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| import os | |
| import sys | |
| os.system('git clone https://github.com/facebookresearch/av_hubert.git') | |
| os.chdir('/home/user/app/av_hubert') | |
| sys.path.append('/home/user/app/av_hubert/avhubert') | |
| print(sys.path) | |
| print(os.listdir()) | |
| print(sys.argv, type(sys.argv)) | |
| sys.argv.append('dummy') | |
| import dlib, cv2, os | |
| import numpy as np | |
| import skvideo | |
| import skvideo.io | |
| from tqdm import tqdm | |
| from preparation.align_mouth import landmarks_interpolate, crop_patch, write_video_ffmpeg | |
| from base64 import b64encode | |
| import torch | |
| import cv2 | |
| import tempfile | |
| from argparse import Namespace | |
| import fairseq | |
| from fairseq import checkpoint_utils, options, tasks, utils | |
| from fairseq.dataclass.configs import GenerationConfig | |
| from huggingface_hub import hf_hub_download | |
| import gradio as gr | |
| from pytube import YouTube | |
| user_dir = "/home/user/app/av_hubert/avhubert" | |
| utils.import_user_module(Namespace(user_dir=user_dir)) | |
| data_dir = "/home/user/app/video" | |
| ckpt_path = hf_hub_download('vumichien/AV-HuBERT', 'model.pt') | |
| face_detector_path = "/home/user/app/mmod_human_face_detector.dat" | |
| face_predictor_path = "/home/user/app/shape_predictor_68_face_landmarks.dat" | |
| mean_face_path = "/home/user/app/20words_mean_face.npy" | |
| mouth_roi_path = "/home/user/app/roi.mp4" | |
| modalities = ["video"] | |
| gen_subset = "test" | |
| gen_cfg = GenerationConfig(beam=20) | |
| models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) | |
| models = [model.eval().cuda() if torch.cuda.is_available() else model.eval() for model in models] | |
| saved_cfg.task.modalities = modalities | |
| saved_cfg.task.data = data_dir | |
| saved_cfg.task.label_dir = data_dir | |
| task = tasks.setup_task(saved_cfg.task) | |
| generator = task.build_generator(models, gen_cfg) | |
| def get_youtube(video_url): | |
| yt = YouTube(video_url) | |
| abs_video_path = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download() | |
| print("Success download video") | |
| print(abs_video_path) | |
| return abs_video_path | |
| def detect_landmark(image, detector, predictor): | |
| gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) | |
| face_locations = detector(gray, 1) | |
| coords = None | |
| for (_, face_location) in enumerate(face_locations): | |
| if torch.cuda.is_available(): | |
| rect = face_location.rect | |
| else: | |
| rect = face_location | |
| shape = predictor(gray, rect) | |
| coords = np.zeros((68, 2), dtype=np.int32) | |
| for i in range(0, 68): | |
| coords[i] = (shape.part(i).x, shape.part(i).y) | |
| return coords | |
| def preprocess_video(input_video_path, input_start): | |
| if torch.cuda.is_available(): | |
| detector = dlib.cnn_face_detection_model_v1(face_detector_path) | |
| else: | |
| detector = dlib.get_frontal_face_detector() | |
| predictor = dlib.shape_predictor(face_predictor_path) | |
| STD_SIZE = (256, 256) | |
| mean_face_landmarks = np.load(mean_face_path) | |
| stablePntsIDs = [33, 36, 39, 42, 45] | |
| videogen = skvideo.io.vread(input_video_path, inputdict={'-ss': str(input_start), '-t': '10'}) | |
| frames = np.array([frame for frame in videogen]) | |
| landmarks = [] | |
| for frame in tqdm(frames): | |
| landmark = detect_landmark(frame, detector, predictor) | |
| landmarks.append(landmark) | |
| preprocessed_landmarks = landmarks_interpolate(landmarks) | |
| rois = crop_patch(input_video_path, preprocessed_landmarks, mean_face_landmarks, stablePntsIDs, STD_SIZE, | |
| window_margin=12, start_idx=48, stop_idx=68, crop_height=96, crop_width=96) | |
| write_video_ffmpeg(rois, mouth_roi_path, "/usr/bin/ffmpeg") | |
| return mouth_roi_path | |
| def predict(process_video): | |
| num_frames = int(cv2.VideoCapture(process_video).get(cv2.CAP_PROP_FRAME_COUNT)) | |
| tsv_cont = ["/\n", f"test-0\t{process_video}\t{None}\t{num_frames}\t{int(16_000*num_frames/25)}\n"] | |
| label_cont = ["DUMMY\n"] | |
| with open(f"{data_dir}/test.tsv", "w") as fo: | |
| fo.write("".join(tsv_cont)) | |
| with open(f"{data_dir}/test.wrd", "w") as fo: | |
| fo.write("".join(label_cont)) | |
| task.load_dataset(gen_subset, task_cfg=saved_cfg.task) | |
| def decode_fn(x): | |
| dictionary = task.target_dictionary | |
| symbols_ignore = generator.symbols_to_strip_from_output | |
| symbols_ignore.add(dictionary.pad()) | |
| return task.datasets[gen_subset].label_processors[0].decode(x, symbols_ignore) | |
| itr = task.get_batch_iterator(dataset=task.dataset(gen_subset)).next_epoch_itr(shuffle=False) | |
| sample = next(itr) | |
| if torch.cuda.is_available(): | |
| sample = utils.move_to_cuda(sample) | |
| hypos = task.inference_step(generator, models, sample) | |
| ref = decode_fn(sample['target'][0].int().cpu()) | |
| hypo = hypos[0][0]['tokens'].int().cpu() | |
| hypo = decode_fn(hypo) | |
| return hypo | |
| # ---- Gradio Layout ----- | |
| youtube_url_in = gr.Textbox(label="Youtube url", lines=1, interactive=True) | |
| video_in = gr.Video(label="Input Video", mirror_webcam=False, interactive=True) | |
| video_start_in = gr.Number(label="Start Time in Seconds", value=0, interactive=True) | |
| video_out = gr.Video(label="Audio Visual Video", mirror_webcam=False, interactive=True) | |
| demo = gr.Blocks() | |
| demo.encrypt = False | |
| text_output = gr.Textbox() | |
| with demo: | |
| gr.Markdown(''' | |
| <div> | |
| <h1 style='text-align: center'>Speech Recognition from Visual Lip Movement by Audio-Visual Hidden Unit BERT Model (AV-HuBERT)</h1> | |
| This space uses AV-HuBERT models from <a href='https://github.com/facebookresearch' target='_blank'><b>Meta Research</b></a> to recoginze the speech from Lip Movement 🤗 | |
| <figure> | |
| <img src="https://huggingface.co/vumichien/AV-HuBERT/resolve/main/lipreading.gif" alt="Audio-Visual Speech Recognition"> | |
| <figcaption> Speech Recognition from visual lip movement | |
| </figcaption> | |
| </figure> | |
| </div> | |
| ''') | |
| with gr.Row(): | |
| gr.Markdown(''' | |
| ### Reading Lip movement with youtube link using Avhubert | |
| ##### Step 1a. Download video from youtube (Note: Only 10 seconds will be analyzed and the face should be stable for better result) | |
| ##### Step 1b. You also can upload video directly | |
| ##### Step 2. Generating landmarks surrounding mouth area | |
| ##### Step 3. Reading lip movement. | |
| ''') | |
| with gr.Row(): | |
| gr.Markdown(''' | |
| ### You can test by following examples: | |
| ''') | |
| examples = gr.Examples(examples= | |
| [ "https://www.youtube.com/watch?v=ZXVDnuepW2s", | |
| "https://www.youtube.com/watch?v=X8_glJn1B8o", | |
| "https://www.youtube.com/watch?v=80yqL2KzBVw"], | |
| label="Examples", inputs=[youtube_url_in]) | |
| with gr.Column(): | |
| youtube_url_in.render() | |
| video_start_in.render() | |
| download_youtube_btn = gr.Button("Download Youtube video") | |
| download_youtube_btn.click(get_youtube, [youtube_url_in], [video_in]) | |
| print(video_in) | |
| with gr.Row(): | |
| video_in.render() | |
| video_out.render() | |
| with gr.Row(): | |
| detect_landmark_btn = gr.Button("Detect landmark") | |
| detect_landmark_btn.click(preprocess_video, [video_in, video_start_in], [video_out]) | |
| predict_btn = gr.Button("Predict") | |
| predict_btn.click(predict, [video_out], [text_output]) | |
| with gr.Row(): | |
| # video_lip = gr.Video(label="Audio Visual Video", mirror_webcam=False) | |
| text_output.render() | |
| demo.launch(debug=True) |