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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')
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')
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
# os.chdir('/home/user/app/av_hubert/avhubert')
user_dir = "/home/user/app/av_hubert/avhubert"
utils.import_user_module(Namespace(user_dir=user_dir))
data_dir = tempfile.mkdtemp()
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
# ---- 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_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 (Avhubert)</h1>
This space uses Avhubert models from <a href='https://github.com/facebookresearch' target='_blank'><b>Meta Research</b></a> to recoginze the speech from Lip Movement 🤗
</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. 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()
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():
# video_lip = gr.Video(label="Audio Visual Video", mirror_webcam=False)
text_output.render()
demo.launch(debug=True) |