| """ | |
| 1.1.1版本兼容 | |
| https://github.com/fishaudio/Bert-VITS2/releases/tag/1.1.1 | |
| """ | |
| import torch | |
| import commons | |
| from .text.cleaner import clean_text, clean_text_fix | |
| from .text import cleaned_text_to_sequence | |
| from .text import get_bert, get_bert_fix | |
| 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 get_text_fix(text, language_str, hps, device): | |
| norm_text, phone, tone, word2ph = clean_text_fix(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_fix(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 | |
| def infer_fix( | |
| 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_fix(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 | |