import torch from transformers import AutoTokenizer, AutoModelForMaskedLM device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large") model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device) def get_bert_feature(text, word2ph): with torch.no_grad(): inputs = tokenizer(text, return_tensors='pt') for i in inputs: inputs[i] = inputs[i].to(device) res = model(**inputs, output_hidden_states=True) res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu() assert len(word2ph) == len(text)+2 word2phone = word2ph phone_level_feature = [] for i in range(len(word2phone)): 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__': # feature = get_bert_feature('你好,我是说的道理。') import torch 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])