|
import os |
|
import sys |
|
import json |
|
|
|
|
|
os.system('git clone https://github.com/facebookresearch/av_hubert.git') |
|
os.chdir('/home/user/app/av_hubert') |
|
os.system('git submodule init') |
|
os.system('git submodule update') |
|
os.chdir('/home/user/app/av_hubert/fairseq') |
|
os.system('pip install ./') |
|
os.system('pip install scipy') |
|
os.system('pip install sentencepiece') |
|
os.system('pip install python_speech_features') |
|
os.system('pip install scikit-video') |
|
os.system('pip install transformers') |
|
os.system('pip install gradio==3.12') |
|
os.system('pip install numpy==1.23.3') |
|
|
|
|
|
|
|
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): |
|
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) |
|
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 |
|
|
|
|
|
|
|
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_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'>Lip Reading Using Machine learning (Audio-Visual Hidden Unit BERT Model (AV-HuBERT))</h1> |
|
</div> |
|
''') |
|
with gr.Row(): |
|
gr.Markdown(''' |
|
### Reading Lip movement with youtube link using Avhubert |
|
##### Step 1a. Download video from youtube (Note: the length of video should be less than 10 seconds if not it will be cut and the face should be stable for better result) |
|
##### Step 1b. Drag and drop videos to upload 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() |
|
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_out]) |
|
predict_btn = gr.Button("Predict") |
|
|
|
predict_btn.click(predict, [video_out], [text_output]) |
|
with gr.Row(): |
|
|
|
text_output.render() |
|
|
|
|
|
|
|
demo.launch(debug=True) |
