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| """ | |
| 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, get_bert | |
| # from clap_wrapper import get_clap_audio_feature, get_clap_text_feature | |
| from text.cleaner import clean_text | |
| import utils | |
| import numpy as np | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| # 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 | |
| # 当前版本信息 | |
| latest_version = "2.3" | |
| # 版本兼容 | |
| SynthesizerTrnMap = { | |
| # "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 = { | |
| # "2.1": 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, version: str, device: str, hps): | |
| if version != latest_version: | |
| net_g = SynthesizerTrnMap[version]( | |
| len(symbolsMap[version]), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model, | |
| ).to(device) | |
| 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, 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 | |
| bert_ori = get_bert( | |
| norm_text, word2ph, language_str, device, style_text, style_weight | |
| ) | |
| del word2ph | |
| assert bert_ori.shape[-1] == len(phone), phone | |
| if language_str == "ZH": | |
| bert = bert_ori | |
| ja_bert = torch.randn(1024, len(phone)) | |
| en_bert = torch.randn(1024, len(phone)) | |
| elif language_str == "JP": | |
| bert = torch.randn(1024, len(phone)) | |
| ja_bert = bert_ori | |
| en_bert = torch.randn(1024, len(phone)) | |
| elif language_str == "EN": | |
| bert = torch.randn(1024, len(phone)) | |
| ja_bert = torch.randn(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, | |
| emotion, | |
| 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 | |
| # 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_V3.keys(): | |
| emotion = 0 | |
| 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) | |
| bert, ja_bert, en_bert, 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:] | |
| 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) | |
| # 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, | |
| 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, 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, | |
| 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 | |