import sys import torch from transformers import DebertaV2Model, DebertaV2Tokenizer from config import config LOCAL_PATH = "./bert/deberta-v3-large" tokenizer = DebertaV2Tokenizer.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] = DebertaV2Model.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) == 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