""" 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 2.3：当前版本 """ import torch import commons from text import cleaned_text_to_sequence # from clap_wrapper import get_clap_audio_feature, get_clap_text_feature from typing import Union from text.cleaner import clean_text import utils from models import SynthesizerTrn from text.symbols import symbols # from oldVersion.V220.models import SynthesizerTrn as V220SynthesizerTrn # from oldVersion.V220.text import symbols as V220symbols # from oldVersion.V210.models import SynthesizerTrn as V210SynthesizerTrn # from oldVersion.V210.text import symbols as V210symbols # from oldVersion.V200.models import SynthesizerTrn as V200SynthesizerTrn # from oldVersion.V200.text import symbols as V200symbols # from oldVersion.V111.models import SynthesizerTrn as V111SynthesizerTrn # from oldVersion.V111.text import symbols as V111symbols # from oldVersion.V110.models import SynthesizerTrn as V110SynthesizerTrn # from oldVersion.V110.text import symbols as V110symbols # from oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn # from oldVersion.V101.text import symbols as V101symbols # # from oldVersion import V111, V110, V101, V200, V210, V220 # 当前版本信息 latest_version = "2.3" # # 版本兼容 # SynthesizerTrnMap = { # "2.2": V220SynthesizerTrn, # "2.1": V210SynthesizerTrn, # "2.0.2-fix": V200SynthesizerTrn, # "2.0.1": V200SynthesizerTrn, # "2.0": V200SynthesizerTrn, # "1.1.1-fix": V111SynthesizerTrn, # "1.1.1": V111SynthesizerTrn, # "1.1": V110SynthesizerTrn, # "1.1.0": V110SynthesizerTrn, # "1.0.1": V101SynthesizerTrn, # "1.0": V101SynthesizerTrn, # "1.0.0": V101SynthesizerTrn, # } # # symbolsMap = { # "2.2": V220symbols, # "2.1": V210symbols, # "2.0.2-fix": V200symbols, # "2.0.1": V200symbols, # "2.0": V200symbols, # "1.1.1-fix": V111symbols, # "1.1.1": V111symbols, # "1.1": V110symbols, # "1.1.0": V110symbols, # "1.0.1": V101symbols, # "1.0": V101symbols, # "1.0.0": V101symbols, # } # def get_emo_(reference_audio, emotion, sid): # emo = ( # torch.from_numpy(get_emo(reference_audio)) # if reference_audio and emotion == -1 # else torch.FloatTensor( # np.load(f"emo_clustering/{sid}/cluster_center_{emotion}.npy") # ) # ) # return emo def get_net_g(model_path: str, device: str, hps): # 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, language_str, hps, device, style_text=None, style_weight=0.7): style_text = None if style_text == "" else style_text # 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 del word2ph phone = torch.LongTensor(phone) tone = torch.LongTensor(tone) language = torch.LongTensor(language) return phone, tone, language def infer( text, emotion: Union[int, str], sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language, hps, net_g, device, reference_audio=None, skip_start=False, skip_end=False, style_text=None, style_weight=0.7, ): # 2.2版本参数位置变了 # inferMap_V4 = { # "2.2": V220.infer, # } # # 2.1 参数新增 emotion reference_audio skip_start skip_end # inferMap_V3 = { # "2.1": V210.infer, # } # # 支持中日英三语版本 # inferMap_V2 = { # "2.0.2-fix": V200.infer, # "2.0.1": V200.infer, # "2.0": V200.infer, # "1.1.1-fix": V111.infer_fix, # "1.1.1": V111.infer, # "1.1": V110.infer, # "1.1.0": V110.infer, # } # # 仅支持中文版本 # # 在测试中，并未发现两个版本的模型不能互相通用 # inferMap_V1 = { # "1.0.1": V101.infer, # "1.0": V101.infer, # "1.0.0": V101.infer, # } # 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: # if version in inferMap_V4.keys(): # return inferMap_V4[version]( # text, # emotion, # sdp_ratio, # noise_scale, # noise_scale_w, # length_scale, # sid, # language, # hps, # net_g, # device, # reference_audio, # skip_start, # skip_end, # style_text, # style_weight, # ) # if version in inferMap_V3.keys(): # return inferMap_V3[version]( # text, # sdp_ratio, # noise_scale, # noise_scale_w, # length_scale, # sid, # language, # hps, # net_g, # device, # reference_audio, # emotion, # skip_start, # skip_end, # style_text, # style_weight, # ) # if version in inferMap_V2.keys(): # return inferMap_V2[version]( # text, # sdp_ratio, # noise_scale, # noise_scale_w, # length_scale, # sid, # language, # hps, # net_g, # device, # ) # if version in inferMap_V1.keys(): # return inferMap_V1[version]( # text, # sdp_ratio, # noise_scale, # noise_scale_w, # length_scale, # sid, # hps, # net_g, # device, # ) # 在此处实现当前版本的推理 # 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) phones, tones, lang_ids = get_text( text, language, hps, device, style_text=style_text, style_weight=style_weight, ) if skip_start: phones = phones[3:] tones = tones[3:] lang_ids = lang_ids[3:] if skip_end: phones = phones[:-2] tones = tones[:-2] lang_ids = lang_ids[:-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) 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, 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, x_tst_lengths, speakers, ) # , 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, ): 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_phones, temp_tones, temp_lang_ids, ) = get_text(txt, lang, hps, device) if _skip_start: temp_phones = temp_phones[3:] temp_tones = temp_tones[3:] temp_lang_ids = temp_lang_ids[3:] if _skip_end: temp_phones = temp_phones[:-2] temp_tones = temp_tones[:-2] temp_lang_ids = temp_lang_ids[:-2] phones.append(temp_phones) tones.append(temp_tones) lang_ids.append(temp_lang_ids) 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) # 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, 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, x_tst_lengths, speakers, ) # , emo if torch.cuda.is_available(): torch.cuda.empty_cache() return audio