Update app.py

app.py

@@ -23,7 +23,34 @@ import ffmpeg
 
 block = gr.Blocks()
 
-
+def pad_image(image):
+    w, h = image.size
+    if w == h:
+        return image
+    elif w > h:
+        new_image = Image.new(image.mode, (w, w), (0, 0, 0))
+        new_image.paste(image, (0, (w - h) // 2))
+        return new_image
+    else:
+        new_image = Image.new(image.mode, (h, h), (0, 0, 0))
+        new_image.paste(image, ((h - w) // 2, 0))
+        return new_image
+
+def calculate(image_in, audio_in):
+    waveform, sample_rate = torchaudio.load(audio_in)
+    waveform = torch.mean(waveform, dim=0, keepdim=True)
+    torchaudio.save("/content/audio.wav", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
+    image = Image.open(image_in)
+    image = pad_image(image)
+    image.save("image.png")
+
+    pocketsphinx_run = subprocess.run(['pocketsphinx', '-phone_align', 'yes', 'single', '/content/audio.wav'], check=True, capture_output=True)
+    jq_run = subprocess.run(['jq', '[.w[]|{word: (.t | ascii_upcase | sub("<S>"; "sil") | sub("<SIL>"; "sil") | sub("\\\(2\\\)"; "") | sub("\\\(3\\\)"; "") | sub("\\\(4\\\)"; "") | sub("\\\[SPEECH\\\]"; "SIL") | sub("\\\[NOISE\\\]"; "SIL")), phones: [.w[]|{ph: .t | sub("\\\+SPN\\\+"; "SIL") | sub("\\\+NSN\\\+"; "SIL"), bg: (.b*100)|floor, ed: (.b*100+.d*100)|floor}]}]'], input=pocketsphinx_run.stdout, capture_output=True)
+    with open("test.json", "w") as f:
+        f.write(jq_run.stdout.decode('utf-8').strip())
+    # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    os.system(f"cd /content/one-shot-talking-face && python3 -B test_script.py --img_path /content/image.png --audio_path /content/audio.wav --phoneme_path /content/test.json --save_dir /content/train")
+    return "/content/train/image_audio.mp4"
     
 def one_shot(image,input_text,gender): 
    if gender == 'Female' or gender == 'female':
@@ -33,19 +60,8 @@ def one_shot(image,input_text,gender):
           f.seek(0)
           sound = AudioSegment.from_file(f.name, format="mp3")
           sound.export("/content/audio.wav", format="wav")
-     waveform, sample_rate = torchaudio.load("/content/audio.wav")
-     torchaudio.save("/content/audio.wav", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
-     image = Image.open(image_in)
-     image = pad_image(image)
-     image.save("/content/image_pre.png")
-     pocketsphinx_run = subprocess.run(['pocketsphinx', '-phone_align', 'yes', 'single', '/content/audio.wav'], check=True, capture_output=True)
-     jq_run = subprocess.run(['jq', '[.w[]|{word: (.t | ascii_upcase | sub("<S>"; "sil") | sub("<SIL>"; "sil") | sub("\\\(2\\\)"; "") | sub("\\\(3\\\)"; "") | sub("\\\(4\\\)"; "") | sub("\\\[SPEECH\\\]"; "SIL") | sub("\\\[NOISE\\\]"; "SIL")), phones: [.w[]|{ph: .t | sub("\\\+SPN\\\+"; "SIL") | sub("\\\+NSN\\\+"; "SIL"), bg: (.b*100)|floor, ed: (.b*100+.d*100)|floor}]}]'], input=pocketsphinx_run.stdout, capture_output=True)
-     with open("test.json", "w") as f:
-         f.write(jq_run.stdout.decode('utf-8').strip())
-    
-     os.system(f"cd /content/one-shot-talking-face && python3 -B test_script.py --img_path /content/image_pre.png --audio_path /content/audio.wav --phoneme_path /content/test.json --save_dir /content/train")
+     calculate(image,"/content/audio.wav")
      
-     return "/content/image_pre.png"
      
        
    elif gender == 'Male' or gender == 'male':