| # 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: | |
| # 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) | |
| # if __name__ == "__main__": | |
| # run() | |
| import torch | |
| print(torch.cuda.is_available()) | |
| print(torch.cuda.device_count()) | |
| print(torch.device(cpu)) | |