| """ |
| 1.1 版本兼容 |
| https://github.com/fishaudio/Bert-VITS2/releases/tag/1.1 |
| """ |
| import torch |
| import commons |
| from .text.cleaner import clean_text |
| from .text import cleaned_text_to_sequence |
| from oldVersion.V111.text import get_bert |
|
|
|
|
| def get_text(text, language_str, hps, device): |
| norm_text, phone, tone, word2ph = clean_text(text, language_str) |
| 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 = get_bert(norm_text, word2ph, language_str, device) |
| del word2ph |
| assert bert.shape[-1] == len(phone), phone |
|
|
| if language_str == "ZH": |
| bert = bert |
| ja_bert = torch.zeros(768, len(phone)) |
| elif language_str == "JP": |
| ja_bert = bert |
| bert = torch.zeros(1024, len(phone)) |
| else: |
| bert = torch.zeros(1024, len(phone)) |
| ja_bert = torch.zeros(768, len(phone)) |
|
|
| 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, phone, tone, language |
|
|
|
|
| def infer( |
| text, |
| sdp_ratio, |
| noise_scale, |
| noise_scale_w, |
| length_scale, |
| sid, |
| language, |
| hps, |
| net_g, |
| device, |
| ): |
| bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps, device) |
| 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) |
| 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, |
| 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, x_tst_lengths, speakers, tones, lang_ids, bert, ja_bert |
| if torch.cuda.is_available(): |
| torch.cuda.empty_cache() |
| return audio |
|
|
