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Dev 2.3. (#242)

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* Fix inputs of duration discriminator

* Add LSTM

* Update models.py

* Update tensorboard scalar

* Noise injection for minimizing modality gap

* Update infer.py

* support bf16 run

* del unused_para flag

* support bf16 config

* add grad clip

* fix(logger and grad):add dur grad,fix grad clip

* Update webui_preprocess.py

* Fix English G2P

* fix(bert_gen):add pass

* Pass SDP to DD

* Update webui_preprocess.py

* Update config.json

* Update webui.py

* Update chinese_bert.py

* Upload webui for deploy

* Update webui.py

* torch.save as pt not npy

* Update config.json

* add freeze emo vq

* Update webui_preprocess.py

* Fix tone_sandhi.py

* Comment up grad clip

* Fix in-place addition

* Add SLM discriminator

* Add DDP for WD

* Feat: Style text: make emotions and style similar to the style text by mixing bert (#240) (#241)

* fix:(oldVersion210) Load on demand Emotion model

* feat: update fastapi.py. 添加更多错误日志信息

* Switch pyopenjtalk to pyopenjtalk-prebuilt

* fix: update fastapi.py. 2.2 reference适配

* Update resample.py

* 修复Onnx导出的BUG (#237)

* Add files via upload

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* Add files via upload

* Add files via upload

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Delete attentions_onnx.py

* Delete models_onnx.py

* Add files via upload

* Add files via upload

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Update __init__.py

* Update __init__.py

* Update __init__.py

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---------



* Fix onnx

* Format export

* Feat: style-text and bert mixing (JA only)

* Ensure the same tensor shape

* Update

* update gradio version

* Fix

* Style text for chinese and english (ver 2.2)

* Style text for chinese and english (ver 2.1)

* Style text in FastAPI

* Translate style text desc in chinese

---------

Co-authored-by: litagin02 <139731664+litagin02@users.noreply.github.com>
Co-authored-by: Sora <654163754@qq.com>
Co-authored-by: Sihan Wang <wangsihan1995@gmail.com>
Co-authored-by: Ναρουσέ·μ·γιουμεμί·Χινακάννα <40709280+NaruseMioShirakana@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Remove CLAP

* Revert "Remove CLAP"

This reverts commit 62fd59bc837c580239840a2bc84b15e0663730fc.

Revert

* Remove CLAP

* bf16 audo grad cilp

* Update webui and infer utils

* Update webui.py

* Update webui.py

* Update webui-preprocess.py

* Update webui_preprocess.py

---------

Co-authored-by: Sihan Wang <wangsihan1995@gmail.com>
Co-authored-by: OedoSoldier <31711261+OedoSoldier@users.noreply.github.com>
Co-authored-by: litagin02 <139731664+litagin02@users.noreply.github.com>
Co-authored-by: Sora <654163754@qq.com>
Co-authored-by: Ναρουσέ·μ·γιουμεμί·Χινακάννα <40709280+NaruseMioShirakana@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Stardust·减
2023-12-19 19:12:26 +08:00
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parent 5479e9039d
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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
2.2：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 = "2.2"
# 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, bert, hps, device):
# 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)
bert_ori = bert[language_str].get_bert_feature(norm_text, word2ph, device)
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,
bert=None,
clap=None,
reference_audio=None,
skip_start=False,
skip_end=False,
):
# 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():
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,
)
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 = clap.get_clap_audio_feature(reference_audio, device)
else:
emo = clap.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, bert, hps, device
)
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,
emo,
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,
bert=None,
clap=None,
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 = clap.get_clap_audio_feature(reference_audio, device)
else:
emo = clap.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(text) - 1) or (skip_end and idx == len(text) - 1)
(
temp_bert,
temp_ja_bert,
temp_en_bert,
temp_phones,
temp_tones,
temp_lang_ids,
) = get_text(txt, lang, bert, 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,
emo,
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