Update app.py

app.py

@@ -1,55 +1,61 @@
-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()
+
+import torch
+print(torch.cuda.is_available())
 
-if __name__ == "__main__":
-    run()
+print(torch.cuda.device_count())
+print(torch.device(cpu))