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Merge branch 'RVC-Boss:main' into main

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Jesse Cheng 2024-02-19 01:58:19 +11:00 committed by GitHub
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18 changed files with 177 additions and 90 deletions
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5
.gitignore vendored
View File

@ -7,5 +7,8 @@ runtime
output
logs
reference
SoVITS_weights
GPT_weights
SoVITS_weights
TEMP

3
GPT_SoVITS/AR/data/data_module.py
View File

@ -41,7 +41,8 @@ class Text2SemanticDataModule(LightningDataModule):
# pad_val=self.config['data']['pad_val'])
def train_dataloader(self):
batch_size = max(min(self.config["train"]["batch_size"],len(self._train_dataset)//4),1)#防止不保存
batch_size=self.config["train"]["batch_size"]//2 if self.config["train"].get("if_dpo",False)==True else self.config["train"]["batch_size"]
batch_size = max(min(batch_size,len(self._train_dataset)//4),1)#防止不保存
sampler = DistributedBucketSampler(self._train_dataset, batch_size=batch_size)
return DataLoader(
self._train_dataset,

4
GPT_SoVITS/AR/models/t2s_lightning_module.py
View File

@ -11,7 +11,6 @@ from AR.models.t2s_model import Text2SemanticDecoder
from AR.modules.lr_schedulers import WarmupCosineLRSchedule
from AR.modules.optim import ScaledAdam
class Text2SemanticLightningModule(LightningModule):
def __init__(self, config, output_dir, is_train=True):
super().__init__()
@ -35,7 +34,8 @@ class Text2SemanticLightningModule(LightningModule):
def training_step(self, batch: Dict, batch_idx: int):
opt = self.optimizers()
scheduler = self.lr_schedulers()
loss, acc = self.model.forward(
forward=self.model.forward if self.config["train"].get("if_dpo",False)==True else self.model.forward_old
loss, acc = forward(
batch["phoneme_ids"],
batch["phoneme_ids_len"],
batch["semantic_ids"],

107
GPT_SoVITS/AR/models/t2s_model.py
View File

@ -337,7 +337,7 @@ class Text2SemanticDecoder(nn.Module):
# AR Decoder
y = prompts
prefix_len = y.shape[1]
x_len = x.shape[1]
x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
stop = False
@ -353,47 +353,41 @@ class Text2SemanticDecoder(nn.Module):
"first_infer": 1,
"stage": 0,
}
for idx in tqdm(range(1500)):
if cache["first_infer"] == 1:
y_emb = self.ar_audio_embedding(y)
else:
y_emb = torch.cat(
[cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1
)
cache["y_emb"] = y_emb
################### first step ##########################
if y is not None:
y_emb = self.ar_audio_embedding(y)
y_len = y_emb.shape[1]
prefix_len = y.shape[1]
y_pos = self.ar_audio_position(y_emb)
# x 和逐渐增长的 y 一起输入给模型
if cache["first_infer"] == 1:
xy_pos = torch.concat([x, y_pos], dim=1)
else:
xy_pos = y_pos[:, -1:]
y_len = y_pos.shape[1]
###以下3个不做缓存
if cache["first_infer"] == 1:
x_attn_mask_pad = F.pad(
xy_pos = torch.concat([x, y_pos], dim=1)
cache["y_emb"] = y_emb
ref_free = False
else:
y_emb = None
y_len = 0
prefix_len = 0
y_pos = None
xy_pos = x
y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
ref_free = True
x_attn_mask_pad = F.pad(
x_attn_mask,
(0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
value=True,
)
y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
(x_len, 0),
value=False,
)
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
y.device
)
else:
###最右边一列（是错的）
# xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
# xy_attn_mask[:,-1]=False
###最下面一行（是对的）
xy_attn_mask = torch.zeros(
(1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
)
# pdb.set_trace()
###缓存重头戏
# print(1111,xy_pos.shape,xy_attn_mask.shape,x_len,y_len)
y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
(x_len, 0),
value=False,
)
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
x.device
)
for idx in tqdm(range(1500)):
xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
logits = self.ar_predict_layer(
xy_dec[:, -1]
@ -404,6 +398,10 @@ class Text2SemanticDecoder(nn.Module):
samples = sample(
logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
)[0].unsqueeze(0)
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
# print(samples.shape)#[1,1]#第一个1是bs
y = torch.concat([y, samples], dim=1)
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
print("use early stop num:", early_stop_num)
stop = True
@ -412,13 +410,38 @@ class Text2SemanticDecoder(nn.Module):
# print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
stop = True
if stop:
if prompts.shape[1] == y.shape[1]:
# if prompts.shape[1] == y.shape[1]:
# y = torch.concat([y, torch.zeros_like(samples)], dim=1)
# print("bad zero prediction")
if y.shape[1]==0:
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
print("bad zero prediction")
print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
break
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
# print(samples.shape)#[1,1]#第一个1是bs
y = torch.concat([y, samples], dim=1)
####################### update next step ###################################
cache["first_infer"] = 0
return y, idx
if cache["y_emb"] is not None:
y_emb = torch.cat(
[cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], dim = 1
)
cache["y_emb"] = y_emb
y_pos = self.ar_audio_position(y_emb)
xy_pos = y_pos[:, -1:]
else:
y_emb = self.ar_audio_embedding(y[:, -1:])
cache["y_emb"] = y_emb
y_pos = self.ar_audio_position(y_emb)
xy_pos = y_pos
y_len = y_pos.shape[1]
###最右边一列（是错的）
# xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
# xy_attn_mask[:,-1]=False
###最下面一行（是对的）
xy_attn_mask = torch.zeros(
(1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
)
if ref_free:
return y[:, :-1], 0
return y[:, :-1], idx-1

3
GPT_SoVITS/AR/models/utils.py
View File

@ -114,7 +114,8 @@ def logits_to_probs(
top_p: Optional[int] = None,
repetition_penalty: float = 1.0,
):
previous_tokens = previous_tokens.squeeze()
if previous_tokens is not None:
previous_tokens = previous_tokens.squeeze()
# print(logits.shape,previous_tokens.shape)
# pdb.set_trace()
if previous_tokens is not None and repetition_penalty != 1.0:

6
GPT_SoVITS/AR/modules/patched_mha_with_cache.py
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@ -5,8 +5,8 @@ from torch.nn.functional import (
_none_or_dtype,
_in_projection_packed,
)
# import torch
from torch.nn import functional as F
import torch
# Tensor = torch.Tensor
# from typing import Callable, List, Optional, Tuple, Union
@ -448,9 +448,11 @@ def multi_head_attention_forward_patched(
k = k.view(bsz, num_heads, src_len, head_dim)
v = v.view(bsz, num_heads, src_len, head_dim)
# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
attn_output = scaled_dot_product_attention(
q, k, v, attn_mask, dropout_p, is_causal
)
attn_output = (
attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
)

45
GPT_SoVITS/inference_webui.py
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@ -248,6 +248,10 @@ def clean_text_inf(text, language):
formattext = ""
language = language.replace("all_","")
for tmp in LangSegment.getTexts(text):
if language == "ja":
if tmp["lang"] == language or tmp["lang"] == "zh":
formattext += tmp["text"] + " "
continue
if tmp["lang"] == language:
formattext += tmp["text"] + " "
while " " in formattext:
@ -279,8 +283,6 @@ def nonen_clean_text_inf(text, language):
for tmp in LangSegment.getTexts(text):
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
print(textlist)
print(langlist)
phones_list = []
word2ph_list = []
norm_text_list = []
@ -365,15 +367,19 @@ def merge_short_text_in_array(texts, threshold):
result[len(result) - 1] += text
return result
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6):
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False):
if prompt_text is None or len(prompt_text) == 0:
ref_free = True
t0 = ttime()
prompt_language = dict_language[prompt_language]
text_language = dict_language[text_language]
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
if not ref_free:
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
print(i18n("实际输入的参考文本:"), prompt_text)
text = text.strip("\n")
if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
print(i18n("实际输入的参考文本:"), prompt_text)
print(i18n("实际输入的目标文本:"), text)
zero_wav = np.zeros(
int(hps.data.sampling_rate * 0.3),
@ -398,11 +404,10 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
1, 2
) # .float()
codes = vq_model.extract_latent(ssl_content)
prompt_semantic = codes[0, 0]
t1 = ttime()
phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
if (how_to_cut == i18n("凑四句一切")):
text = cut1(text)
elif (how_to_cut == i18n("凑50字一切")):
@ -419,7 +424,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
texts = text.split("\n")
texts = merge_short_text_in_array(texts, 5)
audio_opt = []
bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
if not ref_free:
phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
for text in texts:
# 解决输入目标文本的空行导致报错的问题
@ -429,9 +436,13 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
print(i18n("实际输入的目标文本(每句):"), text)
phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
bert = torch.cat([bert1, bert2], 1)
if not ref_free:
bert = torch.cat([bert1, bert2], 1)
all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
else:
bert = bert2
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0)
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
prompt = prompt_semantic.unsqueeze(0).to(device)
@ -441,7 +452,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
pred_semantic, idx = t2s_model.model.infer_panel(
all_phoneme_ids,
all_phoneme_len,
prompt,
None if ref_free else prompt,
bert,
# prompt_phone_len=ph_offset,
top_k=top_k,
@ -607,7 +618,10 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
gr.Markdown(value=i18n("*请上传并填写参考信息"))
with gr.Row():
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
with gr.Column():
ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT"))
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
prompt_language = gr.Dropdown(
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
)
@ -624,6 +638,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
interactive=True,
)
with gr.Row():
gr.Markdown("gpt采样参数(无参考文本时不要太低)：")
top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
@ -632,7 +647,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
inference_button.click(
get_tts_wav,
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut,top_k,top_p,temperature],
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
[output],
)
@ -650,7 +665,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
button3.click(cut3, [text_inp], [text_opt])
button4.click(cut4, [text_inp], [text_opt])
button5.click(cut5, [text_inp], [text_opt])
gr.Markdown(value=i18n("后续将支持混合语种编码文本输入。"))
gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
app.queue(concurrency_count=511, max_size=1022).launch(
server_name="0.0.0.0",

13
GPT_SoVITS/module/models.py
View File

@ -228,6 +228,7 @@ class TextEncoder(nn.Module):
)
y = self.ssl_proj(y * y_mask) * y_mask
y = self.encoder_ssl(y * y_mask, y_mask)
text_mask = torch.unsqueeze(
@ -958,11 +959,13 @@ class SynthesizerTrn(nn.Module):
@torch.no_grad()
def decode(self, codes, text, refer, noise_scale=0.5):
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
refer_mask = torch.unsqueeze(
commons.sequence_mask(refer_lengths, refer.size(2)), 1
).to(refer.dtype)
ge = self.ref_enc(refer * refer_mask, refer_mask)
ge = None
if refer is not None:
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
refer_mask = torch.unsqueeze(
commons.sequence_mask(refer_lengths, refer.size(2)), 1
).to(refer.dtype)
ge = self.ref_enc(refer * refer_mask, refer_mask)
y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)

14
GPT_SoVITS/prepare_datasets/1-get-text.py
View File

@ -33,13 +33,13 @@ from time import time as ttime
import shutil
def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path
dir = os.path.dirname(path)
name = os.path.basename(path)
tmp_path = "%s/%s%s.pth" % (dir, ttime(), i_part)
torch.save(fea, tmp_path)
shutil.move(tmp_path, "%s/%s" % (dir, name))
def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
dir=os.path.dirname(path)
name=os.path.basename(path)
# tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
tmp_path="%s%s.pth"%(ttime(),i_part)
torch.save(fea,tmp_path)
shutil.move(tmp_path,"%s/%s"%(dir,name))
txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)

3
GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py
View File

@ -35,7 +35,8 @@ import shutil
def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
dir=os.path.dirname(path)
name=os.path.basename(path)
tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
# tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
tmp_path="%s%s.pth"%(ttime(),i_part)
torch.save(fea,tmp_path)
shutil.move(tmp_path,"%s/%s"%(dir,name))

12
GPT_SoVITS/process_ckpt.py
View File

@ -1,11 +1,18 @@
import traceback
from collections import OrderedDict
from time import time as ttime
import shutil,os
import torch
from tools.i18n.i18n import I18nAuto
i18n = I18nAuto()
def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
dir=os.path.dirname(path)
name=os.path.basename(path)
tmp_path="%s.pth"%(ttime())
torch.save(fea,tmp_path)
shutil.move(tmp_path,"%s/%s"%(dir,name))
def savee(ckpt, name, epoch, steps, hps):
try:
@ -17,7 +24,8 @@ def savee(ckpt, name, epoch, steps, hps):
opt["weight"][key] = ckpt[key].half()
opt["config"] = hps
opt["info"] = "%sepoch_%siteration" % (epoch, steps)
torch.save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
# torch.save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
return "Success."
except:
return traceback.format_exc()

11
GPT_SoVITS/s1_train.py
View File

@ -24,6 +24,14 @@ torch.set_float32_matmul_precision("high")
from AR.utils import get_newest_ckpt
from collections import OrderedDict
from time import time as ttime
import shutil
def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
dir=os.path.dirname(path)
name=os.path.basename(path)
tmp_path="%s.pth"%(ttime())
torch.save(fea,tmp_path)
shutil.move(tmp_path,"%s/%s"%(dir,name))
class my_model_ckpt(ModelCheckpoint):
@ -70,7 +78,8 @@ class my_model_ckpt(ModelCheckpoint):
to_save_od["weight"][key] = dictt[key].half()
to_save_od["config"] = self.config
to_save_od["info"] = "GPT-e%s" % (trainer.current_epoch + 1)
torch.save(
# torch.save(
my_save(
to_save_od,
"%s/%s-e%s.ckpt"
% (

6
GPT_SoVITS/text/english.py
View File

@ -169,9 +169,9 @@ def read_dict_new():
line = line.strip()
word_split = line.split(" ")
word = word_split[0]
if word not in g2p_dict:
g2p_dict[word] = []
g2p_dict[word].append(word_split[1:])
#if word not in g2p_dict:
g2p_dict[word] = []
g2p_dict[word].append(word_split[1:])
line_index = line_index + 1
line = f.readline()

1
GPT_SoVITS/text/tone_sandhi.py
View File

@ -672,6 +672,7 @@ class ToneSandhi:
and i + 1 < len(seg)
and seg[i - 1][0] == seg[i + 1][0]
and seg[i - 1][1] == "v"
and seg[i + 1][1] == "v"
):
new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
else:

11
GPT_SoVITS/utils.py
View File

@ -64,6 +64,14 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
)
return model, optimizer, learning_rate, iteration
from time import time as ttime
import shutil
def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
dir=os.path.dirname(path)
name=os.path.basename(path)
tmp_path="%s.pth"%(ttime())
torch.save(fea,tmp_path)
shutil.move(tmp_path,"%s/%s"%(dir,name))
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
@ -75,7 +83,8 @@ def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path)
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save(
# torch.save(
my_save(
{
"model": state_dict,
"iteration": iteration,

2
colab_webui.ipynb
View File

@ -82,7 +82,7 @@
"source": [
"# @title launch WebUI 启动WebUI\n",
"!/usr/local/bin/pip install ipykernel\n",
"!sed -i '9s/False/True/' /content/GPT-SoVITS/config.py\n",
"!sed -i '10s/False/True/' /content/GPT-SoVITS/config.py\n",
"%cd /content/GPT-SoVITS/\n",
"!/usr/local/bin/python webui.py"
],

13
docs/cn/Changelog_CN.md
View File

@ -113,12 +113,21 @@
2-DPO Loss实验性训练选项开启，通过构造负样本训练缓解GPT重复漏字问题。推理界面公开几个推理参数。 https://github.com/RVC-Boss/GPT-SoVITS/pull/457
### 20240214更新
1-训练支持中文实验名（原来会报错）
2-DPO训练改为可勾选选项而非必须。如勾选batch size自动减半。修复推理界面新参数不传参的问题。
### 20240216更新
1-支持无参考文本输入
2-修复中文文本前端bug https://github.com/RVC-Boss/GPT-SoVITS/issues/475
todolist：
1-中文多音字推理优化
2-训练支持中文实验名（原来会报错）

6
webui.py
View File

@ -266,7 +266,7 @@ def close1Ba():
return "已终止SoVITS训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
p_train_GPT=None
def open1Bb(batch_size,total_epoch,exp_name,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
global p_train_GPT
if(p_train_GPT==None):
with open("GPT_SoVITS/configs/s1longer.yaml")as f:
@ -283,6 +283,7 @@ def open1Bb(batch_size,total_epoch,exp_name,if_save_latest,if_save_every_weights
data["train"]["save_every_n_epoch"]=save_every_epoch
data["train"]["if_save_every_weights"]=if_save_every_weights
data["train"]["if_save_latest"]=if_save_latest
data["train"]["if_dpo"]=if_dpo
data["train"]["half_weights_save_dir"]=GPT_weight_root
data["train"]["exp_name"]=exp_name
data["train_semantic_path"]="%s/6-name2semantic.tsv"%s1_dir
@ -807,6 +808,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
with gr.Row():
batch_size1Bb = gr.Slider(minimum=1,maximum=40,step=1,label=i18n("每张显卡的batch_size"),value=default_batch_size,interactive=True)
total_epoch1Bb = gr.Slider(minimum=2,maximum=50,step=1,label=i18n("总训练轮数total_epoch"),value=15,interactive=True)
if_dpo = gr.Checkbox(label=i18n("是否开启dpo训练选项(实验性)"), value=False, interactive=True, show_label=True)
if_save_latest1Bb = gr.Checkbox(label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), value=True, interactive=True, show_label=True)
if_save_every_weights1Bb = gr.Checkbox(label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), value=True, interactive=True, show_label=True)
save_every_epoch1Bb = gr.Slider(minimum=1,maximum=50,step=1,label=i18n("保存频率save_every_epoch"),value=5,interactive=True)
@ -817,7 +819,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
info1Bb=gr.Textbox(label=i18n("GPT训练进程输出信息"))
button1Ba_open.click(open1Ba, [batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D], [info1Ba,button1Ba_open,button1Ba_close])
button1Ba_close.click(close1Ba, [], [info1Ba,button1Ba_open,button1Ba_close])
button1Bb_open.click(open1Bb, [batch_size1Bb,total_epoch1Bb,exp_name,if_save_latest1Bb,if_save_every_weights1Bb,save_every_epoch1Bb,gpu_numbers1Bb,pretrained_s1], [info1Bb,button1Bb_open,button1Bb_close])
button1Bb_open.click(open1Bb, [batch_size1Bb,total_epoch1Bb,exp_name,if_dpo,if_save_latest1Bb,if_save_every_weights1Bb,save_every_epoch1Bb,gpu_numbers1Bb,pretrained_s1], [info1Bb,button1Bb_open,button1Bb_close])
button1Bb_close.click(close1Bb, [], [info1Bb,button1Bb_open,button1Bb_close])
with gr.TabItem(i18n("1C-推理")):
gr.Markdown(value=i18n("选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的一个是底模，体验5秒Zero Shot TTS用。"))