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import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
import sys
from text.japanese import text2sep_kata
tokenizer = AutoTokenizer.from_pretrained("ku-nlp/deberta-v2-base-japanese")
# tokenizer = AutoTokenizer.from_pretrained("ku-nlp/deberta-v2-base-japanese-with-auto-jumanpp")
models = dict()
def get_bert_feature(text, word2ph, device=None):
sep_text, _ = text2sep_kata(text)
sep_tokens = [tokenizer.tokenize(t) for t in sep_text]
sep_ids = [tokenizer.convert_tokens_to_ids(t) for t in sep_tokens]
sep_ids = [2] + [item for sublist in sep_ids for item in sublist] + [3]
return get_bert_feature_with_token(sep_ids, word2ph, device)
def get_bert_feature_with_token(tokens, word2ph, device=None):
if (
sys.platform == "darwin"
and torch.backends.mps.is_available()
and device == "cpu"
):
device = "mps"
if not device:
device = "cuda"
if device not in models.keys():
models[device] = AutoModelForMaskedLM.from_pretrained(
"ku-nlp/deberta-v2-base-japanese"
# "ku-nlp/deberta-v2-base-japanese-with-auto-jumanpp"
).to(device)
with torch.no_grad():
inputs = torch.tensor(tokens).to(device).unsqueeze(0)
token_type_ids = torch.zeros_like(inputs).to(device)
attention_mask = torch.ones_like(inputs).to(device)
inputs = {
"input_ids": inputs,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
}
# for i in inputs:
# inputs[i] = inputs[i].to(device)
res = models[device](**inputs, output_hidden_states=True)
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
assert inputs["input_ids"].shape[-1] == len(word2ph)
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 |