440 lines
14 KiB
Python
440 lines
14 KiB
Python
"""
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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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2.3：当前版本
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"""
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import torch
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from . import commons
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from .text import cleaned_text_to_sequence, get_bert
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# from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
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from typing import Union
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from .text.cleaner import clean_text
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from . import utils
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from .models import SynthesizerTrn
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from .text.symbols import symbols
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# from utils.tts.bert_vits2.oldVersion.V220.models import SynthesizerTrn as V220SynthesizerTrn
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# from utils.tts.bert_vits2.oldVersion.V220.text import symbols as V220symbols
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# from utils.tts.bert_vits2.oldVersion.V210.models import SynthesizerTrn as V210SynthesizerTrn
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# from utils.tts.bert_vits2.oldVersion.V210.text import symbols as V210symbols
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# from utils.tts.bert_vits2.oldVersion.V200.models import SynthesizerTrn as V200SynthesizerTrn
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# from utils.tts.bert_vits2.oldVersion.V200.text import symbols as V200symbols
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# from utils.tts.bert_vits2.oldVersion.V111.models import SynthesizerTrn as V111SynthesizerTrn
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# from utils.tts.bert_vits2.oldVersion.V111.text import symbols as V111symbols
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# from utils.tts.bert_vits2.oldVersion.V110.models import SynthesizerTrn as V110SynthesizerTrn
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# from utils.tts.bert_vits2.oldVersion.V110.text import symbols as V110symbols
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# from utils.tts.bert_vits2.oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn
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# from utils.tts.bert_vits2.oldVersion.V101.text import symbols as V101symbols
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# from oldVersion import V111, V110, V101, V200, V210, V220
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# 当前版本信息
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latest_version = "2.3"
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# 版本兼容
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# SynthesizerTrnMap = {
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# "2.2": V220SynthesizerTrn,
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# "2.1": V210SynthesizerTrn,
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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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# "2.2": V220symbols,
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# "2.1": V210symbols,
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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_emo_(reference_audio, emotion, sid):
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# emo = (
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# torch.from_numpy(get_emo(reference_audio))
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# if reference_audio and emotion == -1
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# else torch.FloatTensor(
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# np.load(f"emo_clustering/{sid}/cluster_center_{emotion}.npy")
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# )
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# )
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# return emo
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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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# 当前版本模型 net_g
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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, style_text=None, style_weight=0.7):
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style_text = None if style_text == "" else style_text
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# 在此处实现当前版本的get_text
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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(
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norm_text, word2ph, language_str, device, style_text, style_weight
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)
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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.randn(1024, len(phone))
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en_bert = torch.randn(1024, len(phone))
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elif language_str == "JP":
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bert = torch.randn(1024, len(phone))
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ja_bert = bert_ori
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en_bert = torch.randn(1024, len(phone))
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elif language_str == "EN":
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bert = torch.randn(1024, len(phone))
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ja_bert = torch.randn(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 infer(
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text,
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emotion: Union[int, str],
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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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skip_start=False,
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skip_end=False,
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style_text=None,
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style_weight=0.7,
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):
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# # 2.2版本参数位置变了
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# inferMap_V4 = {
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# "2.2": V220.infer,
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# }
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# # 2.1 参数新增 emotion reference_audio skip_start skip_end
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# inferMap_V3 = {
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# "2.1": V210.infer,
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# }
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# # 支持中日英三语版本
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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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# # 仅支持中文版本
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# # 在测试中，并未发现两个版本的模型不能互相通用
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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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# 非当前版本，根据版本号选择合适的infer
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if version != latest_version:
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if version in inferMap_V4.keys():
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return inferMap_V4[version](
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text,
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emotion,
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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,
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skip_start,
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skip_end,
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style_text,
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style_weight,
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)
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if version in inferMap_V3.keys():
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return inferMap_V3[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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reference_audio,
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emotion,
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skip_start,
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skip_end,
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style_text,
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style_weight,
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)
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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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# 在此处实现当前版本的推理
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# emo = get_emo_(reference_audio, emotion, sid)
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# if isinstance(reference_audio, np.ndarray):
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# emo = get_clap_audio_feature(reference_audio, device)
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# else:
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# emo = get_clap_text_feature(emotion, device)
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# emo = torch.squeeze(emo, dim=1)
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bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
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text,
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language,
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hps,
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device,
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style_text=style_text,
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style_weight=style_weight,
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)
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if skip_start:
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phones = phones[3:]
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tones = tones[3:]
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lang_ids = lang_ids[3:]
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bert = bert[:, 3:]
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ja_bert = ja_bert[:, 3:]
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en_bert = en_bert[:, 3:]
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if skip_end:
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phones = phones[:-2]
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tones = tones[:-2]
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lang_ids = lang_ids[:-2]
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bert = bert[:, :-2]
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ja_bert = ja_bert[:, :-2]
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en_bert = en_bert[:, :-2]
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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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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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en_ratio=1.0
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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 (
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x_tst,
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tones,
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lang_ids,
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bert,
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x_tst_lengths,
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speakers,
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ja_bert,
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en_bert,
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) # , 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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en_ratio=1.0
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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, sid)
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# if isinstance(reference_audio, np.ndarray):
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# emo = get_clap_audio_feature(reference_audio, device)
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# else:
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# emo = get_clap_text_feature(emotion, device)
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# emo = torch.squeeze(emo, dim=1)
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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(language) - 1) or skip_end
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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[:, 3:]
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temp_ja_bert = temp_ja_bert[:, 3:]
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temp_en_bert = temp_en_bert[:, 3:]
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temp_phones = temp_phones[3:]
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temp_tones = temp_tones[3:]
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temp_lang_ids = temp_lang_ids[3:]
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if _skip_end:
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temp_bert = temp_bert[:, :-2]
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temp_ja_bert = temp_ja_bert[:, :-2]
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temp_en_bert = temp_en_bert[:, :-2]
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temp_phones = temp_phones[:-2]
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temp_tones = temp_tones[:-2]
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temp_lang_ids = temp_lang_ids[:-2]
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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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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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en_ratio=en_ratio
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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 (
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x_tst,
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tones,
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lang_ids,
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bert,
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x_tst_lengths,
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speakers,
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ja_bert,
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en_bert,
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) # , 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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