Refactor: moved the module for extracting BERT features from text in each language to style_bert_vits2/text_processing/(language)/bert_feature.py

style_bert_vits2/text_processing/chinese/bert_feature.py

@@ -0,0 +1,138 @@
+import sys
+
+import torch
+from transformers import PreTrainedModel
+
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.text_processing import bert_models
+
+
+models: dict[str, PreTrainedModel] = {}
+
+
+def extract_bert_feature(
+    text: str,
+    word2ph: list[int],
+    device: torch.device | str,
+    assist_text: str | None = None,
+    assist_text_weight: float = 0.7,
+) -> torch.Tensor:
+    """
+    中国語のテキストから BERT の特徴量を抽出する
+
+    Args:
+        text (str): 中国語のテキスト
+        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 (torch.device | str): 推論に利用するデバイス
+        assist_text (str | None, 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
+    """
+
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if device == "cuda" and not torch.cuda.is_available():
+        device = "cpu"
+    if device not in models.keys():
+        models[device] = bert_models.load_model(Languages.ZH).to(device)  # type: ignore
+
+    style_res_mean = None
+    with torch.no_grad():
+        tokenizer = bert_models.load_tokenizer(Languages.ZH)
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)  # type: ignore
+        res = models[device](**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 = models[device](**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
+    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
+
+
+if __name__ == "__main__":
+    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
+    word2phone = [
+        1,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        2,
+        1,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        1,
+    ]
+
+    # 计算总帧数
+    total_frames = sum(word2phone)
+    print(word_level_feature.shape)
+    print(word2phone)
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        print(word_level_feature[i].shape)
+
+        # 对每个词重复word2phone[i]次
+        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    print(phone_level_feature.shape)  # torch.Size([36, 1024])