringed, please contact the researcher via this email: tp054565@mail.apu.edu.my, the researcher will immediately take down the examples used.\
+            "
+            
+examples = [              
+                ['Video1-fake-1-ff.mp4'],
+                ['Video6-real-1-ff.mp4'],
+                ['Video3-fake-3-ff.mp4'],
+                ['Video8-real-3-ff.mp4'],
+                ['real-1.mp4'],
+                ['fake-1.mp4'],
+           ]
+           
+gr.Interface(inference,
+                     inputs = ["video", "text"],
+                     outputs=["text","text", gr.outputs.Video(label="Detected face sequence")],
+                     title=title,
+                     description=description,
+                     examples=examples
+                     ).launch()

requirements.txt

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+https://github.com/explosion/spacy-models/releases/download/en_vectors_web_lg-2.1.0/en_vectors_web_lg-2.1.0.tar.gz