import torch import commons import utils from models import SynthesizerTrn 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 # latest_version = "1.0" class InvalidToneError(ValueError): pass def get_net_g(model_path: str, version: str, device: str, hps): 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, ): # 在此处实现当前版本的get_text norm_text, phone, tone, word2ph = clean_text(text, language_str) 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, ): 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) audio = ( 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, )[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