Update app.py

app.py

@@ -1,61 +1,57 @@
-# import gradio as gr
-# import os, subprocess, torchaudio
-# import torch
-# from PIL import Image
-
-# 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"
+import gradio as gr
+import os, subprocess, torchaudio
+import torch
+from PIL import Image
+
+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 run():
-#   with block:
+def run():
+  with block:
     
-#     with gr.Group():
-#       with gr.Box():
-#         with gr.Row().style(equal_height=True):
-#           image_in = gr.Image(show_label=False, type="filepath")
-#           audio_in = gr.Audio(show_label=False, type='filepath')
-#           video_out = gr.Video(show_label=False)
-#         with gr.Row().style(equal_height=True):
-#           btn = gr.Button("Generate")          
+    with gr.Group():
+      with gr.Box():
+        with gr.Row().style(equal_height=True):
+          image_in = gr.Image(show_label=False, type="filepath")
+          audio_in = gr.Audio(show_label=False, type='filepath')
+          video_out = gr.Video(show_label=False)
+        with gr.Row().style(equal_height=True):
+          btn = gr.Button("Generate")          
 
     
-#     btn.click(calculate, inputs=[image_in, audio_in], outputs=[video_out])
-#     block.queue()
-#     block.launch(server_name="0.0.0.0", server_port=7860)
+    btn.click(calculate, inputs=[image_in, audio_in], outputs=[video_out])
+    block.queue()
+    block.launch(server_name="0.0.0.0", server_port=7860)
 
-# if __name__ == "__main__":
-#     run()
+if __name__ == "__main__":
+    run()
 
-import torch
-print(torch.cuda.is_available())
 
-print(torch.cuda.device_count())
-print(torch.device('cpu'))