""" 版本管理、兼容推理及模型加载实现。 版本说明： 1. 版本号与github的release版本号对应，使用哪个release版本训练的模型即对应其版本号 2. 请在模型的config.json中显示声明版本号，添加一个字段"version" : "你的版本号" 特殊版本说明： 1.1.1-fix： 1.1.1版本训练的模型，但是在推理时使用dev的日语修复 1.1.1-dev： dev开发 2.1：当前版本 """ import torch import commons from text import cleaned_text_to_sequence, get_bert from emo_gen import get_emo from text.cleaner import clean_text import utils from models import SynthesizerTrn from text.symbols import symbols # 当前版本信息 latest_version = "2.1" def get_net_g(model_path: str, version: str, device: str, hps): if version != latest_version: pass else: # 当前版本模型 net_g 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.eval() _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True) return net_g def get_text(text, reference_audio, emotion, language_str, hps, device): # 在此处实现当前版本的get_text 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_ori = get_bert(norm_text, word2ph, language_str, device) 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") emo = ( torch.from_numpy(get_emo(reference_audio)) if reference_audio else torch.Tensor([emotion]) ) 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, emo, phone, tone, language def infer( text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language, hps, net_g, device, reference_audio=None, emotion=None, skip_start=False, skip_end=False, ): version = hps.version if hasattr(hps, "version") else latest_version # 非当前版本，根据版本号选择合适的infer if version != latest_version: pass # 在此处实现当前版本的推理 bert, ja_bert, en_bert, emo, phones, tones, lang_ids = get_text( text, reference_audio, emotion, language, hps, device ) if skip_start: phones = phones[1:] tones = tones[1:] lang_ids = lang_ids[1:] bert = bert[:, 1:] ja_bert = ja_bert[:, 1:] en_bert = en_bert[:, 1:] if skip_end: phones = phones[:-1] tones = tones[:-1] lang_ids = lang_ids[:-1] bert = bert[:, :-1] ja_bert = ja_bert[:, :-1] en_bert = en_bert[:, :-1] 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) emo = emo.to(device).unsqueeze(0) 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, emo, 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 def infer_multilang( text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language, hps, net_g, device, reference_audio=None, emotion=None, skip_start=False, skip_end=False, ): bert, ja_bert, en_bert, emo, phones, tones, lang_ids = [], [], [], [], [], [], [] # bert, ja_bert, en_bert, phones, tones, lang_ids = get_text( # text, language, hps, device # ) for idx, (txt, lang) in enumerate(zip(text, language)): skip_start = (idx != 0) or (skip_start and idx == 0) skip_end = (idx != len(text) - 1) or (skip_end and idx == len(text) - 1) ( temp_bert, temp_ja_bert, temp_en_bert, temp_emo, temp_phones, temp_tones, temp_lang_ids, ) = get_text(txt, reference_audio, emotion, language, hps, device) if skip_start: temp_bert = temp_bert[:, 1:] temp_ja_bert = temp_ja_bert[:, 1:] temp_en_bert = temp_en_bert[:, 1:] temp_emo = temp_emo[:, 1:] temp_phones = temp_phones[1:] temp_tones = temp_tones[1:] temp_lang_ids = temp_lang_ids[1:] if skip_end: temp_bert = temp_bert[:, :-1] temp_ja_bert = temp_ja_bert[:, :-1] temp_en_bert = temp_en_bert[:, :-1] temp_emo = temp_emo[:, :-1] temp_phones = temp_phones[:-1] temp_tones = temp_tones[:-1] temp_lang_ids = temp_lang_ids[:-1] bert.append(temp_bert) ja_bert.append(temp_ja_bert) en_bert.append(temp_en_bert) emo.append(temp_emo) 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) emo = torch.concatenate(emo, 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, emo, 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