fac4f9a8ab
"text_processing" is clearer, but the import statement is longer. "nlp" is shorter and makes it clear that it is natural language processing.
108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
import torch
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from typing import Optional
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from style_bert_vits2.constants import Languages
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from style_bert_vits2.nlp.symbols import (
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LANGUAGE_ID_MAP,
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LANGUAGE_TONE_START_MAP,
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SYMBOLS,
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)
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__symbol_to_id = {s: i for i, s in enumerate(SYMBOLS)}
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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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language: Languages,
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device: torch.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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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): 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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language (Languages): テキストの言語
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device (torch.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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if language == Languages.JP:
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from style_bert_vits2.nlp.japanese.bert_feature import extract_bert_feature
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elif language == Languages.EN:
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from style_bert_vits2.nlp.english.bert_feature import extract_bert_feature
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elif language == Languages.ZH:
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from style_bert_vits2.nlp.chinese.bert_feature import extract_bert_feature
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else:
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raise ValueError(f"Language {language} not supported")
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return extract_bert_feature(text, word2ph, device, assist_text, assist_text_weight)
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def clean_text(
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text: str,
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language: Languages,
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use_jp_extra: bool = True,
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raise_yomi_error: bool = False,
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) -> tuple[str, list[str], list[int], list[int]]:
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"""
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テキストをクリーニングし、音素に変換する
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Args:
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text (str): クリーニングするテキスト
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language (Languages): テキストの言語
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use_jp_extra (bool, optional): テキストが日本語の場合に JP-Extra モデルを利用するかどうか。Defaults to True.
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raise_yomi_error (bool, optional): False の場合、読めない文字が消えたような扱いとして処理される。Defaults to False.
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Returns:
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tuple[str, list[str], list[int], list[int]]: クリーニングされたテキストと、音素・アクセント・元のテキストの各文字に音素が何個割り当てられるかのリスト
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"""
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# Changed to import inside if condition to avoid unnecessary import
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if language == Languages.JP:
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from style_bert_vits2.nlp.japanese import g2p, normalize_text
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norm_text = normalize_text(text)
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phones, tones, word2ph = g2p(norm_text, use_jp_extra, raise_yomi_error)
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elif language == Languages.EN:
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from style_bert_vits2.nlp.english import g2p, normalize_text
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norm_text = normalize_text(text)
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phones, tones, word2ph = g2p(norm_text)
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elif language == Languages.ZH:
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from style_bert_vits2.nlp.chinese import g2p, normalize_text
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norm_text = normalize_text(text)
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phones, tones, word2ph = g2p(norm_text)
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else:
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raise ValueError(f"Language {language} not supported")
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return norm_text, phones, tones, word2ph
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def cleaned_text_to_sequence(cleaned_phones: list[str], tones: list[int], language: Languages) -> tuple[list[int], list[int], list[int]]:
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"""
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テキスト文字列を、テキスト内の記号に対応する一連の ID に変換する
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Args:
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cleaned_phones (list[str]): clean_text() でクリーニングされた音素のリスト (?)
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tones (list[int]): 各音素のアクセント
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language (Languages): テキストの言語
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Returns:
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tuple[list[int], list[int], list[int]]: List of integers corresponding to the symbols in the text
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"""
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phones = [__symbol_to_id[symbol] for symbol in cleaned_phones]
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tone_start = LANGUAGE_TONE_START_MAP[language]
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tones = [i + tone_start for i in tones]
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lang_id = LANGUAGE_ID_MAP[language]
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lang_ids = [lang_id for i in phones]
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return phones, tones, lang_ids
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