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11b4f27 | 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 | import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
bert_models = None
tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large")
def get_bert_feature(text, word2ph, device, language):
global bert_models
if language != "zh":
return torch.zeros(1024, sum(word2ph))
if bert_models == None:
bert_models = AutoModelForMaskedLM.from_pretrained(
"hfl/chinese-roberta-wwm-ext-large"
).to(device)
print('loaded bert model at rank', device)
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(device)
res = bert_models(**inputs, output_hidden_states=True)
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
assert len(word2ph) == len(text)
phone_level_feature = []
for i in range(len(word2ph)):
repeat_feature = res[i].repeat(word2ph[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
return phone_level_feature.T
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