litagin's picture
Bump ver
83d190a
from typing import Optional
import torch
from style_bert_vits2.constants import Languages
from style_bert_vits2.nlp import bert_models
from style_bert_vits2.nlp.japanese.g2p import text_to_sep_kata
def extract_bert_feature(
text: str,
word2ph: list[int],
device: str,
assist_text: Optional[str] = None,
assist_text_weight: float = 0.7,
) -> torch.Tensor:
"""
日本語のテキストから BERT の特徴量を抽出する
Args:
text (str): 日本語のテキスト
word2ph (list[int]): 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
device (str): 推論に利用するデバイス
assist_text (Optional[str], optional): 補助テキスト (デフォルト: None)
assist_text_weight (float, optional): 補助テキストの重み (デフォルト: 0.7)
Returns:
torch.Tensor: BERT の特徴量
"""
# 各単語が何文字かを作る `word2ph` を使う必要があるので、読めない文字は必ず無視する
# でないと `word2ph` の結果とテキストの文字数結果が整合性が取れない
text = "".join(text_to_sep_kata(text, raise_yomi_error=False)[0])
if assist_text:
assist_text = "".join(text_to_sep_kata(assist_text, raise_yomi_error=False)[0])
if device == "cuda" and not torch.cuda.is_available():
device = "cpu"
model = bert_models.load_model(Languages.JP).to(device) # type: ignore
style_res_mean = None
with torch.no_grad():
tokenizer = bert_models.load_tokenizer(Languages.JP)
inputs = tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(device) # type: ignore
res = model(**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) # type: ignore
style_res = model(**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:
assert style_res_mean is not None
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