File size: 3,007 Bytes
1cf1e13 ae80214 1cf1e13 ae80214 1cf1e13 ae80214 1cf1e13 |
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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
import sys
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
from config import config
LOCAL_PATH = "./bert/chinese-roberta-wwm-ext-large"
tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)
models = dict()
def get_bert_feature(
text,
word2ph,
device=config.bert_gen_config.device,
style_text=None,
style_weight=0.7,
):
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(LOCAL_PATH).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()
if style_text:
style_inputs = tokenizer(style_text, return_tensors="pt")
for i in style_inputs:
style_inputs[i] = style_inputs[i].to(device)
style_res = models[device](**style_inputs, output_hidden_states=True)
style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
style_res_mean = style_res.mean(0)
assert len(word2ph) == len(text) + 2
word2phone = word2ph
phone_level_feature = []
for i in range(len(word2phone)):
if style_text:
repeat_feature = (
res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+ style_res_mean.repeat(word2phone[i], 1) * style_weight
)
else:
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__":
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])
|