import sys import torch from transformers import AutoModelForMaskedLM, AutoTokenizer from config import config LOCAL_PATH = "./bert/bert-large-cantonese" tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH) models = dict() def get_bert_feature( text, word2ph, device=config.bert_gen_config.device, style_text=None, style_weight=0.7, ): if ( sys.platform == "darwin" and torch.backends.mps.is_available() and device == "cpu" ): device = "mps" if not device: device = "cuda" if device not in models.keys(): models[device] = AutoModelForMaskedLM.from_pretrained( LOCAL_PATH).to(device) with torch.no_grad(): inputs = tokenizer(text, return_tensors="pt") for i in inputs: inputs[i] = inputs[i].to(device) res = models[device](**inputs, output_hidden_states=True) res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu() if style_text: style_inputs = tokenizer(style_text, return_tensors="pt") for i in style_inputs: style_inputs[i] = style_inputs[i].to(device) style_res = models[device]( **style_inputs, output_hidden_states=True) style_res = torch.cat( style_res["hidden_states"][-3:-2], -1)[0].cpu() style_res_mean = style_res.mean(0) assert len(word2ph) == len(text) + 2 word2phone = word2ph phone_level_feature = [] for i in range(len(word2phone)): if style_text: repeat_feature = ( res[i].repeat(word2phone[i], 1) * (1 - style_weight) + style_res_mean.repeat(word2phone[i], 1) * style_weight ) else: repeat_feature = res[i].repeat(word2phone[i], 1) phone_level_feature.append(repeat_feature) phone_level_feature = torch.cat(phone_level_feature, dim=0) return phone_level_feature.T if __name__ == "__main__": word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征 word2phone = [ 1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, ] # 计算总帧数 total_frames = sum(word2phone) print(word_level_feature.shape) print(word2phone) phone_level_feature = [] for i in range(len(word2phone)): print(word_level_feature[i].shape) # 对每个词重复word2phone[i]次 repeat_feature = word_level_feature[i].repeat(word2phone[i], 1) phone_level_feature.append(repeat_feature) phone_level_feature = torch.cat(phone_level_feature, dim=0) print(phone_level_feature.shape) # torch.Size([36, 1024])