158 lines
5.9 KiB
Python
158 lines
5.9 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Optional, Sequence, Union, cast
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import numpy as np
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from numpy.typing import NDArray
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from style_bert_vits2.constants import Languages
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from style_bert_vits2.nlp import bert_models, onnx_bert_models
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from style_bert_vits2.nlp.japanese.g2p import text_to_sep_kata
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if TYPE_CHECKING:
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import torch
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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 (PyTorch 推論)
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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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import torch
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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, device_map=device)
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bert_models.transfer_model(Languages.JP, 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) # 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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def extract_bert_feature_onnx(
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text: str,
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word2ph: list[int],
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onnx_providers: Sequence[Union[str, tuple[str, dict[str, Any]]]],
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assist_text: Optional[str] = None,
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assist_text_weight: float = 0.7,
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) -> NDArray[Any]:
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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 (ONNX 推論)
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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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onnx_providers (list[str]): ONNX 推論で利用する ExecutionProvider (CPUExecutionProvider, CUDAExecutionProvider など)
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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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NDArray[Any]: 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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tokenizer = onnx_bert_models.load_tokenizer(Languages.JP)
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inputs = tokenizer(text, return_tensors="np")
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session = onnx_bert_models.load_model(
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language=Languages.JP,
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onnx_providers=onnx_providers,
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)
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output_name = session.get_outputs()[0].name
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res = session.run(
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[output_name],
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{
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"input_ids": inputs["input_ids"].astype(np.int64), # type: ignore
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"attention_mask": inputs["attention_mask"].astype(np.int64), # type: ignore
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},
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)[0]
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style_res_mean = None
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if assist_text:
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style_inputs = tokenizer(assist_text, return_tensors="np")
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style_res = session.run(
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[output_name],
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{
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"input_ids": style_inputs["input_ids"].astype(np.int64), # type: ignore
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"attention_mask": style_inputs["attention_mask"].astype(np.int64), # type: ignore
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},
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)[0]
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style_res_mean = np.mean(style_res, axis=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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np.tile(res[i], (word2phone[i], 1)) * (1 - assist_text_weight)
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+ np.tile(style_res_mean, (word2phone[i], 1)) * assist_text_weight
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)
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else:
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repeat_feature = np.tile(res[i], (word2phone[i], 1))
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phone_level_feature.append(repeat_feature)
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phone_level_feature = np.concatenate(phone_level_feature, axis=0)
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return phone_level_feature.T
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