File size: 3,111 Bytes
fe13ef3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
121
122
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,
    assist_text=None,
    assist_text_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 == "cuda" and not torch.cuda.is_available():
        device = "cpu"
    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 assist_text:
            style_inputs = tokenizer(assist_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 assist_text:
            repeat_feature = (
                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
                + style_res_mean.repeat(word2phone[i], 1) * assist_text_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])