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): 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() # 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