Hugging Face's logo

Spaces:
Kyon0x3C8B
/
bert-vits2.3-someone
Sleeping

App Files Community
bert-vits2.3-someone
File size: 1,320 Bytes 
d699cfc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
from onnx_modules.V220_OnnxInference import OnnxInferenceSession
import numpy as np

Session = OnnxInferenceSession(
    {
        "enc": "onnx/BertVits2.2PT/BertVits2.2PT_enc_p.onnx",
        "emb_g": "onnx/BertVits2.2PT/BertVits2.2PT_emb.onnx",
        "dp": "onnx/BertVits2.2PT/BertVits2.2PT_dp.onnx",
        "sdp": "onnx/BertVits2.2PT/BertVits2.2PT_sdp.onnx",
        "flow": "onnx/BertVits2.2PT/BertVits2.2PT_flow.onnx",
        "dec": "onnx/BertVits2.2PT/BertVits2.2PT_dec.onnx",
    },
    Providers=["CPUExecutionProvider"],
)

# 这里的输入和原版是一样的，只需要在原版预处理结果出来之后加上.numpy()即可
x = np.array(
    [
        0,
        97,
        0,
        8,
        0,
        78,
        0,
        8,
        0,
        76,
        0,
        37,
        0,
        40,
        0,
        97,
        0,
        8,
        0,
        23,
        0,
        8,
        0,
        74,
        0,
        26,
        0,
        104,
        0,
    ]
)
tone = np.zeros_like(x)
language = np.zeros_like(x)
sid = np.array([0])
bert = np.random.randn(x.shape[0], 1024)
ja_bert = np.random.randn(x.shape[0], 1024)
en_bert = np.random.randn(x.shape[0], 1024)
emo = np.random.randn(512, 1)

audio = Session(x, tone, language, bert, ja_bert, en_bert, emo, sid)

print(audio)