File size: 14,401 Bytes
5a030e1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import hashlib
import io
import json
import logging
import os
import time
from pathlib import Path
from inference import slicer
import gc

import librosa
import numpy as np
# import onnxruntime
import parselmouth
import soundfile
import torch
import torchaudio

import cluster
from hubert import hubert_model
import utils
from models import SynthesizerTrn

logging.getLogger('matplotlib').setLevel(logging.WARNING)


def read_temp(file_name):
    if not os.path.exists(file_name):
        with open(file_name, "w") as f:
            f.write(json.dumps({"info": "temp_dict"}))
        return {}
    else:
        try:
            with open(file_name, "r") as f:
                data = f.read()
            data_dict = json.loads(data)
            if os.path.getsize(file_name) > 50 * 1024 * 1024:
                f_name = file_name.replace("\\", "/").split("/")[-1]
                print(f"clean {f_name}")
                for wav_hash in list(data_dict.keys()):
                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
                        del data_dict[wav_hash]
        except Exception as e:
            print(e)
            print(f"{file_name} error,auto rebuild file")
            data_dict = {"info": "temp_dict"}
        return data_dict


def write_temp(file_name, data):
    with open(file_name, "w") as f:
        f.write(json.dumps(data))


def timeit(func):
    def run(*args, **kwargs):
        t = time.time()
        res = func(*args, **kwargs)
        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
        return res

    return run


def format_wav(audio_path):
    if Path(audio_path).suffix == '.wav':
        return
    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)


def get_end_file(dir_path, end):
    file_lists = []
    for root, dirs, files in os.walk(dir_path):
        files = [f for f in files if f[0] != '.']
        dirs[:] = [d for d in dirs if d[0] != '.']
        for f_file in files:
            if f_file.endswith(end):
                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
    return file_lists


def get_md5(content):
    return hashlib.new("md5", content).hexdigest()

def fill_a_to_b(a, b):
    if len(a) < len(b):
        for _ in range(0, len(b) - len(a)):
            a.append(a[0])

def mkdir(paths: list):
    for path in paths:
        if not os.path.exists(path):
            os.mkdir(path)

def pad_array(arr, target_length):
    current_length = arr.shape[0]
    if current_length >= target_length:
        return arr
    else:
        pad_width = target_length - current_length
        pad_left = pad_width // 2
        pad_right = pad_width - pad_left
        padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
        return padded_arr
    
def split_list_by_n(list_collection, n, pre=0):
    for i in range(0, len(list_collection), n):
        yield list_collection[i-pre if i-pre>=0 else i: i + n]


class F0FilterException(Exception):
    pass

class Svc(object):
    def __init__(self, net_g_path, config_path,
                 device=None,
                 cluster_model_path="logs/44k/kmeans_10000.pt",
                 nsf_hifigan_enhance = False
                 ):
        self.net_g_path = net_g_path
        if device is None:
            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        else:
            self.dev = torch.device(device)
        self.net_g_ms = None
        self.hps_ms = utils.get_hparams_from_file(config_path)
        self.target_sample = self.hps_ms.data.sampling_rate
        self.hop_size = self.hps_ms.data.hop_length
        self.spk2id = self.hps_ms.spk
        self.nsf_hifigan_enhance = nsf_hifigan_enhance
        # load hubert
        self.hubert_model = utils.get_hubert_model().to(self.dev)
        self.load_model()
        if os.path.exists(cluster_model_path):
            self.cluster_model = cluster.get_cluster_model(cluster_model_path)
        if self.nsf_hifigan_enhance:
            from modules.enhancer import Enhancer
            self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev)

    def load_model(self):
        # get model configuration
        self.net_g_ms = SynthesizerTrn(
            self.hps_ms.data.filter_length // 2 + 1,
            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
            **self.hps_ms.model)
        _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
        if "half" in self.net_g_path and torch.cuda.is_available():
            _ = self.net_g_ms.half().eval().to(self.dev)
        else:
            _ = self.net_g_ms.eval().to(self.dev)



    def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,F0_mean_pooling,cr_threshold=0.05):

        wav, sr = librosa.load(in_path, sr=self.target_sample)

        if F0_mean_pooling == True:
            f0, uv = utils.compute_f0_uv_torchcrepe(torch.FloatTensor(wav), sampling_rate=self.target_sample, hop_length=self.hop_size,device=self.dev,cr_threshold = cr_threshold)
            if f0_filter and sum(f0) == 0:
                raise F0FilterException("No voice detected")
            f0 = torch.FloatTensor(list(f0))
            uv = torch.FloatTensor(list(uv))
        if F0_mean_pooling == False:
            f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
            if f0_filter and sum(f0) == 0:
                raise F0FilterException("No voice detected")
            f0, uv = utils.interpolate_f0(f0)
            f0 = torch.FloatTensor(f0)
            uv = torch.FloatTensor(uv)

        f0 = f0 * 2 ** (tran / 12)
        f0 = f0.unsqueeze(0).to(self.dev)
        uv = uv.unsqueeze(0).to(self.dev)

        wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
        wav16k = torch.from_numpy(wav16k).to(self.dev)
        c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])

        if cluster_infer_ratio !=0:
            cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
            cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
            c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c

        c = c.unsqueeze(0)
        return c, f0, uv

    def infer(self, speaker, tran, raw_path,
              cluster_infer_ratio=0,
              auto_predict_f0=False,
              noice_scale=0.4,
              f0_filter=False,
              F0_mean_pooling=False,
              enhancer_adaptive_key = 0,
              cr_threshold = 0.05
              ):

        speaker_id = self.spk2id.__dict__.get(speaker)
        if not speaker_id and type(speaker) is int:
            if len(self.spk2id.__dict__) >= speaker:
                speaker_id = speaker
        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
        c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling,cr_threshold=cr_threshold)
        if "half" in self.net_g_path and torch.cuda.is_available():
            c = c.half()
        with torch.no_grad():
            start = time.time()
            audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
            if self.nsf_hifigan_enhance:
                audio, _ = self.enhancer.enhance(
                                                                        audio[None,:], 
                                                                        self.target_sample, 
                                                                        f0[:,:,None], 
                                                                        self.hps_ms.data.hop_length, 
                                                                        adaptive_key = enhancer_adaptive_key)
            use_time = time.time() - start
            print("vits use time:{}".format(use_time))
        return audio, audio.shape[-1]

    def clear_empty(self):
        # clean up vram
        torch.cuda.empty_cache()

    def unload_model(self):
        # unload model
        self.net_g_ms = self.net_g_ms.to("cpu")
        del self.net_g_ms
        if hasattr(self,"enhancer"): 
            self.enhancer.enhancer = self.enhancer.enhancer.to("cpu")
            del self.enhancer.enhancer
            del self.enhancer
        gc.collect()

    def slice_inference(self,
                        raw_audio_path,
                        spk,
                        tran,
                        slice_db,
                        cluster_infer_ratio,
                        auto_predict_f0,
                        noice_scale,
                        pad_seconds=0.5,
                        clip_seconds=0,
                        lg_num=0,
                        lgr_num =0.75,
                        F0_mean_pooling = False,
                        enhancer_adaptive_key = 0,
                        cr_threshold = 0.05
                        ):
        wav_path = raw_audio_path
        chunks = slicer.cut(wav_path, db_thresh=slice_db)
        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
        per_size = int(clip_seconds*audio_sr)
        lg_size = int(lg_num*audio_sr)
        lg_size_r = int(lg_size*lgr_num)
        lg_size_c_l = (lg_size-lg_size_r)//2
        lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
        lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
        
        audio = []
        for (slice_tag, data) in audio_data:
            print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
            # padd
            length = int(np.ceil(len(data) / audio_sr * self.target_sample))
            if slice_tag:
                print('jump empty segment')
                _audio = np.zeros(length)
                audio.extend(list(pad_array(_audio, length)))
                continue
            if per_size != 0:
                datas = split_list_by_n(data, per_size,lg_size)
            else:
                datas = [data]
            for k,dat in enumerate(datas):
                per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
                if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
                # padd
                pad_len = int(audio_sr * pad_seconds)
                dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
                raw_path = io.BytesIO()
                soundfile.write(raw_path, dat, audio_sr, format="wav")
                raw_path.seek(0)
                out_audio, out_sr = self.infer(spk, tran, raw_path,
                                                    cluster_infer_ratio=cluster_infer_ratio,
                                                    auto_predict_f0=auto_predict_f0,
                                                    noice_scale=noice_scale,
                                                    F0_mean_pooling = F0_mean_pooling,
                                                    enhancer_adaptive_key = enhancer_adaptive_key,
                                                    cr_threshold = cr_threshold
                                                    )
                _audio = out_audio.cpu().numpy()
                pad_len = int(self.target_sample * pad_seconds)
                _audio = _audio[pad_len:-pad_len]
                _audio = pad_array(_audio, per_length)
                if lg_size!=0 and k!=0:
                    lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
                    lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r]  if lgr_num != 1 else _audio[0:lg_size]
                    lg_pre = lg1*(1-lg)+lg2*lg
                    audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
                    audio.extend(lg_pre)
                    _audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
                audio.extend(list(_audio))
        return np.array(audio)

class RealTimeVC:
    def __init__(self):
        self.last_chunk = None
        self.last_o = None
        self.chunk_len = 16000  # chunk length
        self.pre_len = 3840  # cross fade length, multiples of 640

    # Input and output are 1-dimensional numpy waveform arrays

    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
                cluster_infer_ratio=0,
                auto_predict_f0=False,
                noice_scale=0.4,
                f0_filter=False):

        import maad
        audio, sr = torchaudio.load(input_wav_path)
        audio = audio.cpu().numpy()[0]
        temp_wav = io.BytesIO()
        if self.last_chunk is None:
            input_wav_path.seek(0)

            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
                                        cluster_infer_ratio=cluster_infer_ratio,
                                        auto_predict_f0=auto_predict_f0,
                                        noice_scale=noice_scale,
                                        f0_filter=f0_filter)

            audio = audio.cpu().numpy()
            self.last_chunk = audio[-self.pre_len:]
            self.last_o = audio
            return audio[-self.chunk_len:]
        else:
            audio = np.concatenate([self.last_chunk, audio])
            soundfile.write(temp_wav, audio, sr, format="wav")
            temp_wav.seek(0)

            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
                                        cluster_infer_ratio=cluster_infer_ratio,
                                        auto_predict_f0=auto_predict_f0,
                                        noice_scale=noice_scale,
                                        f0_filter=f0_filter)

            audio = audio.cpu().numpy()
            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
            self.last_chunk = audio[-self.pre_len:]
            self.last_o = audio
            return ret[self.chunk_len:2 * self.chunk_len]