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import sys | |
import torch | |
from transformers import AutoModelForMaskedLM, AutoTokenizer | |
from config import config | |
from .japanese import text2sep_kata | |
LOCAL_PATH = "./bert/deberta-v2-large-japanese" | |
tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH) | |
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(LOCAL_PATH).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 | |