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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 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, 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: 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)
-
- 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
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