Update app.py

app.py

@@ -19,7 +19,6 @@ import torchaudio
 
 
 import torch.nn.functional as F
-import os
 
 from moviepy.editor import VideoFileClip
 
@@ -30,7 +29,6 @@ from huggingface_hub import hf_hub_download
 #!pip install gradio
 import gradio as gr
 
-from moviepy.editor import VideoFileClip
 
 
 
@@ -68,13 +66,12 @@ def seprate_speaker(audio_file, pipeline):
     for speaker, segments in speaker_segments.items():
         # 화자의 모든 발화 구간을 이어붙임
         combined_waveform = torch.cat(segments, dim=1)
-        #current_path = os.getcwd()
-        output_path = "/tmp/wav"    # 경로
+        output_path = "/content/wav"    # 경로
         os.makedirs(output_path, exist_ok=True) # 경로가 없으면 생성
         output_filename = os.path.join(output_path,f"{speaker}.wav")
 
         torchaudio.save(output_filename, combined_waveform, sample_rate) #오디오 파일 저장
-
+        #print(f"Saved {output_filename} for speaker {speaker}")
 
 
 # 간단한 DeepVoice 스타일 모델 정의
@@ -120,28 +117,36 @@ def real_fake_check(list_dir, path, model):
     f_cnt = 0
     prob = {}
     for i in list_dir:      # real / fake 선택
+        #print('------',i)
         input_data = extract_mfcc_path(os.path.join(path, i))
         input_data = torch.tensor(input_data).unsqueeze(0).to('cuda')  # 배치 차원을 추가하여 (1, input_dim, sequence_length)로 맞춤
         result = model(input_data.float())
+        # predicted_class = torch.argmax(result, dim=1).item()
         probabilities = F.softmax(result, dim=1)
         prob[i]='%.2f'%probabilities[0][1].item()
-        predicted_class = 0 if probabilities[0][0] >= THRESHOLD else 1  # 확률값이 기준치보다 크다면 real, 아니면 fake
 
+        predicted_class = 0 if probabilities[0][0] >= THRESHOLD else 1  # 확률값이 기준치보다 크다면 real, 아니면 fake
+        # print('-- %.2f'%probabilities[0][0].item()) #확률 값 출력
         if predicted_class == 0:
+            # print("REAL")
             r_cnt += 1
         else:
+            # print("FAKE")
             f_cnt += 1
-
+    #print()
+    #print('real: ',r_cnt,'/',len(list_dir))
+    #print('fake: ',f_cnt,'/',len(list_dir))
     return {'real: ':f'{r_cnt}/{len(list_dir)}', 'fake: ':f'{f_cnt}/{len(list_dir)}', 'prob: ': prob}
 
 
 def main(file_name):
-    pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
-    device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
+    pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1",)
+                                         
+    #pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     video_file = file_name #deepfake #meganfox.mp4'
-    #current_path = os.getcwd()
-    audio_file = '/tmp/output_audio.wav'  # 저장할 오디오 파일의 경로, 이름 지정
+    audio_file = '/content/output_audio.wav'  # 저장할 오디오 파일의 경로, 이름 지정
 
     extract_audio_from_video(video_file, audio_file)
 
@@ -157,7 +162,7 @@ def main(file_name):
     # 모델
     model_name = hf_hub_download(repo_id="sssssungk/deepfake_voice", filename="deepvoice_model_girl.pth")
     model = DeepVoiceModel(input_dim, hidden_dim, num_classes, dropout_rate, l2_reg).to(device)
-    model.load_state_dict(torch.load(model_name))
+    model.load_state_dict(torch.load(model_name))#("/content/drive/MyDrive/캡스톤 1조/model/deepvoice_model_girl.pth"))
     model.eval()  # 평가 모드로 설정
 
 
@@ -166,25 +171,29 @@ def main(file_name):
     #real_path = '/content/drive/MyDrive/Celeb-DF-v2/Celeb-real'
 
     #real = os.listdir(real_path)
-    #current_path = os.getcwd()
-    fake_path = '/tmp/wav'
+    fake_path = '/content/wav'#'/content/drive/MyDrive/캡스톤 1조/data/deepvoice/fake'
     fake = os.listdir(fake_path)
 
+    #print("\n-------real data---------")
+    #real_fake_check(real, real_path, model) #real dataset
+    #print("\n-------fake data---------")
     rf_check = real_fake_check(fake, fake_path,model)       #fake dataset\
     return rf_check
 
+#Gradio 메인 함수
 def deepvoice_check(video_file):
     results = main(video_file)
     return results
 
 # Gradio 인터페이스 생성
-deepfake = gr.Interface(
-    fn=deepvoice_check,
+iface = gr.Interface(
+    fn=main,
     inputs=gr.Video(label="Upload mp4 File"),
-    outputs=gr.Textbox(label="DeepFaKeVoice Detection Result"),
-    title="DeepFaKeVoice Check",
-    description="Upload an mp4 file to check."
+    outputs=gr.Textbox(label="Deepfake Detection Result"),
+    title="DeepVoice Check",
+    description="Upload an mp4 file to check for DeepVoice indicators."
 )
 
+# Gradio 인터페이스 실행
 if __name__ == "__main__":
-    deepfake.launch(share=True, debug=True)
+    iface.launch()