9c233630ef
BERT models and tokenizers are already stored and managed in the bert_models module and should not be stored here. In addition, since there may be situations where the user would like to use cpu instead of mps for inference when using it as a library, the automatic switching process to mps was removed.
74 lines
2.8 KiB
Python
74 lines
2.8 KiB
Python
from typing import Optional
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import torch
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from style_bert_vits2.constants import Languages
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from style_bert_vits2.nlp import bert_models
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from style_bert_vits2.nlp.japanese.g2p import text_to_sep_kata
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def extract_bert_feature(
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text: str,
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word2ph: list[int],
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device: str,
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assist_text: Optional[str] = None,
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assist_text_weight: float = 0.7,
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) -> torch.Tensor:
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"""
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日本語のテキストから BERT の特徴量を抽出する
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Args:
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text (str): 日本語のテキスト
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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
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device (str): 推論に利用するデバイス
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assist_text (Optional[str], optional): 補助テキスト (デフォルト: None)
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assist_text_weight (float, optional): 補助テキストの重み (デフォルト: 0.7)
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Returns:
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torch.Tensor: BERT の特徴量
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"""
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# 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
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# 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
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text = "".join(text_to_sep_kata(text, raise_yomi_error=False)[0])
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if assist_text:
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assist_text = "".join(text_to_sep_kata(assist_text, raise_yomi_error=False)[0])
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if device == "cuda" and not torch.cuda.is_available():
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device = "cpu"
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model = bert_models.load_model(Languages.JP).to(device) # type: ignore
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style_res_mean = None
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with torch.no_grad():
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tokenizer = bert_models.load_tokenizer(Languages.JP)
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device) # type: ignore
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res = model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
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if assist_text:
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style_inputs = tokenizer(assist_text, return_tensors="pt")
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for i in style_inputs:
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style_inputs[i] = style_inputs[i].to(device) # type: ignore
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style_res = model(**style_inputs, output_hidden_states=True)
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style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
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style_res_mean = style_res.mean(0)
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assert len(word2ph) == len(text) + 2, text
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word2phone = word2ph
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phone_level_feature = []
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for i in range(len(word2phone)):
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if assist_text:
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assert style_res_mean is not None
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repeat_feature = (
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res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
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+ style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
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)
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else:
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repeat_feature = res[i].repeat(word2phone[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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