4f2ae91ce2
* Prevent repeatedly loading the BERT model from the disk. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
101 lines
2.3 KiB
Python
101 lines
2.3 KiB
Python
import torch
|
|
import sys
|
|
from transformers import AutoTokenizer, AutoModelForMaskedLM
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
|
|
|
|
models = dict()
|
|
|
|
|
|
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(
|
|
"./bert/chinese-roberta-wwm-ext-large"
|
|
).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 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
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import torch
|
|
|
|
word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024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, 1024])
|