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TakwayDisplayPlatform/utils/bert_vits2/infer.py

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bert-vits2-utils
2024-06-23 20:39:44 +08:00
"""
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
from . 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
from . import utils
from .models import SynthesizerTrn
from .text.symbols import symbols
# from utils.tts.bert_vits2.oldVersion.V220.models import SynthesizerTrn as V220SynthesizerTrn
# from utils.tts.bert_vits2.oldVersion.V220.text import symbols as V220symbols
# from utils.tts.bert_vits2.oldVersion.V210.models import SynthesizerTrn as V210SynthesizerTrn
# from utils.tts.bert_vits2.oldVersion.V210.text import symbols as V210symbols
# from utils.tts.bert_vits2.oldVersion.V200.models import SynthesizerTrn as V200SynthesizerTrn
# from utils.tts.bert_vits2.oldVersion.V200.text import symbols as V200symbols
# from utils.tts.bert_vits2.oldVersion.V111.models import SynthesizerTrn as V111SynthesizerTrn
# from utils.tts.bert_vits2.oldVersion.V111.text import symbols as V111symbols
# from utils.tts.bert_vits2.oldVersion.V110.models import SynthesizerTrn as V110SynthesizerTrn
# from utils.tts.bert_vits2.oldVersion.V110.text import symbols as V110symbols
# from utils.tts.bert_vits2.oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn
# from utils.tts.bert_vits2.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,
en_ratio=1.0
)[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,
en_ratio=1.0
):
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,
en_ratio=en_ratio
)[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