Spaces:
Sleeping
Sleeping
File size: 18,221 Bytes
20cd47a a648386 20cd47a 7dac13b da97815 20cd47a a648386 20cd47a 8ad7dd5 20cd47a 2aea41f 20cd47a b07f9ec 20cd47a c381ea3 20cd47a 2dc6197 20cd47a f6138ad 44dcd57 d0efd99 44dcd57 95397b0 44dcd57 2aea41f 44dcd57 14622e1 44dcd57 7d797d7 44dcd57 7d797d7 44dcd57 20cd47a a648386 20cd47a 6764da3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 |
from flask import Flask, request, jsonify
import torch
import shutil
import os
import sys
from argparse import ArgumentParser
from time import strftime
from argparse import Namespace
from src.utils.preprocess import CropAndExtract
from src.test_audio2coeff import Audio2Coeff
from src.facerender.animate import AnimateFromCoeff
from src.generate_batch import get_data
from src.generate_facerender_batch import get_facerender_data
# from src.utils.init_path import init_path
import tempfile
from openai import OpenAI
import threading
import elevenlabs
from elevenlabs import set_api_key, generate, play, clone
from flask_cors import CORS, cross_origin
# from flask_swagger_ui import get_swaggerui_blueprint
import uuid
import time
start_time = time.time()
class AnimationConfig:
def __init__(self, driven_audio_path, source_image_path, result_folder,pose_style,expression_scale,still,preprocess,ref_pose_video_path):
self.driven_audio = driven_audio_path
self.source_image = source_image_path
self.ref_eyeblink = None
self.ref_pose = ref_pose_video_path
self.checkpoint_dir = './checkpoints'
self.result_dir = result_folder
self.pose_style = pose_style
self.batch_size = 2
self.expression_scale = expression_scale
self.input_yaw = None
self.input_pitch = None
self.input_roll = None
self.enhancer = None
self.background_enhancer = None
self.cpu = False
self.face3dvis = False
self.still = still
self.preprocess = preprocess
self.verbose = False
self.old_version = False
self.net_recon = 'resnet50'
self.init_path = None
self.use_last_fc = False
self.bfm_folder = './checkpoints/BFM_Fitting/'
self.bfm_model = 'BFM_model_front.mat'
self.focal = 1015.
self.center = 112.
self.camera_d = 10.
self.z_near = 5.
self.z_far = 15.
self.device = 'cpu'
app = Flask(__name__)
CORS(app)
TEMP_DIR = None
app.config['temp_response'] = None
app.config['generation_thread'] = None
app.config['text_prompt'] = None
app.config['final_video_path'] = None
def main(args):
pic_path = args.source_image
audio_path = args.driven_audio
save_dir = args.result_dir
pose_style = args.pose_style
device = args.device
batch_size = args.batch_size
input_yaw_list = args.input_yaw
input_pitch_list = args.input_pitch
input_roll_list = args.input_roll
ref_eyeblink = args.ref_eyeblink
ref_pose = args.ref_pose
preprocess = args.preprocess
dir_path = os.path.dirname(os.path.realpath(__file__))
current_root_path = dir_path
print('current_root_path ',current_root_path)
# sadtalker_paths = init_path(args.checkpoint_dir, os.path.join(current_root_path, 'src/config'), args.size, args.old_version, args.preprocess)
path_of_lm_croper = os.path.join(current_root_path, args.checkpoint_dir, 'shape_predictor_68_face_landmarks.dat')
path_of_net_recon_model = os.path.join(current_root_path, args.checkpoint_dir, 'epoch_20.pth')
dir_of_BFM_fitting = os.path.join(current_root_path, args.checkpoint_dir, 'BFM_Fitting')
wav2lip_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'wav2lip.pth')
audio2pose_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'auido2pose_00140-model.pth')
audio2pose_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2pose.yaml')
audio2exp_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'auido2exp_00300-model.pth')
audio2exp_yaml_path = os.path.join(current_root_path, 'src', 'config', 'auido2exp.yaml')
free_view_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'facevid2vid_00189-model.pth.tar')
if preprocess == 'full':
mapping_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'mapping_00109-model.pth.tar')
facerender_yaml_path = os.path.join(current_root_path, 'src', 'config', 'facerender_still.yaml')
else:
mapping_checkpoint = os.path.join(current_root_path, args.checkpoint_dir, 'mapping_00229-model.pth.tar')
facerender_yaml_path = os.path.join(current_root_path, 'src', 'config', 'facerender.yaml')
# preprocess_model = CropAndExtract(sadtalker_paths, device)
#init model
print(path_of_net_recon_model)
preprocess_model = CropAndExtract(path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, device)
# audio_to_coeff = Audio2Coeff(sadtalker_paths, device)
audio_to_coeff = Audio2Coeff(audio2pose_checkpoint, audio2pose_yaml_path,
audio2exp_checkpoint, audio2exp_yaml_path,
wav2lip_checkpoint, device)
# animate_from_coeff = AnimateFromCoeff(sadtalker_paths, device)
animate_from_coeff = AnimateFromCoeff(free_view_checkpoint, mapping_checkpoint,
facerender_yaml_path, device)
first_frame_dir = os.path.join(save_dir, 'first_frame_dir')
os.makedirs(first_frame_dir, exist_ok=True)
# first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(pic_path, first_frame_dir, args.preprocess,\
# source_image_flag=True, pic_size=args.size)
first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(pic_path, first_frame_dir, args.preprocess, source_image_flag=True)
print('first_coeff_path ',first_coeff_path)
print('crop_pic_path ',crop_pic_path)
if first_coeff_path is None:
print("Can't get the coeffs of the input")
return
if ref_eyeblink is not None:
ref_eyeblink_videoname = os.path.splitext(os.path.split(ref_eyeblink)[-1])[0]
ref_eyeblink_frame_dir = os.path.join(save_dir, ref_eyeblink_videoname)
os.makedirs(ref_eyeblink_frame_dir, exist_ok=True)
# ref_eyeblink_coeff_path, _, _ = preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir, args.preprocess, source_image_flag=False)
ref_eyeblink_coeff_path, _, _ = preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir)
else:
ref_eyeblink_coeff_path=None
print('ref_eyeblink_coeff_path',ref_eyeblink_coeff_path)
if ref_pose is not None:
if ref_pose == ref_eyeblink:
ref_pose_coeff_path = ref_eyeblink_coeff_path
else:
ref_pose_videoname = os.path.splitext(os.path.split(ref_pose)[-1])[0]
ref_pose_frame_dir = os.path.join(save_dir, ref_pose_videoname)
os.makedirs(ref_pose_frame_dir, exist_ok=True)
# ref_pose_coeff_path, _, _ = preprocess_model.generate(ref_pose, ref_pose_frame_dir, args.preprocess, source_image_flag=False)
ref_pose_coeff_path, _, _ = preprocess_model.generate(ref_pose, ref_pose_frame_dir)
else:
ref_pose_coeff_path=None
print('ref_eyeblink_coeff_path',ref_pose_coeff_path)
batch = get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=args.still)
coeff_path = audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path)
if args.face3dvis:
from src.face3d.visualize import gen_composed_video
gen_composed_video(args, device, first_coeff_path, coeff_path, audio_path, os.path.join(save_dir, '3dface.mp4'))
# data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path,
# batch_size, input_yaw_list, input_pitch_list, input_roll_list,
# expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess, size=args.size)
data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path,
batch_size, input_yaw_list, input_pitch_list, input_roll_list,
expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess)
# result, base64_video,temp_file_path= animate_from_coeff.generate(data, save_dir, pic_path, crop_info, \
# enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess, img_size=args.size)
result, base64_video,temp_file_path = animate_from_coeff.generate(data, save_dir, pic_path, crop_info, \
enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess)
print('The generated video is named:')
app.config['temp_response'] = base64_video
app.config['final_video_path'] = temp_file_path
return base64_video, temp_file_path
# shutil.move(result, save_dir+'.mp4')
if not args.verbose:
shutil.rmtree(save_dir)
def create_temp_dir():
return tempfile.TemporaryDirectory()
def save_uploaded_file(file, filename,TEMP_DIR):
unique_filename = str(uuid.uuid4()) + "_" + filename
file_path = os.path.join(TEMP_DIR.name, unique_filename)
file.save(file_path)
return file_path
client = OpenAI(api_key="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")
def translate_text(text_prompt, target_language):
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "system", "content": "You are a helpful language translator assistant."},
{"role": "user", "content": f"Translate completely without hallucination, end to end and the ouput should just be the translation of the text prompt and nothing else, and give the following text to {target_language} language and the text is: {text_prompt}"},
],
max_tokens = len(text_prompt) + 200 # Use the length of the input text
# temperature=0.3,
# stop=["Translate:", "Text:"]
)
return response
@app.route("/run", methods=['POST'])
def generate_video():
global TEMP_DIR
TEMP_DIR = create_temp_dir()
print('request:',request.method)
try:
if request.method == 'POST':
source_image = request.files['source_image']
text_prompt = request.form['text_prompt']
print('Input text prompt: ',text_prompt)
voice_cloning = request.form.get('voice_cloning', 'no')
target_language = request.form.get('target_language', 'original_text')
print('target_language',target_language)
pose_style = int(request.form.get('pose_style', 1))
expression_scale = float(request.form.get('expression_scale', 1))
enhancer = request.form.get('enhancer', None)
voice_gender = request.form.get('voice_gender', 'male')
still_str = request.form.get('still', 'False')
still = still_str.lower() == 'false'
print('still', still)
preprocess = request.form.get('preprocess', 'crop')
print('preprocess selected: ',preprocess)
ref_pose_video = request.files.get('ref_pose', None)
if target_language != 'original_text':
response = translate_text(text_prompt, target_language)
# response = await translate_text_async(text_prompt, target_language)
text_prompt = response.choices[0].message.content.strip()
app.config['text_prompt'] = text_prompt
print('Final text prompt: ',text_prompt)
source_image_path = save_uploaded_file(source_image, 'source_image.png',TEMP_DIR)
print(source_image_path)
# driven_audio_path = await voice_cloning_async(voice_cloning, voice_gender, text_prompt, user_voice)
if voice_cloning == 'no':
if voice_gender == 'male':
voice = 'echo'
print('Entering Audio creation using elevenlabs')
set_api_key("92e149985ea2732b4359c74346c3daee")
audio = generate(text = text_prompt, voice = "Daniel", model = "eleven_multilingual_v2",stream=True, latency=4)
with tempfile.NamedTemporaryFile(suffix=".mp3", prefix="text_to_speech_",dir=TEMP_DIR.name, delete=False) as temp_file:
for chunk in audio:
temp_file.write(chunk)
driven_audio_path = temp_file.name
print('driven_audio_path',driven_audio_path)
print('Audio file saved using elevenlabs')
else:
voice = 'nova'
print('Entering Audio creation using whisper')
response = client.audio.speech.create(model="tts-1-hd",
voice=voice,
input = text_prompt)
print('Audio created using whisper')
with tempfile.NamedTemporaryFile(suffix=".wav", prefix="text_to_speech_",dir=TEMP_DIR.name, delete=False) as temp_file:
driven_audio_path = temp_file.name
response.write_to_file(driven_audio_path)
print('Audio file saved using whisper')
elif voice_cloning == 'yes':
user_voice = request.files['user_voice']
with tempfile.NamedTemporaryFile(suffix=".wav", prefix="user_voice_",dir=TEMP_DIR.name, delete=False) as temp_file:
user_voice_path = temp_file.name
user_voice.save(user_voice_path)
print('user_voice_path',user_voice_path)
set_api_key("92e149985ea2732b4359c74346c3daee")
voice = clone(name = "User Cloned Voice",
files = [user_voice_path] )
audio = generate(text = text_prompt, voice = voice, model = "eleven_multilingual_v2",stream=True, latency=4)
with tempfile.NamedTemporaryFile(suffix=".mp3", prefix="cloned_audio_",dir=TEMP_DIR.name, delete=False) as temp_file:
for chunk in audio:
temp_file.write(chunk)
driven_audio_path = temp_file.name
print('driven_audio_path',driven_audio_path)
# elevenlabs.save(audio, driven_audio_path)
save_dir = tempfile.mkdtemp(dir=TEMP_DIR.name)
result_folder = os.path.join(save_dir, "results")
os.makedirs(result_folder, exist_ok=True)
ref_pose_video_path = None
if ref_pose_video:
with tempfile.NamedTemporaryFile(suffix=".mp4", prefix="ref_pose_",dir=TEMP_DIR.name, delete=False) as temp_file:
ref_pose_video_path = temp_file.name
ref_pose_video.save(ref_pose_video_path)
print('ref_pose_video_path',ref_pose_video_path)
except Exception as e:
app.logger.error(f"An error occurred: {e}")
return "An error occurred", 500
# Example of using the class with some hypothetical paths
args = AnimationConfig(driven_audio_path=driven_audio_path, source_image_path=source_image_path, result_folder=result_folder, pose_style=pose_style, expression_scale=expression_scale,still=still,preprocess=preprocess,ref_pose_video_path=ref_pose_video_path)
if torch.cuda.is_available() and not args.cpu:
args.device = "cuda"
else:
args.device = "cpu"
generation_thread = threading.Thread(target=main, args=(args,))
app.config['generation_thread'] = generation_thread
generation_thread.start()
response_data = {"message": "Video generation started",
"process_id": generation_thread.ident}
return jsonify(response_data)
# base64_video = main(args)
# return jsonify({"base64_video": base64_video})
#else:
# return 'Unsupported HTTP method', 405
@app.route("/status", methods=["GET"])
def check_generation_status():
global TEMP_DIR
response = {"base64_video": "","text_prompt":"", "status": ""}
process_id = request.args.get('process_id', None)
# process_id is required to check the status for that specific process
if process_id:
generation_thread = app.config.get('generation_thread')
if generation_thread and generation_thread.ident == int(process_id) and generation_thread.is_alive():
return jsonify({"status": "in_progress"}), 200
elif app.config.get('temp_response'):
# app.config['temp_response']['status'] = 'completed'
final_response = app.config['temp_response']
response["base64_video"] = final_response
response["text_prompt"] = app.config.get('text_prompt')
response["status"] = "completed"
final_video_path = app.config['final_video_path']
print('final_video_path',final_video_path)
if final_video_path and os.path.exists(final_video_path):
os.remove(final_video_path)
print("Deleted video file:", final_video_path)
TEMP_DIR.cleanup()
# print("Temporary Directory:", TEMP_DIR.name)
# if TEMP_DIR:
# print("Contents of Temporary Directory:")
# for filename in os.listdir(TEMP_DIR.name):
# print(filename)
# else:
# print("Temporary Directory is None or already cleaned up.")
end_time = time.time()
total_time = round(end_time - start_time, 2)
print("Total time taken for execution:", total_time, " seconds")
return jsonify(response)
return jsonify({"error":"No process id provided"})
@app.route("/health", methods=["GET"])
def health_status():
response = {"online": "true"}
return jsonify(response)
if __name__ == '__main__':
app.run(debug=True) |