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import torch
from contants import config
def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, style_text=None, style_weight=0.7,
**kwargs):
with torch.no_grad():
inputs = tokenizer(text, return_tensors='pt')
for i in inputs:
inputs[i] = inputs[i].to(device)
res = model(**inputs, output_hidden_states=True)
res = torch.nn.functional.normalize(torch.cat(res["hidden_states"][-3:-2], -1)[0], dim=0).float().cpu()
if style_text:
style_inputs = tokenizer(style_text, return_tensors="pt")
for i in style_inputs:
style_inputs[i] = style_inputs[i].to(device)
style_res = model(**style_inputs, output_hidden_states=True)
style_res = torch.nn.functional.normalize(
torch.cat(style_res["hidden_states"][-3:-2], -1)[0], dim=0
).float().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 style_text:
repeat_feature = (
res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+ style_res_mean.repeat(word2phone[i], 1) * style_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, 2048) # 12个词,每个词2048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, 2048])
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