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""" |
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版本管理、兼容推理及模型加载实现。 |
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版本说明： |
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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 |
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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" |
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特殊版本说明： |
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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 |
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1.1.1-dev： dev开发 |
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2.1：当前版本 |
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""" |
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import torch |
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import commons |
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from text import cleaned_text_to_sequence, get_bert |
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from emo_gen import get_emo |
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from text.cleaner import clean_text |
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import utils |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from oldVersion.V200.models import SynthesizerTrn as V200SynthesizerTrn |
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from oldVersion.V200.text import symbols as V200symbols |
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from oldVersion.V111.models import SynthesizerTrn as V111SynthesizerTrn |
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from oldVersion.V111.text import symbols as V111symbols |
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from oldVersion.V110.models import SynthesizerTrn as V110SynthesizerTrn |
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from oldVersion.V110.text import symbols as V110symbols |
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from oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn |
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from oldVersion.V101.text import symbols as V101symbols |
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from oldVersion import V111, V110, V101, V200 |
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latest_version = "2.1" |
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SynthesizerTrnMap = { |
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"2.0.2-fix": V200SynthesizerTrn, |
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"2.0.1": V200SynthesizerTrn, |
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"2.0": V200SynthesizerTrn, |
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"1.1.1-fix": V111SynthesizerTrn, |
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"1.1.1": V111SynthesizerTrn, |
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"1.1": V110SynthesizerTrn, |
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"1.1.0": V110SynthesizerTrn, |
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"1.0.1": V101SynthesizerTrn, |
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"1.0": V101SynthesizerTrn, |
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"1.0.0": V101SynthesizerTrn, |
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} |
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symbolsMap = { |
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"2.0.2-fix": V200symbols, |
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"2.0.1": V200symbols, |
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"2.0": V200symbols, |
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"1.1.1-fix": V111symbols, |
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"1.1.1": V111symbols, |
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"1.1": V110symbols, |
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"1.1.0": V110symbols, |
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"1.0.1": V101symbols, |
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"1.0": V101symbols, |
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"1.0.0": V101symbols, |
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} |
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def get_net_g(model_path: str, version: str, device: str, hps): |
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if version != latest_version: |
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net_g = SynthesizerTrnMap[version]( |
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len(symbolsMap[version]), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model, |
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).to(device) |
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else: |
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net_g = SynthesizerTrn( |
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len(symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model, |
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).to(device) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True) |
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return net_g |
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def get_text(text, language_str, hps, device): |
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norm_text, phone, tone, word2ph = clean_text(text, language_str) |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) |
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if hps.data.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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bert_ori = get_bert(norm_text, word2ph, language_str, device) |
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del word2ph |
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assert bert_ori.shape[-1] == len(phone), phone |
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if language_str == "ZH": |
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bert = bert_ori |
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ja_bert = torch.zeros(1024, len(phone)) |
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en_bert = torch.zeros(1024, len(phone)) |
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elif language_str == "JP": |
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bert = torch.zeros(1024, len(phone)) |
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ja_bert = bert_ori |
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en_bert = torch.zeros(1024, len(phone)) |
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elif language_str == "EN": |
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bert = torch.zeros(1024, len(phone)) |
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ja_bert = torch.zeros(1024, len(phone)) |
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en_bert = bert_ori |
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else: |
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raise ValueError("language_str should be ZH, JP or EN") |
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assert bert.shape[-1] == len( |
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phone |
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), f"Bert seq len {bert.shape[-1]} != {len(phone)}" |
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phone = torch.LongTensor(phone) |
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tone = torch.LongTensor(tone) |
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language = torch.LongTensor(language) |
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return bert, ja_bert, en_bert, phone, tone, language |
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def get_emo_(reference_audio, emotion): |
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emo = ( |
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torch.from_numpy(get_emo(reference_audio)) |
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if reference_audio |
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else torch.Tensor([emotion]) |
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) |
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return emo |
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def infer( |
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text, |
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sdp_ratio, |
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noise_scale, |
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noise_scale_w, |
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length_scale, |
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sid, |
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language, |
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hps, |
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net_g, |
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device, |
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reference_audio=None, |
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emotion=None, |
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skip_start=False, |
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skip_end=False, |
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): |
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inferMap_V2 = { |
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"2.0.2-fix": V200.infer, |
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"2.0.1": V200.infer, |
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"2.0": V200.infer, |
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"1.1.1-fix": V111.infer_fix, |
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"1.1.1": V111.infer, |
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"1.1": V110.infer, |
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"1.1.0": V110.infer, |
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} |
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inferMap_V1 = { |
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"1.0.1": V101.infer, |
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"1.0": V101.infer, |
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"1.0.0": V101.infer, |
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} |
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version = hps.version if hasattr(hps, "version") else latest_version |
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if version != latest_version: |
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if version in inferMap_V2.keys(): |
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return inferMap_V2[version]( |
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text, |
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sdp_ratio, |
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noise_scale, |
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noise_scale_w, |
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length_scale, |
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sid, |
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language, |
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hps, |
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net_g, |
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device, |
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) |
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if version in inferMap_V1.keys(): |
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return inferMap_V1[version]( |
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text, |
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sdp_ratio, |
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noise_scale, |
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noise_scale_w, |
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length_scale, |
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sid, |
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hps, |
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net_g, |
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device, |
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) |
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bert, ja_bert, en_bert, phones, tones, lang_ids = get_text( |
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text, language, hps, device |
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) |
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emo = get_emo_(reference_audio, emotion) |
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if skip_start: |
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phones = phones[1:] |
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tones = tones[1:] |
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lang_ids = lang_ids[1:] |
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bert = bert[:, 1:] |
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ja_bert = ja_bert[:, 1:] |
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en_bert = en_bert[:, 1:] |
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if skip_end: |
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phones = phones[:-1] |
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tones = tones[:-1] |
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lang_ids = lang_ids[:-1] |
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bert = bert[:, :-1] |
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ja_bert = ja_bert[:, :-1] |
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en_bert = en_bert[:, :-1] |
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with torch.no_grad(): |
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x_tst = phones.to(device).unsqueeze(0) |
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tones = tones.to(device).unsqueeze(0) |
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lang_ids = lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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ja_bert = ja_bert.to(device).unsqueeze(0) |
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en_bert = en_bert.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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emo = emo.to(device).unsqueeze(0) |
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del phones |
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) |
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audio = ( |
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net_g.infer( |
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x_tst, |
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x_tst_lengths, |
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speakers, |
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tones, |
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lang_ids, |
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bert, |
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ja_bert, |
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en_bert, |
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emo, |
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sdp_ratio=sdp_ratio, |
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noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, |
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length_scale=length_scale, |
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)[0][0, 0] |
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.data.cpu() |
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.float() |
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.numpy() |
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) |
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del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert, emo |
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if torch.cuda.is_available(): |
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torch.cuda.empty_cache() |
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return audio |
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def infer_multilang( |
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text, |
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sdp_ratio, |
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noise_scale, |
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noise_scale_w, |
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length_scale, |
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sid, |
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language, |
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hps, |
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net_g, |
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device, |
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reference_audio=None, |
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emotion=None, |
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skip_start=False, |
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skip_end=False, |
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): |
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bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], [] |
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emo = get_emo_(reference_audio, emotion) |
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for idx, (txt, lang) in enumerate(zip(text, language)): |
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skip_start = (idx != 0) or (skip_start and idx == 0) |
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skip_end = (idx != len(text) - 1) or (skip_end and idx == len(text) - 1) |
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( |
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temp_bert, |
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temp_ja_bert, |
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temp_en_bert, |
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temp_phones, |
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temp_tones, |
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temp_lang_ids, |
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) = get_text(txt, lang, hps, device) |
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if skip_start: |
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temp_bert = temp_bert[:, 1:] |
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temp_ja_bert = temp_ja_bert[:, 1:] |
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temp_en_bert = temp_en_bert[:, 1:] |
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temp_phones = temp_phones[1:] |
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temp_tones = temp_tones[1:] |
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temp_lang_ids = temp_lang_ids[1:] |
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if skip_end: |
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temp_bert = temp_bert[:, :-1] |
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temp_ja_bert = temp_ja_bert[:, :-1] |
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temp_en_bert = temp_en_bert[:, :-1] |
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temp_phones = temp_phones[:-1] |
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temp_tones = temp_tones[:-1] |
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temp_lang_ids = temp_lang_ids[:-1] |
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bert.append(temp_bert) |
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ja_bert.append(temp_ja_bert) |
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en_bert.append(temp_en_bert) |
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phones.append(temp_phones) |
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tones.append(temp_tones) |
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lang_ids.append(temp_lang_ids) |
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bert = torch.concatenate(bert, dim=1) |
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ja_bert = torch.concatenate(ja_bert, dim=1) |
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en_bert = torch.concatenate(en_bert, dim=1) |
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phones = torch.concatenate(phones, dim=0) |
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tones = torch.concatenate(tones, dim=0) |
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lang_ids = torch.concatenate(lang_ids, dim=0) |
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with torch.no_grad(): |
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x_tst = phones.to(device).unsqueeze(0) |
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tones = tones.to(device).unsqueeze(0) |
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lang_ids = lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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ja_bert = ja_bert.to(device).unsqueeze(0) |
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en_bert = en_bert.to(device).unsqueeze(0) |
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emo = emo.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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del phones |
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) |
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audio = ( |
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net_g.infer( |
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x_tst, |
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x_tst_lengths, |
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speakers, |
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tones, |
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lang_ids, |
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bert, |
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ja_bert, |
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en_bert, |
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emo, |
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sdp_ratio=sdp_ratio, |
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noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, |
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length_scale=length_scale, |
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)[0][0, 0] |
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.data.cpu() |
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.float() |
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.numpy() |
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) |
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del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert, emo |
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if torch.cuda.is_available(): |
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torch.cuda.empty_cache() |
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return audio |
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