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.cat(res["hidden_states"][-3:-2], -1)[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.cat(style_res["hidden_states"][-3:-2], -1)[0].float().cpu() style_res_mean = style_res.mean(0) assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph)) 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