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| import sys | |
| import torch | |
| from transformers import AutoModelForMaskedLM, AutoTokenizer | |
| from config import config | |
| from text.japanese import text2sep_kata | |
| 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, | |
| ): | |
| text = "".join(text2sep_kata(text)[0]) | |
| 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 | |