File size: 3,520 Bytes
28b3671 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
from typing import Optional
import torch
from style_bert_vits2.constants import Languages
from style_bert_vits2.nlp import bert_models
def extract_bert_feature(
text: str,
word2ph: list[int],
device: str,
assist_text: Optional[str] = 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 (str): 推論に利用するデバイス
assist_text (Optional[str], 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 device == "cuda" and not torch.cuda.is_available():
device = "cpu"
model = 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 = model(**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 = model(**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])
|