File size: 5,358 Bytes
4de73fc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
# Copyright (c) 2022, Yongqiang Li (yongqiangli@alumni.hust.edu.cn)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
from text import text_to_sequence
import numpy as np
from scipy.io import wavfile
import torch
import json
import commons
import utils
import sys
import pathlib

try:
    import onnxruntime as ort
except ImportError:
    print('Please install onnxruntime!')
    sys.exit(1)


def to_numpy(tensor: torch.Tensor):
    return tensor.detach().cpu().numpy() if tensor.requires_grad \
        else tensor.detach().numpy()


def get_args():
    parser = argparse.ArgumentParser(description='inference')
    parser.add_argument('--onnx_model', required=True, help='onnx model')
    parser.add_argument('--cfg', required=True, help='config file')
    parser.add_argument('--outdir', default="onnx_output",
                        help='ouput directory')
    # parser.add_argument('--phone_table',
    #                     required=True,
    #                     help='input phone dict')
    # parser.add_argument('--speaker_table', default=None, help='speaker table')
    parser.add_argument('--test_file', required=True, help='test file')
    args = parser.parse_args()
    return args


def get_symbols_from_json(path):
    import os
    assert os.path.isfile(path)
    with open(path, 'r') as f:
        data = json.load(f)
    return data['symbols']


def main():
    args = get_args()
    print(args)
    if not pathlib.Path(args.outdir).exists():
        pathlib.Path(args.outdir).mkdir(exist_ok=True, parents=True)
    # phones =
    symbols = get_symbols_from_json(args.cfg)
    phone_dict = {
        symbol: i for i, symbol in enumerate(symbols)
    }

    # speaker_dict = {}
    # if args.speaker_table is not None:
    #     with open(args.speaker_table) as p_f:
    #         for line in p_f:
    #             arr = line.strip().split()
    #             assert len(arr) == 2
    #             speaker_dict[arr[0]] = int(arr[1])
    hps = utils.get_hparams_from_file(args.cfg)

    ort_sess = ort.InferenceSession(args.onnx_model)

    with open(args.test_file) as fin:
        for line in fin:
            arr = line.strip().split("|")
            audio_path = arr[0]
        
            # TODO: 控制说话人编号
            sid = 3
            text = '[ZH]你好,重庆市位于四川省东边[ZH]'
            # else:
            #     sid = speaker_dict[arr[1]]
            #     text = arr[2]
            seq = text_to_sequence(text, cleaner_names=hps.data.text_cleaners
                                   )
            if hps.data.add_blank:
                seq = commons.intersperse(seq, 0)

            # if hps.data.add_blank:
            #     seq = commons.intersperse(seq, 0)
            with torch.no_grad():
                #         x = torch.LongTensor([seq])
                #         x_len = torch.IntTensor([x.size(1)]).long()
                #         sid = torch.LongTensor([sid]).long()
                #         scales = torch.FloatTensor([0.667, 1.0, 1])
                # # make triton dynamic shape happy
                #         scales = scales.unsqueeze(0)

                # use numpy to replace torch
                x = np.array([seq], dtype=np.int64)
                x_len = np.array([x.shape[1]], dtype=np.int64)
                sid = np.array([sid], dtype=np.int64)
                # noise(可用于控制感情等变化程度) lenth(可用于控制整体语速) noisew(控制音素发音长度变化程度)
                # 参考 https://github.com/gbxh/genshinTTS
                scales = np.array([0.667, 0.8, 1], dtype=np.float32)
                # scales = scales[np.newaxis, :]
                # scales.reshape(1, -1)
                scales.resize(1, 3)

                ort_inputs = {
                    'input': x,
                    'input_lengths': x_len,
                    'scales': scales,
                    'sid': sid
                }

                # ort_inputs = {
                #     'input': to_numpy(x),
                #     'input_lengths': to_numpy(x_len),
                #     'scales': to_numpy(scales),
                #     'sid': to_numpy(sid)
                # }
                import time
                # start_time = time.time()
                start_time = time.perf_counter()
                audio = np.squeeze(ort_sess.run(None, ort_inputs))
                audio *= 32767.0 / max(0.01, np.max(np.abs(audio))) * 0.6
                audio = np.clip(audio, -32767.0, 32767.0)
                end_time = time.perf_counter()
                # end_time = time.time()
                print("infer time cost: ", end_time - start_time, "s")

                wavfile.write(args.outdir + "/" + audio_path.split("/")[-1],
                              hps.data.sampling_rate, audio.astype(np.int16))


if __name__ == '__main__':
    main()