ml-talking-face / app.py
deepkyu
update return text format ( `1452164` in https://bitbucket.org/maum-system/cvpr22-demo-gradio )
31a0492
raw
history blame
8.17 kB
import os
import subprocess
REST_IP = os.environ['REST_IP']
SERVICE_PORT = int(os.environ['SERVICE_PORT'])
TRANSLATION_APIKEY_URL = os.environ['TRANSLATION_APIKEY_URL']
GOOGLE_APPLICATION_CREDENTIALS = os.environ['GOOGLE_APPLICATION_CREDENTIALS']
subprocess.call(f"wget --no-check-certificate -O {GOOGLE_APPLICATION_CREDENTIALS} {TRANSLATION_APIKEY_URL}", shell=True)
TOXICITY_THRESHOLD = float(os.getenv('TOXICITY_THRESHOLD', 0.7))
import gradio as gr
from toxicity_estimator import PerspectiveAPI
from translator import Translator
from client_rest import RestAPIApplication
from pathlib import Path
import argparse
import threading
import yaml
TITLE = Path("docs/title.txt").read_text()
DESCRIPTION = Path("docs/description.md").read_text()
class GradioApplication:
def __init__(self, rest_ip, rest_port, max_seed):
self.lang_list = {
'ko': 'ko_KR',
'en': 'en_US',
'ja': 'ja_JP',
'zh': 'zh_CN',
'zh-CN': 'zh_CN'
}
self.background_list = [None,
"background_image/cvpr.png",
"background_image/black.png",
"background_image/river.mp4",
"background_image/sky.mp4"]
self.perspective_api = PerspectiveAPI()
self.translator = Translator()
self.rest_application = RestAPIApplication(rest_ip, rest_port)
self.output_dir = Path("output_file")
inputs = prepare_input()
outputs = prepare_output()
self.iface = gr.Interface(fn=self.infer,
title=TITLE,
description=DESCRIPTION,
inputs=inputs,
outputs=outputs,
allow_flagging='never',
article=Path("docs/article.md").read_text())
self.max_seed = max_seed
self._file_seed = 0
self.lock = threading.Lock()
def _get_file_seed(self):
return f"{self._file_seed % self.max_seed:02d}"
def _reset_file_seed(self):
self._file_seed = 0
def _counter_file_seed(self):
with self.lock:
self._file_seed += 1
def get_lang_code(self, lang):
return self.lang_list[lang]
def get_background_data(self, background_index):
# get background filename and its extension
data_path = self.background_list[background_index]
if data_path is not None:
with open(data_path, 'rb') as rf:
background_data = rf.read()
is_video_background = str(data_path).endswith(".mp4")
else:
background_data = None
is_video_background = False
return background_data, is_video_background
@staticmethod
def return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=""):
return {'Toxicity': toxicity_prob}, f"Language: {lang_dest}\nText: {target_text}\n-\nDetails: {detail}", str(video_filename)
def infer(self, text, lang, duration_rate, action, background_index):
self._counter_file_seed()
print(f"File Seed: {self._file_seed}")
toxicity_prob = 0.0
target_text = ""
lang_dest = ""
video_filename = "vacant.mp4"
# Toxicity estimation
try:
toxicity_prob = self.perspective_api.get_score(text)
except Exception as e: # when Perspective API doesn't work
pass
if toxicity_prob > TOXICITY_THRESHOLD:
detail = "Sorry, it seems that the input text is too toxic."
return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {detail}")
# Google Translate API
try:
target_text, lang_dest = self.translator.get_translation(text, lang)
except Exception as e:
target_text = ""
lang_dest = ""
detail = f"Error from language translation: ({e})"
return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {detail}")
try:
self.translator.length_check(lang_dest, target_text) # assertion check
except AssertionError as e:
return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {str(e)}")
lang_rpc_code = self.get_lang_code(lang_dest)
# Video Inference
background_data, is_video_background = self.get_background_data(background_index)
video_data = self.rest_application.get_video(target_text, lang_rpc_code, duration_rate, action.lower(),
background_data, is_video_background)
print(f"Video data size: {len(video_data)}")
video_filename = self.output_dir / f"{self._file_seed:02d}.mkv"
with open(video_filename, "wb") as video_file:
video_file.write(video_data)
return self.return_format(toxicity_prob, target_text, lang_dest, video_filename)
def run(self, server_port=7860, share=False):
try:
self.iface.launch(height=900,
share=share, server_port=server_port,
enable_queue=True)
except KeyboardInterrupt:
gr.close_all()
def prepare_input():
text_input = gr.Textbox(lines=2,
placeholder="Type your text with English, Chinese, Korean, and Japanese.",
value="Hello, this is demonstration for talking face generation "
"with multilingual text-to-speech.",
label="Text")
lang_input = gr.Radio(['Korean', 'English', 'Japanese', 'Chinese'],
type='value',
value=None,
label="Language")
duration_rate_input = gr.Slider(minimum=0.8,
maximum=1.2,
step=0.01,
value=1.0,
label="Duration (The bigger the value, the slower the speech)")
action_input = gr.Radio(['Default', 'Hand', 'BothHand', 'HandDown', 'Sorry'],
type='value',
value='Default',
label="Select an action ...")
background_input = gr.Radio(['None', 'CVPR', 'Black', 'River', 'Sky'],
type='index',
value='None',
label="Select a background image/video ...")
return [text_input, lang_input, duration_rate_input,
action_input, background_input]
def prepare_output():
toxicity_output = gr.Label(num_top_classes=1, label="Toxicity (from Perspective API)")
translation_result_otuput = gr.Textbox(type="str", label="Translation Result")
video_output = gr.Video(format='mp4')
return [toxicity_output, translation_result_otuput, video_output]
def parse_args():
parser = argparse.ArgumentParser(
description='GRADIO DEMO for talking face generation submitted to CVPR2022')
parser.add_argument('-p', '--port', dest='gradio_port', type=int, default=7860, help="Port for gradio")
parser.add_argument('--rest_ip', type=str, default=REST_IP, help="IP for REST API")
parser.add_argument('--rest_port', type=int, default=SERVICE_PORT, help="Port for REST API")
parser.add_argument('--max_seed', type=int, default=20, help="Max seed for saving video")
parser.add_argument('--share', action='store_true', help='get publicly sharable link')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
gradio_application = GradioApplication(args.rest_ip, args.rest_port, args.max_seed)
gradio_application.run(server_port=args.gradio_port, share=args.share)