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Fix Bugs, Modified Layout (#2434)

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Co-authored-by: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com>
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XXXXRT666 2 months ago committed by GitHub
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commit 132f6e7b8b
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4 changed files with 215 additions and 62 deletions
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117
GPT_SoVITS/inference_webui.py
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@ -31,9 +31,11 @@ warnings.simplefilter(action="ignore", category=FutureWarning)
version = model_version = os.environ.get("version", "v2")
from config import name2sovits_path,name2gpt_path,change_choices,get_weights_names
from config import change_choices, get_weights_names, name2gpt_path, name2sovits_path
SoVITS_names, GPT_names = get_weights_names()
from config import pretrained_sovits_name
path_sovits_v3 = pretrained_sovits_name["v3"]
path_sovits_v4 = pretrained_sovits_name["v4"]
is_exist_s2gv3 = os.path.exists(path_sovits_v3)
@ -108,6 +110,7 @@ from peft import LoraConfig, get_peft_model
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
from tools.assets import css, js, top_html
from tools.i18n.i18n import I18nAuto, scan_language_list
language = os.environ.get("language", "Auto")
@ -208,8 +211,11 @@ else:
from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
v3v4set = {"v3", "v4"}
def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
if "！"in sovits_path:sovits_path=name2sovits_path[sovits_path]
if "！" in sovits_path:
sovits_path = name2sovits_path[sovits_path]
global vq_model, hps, version, model_version, dict_language, if_lora_v3
version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
print(sovits_path, version, model_version, if_lora_v3)
@ -272,7 +278,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
version = hps.model.version
# print("sovits版本:",hps.model.version)
if model_version not in v3v4set:
if "Pro"not in model_version:
if "Pro" not in model_version:
model_version = version
else:
hps.model.version = model_version
@ -355,7 +361,8 @@ except:
def change_gpt_weights(gpt_path):
if "！"in gpt_path:gpt_path=name2gpt_path[gpt_path]
if "！" in gpt_path:
gpt_path = name2gpt_path[gpt_path]
global hz, max_sec, t2s_model, config
hz = 50
dict_s1 = torch.load(gpt_path, map_location="cpu", weights_only=False)
@ -383,6 +390,7 @@ import torch
now_dir = os.getcwd()
def clean_hifigan_model():
global hifigan_model
if hifigan_model:
@ -392,6 +400,8 @@ def clean_hifigan_model():
torch.cuda.empty_cache()
except:
pass
def clean_bigvgan_model():
global bigvgan_model
if bigvgan_model:
@ -401,6 +411,8 @@ def clean_bigvgan_model():
torch.cuda.empty_cache()
except:
pass
def clean_sv_cn_model():
global sv_cn_model
if sv_cn_model:
@ -411,8 +423,9 @@ def clean_sv_cn_model():
except:
pass
def init_bigvgan():
global bigvgan_model, hifigan_model,sv_cn_model
global bigvgan_model, hifigan_model, sv_cn_model
from BigVGAN import bigvgan
bigvgan_model = bigvgan.BigVGAN.from_pretrained(
@ -429,8 +442,9 @@ def init_bigvgan():
else:
bigvgan_model = bigvgan_model.to(device)
def init_hifigan():
global hifigan_model, bigvgan_model,sv_cn_model
global hifigan_model, bigvgan_model, sv_cn_model
hifigan_model = Generator(
initial_channel=100,
resblock="1",
@ -445,7 +459,9 @@ def init_hifigan():
hifigan_model.eval()
hifigan_model.remove_weight_norm()
state_dict_g = torch.load(
"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu", weights_only=False
"%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
map_location="cpu",
weights_only=False,
)
print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
clean_bigvgan_model()
@ -455,9 +471,12 @@ def init_hifigan():
else:
hifigan_model = hifigan_model.to(device)
from sv import SV
def init_sv_cn():
global hifigan_model, bigvgan_model,sv_cn_model
global hifigan_model, bigvgan_model, sv_cn_model
sv_cn_model = SV(device, is_half)
clean_bigvgan_model()
clean_hifigan_model()
@ -468,34 +487,37 @@ if model_version == "v3":
init_bigvgan()
if model_version == "v4":
init_hifigan()
if model_version in {"v2Pro","v2ProPlus"}:
if model_version in {"v2Pro", "v2ProPlus"}:
init_sv_cn()
resample_transform_dict={}
def resample(audio_tensor, sr0,sr1,device):
resample_transform_dict = {}
def resample(audio_tensor, sr0, sr1, device):
global resample_transform_dict
key="%s-%s-%s"%(sr0,sr1,str(device))
key = "%s-%s-%s" % (sr0, sr1, str(device))
if key not in resample_transform_dict:
resample_transform_dict[key] = torchaudio.transforms.Resample(
sr0, sr1
).to(device)
resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
return resample_transform_dict[key](audio_tensor)
def get_spepc(hps, filename,dtype,device,is_v2pro=False):
def get_spepc(hps, filename, dtype, device, is_v2pro=False):
# audio = load_audio(filename, int(hps.data.sampling_rate))
# audio, sampling_rate = librosa.load(filename, sr=int(hps.data.sampling_rate))
# audio = torch.FloatTensor(audio)
sr1=int(hps.data.sampling_rate)
audio, sr0=torchaudio.load(filename)
if sr0!=sr1:
audio=audio.to(device)
if(audio.shape[0]==2):audio=audio.mean(0).unsqueeze(0)
audio=resample(audio,sr0,sr1,device)
sr1 = int(hps.data.sampling_rate)
audio, sr0 = torchaudio.load(filename)
if sr0 != sr1:
audio = audio.to(device)
if audio.shape[0] == 2:
audio = audio.mean(0).unsqueeze(0)
audio = resample(audio, sr0, sr1, device)
else:
audio=audio.to(device)
if(audio.shape[0]==2):audio=audio.mean(0).unsqueeze(0)
audio = audio.to(device)
if audio.shape[0] == 2:
audio = audio.mean(0).unsqueeze(0)
maxx = audio.abs().max()
if maxx > 1:
@ -508,10 +530,10 @@ def get_spepc(hps, filename,dtype,device,is_v2pro=False):
hps.data.win_length,
center=False,
)
spec=spec.to(dtype)
if is_v2pro==True:
audio=resample(audio,sr1,16000,device).to(dtype)
return spec,audio
spec = spec.to(dtype)
if is_v2pro == True:
audio = resample(audio, sr1, 16000, device).to(dtype)
return spec, audio
def clean_text_inf(text, language, version):
@ -744,7 +766,7 @@ def get_tts_wav(
ref_free = False # s2v3暂不支持ref_free
else:
if_sr = False
if model_version not in {"v3","v4","v2Pro","v2ProPlus"}:
if model_version not in {"v3", "v4", "v2Pro", "v2ProPlus"}:
clean_bigvgan_model()
clean_hifigan_model()
clean_sv_cn_model()
@ -851,35 +873,39 @@ def get_tts_wav(
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
cache[i_text] = pred_semantic
t3 = ttime()
is_v2pro=model_version in {"v2Pro","v2ProPlus"}
is_v2pro = model_version in {"v2Pro", "v2ProPlus"}
# print(23333,is_v2pro,model_version)
###v3不存在以下逻辑和inp_refs
if model_version not in v3v4set:
refers = []
if is_v2pro:
sv_emb=[]
sv_emb = []
if sv_cn_model == None:
init_sv_cn()
if inp_refs:
for path in inp_refs:
try:#####这里加上提取sv的逻辑，要么一堆sv一堆refer，要么单个sv单个refer
refer,audio_tensor = get_spepc(hps, path.name,dtype,device,is_v2pro)
try: #####这里加上提取sv的逻辑，要么一堆sv一堆refer，要么单个sv单个refer
refer, audio_tensor = get_spepc(hps, path.name, dtype, device, is_v2pro)
refers.append(refer)
if is_v2pro:
sv_emb.append(sv_cn_model.compute_embedding3(audio_tensor))
except:
traceback.print_exc()
if len(refers) == 0:
refers,audio_tensor = get_spepc(hps, ref_wav_path,dtype,device,is_v2pro)
refers=[refers]
refers, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device, is_v2pro)
refers = [refers]
if is_v2pro:
sv_emb=[sv_cn_model.compute_embedding3(audio_tensor)]
sv_emb = [sv_cn_model.compute_embedding3(audio_tensor)]
if is_v2pro:
audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed,sv_emb=sv_emb)[0][0]
audio = vq_model.decode(
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed, sv_emb=sv_emb
)[0][0]
else:
audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed)[0][0]
audio = vq_model.decode(
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
)[0][0]
else:
refer,audio_tensor = get_spepc(hps, ref_wav_path,dtype,device)
refer, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device)
phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
phoneme_ids1 = torch.LongTensor(phones2).to(device).unsqueeze(0)
fea_ref, ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)
@ -889,7 +915,7 @@ def get_tts_wav(
ref_audio = ref_audio.mean(0).unsqueeze(0)
tgt_sr = 24000 if model_version == "v3" else 32000
if sr != tgt_sr:
ref_audio = resample(ref_audio, sr, tgt_sr,device)
ref_audio = resample(ref_audio, sr, tgt_sr, device)
# print("ref_audio",ref_audio.abs().mean())
mel2 = mel_fn(ref_audio) if model_version == "v3" else mel_fn_v4(ref_audio)
mel2 = norm_spec(mel2)
@ -1076,6 +1102,7 @@ def process_text(texts):
_text.append(text)
return _text
def html_center(text, label="p"):
return f"""<div style="text-align: center; margin: 100; padding: 50;">
<{label} style="margin: 0; padding: 0;">{text}</{label}>
@ -1088,11 +1115,13 @@ def html_left(text, label="p"):
</div>"""
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
gr.Markdown(
value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
+ "<br>"
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
gr.HTML(
top_html.format(
i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
+ i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
),
elem_classes="markdown",
)
with gr.Group():
gr.Markdown(html_center(i18n("模型切换"), "h3"))

28
GPT_SoVITS/inference_webui_fast.py
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@ -47,6 +47,7 @@ import gradio as gr
from TTS_infer_pack.text_segmentation_method import get_method
from TTS_infer_pack.TTS import NO_PROMPT_ERROR, TTS, TTS_Config
from tools.assets import css, js, top_html
from tools.i18n.i18n import I18nAuto, scan_language_list
language = os.environ.get("language", "Auto")
@ -98,9 +99,11 @@ cut_method = {
i18n("按标点符号切"): "cut5",
}
from config import name2sovits_path,name2gpt_path,change_choices,get_weights_names
from config import change_choices, get_weights_names, name2gpt_path, name2sovits_path
SoVITS_names, GPT_names = get_weights_names()
from config import pretrained_sovits_name
path_sovits_v3 = pretrained_sovits_name["v3"]
path_sovits_v4 = pretrained_sovits_name["v4"]
is_exist_s2gv3 = os.path.exists(path_sovits_v3)
@ -111,10 +114,12 @@ tts_config.device = device
tts_config.is_half = is_half
tts_config.version = version
if gpt_path is not None:
if "！"in gpt_path:gpt_path=name2gpt_path[gpt_path]
if "！" in gpt_path:
gpt_path = name2gpt_path[gpt_path]
tts_config.t2s_weights_path = gpt_path
if sovits_path is not None:
if "！"in sovits_path:sovits_path=name2sovits_path[sovits_path]
if "！" in sovits_path:
sovits_path = name2sovits_path[sovits_path]
tts_config.vits_weights_path = sovits_path
if cnhubert_base_path is not None:
tts_config.cnhuhbert_base_path = cnhubert_base_path
@ -189,6 +194,7 @@ def custom_sort_key(s):
parts = [int(part) if part.isdigit() else part for part in parts]
return parts
if os.path.exists("./weight.json"):
pass
else:
@ -206,9 +212,13 @@ with open("./weight.json", "r", encoding="utf-8") as file:
sovits_path = sovits_path[0]
from process_ckpt import get_sovits_version_from_path_fast
v3v4set = {"v3", "v4"}
def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
if "！"in sovits_path:sovits_path=name2sovits_path[sovits_path]
if "！" in sovits_path:
sovits_path = name2sovits_path[sovits_path]
global version, model_version, dict_language, if_lora_v3
version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
# print(sovits_path,version, model_version, if_lora_v3)
@ -273,11 +283,13 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
f.write(json.dumps(data))
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
gr.Markdown(
value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
+ "<br>"
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
gr.HTML(
top_html.format(
i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
+ i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
),
elem_classes="markdown",
)
with gr.Column():

112
tools/assets.py
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@ -0,0 +1,112 @@
js = """
function createGradioAnimation() {
const params = new URLSearchParams(window.location.search);
if (params.get('__theme') !== 'light') {
params.set('__theme', 'light'); // 仅当 __theme 不是 'light' 时设置为 'light'
window.location.search = params.toString(); // 更新 URL，触发页面刷新
}
var container = document.createElement('div');
container.id = 'gradio-animation';
container.style.fontSize = '2em';
container.style.fontWeight = '500';
container.style.textAlign = 'center';
container.style.marginBottom = '20px';
container.style.fontFamily = '-apple-system, sans-serif, Arial, Calibri';
var text = 'Welcome to GPT-SoVITS !';
for (var i = 0; i < text.length; i++) {
(function(i){
setTimeout(function(){
var letter = document.createElement('span');
letter.style.opacity = '0';
letter.style.transition = 'opacity 0.5s';
letter.innerText = text[i];
container.appendChild(letter);
setTimeout(function() {
letter.style.opacity = '1';
}, 50);
}, i * 250);
})(i);
}
return 'Animation created';
}
"""
css = """
/* CSSStyleRule */
.markdown {
background-color: lightblue;
padding: 10px;
}
.checkbox_info {
color: var(--block-title-text-color) !important;
font-size: var(--block-title-text-size) !important;
font-weight: var(--block-title-text-weight) !important;
height: 22px;
margin-bottom: 8px !important;
}
::selection {
background: #ffc078; !important;
}
#checkbox_train_dpo input[type="checkbox"]{
margin-top: 6px;
}
#checkbox_train_dpo span {
margin-top: 6px;
}
#checkbox_align_train {
padding-top: 18px;
padding-bottom: 18px;
}
#checkbox_align_infer input[type="checkbox"] {
margin-top: 10px;
}
#checkbox_align_infer span {
margin-top: 10px;
}
footer {
height: 50px !important; /* 设置页脚高度 */
background-color: transparent !important; /* 背景透明 */
display: flex;
justify-content: center; /* 居中对齐 */
align-items: center; /* 垂直居中 */
}
footer * {
display: none !important; /* 隐藏所有子元素 */
}
"""
top_html = """
<div align="center">
<div style="margin-bottom: 20px; font-size: 15px;">{}</div>
<div style="display: flex; gap: 80px; justify-content: center;">
<a href="https://github.com/RVC-Boss/GPT-SoVITS" target="_blank">
<img src="https://img.shields.io/badge/GitHub-GPT--SoVITS-blue.svg?style=for-the-badge&logo=github" style="width: auto; height: 30px;">
</a>
<a href="https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e" target="_blank">
<img src="https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white" style="width: auto; height: 30px;">
</a>
<a href="https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e" target="_blank">
<img src="https://img.shields.io/badge/English-READ%20DOCS-blue?style=for-the-badge&logo=googledocs&logoColor=white" style="width: auto; height: 30px;">
</a>
<a href="https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE" target="_blank">
<img src="https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative" style="width: auto; height: 30px;">
</a>
</div>
</div>
"""

14
webui.py
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@ -60,6 +60,7 @@ import shutil
import subprocess
from subprocess import Popen
from tools.assets import css, js, top_html
from tools.i18n.i18n import I18nAuto, scan_language_list
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else "Auto"
@ -1299,14 +1300,13 @@ def sync(text):
return {"__type__": "update", "value": text}
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False) as app:
gr.Markdown(
value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
gr.HTML(
top_html.format(
i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
+ i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
+ "<br>"
+ i18n("中文教程文档")
+ ": "
+ "https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e"
),
elem_classes="markdown",
)
with gr.Tabs():

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