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,enhancer,still,preprocess,ref_pose_video_path): self.driven_audio = driven_audio_path self.source_image = source_image_path self.ref_eyeblink = ref_pose_video_path 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 = enhancer 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/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-4-0125-preview", messages=[{"role": "system", "content": "You are a helpful language translator assistant."}, {"role": "user", "content": f"Translate completely without hallucination, end to end, 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']) async def generate_video(): global TEMP_DIR TEMP_DIR = create_temp_dir() 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 = int(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() == 'true' 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 = 'onyx' 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') 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("87792fce164425fbe1204e9fd1fe25cd") 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) # 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, enhancer=enhancer,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)