import sys import torch from transformers import AutoModelForMaskedLM, AutoTokenizer from config import config from text.japanese import text2sep_kata, text_normalize LOCAL_PATH = "./bert/deberta-v2-large-japanese-char-wwm" tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH) models = dict() def get_bert_feature( text, word2ph, device=config.bert_gen_config.device, assist_text=None, assist_text_weight=0.7, ignore_unknown=False, ): text = "".join(text2sep_kata(text, ignore_unknown=ignore_unknown)[0]) # text = text_normalize(text) if assist_text: assist_text = "".join(text2sep_kata(assist_text)[0]) if ( sys.platform == "darwin" and torch.backends.mps.is_available() and device == "cpu" ): device = "mps" if not device: device = "cuda" if device == "cuda" and not torch.cuda.is_available(): device = "cpu" if device not in models.keys(): models[device] = AutoModelForMaskedLM.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 assist_text: style_inputs = tokenizer(assist_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) == len(text) + 2, text word2phone = word2ph phone_level_feature = [] for i in range(len(word2phone)): if assist_text: repeat_feature = ( res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight) + style_res_mean.repeat(word2phone[i], 1) * assist_text_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