File size: 9,532 Bytes
d232606
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import torch

import commons
import utils
from models import SynthesizerTrn
from models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
from text.symbols import symbols
from common.log import logger


class InvalidToneError(ValueError):
    pass


def get_net_g(model_path: str, version: str, device: str, hps):
    if version.endswith("JP-Extra"):
        logger.info("Using JP-Extra model")
        net_g = SynthesizerTrnJPExtra(
            len(symbols),
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            **hps.model,
        ).to(device)
    else:
        logger.info("Using normal model")
        net_g = SynthesizerTrn(
            len(symbols),
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            **hps.model,
        ).to(device)
    net_g.state_dict()
    _ = net_g.eval()
    if model_path.endswith(".pth") or model_path.endswith(".pt"):
        _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
    elif model_path.endswith(".safetensors"):
        _ = utils.load_safetensors(model_path, net_g, True)
    else:
        raise ValueError(f"Unknown model format: {model_path}")
    return net_g


def get_text(
    text,
    language_str,
    hps,
    device,
    assist_text=None,
    assist_text_weight=0.7,
    given_tone=None,
):
    use_jp_extra = hps.version.endswith("JP-Extra")
    norm_text, phone, tone, word2ph = clean_text(text, language_str, use_jp_extra)
    if given_tone is not None:
        if len(given_tone) != len(phone):
            raise InvalidToneError(
                f"Length of given_tone ({len(given_tone)}) != length of phone ({len(phone)})"
            )
        tone = given_tone
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)

    if hps.data.add_blank:
        phone = commons.intersperse(phone, 0)
        tone = commons.intersperse(tone, 0)
        language = commons.intersperse(language, 0)
        for i in range(len(word2ph)):
            word2ph[i] = word2ph[i] * 2
        word2ph[0] += 1
    bert_ori = get_bert(
        norm_text, word2ph, language_str, device, assist_text, assist_text_weight
    )
    del word2ph
    assert bert_ori.shape[-1] == len(phone), phone

    if language_str == "ZH":
        bert = bert_ori
        ja_bert = torch.zeros(1024, len(phone))
        en_bert = torch.zeros(1024, len(phone))
    elif language_str == "JP":
        bert = torch.zeros(1024, len(phone))
        ja_bert = bert_ori
        en_bert = torch.zeros(1024, len(phone))
    elif language_str == "EN":
        bert = torch.zeros(1024, len(phone))
        ja_bert = torch.zeros(1024, len(phone))
        en_bert = bert_ori
    else:
        raise ValueError("language_str should be ZH, JP or EN")

    assert bert.shape[-1] == len(
        phone
    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"

    phone = torch.LongTensor(phone)
    tone = torch.LongTensor(tone)
    language = torch.LongTensor(language)
    return bert, ja_bert, en_bert, phone, tone, language


def infer(
    text,
    style_vec,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid: int,  # In the original Bert-VITS2, its speaker_name: str, but here it's id
    language,
    hps,
    net_g,
    device,
    skip_start=False,
    skip_end=False,
    assist_text=None,
    assist_text_weight=0.7,
    given_tone=None,
):
    is_jp_extra = hps.version.endswith("JP-Extra")
    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
        text,
        language,
        hps,
        device,
        assist_text=assist_text,
        assist_text_weight=assist_text_weight,
        given_tone=given_tone,
    )
    if skip_start:
        phones = phones[3:]
        tones = tones[3:]
        lang_ids = lang_ids[3:]
        bert = bert[:, 3:]
        ja_bert = ja_bert[:, 3:]
        en_bert = en_bert[:, 3:]
    if skip_end:
        phones = phones[:-2]
        tones = tones[:-2]
        lang_ids = lang_ids[:-2]
        bert = bert[:, :-2]
        ja_bert = ja_bert[:, :-2]
        en_bert = en_bert[:, :-2]
    with torch.no_grad():
        x_tst = phones.to(device).unsqueeze(0)
        tones = tones.to(device).unsqueeze(0)
        lang_ids = lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        ja_bert = ja_bert.to(device).unsqueeze(0)
        en_bert = en_bert.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        style_vec = torch.from_numpy(style_vec).to(device).unsqueeze(0)
        del phones
        sid_tensor = torch.LongTensor([sid]).to(device)
        if is_jp_extra:
            output = net_g.infer(
                x_tst,
                x_tst_lengths,
                sid_tensor,
                tones,
                lang_ids,
                ja_bert,
                style_vec=style_vec,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )
        else:
            output = net_g.infer(
                x_tst,
                x_tst_lengths,
                sid_tensor,
                tones,
                lang_ids,
                bert,
                ja_bert,
                en_bert,
                style_vec=style_vec,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )
        audio = output[0][0, 0].data.cpu().float().numpy()
        del (
            x_tst,
            tones,
            lang_ids,
            bert,
            x_tst_lengths,
            sid_tensor,
            ja_bert,
            en_bert,
            style_vec,
        )  # , emo
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return audio


def infer_multilang(
    text,
    style_vec,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    language,
    hps,
    net_g,
    device,
    skip_start=False,
    skip_end=False,
):
    bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], []
    # emo = get_emo_(reference_audio, emotion, sid)
    # if isinstance(reference_audio, np.ndarray):
    #     emo = get_clap_audio_feature(reference_audio, device)
    # else:
    #     emo = get_clap_text_feature(emotion, device)
    # emo = torch.squeeze(emo, dim=1)
    for idx, (txt, lang) in enumerate(zip(text, language)):
        _skip_start = (idx != 0) or (skip_start and idx == 0)
        _skip_end = (idx != len(language) - 1) or skip_end
        (
            temp_bert,
            temp_ja_bert,
            temp_en_bert,
            temp_phones,
            temp_tones,
            temp_lang_ids,
        ) = get_text(txt, lang, hps, device)
        if _skip_start:
            temp_bert = temp_bert[:, 3:]
            temp_ja_bert = temp_ja_bert[:, 3:]
            temp_en_bert = temp_en_bert[:, 3:]
            temp_phones = temp_phones[3:]
            temp_tones = temp_tones[3:]
            temp_lang_ids = temp_lang_ids[3:]
        if _skip_end:
            temp_bert = temp_bert[:, :-2]
            temp_ja_bert = temp_ja_bert[:, :-2]
            temp_en_bert = temp_en_bert[:, :-2]
            temp_phones = temp_phones[:-2]
            temp_tones = temp_tones[:-2]
            temp_lang_ids = temp_lang_ids[:-2]
        bert.append(temp_bert)
        ja_bert.append(temp_ja_bert)
        en_bert.append(temp_en_bert)
        phones.append(temp_phones)
        tones.append(temp_tones)
        lang_ids.append(temp_lang_ids)
    bert = torch.concatenate(bert, dim=1)
    ja_bert = torch.concatenate(ja_bert, dim=1)
    en_bert = torch.concatenate(en_bert, dim=1)
    phones = torch.concatenate(phones, dim=0)
    tones = torch.concatenate(tones, dim=0)
    lang_ids = torch.concatenate(lang_ids, dim=0)
    with torch.no_grad():
        x_tst = phones.to(device).unsqueeze(0)
        tones = tones.to(device).unsqueeze(0)
        lang_ids = lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        ja_bert = ja_bert.to(device).unsqueeze(0)
        en_bert = en_bert.to(device).unsqueeze(0)
        # emo = emo.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        del phones
        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
        audio = (
            net_g.infer(
                x_tst,
                x_tst_lengths,
                speakers,
                tones,
                lang_ids,
                bert,
                ja_bert,
                en_bert,
                style_vec=style_vec,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )[0][0, 0]
            .data.cpu()
            .float()
            .numpy()
        )
        del (
            x_tst,
            tones,
            lang_ids,
            bert,
            x_tst_lengths,
            speakers,
            ja_bert,
            en_bert,
        )  # , emo
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return audio