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| import torch | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM | |
| import sys | |
| from .japanese import text2sep_kata | |
| from config import config | |
| tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3") | |
| models = dict() | |
| def get_bert_feature(text, word2ph, device=config.bert_gen_config.device): | |
| sep_text, _ = text2sep_kata(text) | |
| sep_tokens = [tokenizer.tokenize(t) for t in sep_text] | |
| sep_ids = [tokenizer.convert_tokens_to_ids(t) for t in sep_tokens] | |
| sep_ids = [2] + [item for sublist in sep_ids for item in sublist] + [3] | |
| return get_bert_feature_with_token(sep_ids, word2ph, device) | |
| def get_bert_feature_with_token(tokens, 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] = AutoModelForMaskedLM.from_pretrained( | |
| "./bert/bert-base-japanese-v3" | |
| ).to(device) | |
| with torch.no_grad(): | |
| inputs = torch.tensor(tokens).to(device).unsqueeze(0) | |
| token_type_ids = torch.zeros_like(inputs).to(device) | |
| attention_mask = torch.ones_like(inputs).to(device) | |
| inputs = { | |
| "input_ids": inputs, | |
| "token_type_ids": token_type_ids, | |
| "attention_mask": attention_mask, | |
| } | |
| # 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 inputs["input_ids"].shape[-1] == len(word2ph) | |
| 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 | |