File size: 27,509 Bytes
3b7b011
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
"""
0416后的更新:
    引入config中half
    重建npy而不用填写
    v2支持
    无f0模型支持
    修复

    int16:
    增加无索引支持
    f0算法改harvest(怎么看就只有这个会影响CPU占用),但是不这么改效果不好
"""
import os, sys, traceback, re

import json

now_dir = os.getcwd()
sys.path.append(now_dir)
from assets.configs.config import Config

Config = Config()

import torch_directml
import PySimpleGUI as sg
import sounddevice as sd
import noisereduce as nr
import numpy as np
from fairseq import checkpoint_utils
import librosa, torch, pyworld, faiss, time, threading
import torch.nn.functional as F
import torchaudio.transforms as tat
import scipy.signal as signal


# import matplotlib.pyplot as plt
from lib.infer.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from assets.i18n.i18n import I18nAuto

i18n = I18nAuto()
device = torch_directml.device(torch_directml.default_device())
current_dir = os.getcwd()


class RVC:
    def __init__(
        self, key, hubert_path, pth_path, index_path, npy_path, index_rate
    ) -> None:
        """
        初始化
        """
        try:
            self.f0_up_key = key
            self.time_step = 160 / 16000 * 1000
            self.f0_min = 50
            self.f0_max = 1100
            self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
            self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
            self.sr = 16000
            self.window = 160
            if index_rate != 0:
                self.index = faiss.read_index(index_path)
                # self.big_npy = np.load(npy_path)
                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
                print("index search enabled")
            self.index_rate = index_rate
            model_path = hubert_path
            print("load model(s) from {}".format(model_path))
            models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
                [model_path],
                suffix="",
            )
            self.model = models[0]
            self.model = self.model.to(device)
            if Config.is_half:
                self.model = self.model.half()
            else:
                self.model = self.model.float()
            self.model.eval()
            cpt = torch.load(pth_path, map_location="cpu")
            self.tgt_sr = cpt["config"][-1]
            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
            self.if_f0 = cpt.get("f0", 1)
            self.version = cpt.get("version", "v1")
            if self.version == "v1":
                if self.if_f0 == 1:
                    self.net_g = SynthesizerTrnMs256NSFsid(
                        *cpt["config"], is_half=Config.is_half
                    )
                else:
                    self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
            elif self.version == "v2":
                if self.if_f0 == 1:
                    self.net_g = SynthesizerTrnMs768NSFsid(
                        *cpt["config"], is_half=Config.is_half
                    )
                else:
                    self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
            del self.net_g.enc_q
            print(self.net_g.load_state_dict(cpt["weight"], strict=False))
            self.net_g.eval().to(device)
            if Config.is_half:
                self.net_g = self.net_g.half()
            else:
                self.net_g = self.net_g.float()
        except:
            print(traceback.format_exc())

    def get_f0(self, x, f0_up_key, inp_f0=None):
        x_pad = 1
        f0_min = 50
        f0_max = 1100
        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
        f0, t = pyworld.harvest(
            x.astype(np.double),
            fs=self.sr,
            f0_ceil=f0_max,
            f0_floor=f0_min,
            frame_period=10,
        )
        f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
        f0 = signal.medfilt(f0, 3)
        f0 *= pow(2, f0_up_key / 12)
        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
        tf0 = self.sr // self.window  # 每秒f0点数
        if inp_f0 is not None:
            delta_t = np.round(
                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
            ).astype("int16")
            replace_f0 = np.interp(
                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
            )
            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
        f0bak = f0.copy()
        f0_mel = 1127 * np.log(1 + f0 / 700)
        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
            f0_mel_max - f0_mel_min
        ) + 1
        f0_mel[f0_mel <= 1] = 1
        f0_mel[f0_mel > 255] = 255
        f0_coarse = np.rint(f0_mel).astype(np.int)
        return f0_coarse, f0bak  # 1-0

    def infer(self, feats: torch.Tensor) -> np.ndarray:
        """
        推理函数
        """
        audio = feats.clone().cpu().numpy()
        assert feats.dim() == 1, feats.dim()
        feats = feats.view(1, -1)
        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
        if Config.is_half:
            feats = feats.half()
        else:
            feats = feats.float()
        inputs = {
            "source": feats.to(device),
            "padding_mask": padding_mask.to(device),
            "output_layer": 9 if self.version == "v1" else 12,
        }
        torch.cuda.synchronize()
        with torch.no_grad():
            logits = self.model.extract_features(**inputs)
            feats = (
                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
            )

        ####索引优化
        try:
            if (
                hasattr(self, "index")
                and hasattr(self, "big_npy")
                and self.index_rate != 0
            ):
                npy = feats[0].cpu().numpy().astype("float32")
                score, ix = self.index.search(npy, k=8)
                weight = np.square(1 / score)
                weight /= weight.sum(axis=1, keepdims=True)
                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
                if Config.is_half:
                    npy = npy.astype("float16")
                feats = (
                    torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
                    + (1 - self.index_rate) * feats
                )
            else:
                print("index search FAIL or disabled")
        except:
            traceback.print_exc()
            print("index search FAIL")
        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
        torch.cuda.synchronize()
        print(feats.shape)
        if self.if_f0 == 1:
            pitch, pitchf = self.get_f0(audio, self.f0_up_key)
            p_len = min(feats.shape[1], 13000, pitch.shape[0])  # 太大了爆显存
        else:
            pitch, pitchf = None, None
            p_len = min(feats.shape[1], 13000)  # 太大了爆显存
        torch.cuda.synchronize()
        # print(feats.shape,pitch.shape)
        feats = feats[:, :p_len, :]
        if self.if_f0 == 1:
            pitch = pitch[:p_len]
            pitchf = pitchf[:p_len]
            pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
            pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
        p_len = torch.LongTensor([p_len]).to(device)
        ii = 0  # sid
        sid = torch.LongTensor([ii]).to(device)
        with torch.no_grad():
            if self.if_f0 == 1:
                infered_audio = (
                    self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
                    .data.cpu()
                    .float()
                )
            else:
                infered_audio = (
                    self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
                )
        torch.cuda.synchronize()
        return infered_audio


class GUIConfig:
    def __init__(self) -> None:
        self.hubert_path: str = ""
        self.pth_path: str = ""
        self.index_path: str = ""
        self.npy_path: str = ""
        self.pitch: int = 12
        self.samplerate: int = 44100
        self.block_time: float = 1.0  # s
        self.buffer_num: int = 1
        self.threhold: int = -30
        self.crossfade_time: float = 0.08
        self.extra_time: float = 0.04
        self.I_noise_reduce = False
        self.O_noise_reduce = False
        self.index_rate = 0.3


class GUI:
    def __init__(self) -> None:
        self.config = GUIConfig()
        self.flag_vc = False

        self.launcher()

    def load(self):
        (
            input_devices,
            output_devices,
            input_devices_indices,
            output_devices_indices,
        ) = self.get_devices()
        try:
            with open("values1.json", "r") as j:
                data = json.load(j)
        except:
            with open("values1.json", "w") as j:
                data = {
                    "pth_path": "",
                    "index_path": "",
                    "sg_input_device": input_devices[
                        input_devices_indices.index(sd.default.device[0])
                    ],
                    "sg_output_device": output_devices[
                        output_devices_indices.index(sd.default.device[1])
                    ],
                    "threhold": "-45",
                    "pitch": "0",
                    "index_rate": "0",
                    "block_time": "1",
                    "crossfade_length": "0.04",
                    "extra_time": "1",
                }
        return data

    def launcher(self):
        data = self.load()
        sg.theme("LightBlue3")
        input_devices, output_devices, _, _ = self.get_devices()
        layout = [
            [
                sg.Frame(
                    title=i18n("Load model"),
                    layout=[
                        [
                            sg.Input(
                                default_text="hubert_base.pt",
                                key="hubert_path",
                                disabled=True,
                            ),
                            sg.FileBrowse(
                                i18n("Hubert Model"),
                                initial_folder=os.path.join(os.getcwd()),
                                file_types=(("pt files", "*.pt"),),
                            ),
                        ],
                        [
                            sg.Input(
                                default_text=data.get("pth_path", ""),
                                key="pth_path",
                            ),
                            sg.FileBrowse(
                                i18n("Select the .pth file"),
                                initial_folder=os.path.join(os.getcwd(), "weights"),
                                file_types=(("weight files", "*.pth"),),
                            ),
                        ],
                        [
                            sg.Input(
                                default_text=data.get("index_path", ""),
                                key="index_path",
                            ),
                            sg.FileBrowse(
                                i18n("Select the .index file"),
                                initial_folder=os.path.join(os.getcwd(), "logs"),
                                file_types=(("index files", "*.index"),),
                            ),
                        ],
                        [
                            sg.Input(
                                default_text="你不需要填写这个You don't need write this.",
                                key="npy_path",
                                disabled=True,
                            ),
                            sg.FileBrowse(
                                i18n("Select the .npy file"),
                                initial_folder=os.path.join(os.getcwd(), "logs"),
                                file_types=(("feature files", "*.npy"),),
                            ),
                        ],
                    ],
                )
            ],
            [
                sg.Frame(
                    layout=[
                        [
                            sg.Text(i18n("Input device")),
                            sg.Combo(
                                input_devices,
                                key="sg_input_device",
                                default_value=data.get("sg_input_device", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("Output device")),
                            sg.Combo(
                                output_devices,
                                key="sg_output_device",
                                default_value=data.get("sg_output_device", ""),
                            ),
                        ],
                    ],
                    title=i18n("Audio device (please use the same type of driver)"),
                )
            ],
            [
                sg.Frame(
                    layout=[
                        [
                            sg.Text(i18n("Response threshold")),
                            sg.Slider(
                                range=(-60, 0),
                                key="threhold",
                                resolution=1,
                                orientation="h",
                                default_value=data.get("threhold", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("Pitch settings")),
                            sg.Slider(
                                range=(-24, 24),
                                key="pitch",
                                resolution=1,
                                orientation="h",
                                default_value=data.get("pitch", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("Index Rate")),
                            sg.Slider(
                                range=(0.0, 1.0),
                                key="index_rate",
                                resolution=0.01,
                                orientation="h",
                                default_value=data.get("index_rate", ""),
                            ),
                        ],
                    ],
                    title=i18n("General settings"),
                ),
                sg.Frame(
                    layout=[
                        [
                            sg.Text(i18n("Sample length")),
                            sg.Slider(
                                range=(0.1, 3.0),
                                key="block_time",
                                resolution=0.1,
                                orientation="h",
                                default_value=data.get("block_time", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("Fade length")),
                            sg.Slider(
                                range=(0.01, 0.15),
                                key="crossfade_length",
                                resolution=0.01,
                                orientation="h",
                                default_value=data.get("crossfade_length", ""),
                            ),
                        ],
                        [
                            sg.Text(i18n("Extra推理时长")),
                            sg.Slider(
                                range=(0.05, 3.00),
                                key="extra_time",
                                resolution=0.01,
                                orientation="h",
                                default_value=data.get("extra_time", ""),
                            ),
                        ],
                        [
                            sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
                            sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
                        ],
                    ],
                    title=i18n("Performance settings"),
                ),
            ],
            [
                sg.Button(i18n("开始音频Convert"), key="start_vc"),
                sg.Button(i18n("停止音频Convert"), key="stop_vc"),
                sg.Text(i18n("Inference time (ms):")),
                sg.Text("0", key="infer_time"),
            ],
        ]
        self.window = sg.Window("RVC - GUI", layout=layout)
        self.event_handler()

    def event_handler(self):
        while True:
            event, values = self.window.read()
            if event == sg.WINDOW_CLOSED:
                self.flag_vc = False
                exit()
            if event == "start_vc" and self.flag_vc == False:
                if self.set_values(values) == True:
                    print("using_cuda:" + str(torch.cuda.is_available()))
                    self.start_vc()
                    settings = {
                        "pth_path": values["pth_path"],
                        "index_path": values["index_path"],
                        "sg_input_device": values["sg_input_device"],
                        "sg_output_device": values["sg_output_device"],
                        "threhold": values["threhold"],
                        "pitch": values["pitch"],
                        "index_rate": values["index_rate"],
                        "block_time": values["block_time"],
                        "crossfade_length": values["crossfade_length"],
                        "extra_time": values["extra_time"],
                    }
                    with open("values1.json", "w") as j:
                        json.dump(settings, j)
            if event == "stop_vc" and self.flag_vc == True:
                self.flag_vc = False

    def set_values(self, values):
        if len(values["pth_path"].strip()) == 0:
            sg.popup(i18n("Select the pth file"))
            return False
        if len(values["index_path"].strip()) == 0:
            sg.popup(i18n("Select the index file"))
            return False
        pattern = re.compile("[^\x00-\x7F]+")
        if pattern.findall(values["hubert_path"]):
            sg.popup(i18n("The hubert model path must not contain Chinese characters"))
            return False
        if pattern.findall(values["pth_path"]):
            sg.popup(i18n("The pth file path must not contain Chinese characters."))
            return False
        if pattern.findall(values["index_path"]):
            sg.popup(i18n("The index file path must not contain Chinese characters."))
            return False
        self.set_devices(values["sg_input_device"], values["sg_output_device"])
        self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
        self.config.pth_path = values["pth_path"]
        self.config.index_path = values["index_path"]
        self.config.npy_path = values["npy_path"]
        self.config.threhold = values["threhold"]
        self.config.pitch = values["pitch"]
        self.config.block_time = values["block_time"]
        self.config.crossfade_time = values["crossfade_length"]
        self.config.extra_time = values["extra_time"]
        self.config.I_noise_reduce = values["I_noise_reduce"]
        self.config.O_noise_reduce = values["O_noise_reduce"]
        self.config.index_rate = values["index_rate"]
        return True

    def start_vc(self):
        torch.cuda.empty_cache()
        self.flag_vc = True
        self.block_frame = int(self.config.block_time * self.config.samplerate)
        self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
        self.sola_search_frame = int(0.012 * self.config.samplerate)
        self.delay_frame = int(0.01 * self.config.samplerate)  # 往前预留0.02s
        self.extra_frame = int(self.config.extra_time * self.config.samplerate)
        self.rvc = None
        self.rvc = RVC(
            self.config.pitch,
            self.config.hubert_path,
            self.config.pth_path,
            self.config.index_path,
            self.config.npy_path,
            self.config.index_rate,
        )
        self.input_wav: np.ndarray = np.zeros(
            self.extra_frame
            + self.crossfade_frame
            + self.sola_search_frame
            + self.block_frame,
            dtype="float32",
        )
        self.output_wav: torch.Tensor = torch.zeros(
            self.block_frame, device=device, dtype=torch.float32
        )
        self.sola_buffer: torch.Tensor = torch.zeros(
            self.crossfade_frame, device=device, dtype=torch.float32
        )
        self.fade_in_window: torch.Tensor = torch.linspace(
            0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
        )
        self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
        self.resampler1 = tat.Resample(
            orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
        )
        self.resampler2 = tat.Resample(
            orig_freq=self.rvc.tgt_sr,
            new_freq=self.config.samplerate,
            dtype=torch.float32,
        )
        thread_vc = threading.Thread(target=self.soundinput)
        thread_vc.start()

    def soundinput(self):
        """
        接受音频输入
        """
        with sd.Stream(
            channels=2,
            callback=self.audio_callback,
            blocksize=self.block_frame,
            samplerate=self.config.samplerate,
            dtype="float32",
        ):
            while self.flag_vc:
                time.sleep(self.config.block_time)
                print("Audio block passed.")
        print("ENDing VC")

    def audio_callback(
        self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
    ):
        """
        音频处理
        """
        start_time = time.perf_counter()
        indata = librosa.to_mono(indata.T)
        if self.config.I_noise_reduce:
            indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)

        """noise gate"""
        frame_length = 2048
        hop_length = 1024
        rms = librosa.feature.rms(
            y=indata, frame_length=frame_length, hop_length=hop_length
        )
        db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
        # print(rms.shape,db.shape,db)
        for i in range(db_threhold.shape[0]):
            if db_threhold[i]:
                indata[i * hop_length : (i + 1) * hop_length] = 0
        self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)

        # infer
        print("input_wav:" + str(self.input_wav.shape))
        # print('infered_wav:'+str(infer_wav.shape))
        infer_wav: torch.Tensor = self.resampler2(
            self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
        )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
            device
        )
        print("infer_wav:" + str(infer_wav.shape))

        # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
        cor_nom = F.conv1d(
            infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
            self.sola_buffer[None, None, :],
        )
        cor_den = torch.sqrt(
            F.conv1d(
                infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
                ** 2,
                torch.ones(1, 1, self.crossfade_frame, device=device),
            )
            + 1e-8
        )
        sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
        print("sola offset: " + str(int(sola_offset)))

        # crossfade
        self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
        self.output_wav[: self.crossfade_frame] *= self.fade_in_window
        self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
        if sola_offset < self.sola_search_frame:
            self.sola_buffer[:] = (
                infer_wav[
                    -self.sola_search_frame
                    - self.crossfade_frame
                    + sola_offset : -self.sola_search_frame
                    + sola_offset
                ]
                * self.fade_out_window
            )
        else:
            self.sola_buffer[:] = (
                infer_wav[-self.crossfade_frame :] * self.fade_out_window
            )

        if self.config.O_noise_reduce:
            outdata[:] = np.tile(
                nr.reduce_noise(
                    y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
                ),
                (2, 1),
            ).T
        else:
            outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
        total_time = time.perf_counter() - start_time
        self.window["infer_time"].update(int(total_time * 1000))
        print("infer time:" + str(total_time))

    def get_devices(self, update: bool = True):
        """获取设备列表"""
        if update:
            sd._terminate()
            sd._initialize()
        devices = sd.query_devices()
        hostapis = sd.query_hostapis()
        for hostapi in hostapis:
            for device_idx in hostapi["devices"]:
                devices[device_idx]["hostapi_name"] = hostapi["name"]
        input_devices = [
            f"{d['name']} ({d['hostapi_name']})"
            for d in devices
            if d["max_input_channels"] > 0
        ]
        output_devices = [
            f"{d['name']} ({d['hostapi_name']})"
            for d in devices
            if d["max_output_channels"] > 0
        ]
        input_devices_indices = [
            d["index"] if "index" in d else d["name"]
            for d in devices
            if d["max_input_channels"] > 0
        ]
        output_devices_indices = [
            d["index"] if "index" in d else d["name"]
            for d in devices
            if d["max_output_channels"] > 0
        ]
        return (
            input_devices,
            output_devices,
            input_devices_indices,
            output_devices_indices,
        )

    def set_devices(self, input_device, output_device):
        """设置输出设备"""
        (
            input_devices,
            output_devices,
            input_device_indices,
            output_device_indices,
        ) = self.get_devices()
        sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
        sd.default.device[1] = output_device_indices[
            output_devices.index(output_device)
        ]
        print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
        print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))


gui = GUI()