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import torch | |
from contants import config | |
def get_bert_feature(text, word2ph, tokenizer, model, device=config.system.device, **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.cat(res["hidden_states"][-3:-2], -1)[0].float().cpu() | |
# assert len(word2ph) == len(text)+2 | |
word2phone = word2ph | |
phone_level_feature = [] | |
for i in range(len(word2phone)): | |
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 | |