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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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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) |
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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) |
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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) |
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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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