diff --git a/0 一键启动脚本/Cfg/About.txt b/0 一键启动脚本/Cfg/About.txt index 602808da..e69de29b 100644 --- a/0 一键启动脚本/Cfg/About.txt +++ b/0 一键启动脚本/Cfg/About.txt @@ -1,25 +0,0 @@ -ʹ˵ - -ʱ뿪 TTS̨ TTS WebUI -ģʱ뿪 TTSģ͹ TTS̨ TTS WebUI -ѵʱֻ迪 GPTsoVITS WebUI - - -GPT-soVITS Start V ػ汾 -汾1.3.20240311.1 -ߣС -QQ/Wechat406267780 - -䣺ػ2.1 by Xter - -ػϢ -forkַ by XTerhttps://github.com/X-T-E-R/GPT-SoVITS-Inference -ģ͹ģַ by XTerhttps://github.com/X-T-E-R/TTS-for-GPT-soVITS -ԭĿ by RVC-Bossޣhttps://github.com/RVC-Boss/GPT-SoVITS - -ػȺ863760614 - -Ȩ˵ -Ϊ˹ϳĿһʹע⣬ԴģʹþӦԭʼȨߵȨ档ʹñκݣûгеӦİȨΡ -ûӦ⣬ʹòκԴģ͵İȨɻʹȨʹñذȨɺЭ飬ȷݵĺϷȡʹá -粻Ͽɱ򲻳еӦΣֹͣʹñɾļʹüΪܱԸ涨 \ No newline at end of file diff --git a/0 一键启动脚本/Cfg/Cfg.ini b/0 一键启动脚本/Cfg/Cfg.ini index 510164ef..812c6f0e 100644 --- a/0 一键启动脚本/Cfg/Cfg.ini +++ b/0 一键启动脚本/Cfg/Cfg.ini @@ -1,30 +1,73 @@ +[Title] +Text=ػ2.1 by Xter | ԭĿ by RVC-Bossޣ [HomeImage] Route=\Cfg\HomeImage.jpg -[TTSModuleManagerStart] -Route=10 ģ͹棨ѡ.bat [TTSBackgroundServiceStart] +FolderName=TTS ̨ +ButtonName=TTS ̨ Route=3 ˳.bat [TTSWebUIStart] +FolderName=TTS WebUI +Hide=N +ButtonName=TTS WebUI Route=4 ǰ˺ϳɳ򣨿ѡˣ.bat [GPTsoVITSWebUIStart] +FolderName=GPTsV WebUI +Hide=N +ButtonName=GPTsV WebUI Route=11 ԭĿѵ棨Сױ𿪣ҳϵĵоȺ.bat -[TTSModuleFolder] -Route=..\trained -[TTSOnekeyStartFolder] -Route=..\0 һű -[GPTsoVITSRootFolder] -Route=..\ -[TTSJsonConfig] -Route=..\Inference\config.json +[TTSModuleManagerStart] +FolderName=TTS ģ͹ +Hide=N +ButtonName=TTS ģ͹ +Route=10 ģ͹棨ѡ.bat [UpdateProgramStart] +FolderName=Ŀ +ButtonName=Ŀ Route=0 һĿ.bat [UpdateDependenciesStart] +FolderName= +Hide=N +ButtonName= Route=1 һ±ĿҪ.bat [ForceUpdateStart] +FolderName=ǿƸ +Hide=N +ButtonName=ǿƸ Route=999 ǿƸ£Ḳãã0.bat +[TTSModuleFolder] +ButtonName=TTSģĿ¼ +Hide=N +Route=..\trained +[TTSOnekeyStartFolder] +ButtonName=TTSһĿ¼ +Hide=N +Route=..\0 һű +[GPTsoVITSRootFolder] +ButtonName=GPTsoVITSĿ¼ +Hide=N +Route=..\ [TTSDocument] +ButtonName=TTSĿĵ +Hide=N Route=https://www.yuque.com/xter/zibxlp [GPTsoVITSDocument] +ButtonName=GPTsoVITSĿĵ +Hide=N Route=https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e +[StartDocument] +ButtonName=Ŀĵ +Hide=N +Route=https://note.youdao.com/s/DlzSyaLl +[JsonConfig] +Route=..\Inference\config.json [AboutTxt] Route=\Cfg\About.txt +[Status] +Hide=N +TWU Hide=N +GWU Hide=N +TMM Hide=N +UpD Hide=N +FUp Hide=N +Line Hide=156 diff --git a/0 一键启动脚本/GPT-soVITS Start.exe b/0 一键启动脚本/GPT-soVITS Start.exe index 7b6a3878..ff6270de 100644 Binary files a/0 一键启动脚本/GPT-soVITS Start.exe and b/0 一键启动脚本/GPT-soVITS Start.exe differ diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index 4e135a2c..dfd6eb0d 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -14,7 +14,7 @@ from AR.models.utils import ( logits_to_probs, multinomial_sample_one_no_sync, dpo_loss, - make_reject_y, + make_reject_y, get_batch_logps ) from AR.modules.embedding import SinePositionalEmbedding @@ -26,11 +26,6 @@ from torch import nn from torch.nn import functional as F from torchmetrics.classification import MulticlassAccuracy -try: - from flash_attn import flash_attn_with_kvcache -except ImportError: - flash_attn_with_kvcache = None - default_config = { "embedding_dim": 512, "hidden_dim": 512, @@ -302,7 +297,7 @@ class Text2SemanticDecoder(nn.Module): (0, y_len), value=True, ) - + y_attn_mask = F.pad( torch.triu( torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), @@ -363,7 +358,7 @@ class Text2SemanticDecoder(nn.Module): A_logits, R_logits = get_batch_logps(logits, reject_logits, targets, reject_targets) loss_2, _, _ = dpo_loss(A_logits, R_logits, 0, 0, 0.2, reference_free=True) - + loss = loss_1 + loss_2 return loss, acc @@ -432,14 +427,14 @@ class Text2SemanticDecoder(nn.Module): # 需要看下这个函数和 forward 的区别以及没有 semantic 的时候 prompts 输入什么 def infer( - self, - x, - x_lens, - prompts, - bert_feature, - top_k: int = -100, - early_stop_num: int = -1, - temperature: float = 1.0, + self, + x, + x_lens, + prompts, + bert_feature, + top_k: int = -100, + early_stop_num: int = -1, + temperature: float = 1.0, ): x = self.ar_text_embedding(x) x = x + self.bert_proj(bert_feature.transpose(1, 2)) @@ -678,18 +673,22 @@ class Text2SemanticDecoder(nn.Module): # AR Decoder y = prompts - + x_len = x.shape[1] x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool) stop = False # print(1111111,self.num_layers) - - if flash_attn_with_kvcache is not None: - k_cache = [torch.empty(x.shape[0], 2048, 16, 32, dtype=x.dtype, device=x.device) for _ in range(self.num_layers)] - v_cache = [torch.empty(x.shape[0], 2048, 16, 32, dtype=x.dtype, device=x.device) for _ in range(self.num_layers)] - else: - k_cache = [None] * self.num_layers - v_cache = [None] * self.num_layers + cache = { + "all_stage": self.num_layers, + "k": [None] * self.num_layers, ###根据配置自己手写 + "v": [None] * self.num_layers, + # "xy_pos":None,##y_pos位置编码每次都不一样的没法缓存，每次都要重新拼xy_pos.主要还是写法原因，其实是可以历史统一一样的，但也没啥计算量就不管了 + "y_emb": None, ##只需要对最新的samples求emb，再拼历史的就行 + # "logits":None,###原版就已经只对结尾求再拼接了，不用管 + # "xy_dec":None,###不需要，本来只需要最后一个做logits + "first_infer": 1, + "stage": 0, + } ################### first step ########################## if y is not None: y_emb = self.ar_audio_embedding(y) @@ -697,6 +696,7 @@ class Text2SemanticDecoder(nn.Module): prefix_len = y.shape[1] y_pos = self.ar_audio_position(y_emb) xy_pos = torch.concat([x, y_pos], dim=1) + cache["y_emb"] = y_emb ref_free = False else: y_emb = None @@ -708,10 +708,10 @@ class Text2SemanticDecoder(nn.Module): ref_free = True x_attn_mask_pad = F.pad( - x_attn_mask, - (0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y) - value=True, - ) + x_attn_mask, + (0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y) + value=True, + ) y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y) torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1), (x_len, 0), @@ -725,16 +725,14 @@ class Text2SemanticDecoder(nn.Module): batch_idx_map = list(range(y.shape[0])) idx_list = [None]*y.shape[0] for idx in tqdm(range(1500)): - logits = self.infer_one_step(xy_pos, xy_attn_mask, k_cache, v_cache, cache_seqlens) - - if idx == 0: - cache_seqlens += xy_pos.shape[1] - else: - cache_seqlens += 1 - xy_attn_mask = None - - if idx == 0: - logits = logits[:, :-1] + + xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache) + logits = self.ar_predict_layer( + xy_dec[:, -1] + ) ##不用改，如果用了cache的默认就是只有一帧，取最后一帧一样的 + # samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature) + if(idx==0):###第一次跑不能EOS否则没有了 + logits = logits[:, :-1] ###刨除1024终止符号的概率 samples = sample( logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature )[0] @@ -778,12 +776,15 @@ class Text2SemanticDecoder(nn.Module): # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS) stop = True if stop: - if y.shape[1] == 0: + # if prompts.shape[1] == y.shape[1]: + # y = torch.concat([y, torch.zeros_like(samples)], dim=1) + # print("bad zero prediction") + if y.shape[1]==0: y = torch.concat([y, torch.zeros_like(samples)], dim=1) print("bad zero prediction") print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]") break - + ####################### update next step ################################### cache["first_infer"] = 0 if cache["y_emb"] is not None: diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index e583c9f9..61ba7be1 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -1,3 +1,4 @@ +from copy import deepcopy import math import os, sys import random @@ -50,22 +51,7 @@ custom: class TTS_Config: - def __init__(self, configs: Union[dict, str]): - if isinstance(configs, str) and configs=="": - self.default_configs:dict = None - self.configs_path = "GPT_SoVITS/configs/tts_infer.yaml" - else: - configs_base_path:str = "GPT_SoVITS/configs/" - os.makedirs(configs_base_path, exist_ok=True) - self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml") - if isinstance(configs, str): - self.configs_path = configs - configs:dict = self._load_configs(configs) - - # assert isinstance(configs, dict) - self.default_configs:dict = configs.get("default", None) - if self.default_configs is None: - self.default_configs={ + default_configs={ "device": "cpu", "is_half": False, "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", @@ -74,18 +60,54 @@ class TTS_Config: "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", "flash_attn_enabled": True } - if isinstance(configs, dict): - self.configs:dict = configs.get("custom", self.default_configs) - else: - self.configs:dict = self.default_configs + configs:dict = None + def __init__(self, configs: Union[dict, str]=None): - self.device = self.configs.get("device") - self.is_half = self.configs.get("is_half") - self.t2s_weights_path = self.configs.get("t2s_weights_path") - self.vits_weights_path = self.configs.get("vits_weights_path") - self.bert_base_path = self.configs.get("bert_base_path") - self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path") - self.flash_attn_enabled = self.configs.get("flash_attn_enabled") + # 设置默认配置文件路径 + configs_base_path:str = "GPT_SoVITS/configs/" + os.makedirs(configs_base_path, exist_ok=True) + self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml") + + if configs in ["", None]: + if not os.path.exists(self.configs_path): + self.save_configs() + print(f"Create default config file at {self.configs_path}") + configs:dict = {"default": deepcopy(self.default_configs)} + + if isinstance(configs, str): + self.configs_path = configs + configs:dict = self._load_configs(self.configs_path) + + assert isinstance(configs, dict) + default_configs:dict = configs.get("default", None) + if default_configs is not None: + self.default_configs = default_configs + + self.configs:dict = configs.get("custom", deepcopy(self.default_configs)) + + + self.device = self.configs.get("device", torch.device("cpu")) + self.is_half = self.configs.get("is_half", False) + self.flash_attn_enabled = self.configs.get("flash_attn_enabled", True) + self.t2s_weights_path = self.configs.get("t2s_weights_path", None) + self.vits_weights_path = self.configs.get("vits_weights_path", None) + self.bert_base_path = self.configs.get("bert_base_path", None) + self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None) + + + if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)): + self.t2s_weights_path = self.default_configs['t2s_weights_path'] + print(f"fall back to default t2s_weights_path: {self.t2s_weights_path}") + if (self.vits_weights_path in [None, ""]) or (not os.path.exists(self.vits_weights_path)): + self.vits_weights_path = self.default_configs['vits_weights_path'] + print(f"fall back to default vits_weights_path: {self.vits_weights_path}") + if (self.bert_base_path in [None, ""]) or (not os.path.exists(self.bert_base_path)): + self.bert_base_path = self.default_configs['bert_base_path'] + print(f"fall back to default bert_base_path: {self.bert_base_path}") + if (self.cnhuhbert_base_path in [None, ""]) or (not os.path.exists(self.cnhuhbert_base_path)): + self.cnhuhbert_base_path = self.default_configs['cnhuhbert_base_path'] + print(f"fall back to default cnhuhbert_base_path: {self.cnhuhbert_base_path}") + self.update_configs() self.max_sec = None @@ -109,24 +131,18 @@ class TTS_Config: def save_configs(self, configs_path:str=None)->None: configs={ - "default": { - "device": "cpu", - "is_half": False, - "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", - "vits_weights_path": "GPT_SoVITS/pretrained_models/s2G488k.pth", - "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", - "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", - "flash_attn_enabled": True - }, - "custom": self.update_configs() + "default":self.default_configs, } + if self.configs is not None: + configs["custom"] = self.update_configs() + if configs_path is None: configs_path = self.configs_path with open(configs_path, 'w') as f: yaml.dump(configs, f) def update_configs(self): - config = { + self.config = { "device" : str(self.device), "is_half" : self.is_half, "t2s_weights_path" : self.t2s_weights_path, @@ -135,7 +151,7 @@ class TTS_Config: "cnhuhbert_base_path": self.cnhuhbert_base_path, "flash_attn_enabled" : self.flash_attn_enabled } - return config + return self.config def __str__(self): self.configs = self.update_configs() @@ -144,6 +160,9 @@ class TTS_Config: string += f"{str(k).ljust(20)}: {str(v)}\n" string += "-" * 100 + '\n' return string + + def __repr__(self): + return self.__str__() class TTS: @@ -180,35 +199,40 @@ class TTS: self.stop_flag:bool = False + self.precison:torch.dtype = torch.float16 if self.configs.is_half else torch.float32 def _init_models(self,): self.init_t2s_weights(self.configs.t2s_weights_path) self.init_vits_weights(self.configs.vits_weights_path) self.init_bert_weights(self.configs.bert_base_path) self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path) + # self.enable_half_precision(self.configs.is_half) def init_cnhuhbert_weights(self, base_path: str): + print(f"Loading CNHuBERT weights from {base_path}") self.cnhuhbert_model = CNHubert(base_path) self.cnhuhbert_model=self.cnhuhbert_model.eval() - if self.configs.is_half == True: - self.cnhuhbert_model = self.cnhuhbert_model.half() self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) + if self.configs.is_half: + self.cnhuhbert_model = self.cnhuhbert_model.half() def init_bert_weights(self, base_path: str): + print(f"Loading BERT weights from {base_path}") self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path) self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path) self.bert_model=self.bert_model.eval() + self.bert_model = self.bert_model.to(self.configs.device) if self.configs.is_half: self.bert_model = self.bert_model.half() - self.bert_model = self.bert_model.to(self.configs.device) def init_vits_weights(self, weights_path: str): + print(f"Loading VITS weights from {weights_path}") self.configs.vits_weights_path = weights_path self.configs.save_configs() dict_s2 = torch.load(weights_path, map_location=self.configs.device) @@ -231,15 +255,16 @@ class TTS: if hasattr(vits_model, "enc_q"): del vits_model.enc_q - if self.configs.is_half: - vits_model = vits_model.half() vits_model = vits_model.to(self.configs.device) vits_model = vits_model.eval() vits_model.load_state_dict(dict_s2["weight"], strict=False) self.vits_model = vits_model + if self.configs.is_half: + self.vits_model = self.vits_model.half() def init_t2s_weights(self, weights_path: str): + print(f"Loading Text2Semantic weights from {weights_path}") self.configs.t2s_weights_path = weights_path self.configs.save_configs() self.configs.hz = 50 @@ -249,11 +274,61 @@ class TTS: t2s_model = Text2SemanticLightningModule(config, "****", is_train=False, flash_attn_enabled=self.configs.flash_attn_enabled) t2s_model.load_state_dict(dict_s1["weight"]) - if self.configs.is_half: - t2s_model = t2s_model.half() t2s_model = t2s_model.to(self.configs.device) t2s_model = t2s_model.eval() self.t2s_model = t2s_model + if self.configs.is_half: + self.t2s_model = self.t2s_model.half() + + def enable_half_precision(self, enable: bool = True): + ''' + To enable half precision for the TTS model. + Args: + enable: bool, whether to enable half precision. + + ''' + if self.configs.device == "cpu" and enable: + print("Half precision is not supported on CPU.") + return + + self.configs.is_half = enable + self.precison = torch.float16 if enable else torch.float32 + self.configs.save_configs() + if enable: + if self.t2s_model is not None: + self.t2s_model =self.t2s_model.half() + if self.vits_model is not None: + self.vits_model = self.vits_model.half() + if self.bert_model is not None: + self.bert_model =self.bert_model.half() + if self.cnhuhbert_model is not None: + self.cnhuhbert_model = self.cnhuhbert_model.half() + else: + if self.t2s_model is not None: + self.t2s_model = self.t2s_model.float() + if self.vits_model is not None: + self.vits_model = self.vits_model.float() + if self.bert_model is not None: + self.bert_model = self.bert_model.float() + if self.cnhuhbert_model is not None: + self.cnhuhbert_model = self.cnhuhbert_model.float() + + def set_device(self, device: torch.device): + ''' + To set the device for all models. + Args: + device: torch.device, the device to use for all models. + ''' + self.configs.device = device + self.configs.save_configs() + if self.t2s_model is not None: + self.t2s_model = self.t2s_model.to(device) + if self.vits_model is not None: + self.vits_model = self.vits_model.to(device) + if self.bert_model is not None: + self.bert_model = self.bert_model.to(device) + if self.cnhuhbert_model is not None: + self.cnhuhbert_model = self.cnhuhbert_model.to(device) def set_ref_audio(self, ref_audio_path:str): ''' @@ -354,7 +429,7 @@ class TTS: pos_end = min(pos+batch_size,index_and_len_list.shape[0]) while pos < pos_end: batch=index_and_len_list[pos:pos_end, 1].astype(np.float32) - score=batch[(pos_end-pos)//2]/batch.mean() + score=batch[(pos_end-pos)//2]/(batch.mean()+1e-8) if (score>=threshold) or (pos_end-pos==1): batch_index=index_and_len_list[pos:pos_end, 0].tolist() batch_index_list_len += len(batch_index) @@ -386,13 +461,13 @@ class TTS: for item in item_list: if prompt_data is not None: all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\ - .to(dtype=torch.float32 if not self.configs.is_half else torch.float16) + .to(dtype=self.precison) all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]) phones = torch.LongTensor(item["phones"]) # norm_text = prompt_data["norm_text"]+item["norm_text"] else: all_bert_features = item["bert_features"]\ - .to(dtype=torch.float32 if not self.configs.is_half else torch.float16) + .to(dtype=self.precison) phones = torch.LongTensor(item["phones"]) all_phones = phones # norm_text = item["norm_text"] @@ -412,7 +487,7 @@ class TTS: # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len) all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len) # all_bert_features_batch = all_bert_features_list - all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=torch.float32) + all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=self.precison) for idx, item in enumerate(all_bert_features_list): all_bert_features_batch[idx, :, : item.shape[-1]] = item @@ -542,6 +617,11 @@ class TTS: ###### text preprocessing ######## data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) + if len(data) == 0: + yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate * 0.3), + dtype=np.int16) + return + t1 = ttime() data, batch_index_list = self.to_batch(data, prompt_data=self.prompt_cache if not no_prompt_text else None, @@ -594,10 +674,8 @@ class TTS: t4 = ttime() t_34 += t4 - t3 - refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"].to(self.configs.device) - if self.configs.is_half: - refer_audio_spepc = refer_audio_spepc.half() - + refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"]\ + .to(dtype=self.precison, device=self.configs.device) batch_audio_fragment = [] @@ -679,7 +757,7 @@ class TTS: split_bucket:bool=True)->tuple[int, np.ndarray]: zero_wav = torch.zeros( int(self.configs.sampling_rate * 0.3), - dtype=torch.float16 if self.configs.is_half else torch.float32, + dtype=self.precison, device=self.configs.device ) @@ -697,13 +775,9 @@ class TTS: # audio = [item for batch in audio for item in batch] audio = sum(audio, []) - - try: - audio = np.concatenate(audio, 0) - audio = (audio * 32768).astype(np.int16) - except: - audio = np.array(audio) - audio = (audio * 32768).astype(np.int16) + + audio = np.concatenate(audio, 0) + audio = (audio * 32768).astype(np.int16) try: if speed_factor != 1.0: diff --git a/Inference b/Inference index d0716731..66f358cc 160000 --- a/Inference +++ b/Inference @@ -1 +1 @@ -Subproject commit d0716731840c8f405a8c1c60277a646029a2c2ae +Subproject commit 66f358cc1140490bb1f06ae4550e5c8a0dedd86f