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import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
import sys
import os
from text.japanese import text2sep_kata
tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3")
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(text, 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(
# "cl-tohoku/bert-base-japanese-v3"
# ).to(device)
# with torch.no_grad():
# inputs = tokenizer(text, return_tensors="pt")
# 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
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(
"./bert/bert-base-japanese-v3"
).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
if __name__ == "__main__":
print(get_bert_feature("観覧車",[4,2]))
pass |