import torch from contants import config def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, style_text=None, style_weight=0.7, **kwargs): 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.nn.functional.normalize(torch.cat(res["hidden_states"][-3:-2], -1)[0], dim=0).float().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 = model(**style_inputs, output_hidden_states=True) style_res = torch.nn.functional.normalize( torch.cat(style_res["hidden_states"][-3:-2], -1)[0], dim=0 ).float().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, 2048) # 12个词,每个词2048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, 2048])