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Main.py
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1028 lines (765 loc) · 40 KB
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import sys
sys.path.append("SadTalker")
from SadTalker.Inference import SadTalker_Model
sys.path.append("VITS")
sys.path.append("VITS/GPT_SoVITS")
from VITS.Inference import GPT_SoVITS_Model
from VITS.train import GPT_SoVITS_Tarin
sys.path.append("Easy_Wav2Lip")
from Easy_Wav2Lip.Motion_Inference import Wav2Lip_Model
from util.PPT2Video import Ppt_2_Video
from util.Function import Clear_File, Change_image_Size, Sort_Key, Write_Json
from util.WavJoin import Add_Wav_Processor
import json
import os
import shutil
import yaml
import wave
#####################################################################################
# 需要线程池完成的任务功能 #
#####################################################################################
#推理VITS跟Sadtalker
def VITS_Sadtalker_Inference(result_vits_user_path, result_sadtalker_user_path, save_user_path):
DH = VITS_Sadtalker_Join(result_vits_user_path, result_sadtalker_user_path, save_user_path)
sad_parames_yaml_path, vits_parames_yaml_path, _ = Get_Parmes(save_user_path)
DH.Set_Params_and_Model(sad_parames_yaml_path, vits_parames_yaml_path)
ref_wav_path = os.path.join(save_user_path,"Ref_Wav.wav")
with open(os.path.join(save_user_path,"Ref_text.json"), "r", encoding='utf-8') as f:
data = json.load(f)
ref_text = data["Text"]
print(ref_wav_path)
print(ref_text)
DH.Inference_VITS(ref_wav_path,ref_text)
imag_path = os.path.join(save_user_path,"Image.png")
DH.Inference_SadTalker(imag_path)
Task_State(save_user_path, "Audio_Video_Inference", True)
# 推理用户音频跟Sadtalker
def User_Wav_Sadtalker_Inference(result_sadtalker_user_path, save_user_path):
ppt_audio_dir = os.path.join(save_user_path, "PPT_Audio")
audio_json_save_path = os.path.join(save_user_path, "Audio_save_path.json")
video_json_save_path =os.path.join(save_user_path,"Video_save_path.json")
image = os.path.join(save_user_path, "Image.png")
Sad = SadTalker_Model()
sad_parames_yaml_path, _, _ = Get_Parmes(save_user_path)
Sad.Initialize_Parames(save_user_path, sad_parames_yaml_path)
Sad.Initialize_Models()
Write_Json(ppt_audio_dir, audio_json_save_path)
#读取Audio_save_path.json里面的键值对
with open(audio_json_save_path, "r", encoding="utf-8") as f:
data = json.load(f)
#把键跟值分别保存list
ip_keys = list(data.keys())
ip_values = list(data.values())
dict = {}
for i in range(len(ip_keys)):
save_path = os.path.join(result_sadtalker_user_path,ip_keys[i])
Sad.Perform_Inference(image, ip_values[i],save_path)
dict[ip_keys[i]] = save_path + ".mp4"
# 使用with语句打开文件,确保在写入完成后自动关闭文件
with open(video_json_save_path, 'w',encoding='utf-8') as json_file:
# 使用json.dump()函数将数据写入JSON文件
json.dump(dict, json_file)
Task_State(save_user_path, "Audio_Video_Inference", True)
#推理VITS跟Wav2Lip
def VITS_Wav2Lip_Inference(result_vits_user_path, result_wav2lip_user_path, save_user_path):
VWJ = VITS_Wav2Lip_Join(result_vits_user_path, result_wav2lip_user_path, save_user_path)
_, vits_parames_yaml_path, w2p_parames_yaml_path = Get_Parmes(save_user_path)
VWJ.Set_Params_and_Model(w2p_parames_yaml_path, vits_parames_yaml_path)
ref_wav_path = os.path.join(save_user_path,"Ref_Wav.wav")
with open(os.path.join(save_user_path,"Ref_text.json"), "r", encoding='utf-8') as f:
data = json.load(f)
ref_text = data["Text"]
print(ref_text)
VWJ.Inference_VITS(ref_wav_path,ref_text)
video_path = os.path.join(save_user_path,"Video.mp4")
VWJ.Inference_Wav2Lip(video_path)
Task_State(save_user_path, "Audio_Video_Inference", True)
# 推理用户音频跟Wav2Lip
def User_Wav_Wav2Lip_Inference(user_result_wav2lip_path, save_user_path):
ppt_audio_dir = os.path.join(save_user_path, "PPT_Audio")
audio_json_save_path = os.path.join(save_user_path, "Audio_save_path.json")
json_file_path =os.path.join(save_user_path,"Video_save_path.json")
video_path = os.path.join(save_user_path, "Video.mp4")
Write_Json(ppt_audio_dir, audio_json_save_path)
WM = Wav2Lip_Model()
WM.Initialize_Parames(save_user_path)
WM.Initialize_Models()
#修改FPS
fps_video = os.path.join(WM.wav2lip_temp, "fps_video.mp4")
print("Change Video Fps")
WM.Change_Video_Fps(video_path, fps_video, 25.0)
path = os.path.join(save_user_path,"Audio_save_path.json")
#读取Audio_save_path.json里面的键值对
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
#把键跟值分别保存list
ip_keys = list(data.keys())
audio_path_values = list(data.values())
dict = {}
for i in range(len(ip_keys)):
save_path = os.path.join(user_result_wav2lip_path,ip_keys[i] + ".mp4")
shear_video = WM.Shear_Video(fps_video, audio_path_values[i])
WM.Perform_Inference(shear_video, audio_path_values[i], save_path)
dict[ip_keys[i]] = save_path
# 使用with语句打开文件,确保在写入完成后自动关闭文件
with open(json_file_path, 'w',encoding='utf-8') as json_file:
# 使用json.dump()函数将数据写入JSON文件
json.dump(dict, json_file)
Task_State(save_user_path, "Audio_Video_Inference", True)
#推理VITS
def VITS_Multiple_Inference(result_vits_user_path, result_sadtalker_user_path, save_user_path):
DH = VITS_Sadtalker_Join(result_vits_user_path, result_sadtalker_user_path, save_user_path)
sad_parames_yaml_path, vits_parames_yaml_path, _ = Get_Parmes(save_user_path)
DH.Set_Params_and_Model(sad_parames_yaml_path, vits_parames_yaml_path)
ref_wav_path = os.path.join(save_user_path,"Ref_Wav.wav")
with open(os.path.join(save_user_path,"Ref_text.json"), "r", encoding='utf-8') as f:
data = json.load(f)
ref_text = data["Text"]
print(ref_wav_path)
print(ref_text)
DH.Inference_VITS(ref_wav_path,ref_text)
Task_State(save_user_path, "VITS_Inference", True)
#生成ppt融合视频(全插入)
def Video_Merge(save_user_path):
Remove_Video_Background(save_user_path)
video_path = Video_Joint(save_user_path)
Last_Video_Join_Audio(save_user_path, video_path)
Task_State(save_user_path, "Video_Merge", True)
#生成ppt融合视频(可选择插入)
def Video_Merge_Select_Into(save_user_path):
Remove_Video_Background(save_user_path)
video_path = Video_Joint_Select(save_user_path)
Last_Video_Join_Audio(save_user_path, video_path)
Task_State(save_user_path, "Video_Merge", True)
#训练VITS
def Train_VITS(save_user_path, user, json_data):
VT = VITS_Train(user)
list_file_path, audio_data_path = VT.Create_Audio_Label(save_user_path,json_data)
Clear_File(os.path.join(save_user_path,"Weight"))
Clear_File(os.path.join("VITS/logs",user))
#格式化
VT.Format_Data(list_file_path, audio_data_path)
#训练SoVITS
VT.Train_SoVITS(save_user_path,10,10)
#训练GPT
VT.Train_GPT(save_user_path,18,18)
Task_State(save_user_path, "VITS_Train", True)
#####################################################################################
# 一些功能 #
#####################################################################################
# 创建user_path文件夹
def Create_File(user_name):
user_result_vits_path = os.path.join("Result/VITS", f"{user_name}")
user_result_sadtalker_path = os.path.join("Result/SadTalker", f"{user_name}")
user_result_wav2lip_path = os.path.join("Result/Wav2Lip", f"{user_name}")
user_data_save_path = os.path.join("Data", f"{user_name}")
if not os.path.exists(user_result_vits_path):
os.makedirs(user_result_vits_path)
if not os.path.exists(user_result_sadtalker_path):
os.makedirs(user_result_sadtalker_path)
if not os.path.exists(user_result_wav2lip_path):
os.makedirs(user_result_wav2lip_path)
if not os.path.exists(user_data_save_path):
os.makedirs(user_data_save_path)
os.makedirs(os.path.join(user_data_save_path,"output_frames"))
os.makedirs(os.path.join(user_data_save_path,"input_frames"))
os.makedirs(os.path.join(user_data_save_path,"Mov_Video"))
os.makedirs(os.path.join(user_data_save_path,"PPT_Video"))
os.makedirs(os.path.join(user_data_save_path,"Audio_Data"))
os.makedirs(os.path.join(user_data_save_path,"Weight"))
os.makedirs(os.path.join(user_data_save_path, "wav2lip_temp"))
os.makedirs(os.path.join(user_data_save_path, "PPT_Audio"))
#创建空的json文件
audio_filename = os.path.join(user_data_save_path,"Audio_save_path.json")
with open(audio_filename, "w") as json_file:
json.dump({}, json_file)
video_filename = os.path.join(user_data_save_path,"Video_save_path.json")
with open(video_filename, "w") as json_file:
json.dump({}, json_file)
ppt_remake_filename = os.path.join(user_data_save_path,"PPT_Remake.json")
with open(ppt_remake_filename, "w") as json_file:
json.dump({}, json_file)
ref_wav_text = os.path.join(user_data_save_path,"Ref_text.json")
with open(ref_wav_text, "w") as json_file:
json.dump({}, json_file)
wav_time = os.path.join(user_data_save_path,"Time.json")
with open(wav_time, "w") as json_file:
json.dump({}, json_file)
state = os.path.join(user_data_save_path,"State.json")
with open(state, "w") as json_file:
json.dump({}, json_file)
people_location = os.path.join(user_data_save_path,"People_Location.json")
with open(people_location, "w") as json_file:
json.dump({}, json_file)
return user_result_vits_path, user_result_sadtalker_path, user_result_wav2lip_path, user_data_save_path
# 初始化文件夹
def Init_File(user_result_vits_path, user_result_sadtalker_path, user_result_wav2lip_path, user_data_save_path):
Clear_File(os.path.join(user_data_save_path, "Audio_Data"))
Clear_File(os.path.join(user_data_save_path, "PPT_Audio"))
Clear_File(os.path.join(user_data_save_path, "output_frames"))
Clear_File(os.path.join(user_data_save_path, "input_frames"))
Clear_File(os.path.join(user_data_save_path, "wav2lip_temp"))
Clear_File(os.path.join(user_data_save_path, "Mov_Video"))
Clear_File(os.path.join(user_data_save_path, "PPT_Video"))
Clear_File(user_result_vits_path)
Clear_File(user_result_sadtalker_path)
Clear_File(user_result_wav2lip_path)
# 设置跟拉取任务状态
def Task_State(user_data_save_path, task, methods=None):
json_file_path = os.path.join(user_data_save_path, "State.json")
# 读取目标文件
with open(json_file_path, 'r', encoding='utf-8') as f:
target_data = yaml.safe_load(f)
if methods != None:
target_data[str(task)] = methods
with open(json_file_path, "w", encoding='utf-8') as json_file:
json.dump(target_data, json_file, ensure_ascii=False)
return target_data[str(task)]
# 配置SadTalker参数
def Config_SadTalker_Parmes(user_data_save_path, json_dict):
target_file_path = os.path.join(user_data_save_path, "SadTalker_config.yaml")
# 检查文件是否存在
if not os.path.exists(target_file_path):
shutil.copy("SadTalker/SadTalker_config.yaml", user_data_save_path)
# 读取目标文件
with open(target_file_path, 'r', encoding='utf-8') as f:
target_data = yaml.safe_load(f)
# 替换值
for key, value in json_dict.items():
if key in target_data:
target_data[key] = value
# 写入目标文件
with open(target_file_path, 'w', encoding='utf-8') as f:
yaml.dump(target_data, f)
return target_file_path
# 配置VITS参数
def Config_VITS_Parmes(user_data_save_path, json_dict):
target_file_path = os.path.join(user_data_save_path, "GPT-SoVITS_config.yaml")
# 检查文件是否存在
if not os.path.exists(target_file_path):
shutil.copy("VITS/GPT-SoVITS_config.yaml", user_data_save_path)
# 读取目标文件
with open(target_file_path, 'r', encoding='utf-8') as f:
target_data = yaml.safe_load(f)
# 替换值
for key, value in json_dict.items():
if key in target_data:
target_data[key] = value
# 写入目标文件
with open(target_file_path, 'w', encoding='utf-8') as f:
yaml.dump(target_data, f)
return target_file_path
# 配置wav2lip参数
def Config_Wav2Lip_Parmes(user_data_save_path, json_dict):
target_file_path = os.path.join(user_data_save_path, "Wav2Lip_config.yaml")
# 检查文件是否存在
if not os.path.exists(target_file_path):
shutil.copy("Easy_Wav2Lip/Wav2Lip_config.yaml", user_data_save_path)
# 读取目标文件
with open(target_file_path, 'r', encoding='utf-8') as f:
target_data = yaml.safe_load(f)
# 替换值
for key, value in json_dict.items():
if key in target_data:
target_data[key] = value
# 写入目标文件
with open(target_file_path, 'w', encoding='utf-8') as f:
yaml.dump(target_data, f)
return target_file_path
# 获取参数
def Get_Parmes(user_data_save_path):
vits = os.path.join(user_data_save_path, "GPT-SoVITS_config.yaml")
sadtdlker = os.path.join(user_data_save_path, "SadTalker_config.yaml")
wav2lip = os.path.join(user_data_save_path,"Wav2Lip_config.yaml")
# 检查文件是否存在
if not os.path.exists(vits):
shutil.copy("VITS/GPT-SoVITS_config.yaml", user_data_save_path)
if not os.path.exists(sadtdlker):
shutil.copy("SadTalker/SadTalker_config.yaml", user_data_save_path)
if not os.path.exists(wav2lip):
shutil.copy("Easy_Wav2Lip/Wav2Lip_config.yaml", user_data_save_path)
return sadtdlker, vits, wav2lip
# 保存PPT备注
def Save_PPT_Remake(user_data_save_path, ppt_remakes):
ppt_remake_filename = os.path.join(user_data_save_path, "PPT_Remake.json")
with open(ppt_remake_filename, "w", encoding='utf-8') as json_file:
json.dump(ppt_remakes, json_file, ensure_ascii=False)
return ppt_remake_filename
# 保存数字人插入页数的json
def Save_People_Location(user_data_save_path, people_location):
people_location_filename = os.path.join(user_data_save_path, "People_Location.json")
with open(people_location_filename, "w", encoding='utf-8') as json_file:
json.dump(people_location, json_file, ensure_ascii=False)
return people_location_filename
# 保存真人照片
def Save_Image(user_data_save_path, image_path):
img_name = os.path.join(user_data_save_path, "Image.png")
if os.path.exists(img_name):
os.remove(img_name)
#移动
shutil.move(image_path, user_data_save_path)
#修改保存在save_user_path照片的名字
os.rename(os.path.join(user_data_save_path, image_path.split("/")[-1]), img_name)
Change_image_Size(img_name)
# 保存接收的视频
def Save_Video(user_data_save_path, video_at_path):
video_name = os.path.join(user_data_save_path, "PPT_Video.mp4")
if os.path.exists(video_name):
os.remove(video_name)
#移动
shutil.move(video_at_path, user_data_save_path)
os.rename(os.path.join(user_data_save_path, video_at_path.split("/")[-1]), video_name)
# 保存音频时长
def Save_Tiem(user_data_save_path, audio_dir):
wav_tiem = os.path.join(user_data_save_path,"Time.json")
time_dict = {}
audio_dir_all_file = os.path.join(user_data_save_path, audio_dir)
file_dir = os.listdir(audio_dir_all_file)
file_list = sorted(file_dir, key=Sort_Key)
if (len(file_list) > 0):
#获取音频文件时长
for _, file in enumerate(file_list):
#获取文件名
file_name = file.split(".")[0]
file_path = os.path.join(audio_dir_all_file, file)
with wave.open(file_path, 'rb') as wav_file:
# 获取帧数
frames = wav_file.getnframes()
# 获取帧速率(每秒的帧数)
frame_rate = wav_file.getframerate()
# 计算时长(以秒为单位)
duration = frames / float(frame_rate)
time_dict[str(file_name)] = duration
with open(wav_tiem, 'w', encoding='utf-8') as f:
json.dump(time_dict, f)
return time_dict
return None
# 保存用于训练的音频
def Save_Train_Audio(user_data_save_path, name, audio_data):
audio_data_path = os.path.join(user_data_save_path, "Audio_Data")
audio = os.path.join(audio_data_path, name)
if os.path.exists(audio):
os.remove(audio)
#保存到文件
with open(audio, "wb") as img_file:
img_file.write(audio_data)
# 保存插入ppt的音频
def Save_Insert_Audio(user_data_save_path, name, audio_data):
insert_audio_path = os.path.join(user_data_save_path, "PPT_Audio")
audio = os.path.join(insert_audio_path, name)
if os.path.exists(audio):
os.remove(audio)
#保存到文件
with open(audio, "wb") as img_file:
img_file.write(audio_data)
#####################################################################################
# VITS功能 #
#####################################################################################
#训练VITS模型
class VITS_Train():
def __init__(self, user):
self.GST = GPT_SoVITS_Tarin(user)
#对音频标注保存
def Create_Audio_Label(self, user_data_save_path, data_json):
audio_data_path = os.path.join(user_data_save_path, "Audio_Data")
list_file_path = os.path.join(user_data_save_path, "Train.list")
with open(list_file_path, "w", encoding='utf-8') as text_file:
for key, value in data_json.items():
name = os.path.join(audio_data_path,key)
text_file.write(f"{name}|split|ZH|{value}\n")
return list_file_path, audio_data_path
#数据格式化
def Format_Data(self, train_list, train_audio_path):
if(self.GST.Format_Data(train_list,train_audio_path)):
return True
return False
#训练VITS模型
def Train_SoVITS(self, user_data_save_path, total_epoch, save_every_epoch):
model_path = self.GST.Train_SoVITS(total_epoch, save_every_epoch)
if(model_path != None):
path = os.path.join(user_data_save_path, "Weight")
shutil.move(model_path, path)
return True
return False
#训练GPT模型
def Train_GPT(self, user_data_save_path, total_epoch, save_every_epoch):
model_path = self.GST.Train_GPT(total_epoch, save_every_epoch)
if(model_path != None):
path = os.path.join(user_data_save_path, "Weight")
shutil.move(model_path, path)
return True
return False
#保存VITS的参照音频跟文字
def Save_VITS_Ref_Wav_And_Text(user_data_save_path, wav_path, ref_text, methods="move"):
wav_name = os.path.join(user_data_save_path, "Ref_Wav.wav")
if os.path.exists(wav_name):
os.remove(wav_name)
#移动或者copy
if(methods == "copy"):
shutil.copy(wav_path, user_data_save_path)
elif(methods == "move"):
shutil.move(wav_path, user_data_save_path)
os.rename(os.path.join(user_data_save_path, wav_path.split("/")[-1]), wav_name)
ref_wav_text = os.path.join(user_data_save_path,"Ref_text.json")
with open(ref_wav_text, "w", encoding='utf-8') as json_file:
json.dump(ref_text, json_file, ensure_ascii=False)
return ref_wav_text
#//////////////////////////////// VITS模型二选一(改模型路径) /////////////////////////////////////////////////////////////////////////////////////////////
# 选择自定义的VITS模型
def Select_Train_VITS_Model(user_data_save_path, user):
pth = os.path.join(user_data_save_path, "Weight", f"{user}.pth")
ckpt = os.path.join(user_data_save_path, "Weight", f"{user}.ckpt")
model_dict = {
"GPT_model_path" : ckpt,
"SoVITS_model_path" : pth
}
Config_VITS_Parmes(user_data_save_path, model_dict)
# 选择预训练的VITS模型 (把ref_wav和ref_test复制到保存路径,然后再改掉GPT-SoVITS_config.yaml模型位置,改掉参考文字)
def Select_VITS_Model(user_data_save_path, index):
target_file_path = os.path.join(user_data_save_path, "GPT-SoVITS_config.yaml")
# 检查文件是否存在
if not os.path.exists(target_file_path):
shutil.copy("VITS/GPT-SoVITS_config.yaml", user_data_save_path)
with open("VITS/Model.json",'r', encoding='utf-8') as f:
model_json = json.load(f)
model = model_json[str(index)]
Config_VITS_Parmes(user_data_save_path, model)
Save_VITS_Ref_Wav_And_Text(user_data_save_path, model["Ref_Wav"], {"Text" : model["Ref_Text"]}, "copy")
return target_file_path
#///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
#####################################################################################
# 视频拼接 #
#####################################################################################
#把sadtalker的每一个视频去背景
def Remove_Video_Background(user_data_save_path):
output_frames = os.path.join(user_data_save_path, "output_frames")
input_frames = os.path.join(user_data_save_path, "input_frames")
mov_video = os.path.join(user_data_save_path, "Mov_Video")
Clear_File(output_frames)
Clear_File(input_frames)
Clear_File(mov_video)
PV = Ppt_2_Video(output_frames, input_frames, mov_video)
with open(os.path.join(user_data_save_path, "Video_save_path.json"), 'r', encoding='utf-8') as f:
data = json.load(f)
for key, video_file_path in enumerate(data.values()):
PV.Video_To_Frames(video_file_path)
PV.Remove_Background()
PV.Create_Video(key)
#Mov视频跟PPT合成最终视频(全插入)
def Video_Joint(user_data_save_path):
output_frames = os.path.join(user_data_save_path, "output_frames")
input_frames = os.path.join(user_data_save_path, "input_frames")
Mov_Video = os.path.join(user_data_save_path, "Mov_Video")
ppt_video_path = os.path.join(user_data_save_path, "PPT_Video.mp4")
output_ppt_video = os.path.join(user_data_save_path, "PPT_Video")
Clear_File(output_ppt_video)
PV = Ppt_2_Video(output_frames, input_frames, Mov_Video)
transition_time = 0
with open(os.path.join(user_data_save_path, "Time.json"), 'r', encoding='utf-8') as f:
data = json.load(f)
# keys = list(data.keys())
time_list = list(data.values())
for i in range(len(time_list)):
# mov_path = os.path.join(Mov_Video, mov_list[i])
mov_path = os.path.join(Mov_Video, f"{i}" + "_Mov.mov")
if i == 0:
one_ppt_video_path = os.path.join(output_ppt_video, f"{i}_ppt_video.mp4")
transition_time += 3
PV.Insert_Video(ppt_video_path, mov_path, one_ppt_video_path, transition_time, time_list[i])
else:
one_ppt_video_path = os.path.join(output_ppt_video, f"{i - 1}_ppt_video.mp4")
two_ppt_video_path = os.path.join(output_ppt_video, f"{i}_ppt_video.mp4")
transition_time += 4
transition_time += time_list[i - 1]
PV.Insert_Video(one_ppt_video_path, mov_path, two_ppt_video_path, transition_time, time_list[i])
return two_ppt_video_path
#Mov视频跟PPT合成最终视频(可选择插入)
def Video_Joint_Select(user_data_save_path):
output_frames = os.path.join(user_data_save_path, "output_frames")
input_frames = os.path.join(user_data_save_path, "input_frames")
Mov_Video = os.path.join(user_data_save_path, "Mov_Video")
ppt_video_path = os.path.join(user_data_save_path, "PPT_Video.mp4")
output_ppt_video = os.path.join(user_data_save_path, "PPT_Video")
last_video_path = ""
Clear_File(output_ppt_video)
PV = Ppt_2_Video(output_frames, input_frames, Mov_Video)
transition_time = 0
OnePPTVideoIndex = -1
with open(os.path.join(user_data_save_path, "Time.json"), 'r', encoding='utf-8') as f:
data = json.load(f)
with open(os.path.join(user_data_save_path, "People_Location.json"), 'r', encoding='utf-8') as f:
select = json.load(f)
# keys = list(data.keys())
time_list = list(data.values())
for i in range(len(time_list)):
# mov_path = os.path.join(Mov_Video, mov_list[i])
mov_path = os.path.join(Mov_Video, f"{i}" + "_Mov.mov")
if i == 0:
one_ppt_video_path = os.path.join(output_ppt_video, f"{i}_ppt_video.mp4")
transition_time += 3
if(select[str(i)] == "True"):
OnePPTVideoIndex = i
PV.Insert_Video(ppt_video_path, mov_path, one_ppt_video_path, transition_time, time_list[i])
last_video_path = one_ppt_video_path
else:
if(OnePPTVideoIndex == -1):
one_ppt_video_path = ppt_video_path
else:
one_ppt_video_path = os.path.join(output_ppt_video, f"{OnePPTVideoIndex}_ppt_video.mp4")
two_ppt_video_path = os.path.join(output_ppt_video, f"{i}_ppt_video.mp4")
transition_time += 4
transition_time += time_list[i - 1]
if(select[str(i)] == "True"):
OnePPTVideoIndex = i
PV.Insert_Video(one_ppt_video_path, mov_path, two_ppt_video_path, transition_time, time_list[i])
last_video_path = two_ppt_video_path
return last_video_path
#最终的PPT视频拼接音频
def Last_Video_Join_Audio(user_data_save_path, input_video):
AWP = Add_Wav_Processor()
audio_save_path = os.path.join(user_data_save_path, "Audio_save_path.json")
last_video = os.path.join(user_data_save_path, "last_video.mp4")
join_audio = os.path.join(user_data_save_path, "Join_Audio.wav")
with open(audio_save_path, 'r', encoding='utf-8') as f:
path_dict = json.load(f)
for i in path_dict.keys():
if i == "0":
print(path_dict[str(i)])
result_audio = AWP.Add_Silence_At_Beginning(path_dict[str(i)])
result_audio.export(join_audio, format="wav")
else:
print(path_dict[str(i)])
result_audio = AWP.Add_Silence_Between_Tracks(join_audio,path_dict[str(i)])
result_audio.export(join_audio, format="wav")
print(i)
# 当不是第一个视频的时候就执行add_silence_between_tracks和上一个wav结合空出几秒自己选择
# 生成完成add_audio_to_video贴入MP4
AWP.Add_Audio_To_Video(input_video, join_audio, last_video)
return last_video
#PPT视频拼接音频
def Video_Join_Audio(user_data_save_path):
AWP = Add_Wav_Processor()
input_video = os.path.join(user_data_save_path, "PPT_Video.mp4")
audio_save_path = os.path.join(user_data_save_path, "Audio_save_path.json")
last_video = os.path.join(user_data_save_path, "last_video.mp4")
join_audio = os.path.join(user_data_save_path, "Join_Audio.wav")
with open(audio_save_path, 'r', encoding='utf-8') as f:
path_dict = json.load(f)
for i in path_dict.keys():
if i == "0":
print(path_dict[str(i)])
result_audio = AWP.Add_Silence_At_Beginning(path_dict[str(i)])
result_audio.export(join_audio, format="wav")
else:
print(path_dict[str(i)])
result_audio = AWP.Add_Silence_Between_Tracks(join_audio,path_dict[str(i)])
result_audio.export(join_audio, format="wav")
print(i)
# 当不是第一个视频的时候就执行add_silence_between_tracks和上一个wav结合空出几秒自己选择
# 生成完成add_audio_to_video贴入MP4
AWP.Add_Audio_To_Video(input_video, join_audio, last_video)
return last_video
#####################################################################################
# 模型推理 #
#####################################################################################
#VITS跟Sadtalker结合
class VITS_Sadtalker_Join():
def __init__(self,vits,sadtalker,save):
#声明模型
self.VITS = None
self.Sad = None
#实例化
self.VITS = GPT_SoVITS_Model()
self.Sad = SadTalker_Model()
#全局变量
self.user_result_vits_path = vits
self.user_result_sadtalker_path = sadtalker
self.user_data_save_path = save
#清空文件
Clear_File(self.user_result_vits_path)
Clear_File(self.user_result_sadtalker_path)
#预加载参数和模型
def Set_Params_and_Model(self,sad_parames_yaml_path=None,vits_parames_yaml_path=None):
self.VITS.Initialize_Parames(vits_parames_yaml_path)
self.Sad.Initialize_Parames(self.user_data_save_path, sad_parames_yaml_path)
self.Sad.Initialize_Models()
#根据备注推理音频
def Inference_VITS(self, ref_wav_path, ref_text):
path = os.path.join(self.user_data_save_path,"PPT_Remake.json")
#读取Json_Data/PPT_Remake.json里面的键值对
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
#把键跟值分别保存list
ip_keys = list(data.keys())
PPT_Remake_values = list(data.values())
dict = {}
for i in range(len(ip_keys)):
text = PPT_Remake_values[i]
output_path = os.path.join(self.user_result_vits_path, f"{ip_keys[i]}.wav")
self.VITS.Initialize_Models()
self.VITS.Perform_Inference(
ref_wav_path = ref_wav_path,
prompt_text = ref_text,
prompt_languageself = self.VITS.i18n("中文"),
target_text = text,
target_text_language = self.VITS.i18n("中文"),
cut = self.VITS.i18n("凑50字一切"),
output_path=output_path
)
dict[ip_keys[i]] = output_path
# 指定要写入的JSON文件路径
json_file_path =os.path.join(self.user_data_save_path,"Audio_save_path.json")
# 使用with语句打开文件,确保在写入完成后自动关闭文件
with open(json_file_path, 'w', encoding='utf-8') as json_file:
# 使用json.dump()函数将数据写入JSON文件
json.dump(dict, json_file)
Save_Tiem(self.user_data_save_path, self.user_result_vits_path)
#生成数字人视频
def Inference_SadTalker(self,image):
path = os.path.join(self.user_data_save_path,"Audio_save_path.json")
#读取Audio_save_path.json里面的键值对
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
#把键跟值分别保存list
ip_keys = list(data.keys())
audio_path_values = list(data.values())
dict = {}
for i in range(len(ip_keys)):
save_path = os.path.join(self.user_result_sadtalker_path,ip_keys[i])
self.Sad.Perform_Inference(image, audio_path_values[i],save_path)
dict[ip_keys[i]] = save_path + ".mp4"
# 指定要写入的JSON文件路径
json_file_path =os.path.join(self.user_data_save_path,"Video_save_path.json")
# 使用with语句打开文件,确保在写入完成后自动关闭文件
with open(json_file_path, 'w',encoding='utf-8') as json_file:
# 使用json.dump()函数将数据写入JSON文件
json.dump(dict, json_file)
#根据备注推理音频_test
def Inference_VITS_test(self, ref_wav_path, ref_text, text):
output_path = os.path.join(self.user_data_save_path, "Test_VITS.wav")
self.VITS.Initialize_Models()
self.VITS.Perform_Inference(
ref_wav_path = ref_wav_path,
prompt_text = ref_text,
prompt_languageself = self.VITS.i18n("中文"),
target_text = text,
target_text_language = self.VITS.i18n("中文"),
cut = self.VITS.i18n("凑50字一切"),
output_path=output_path
)
return output_path
#生成数字人视频_test
def Inference_SadTalker_test(self, image, audio_path, save_path):
self.Sad.Perform_Inference(image, audio_path, save_path)
#VITS跟Wav2Lip结合
class VITS_Wav2Lip_Join():
def __init__(self,vits,wav2lip,save):
#声明模型
self.VITS = None
self.WM = None
#实例化
self.VITS = GPT_SoVITS_Model()
self.WM = Wav2Lip_Model()
#全局变量
self.user_result_vits_path = vits
self.user_result_wav2lip_path = wav2lip
self.user_data_save_path = save
#清空文件
Clear_File(self.user_result_vits_path)
Clear_File(self.user_result_wav2lip_path)
#预加载参数和模型
def Set_Params_and_Model(self,w2l_parames_yaml_path=None,vits_parames_yaml_path=None):
self.VITS.Initialize_Parames(vits_parames_yaml_path)
self.WM.Initialize_Parames(self.user_data_save_path, w2l_parames_yaml_path)
Clear_File(self.WM.wav2lip_temp)
self.WM.Initialize_Models()
#根据备注推理音频
def Inference_VITS(self, ref_wav_path, ref_text):
path = os.path.join(self.user_data_save_path,"PPT_Remake.json")
#读取Json_Data/PPT_Remake.json里面的键值对
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
#把键跟值分别保存list
ip_keys = list(data.keys())
PPT_Remake_values = list(data.values())
dict = {}
for i in range(len(ip_keys)):
text = PPT_Remake_values[i]
output_path = os.path.join(self.user_result_vits_path, f"{ip_keys[i]}.wav")
self.VITS.Initialize_Models()
self.VITS.Perform_Inference(
ref_wav_path = ref_wav_path,
prompt_text = ref_text,
prompt_languageself = self.VITS.i18n("中文"),
target_text = text,
target_text_language = self.VITS.i18n("中文"),
cut = self.VITS.i18n("凑50字一切"),
output_path=output_path
)
dict[ip_keys[i]] = output_path
# 指定要写入的JSON文件路径
json_file_path =os.path.join(self.user_data_save_path,"Audio_save_path.json")
# 使用with语句打开文件,确保在写入完成后自动关闭文件
with open(json_file_path, 'w', encoding='utf-8') as json_file:
# 使用json.dump()函数将数据写入JSON文件
json.dump(dict, json_file)
Save_Tiem(self.user_data_save_path, self.user_result_vits_path)
#生成数字人视频带动作
def Inference_Wav2Lip(self, video_path):
#修改FPS
fps_video = os.path.join(self.WM.wav2lip_temp, "fps_video.mp4")
print("Change Video Fps")
self.WM.Change_Video_Fps(video_path, fps_video, 25.0)
path = os.path.join(self.user_data_save_path,"Audio_save_path.json")
#读取Audio_save_path.json里面的键值对
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
#把键跟值分别保存list
ip_keys = list(data.keys())
audio_path_values = list(data.values())
dict = {}
for i in range(len(ip_keys)):
save_path = os.path.join(self.user_result_wav2lip_path,ip_keys[i] + ".mp4")
shear_video = self.WM.Shear_Video(fps_video, audio_path_values[i])
self.WM.Perform_Inference(shear_video, audio_path_values[i], save_path)
dict[ip_keys[i]] = save_path
# 指定要写入的JSON文件路径
json_file_path =os.path.join(self.user_data_save_path,"Video_save_path.json")
# 使用with语句打开文件,确保在写入完成后自动关闭文件
with open(json_file_path, 'w',encoding='utf-8') as json_file:
# 使用json.dump()函数将数据写入JSON文件
json.dump(dict, json_file)
if __name__ == '__main__':
result_vits_user_path, result_sadtalker_user_path, result_wav2lip_user_path, user_data_save_path = Create_File("Hui")
# di = {
# "1.wav":"首先你要准备一个说话人的音频的数据集。",
# "2.wav":"然后根据这个数据集,让AI去训练模型。"
# }
# VT = VITS_Train("Hui")
# list_file_path, audio_data_path = VT.Create_Audio_Label(user_data_save_path,di)
# VT.Format_Data(list_file_path, audio_data_path)
# VT.Train_SoVITS(user_data_save_path,8,8)
# VT.Train_GPT(user_data_save_path,15,15)
# Remove_Video_Background(user_data_save_path)
# Video_Joint(user_data_save_path)
#Video_Join_Audeo(user_data_save_path, "Data/Hui/PPT_Video/4_ppt_video.mp4")
# Select_VITS_Model(save_user_path,"0")