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Update inference_webui.py

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RVC-Boss 2024-01-30 14:56:47 +08:00 committed by GitHub
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GPT_SoVITS/inference_webui.py
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@ -1,4 +1,5 @@
import os,re,logging import os, re, logging
logging.getLogger("markdown_it").setLevel(logging.ERROR) logging.getLogger("markdown_it").setLevel(logging.ERROR)
logging.getLogger("urllib3").setLevel(logging.ERROR) logging.getLogger("urllib3").setLevel(logging.ERROR)
logging.getLogger("httpcore").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR)
@ -10,16 +11,16 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
import pdb import pdb
if os.path.exists("./gweight.txt"): if os.path.exists("./gweight.txt"):
with open("./gweight.txt", 'r',encoding="utf-8") as file: with open("./gweight.txt", 'r', encoding="utf-8") as file:
gweight_data = file.read() gweight_data = file.read()
gpt_path = os.environ.get( gpt_path = os.environ.get(
"gpt_path", gweight_data) "gpt_path", gweight_data)
else: else:
gpt_path = os.environ.get( gpt_path = os.environ.get(
"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
if os.path.exists("./sweight.txt"): if os.path.exists("./sweight.txt"):
with open("./sweight.txt", 'r',encoding="utf-8") as file: with open("./sweight.txt", 'r', encoding="utf-8") as file:
sweight_data = file.read() sweight_data = file.read()
sovits_path = os.environ.get("sovits_path", sweight_data) sovits_path = os.environ.get("sovits_path", sweight_data)
else: else:
@ -37,16 +38,17 @@ bert_path = os.environ.get(
infer_ttswebui = os.environ.get("infer_ttswebui", 9872) infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
infer_ttswebui = int(infer_ttswebui) infer_ttswebui = int(infer_ttswebui)
is_share = os.environ.get("is_share", "False") is_share = os.environ.get("is_share", "False")
is_share=eval(is_share) is_share = eval(is_share)
if "_CUDA_VISIBLE_DEVICES" in os.environ: if "_CUDA_VISIBLE_DEVICES" in os.environ:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
is_half = eval(os.environ.get("is_half", "True")) is_half = eval(os.environ.get("is_half", "True"))
import gradio as gr import gradio as gr
from transformers import AutoModelForMaskedLM, AutoTokenizer from transformers import AutoModelForMaskedLM, AutoTokenizer
import numpy as np import numpy as np
import librosa,torch import librosa, torch
from feature_extractor import cnhubert from feature_extractor import cnhubert
cnhubert.cnhubert_base_path=cnhubert_base_path
cnhubert.cnhubert_base_path = cnhubert_base_path
from module.models import SynthesizerTrn from module.models import SynthesizerTrn
from AR.models.t2s_lightning_module import Text2SemanticLightningModule from AR.models.t2s_lightning_module import Text2SemanticLightningModule
@ -56,9 +58,10 @@ from time import time as ttime
from module.mel_processing import spectrogram_torch from module.mel_processing import spectrogram_torch
from my_utils import load_audio from my_utils import load_audio
from tools.i18n.i18n import I18nAuto from tools.i18n.i18n import I18nAuto
i18n = I18nAuto() i18n = I18nAuto()
os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
if torch.cuda.is_available(): if torch.cuda.is_available():
device = "cuda" device = "cuda"
@ -74,6 +77,7 @@ if is_half == True:
else: else:
bert_model = bert_model.to(device) bert_model = bert_model.to(device)
def get_bert_feature(text, word2ph): def get_bert_feature(text, word2ph):
with torch.no_grad(): with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt") inputs = tokenizer(text, return_tensors="pt")
@ -89,6 +93,7 @@ def get_bert_feature(text, word2ph):
phone_level_feature = torch.cat(phone_level_feature, dim=0) phone_level_feature = torch.cat(phone_level_feature, dim=0)
return phone_level_feature.T return phone_level_feature.T
class DictToAttrRecursive(dict): class DictToAttrRecursive(dict):
def __init__(self, input_dict): def __init__(self, input_dict):
super().__init__(input_dict) super().__init__(input_dict)
@ -123,10 +128,11 @@ if is_half == True:
else: else:
ssl_model = ssl_model.to(device) ssl_model = ssl_model.to(device)
def change_sovits_weights(sovits_path): def change_sovits_weights(sovits_path):
global vq_model,hps global vq_model, hps
dict_s2=torch.load(sovits_path,map_location="cpu") dict_s2 = torch.load(sovits_path, map_location="cpu")
hps=dict_s2["config"] hps = dict_s2["config"]
hps = DictToAttrRecursive(hps) hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz" hps.model.semantic_frame_rate = "25hz"
vq_model = SynthesizerTrn( vq_model = SynthesizerTrn(
@ -135,7 +141,7 @@ def change_sovits_weights(sovits_path):
n_speakers=hps.data.n_speakers, n_speakers=hps.data.n_speakers,
**hps.model **hps.model
) )
if("pretrained"not in sovits_path): if ("pretrained" not in sovits_path):
del vq_model.enc_q del vq_model.enc_q
if is_half == True: if is_half == True:
vq_model = vq_model.half().to(device) vq_model = vq_model.half().to(device)
@ -143,11 +149,15 @@ def change_sovits_weights(sovits_path):
vq_model = vq_model.to(device) vq_model = vq_model.to(device)
vq_model.eval() vq_model.eval()
print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
with open("./sweight.txt","w",encoding="utf-8")as f:f.write(sovits_path) with open("./sweight.txt", "w", encoding="utf-8") as f:
f.write(sovits_path)
change_sovits_weights(sovits_path) change_sovits_weights(sovits_path)
def change_gpt_weights(gpt_path): def change_gpt_weights(gpt_path):
global hz,max_sec,t2s_model,config global hz, max_sec, t2s_model, config
hz = 50 hz = 50
dict_s1 = torch.load(gpt_path, map_location="cpu") dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["config"] config = dict_s1["config"]
@ -160,9 +170,12 @@ def change_gpt_weights(gpt_path):
t2s_model.eval() t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()]) total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6)) print("Number of parameter: %.2fM" % (total / 1e6))
with open("./gweight.txt","w",encoding="utf-8")as f:f.write(gpt_path) with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
change_gpt_weights(gpt_path) change_gpt_weights(gpt_path)
def get_spepc(hps, filename): def get_spepc(hps, filename):
audio = load_audio(filename, int(hps.data.sampling_rate)) audio = load_audio(filename, int(hps.data.sampling_rate))
audio = torch.FloatTensor(audio) audio = torch.FloatTensor(audio)
@ -179,10 +192,10 @@ def get_spepc(hps, filename):
return spec return spec
dict_language={ dict_language = {
i18n("中文"):"zh", i18n("中文"): "zh",
i18n("英文"):"en", i18n("英文"): "en",
i18n("日文"):"ja" i18n("日文"): "ja"
} }
@ -262,27 +275,31 @@ def nonen_get_bert_inf(text, language):
return bert return bert
splits = {"，","。","？","！",",",".","?","!","~",":","：","—","…",}
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
def get_first(text): def get_first(text):
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
text = re.split(pattern, text)[0].strip() text = re.split(pattern, text)[0].strip()
return text return text
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")):
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切")):
t0 = ttime() t0 = ttime()
prompt_text = prompt_text.strip("\n") prompt_text = prompt_text.strip("\n")
if(prompt_text[-1]not in splits):prompt_text+="。"if prompt_language!="en"else "." if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
text = text.strip("\n") text = text.strip("\n")
if(text[0]not in splits and len(get_first(text))<4):text="。"+text if text_language!="en"else "."+text 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("实际输入的参考文本:"), prompt_text)
print(i18n("实际输入的目标文本:"),text) print(i18n("实际输入的目标文本:"), text)
zero_wav = np.zeros( zero_wav = np.zeros(
int(hps.data.sampling_rate * 0.3), int(hps.data.sampling_rate * 0.3),
dtype=np.float16 if is_half == True else np.float32, dtype=np.float16 if is_half == True else np.float32,
) )
with torch.no_grad(): with torch.no_grad():
wav16k, sr = librosa.load(ref_wav_path, sr=16000) wav16k, sr = librosa.load(ref_wav_path, sr=16000)
if(wav16k.shape[0]>160000 or wav16k.shape[0]<48000): if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
wav16k = torch.from_numpy(wav16k) wav16k = torch.from_numpy(wav16k)
zero_wav_torch = torch.from_numpy(zero_wav) zero_wav_torch = torch.from_numpy(zero_wav)
@ -292,7 +309,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
else: else:
wav16k = wav16k.to(device) wav16k = wav16k.to(device)
zero_wav_torch = zero_wav_torch.to(device) zero_wav_torch = zero_wav_torch.to(device)
wav16k=torch.cat([wav16k,zero_wav_torch]) wav16k = torch.cat([wav16k, zero_wav_torch])
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
"last_hidden_state" "last_hidden_state"
].transpose( ].transpose(
@ -307,13 +324,19 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language) phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
else: else:
phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language) phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
if(how_to_cut==i18n("凑四句一切")):text=cut1(text) if (how_to_cut == i18n("凑四句一切")):
elif(how_to_cut==i18n("凑50字一切")):text=cut2(text) text = cut1(text)
elif(how_to_cut==i18n("按中文句号。切")):text=cut3(text) elif (how_to_cut == i18n("凑50字一切")):
elif(how_to_cut==i18n("按英文句号.切")):text=cut4(text) text = cut2(text)
text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n") elif (how_to_cut == i18n("按中文句号。切")):
print(i18n("实际输入的目标文本(切句后):"),text) text = cut3(text)
texts=text.split("\n") elif (how_to_cut == i18n("按英文句号.切")):
text = cut4(text)
elif (how_to_cut == i18n("按标点符号切")):
text = cut5(text)
text = text.replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n")
print(i18n("实际输入的目标文本(切句后):"), text)
texts = text.split("\n")
audio_opt = [] audio_opt = []
if prompt_language == "en": if prompt_language == "en":
bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language) bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
@ -368,9 +391,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
vq_model.decode( vq_model.decode(
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
) )
.detach() .detach()
.cpu() .cpu()
.numpy()[0, 0] .numpy()[0, 0]
) ###试试重建不带上prompt部分 ) ###试试重建不带上prompt部分
audio_opt.append(audio) audio_opt.append(audio)
audio_opt.append(zero_wav) audio_opt.append(zero_wav)
@ -380,6 +403,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
np.int16 np.int16
) )
def split(todo_text): def split(todo_text):
todo_text = todo_text.replace("……", "。").replace("——", "，") todo_text = todo_text.replace("……", "。").replace("——", "，")
if todo_text[-1] not in splits: if todo_text[-1] not in splits:
@ -407,7 +431,7 @@ def cut1(inp):
if len(split_idx) > 1: if len(split_idx) > 1:
opts = [] opts = []
for idx in range(len(split_idx) - 1): for idx in range(len(split_idx) - 1):
opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]])) opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
else: else:
opts = [inp] opts = [inp]
return "\n".join(opts) return "\n".join(opts)
@ -431,7 +455,7 @@ def cut2(inp):
if tmp_str != "": if tmp_str != "":
opts.append(tmp_str) opts.append(tmp_str)
# print(opts) # print(opts)
if len(opts)>1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起 if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
opts[-2] = opts[-2] + opts[-1] opts[-2] = opts[-2] + opts[-1]
opts = opts[:-1] opts = opts[:-1]
return "\n".join(opts) return "\n".join(opts)
@ -440,10 +464,25 @@ def cut2(inp):
def cut3(inp): def cut3(inp):
inp = inp.strip("\n") inp = inp.strip("\n")
return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
def cut4(inp): def cut4(inp):
inp = inp.strip("\n") inp = inp.strip("\n")
return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
def cut5(inp):
# if not re.search(r'[^\w\s]', inp[-1]):
# inp += '。'
inp = inp.strip("\n")
punds = r'[,.;?!、，。？！;：]'
items = re.split(f'({punds})', inp)
items = ["".join(group) for group in zip(items[::2], items[1::2])]
opt = "\n".join(items)
return opt
def custom_sort_key(s): def custom_sort_key(s):
# 使用正则表达式提取字符串中的数字部分和非数字部分 # 使用正则表达式提取字符串中的数字部分和非数字部分
parts = re.split('(\d+)', s) parts = re.split('(\d+)', s)
@ -451,25 +490,31 @@ def custom_sort_key(s):
parts = [int(part) if part.isdigit() else part for part in parts] parts = [int(part) if part.isdigit() else part for part in parts]
return parts return parts
def change_choices(): def change_choices():
SoVITS_names, GPT_names = get_weights_names() SoVITS_names, GPT_names = get_weights_names()
return {"choices": sorted(SoVITS_names,key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names,key=custom_sort_key), "__type__": "update"} return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
SoVITS_weight_root = "SoVITS_weights"
GPT_weight_root = "GPT_weights"
os.makedirs(SoVITS_weight_root, exist_ok=True)
os.makedirs(GPT_weight_root, exist_ok=True)
pretrained_sovits_name="GPT_SoVITS/pretrained_models/s2G488k.pth"
pretrained_gpt_name="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
SoVITS_weight_root="SoVITS_weights"
GPT_weight_root="GPT_weights"
os.makedirs(SoVITS_weight_root,exist_ok=True)
os.makedirs(GPT_weight_root,exist_ok=True)
def get_weights_names(): def get_weights_names():
SoVITS_names = [pretrained_sovits_name] SoVITS_names = [pretrained_sovits_name]
for name in os.listdir(SoVITS_weight_root): for name in os.listdir(SoVITS_weight_root):
if name.endswith(".pth"):SoVITS_names.append("%s/%s"%(SoVITS_weight_root,name)) if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
GPT_names = [pretrained_gpt_name] GPT_names = [pretrained_gpt_name]
for name in os.listdir(GPT_weight_root): for name in os.listdir(GPT_weight_root):
if name.endswith(".ckpt"): GPT_names.append("%s/%s"%(GPT_weight_root,name)) if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
return SoVITS_names,GPT_names return SoVITS_names, GPT_names
SoVITS_names,GPT_names = get_weights_names()
SoVITS_names, GPT_names = get_weights_names()
with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Blocks(title="GPT-SoVITS WebUI") as app:
gr.Markdown( gr.Markdown(
@ -478,29 +523,29 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
with gr.Group(): with gr.Group():
gr.Markdown(value=i18n("模型切换")) gr.Markdown(value=i18n("模型切换"))
with gr.Row(): with gr.Row():
GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path,interactive=True) GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path,interactive=True) SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
SoVITS_dropdown.change(change_sovits_weights,[SoVITS_dropdown],[]) SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
GPT_dropdown.change(change_gpt_weights,[GPT_dropdown],[]) GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
gr.Markdown(value=i18n("*请上传并填写参考信息")) gr.Markdown(value=i18n("*请上传并填写参考信息"))
with gr.Row(): 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") 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="") prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
prompt_language = gr.Dropdown( prompt_language = gr.Dropdown(
label=i18n("参考音频的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文") label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文")
) )
gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。")) gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。"))
with gr.Row(): with gr.Row():
text = gr.Textbox(label=i18n("需要合成的文本"), value="") text = gr.Textbox(label=i18n("需要合成的文本"), value="")
text_language = gr.Dropdown( text_language = gr.Dropdown(
label=i18n("需要合成的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文") label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文")
) )
how_to_cut = gr.Radio( how_to_cut = gr.Radio(
label=i18n("怎么切"), label=i18n("怎么切"),
choices=[i18n("不切"),i18n("凑四句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.切"),], choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
value=i18n("凑50字一切"), value=i18n("凑四句一切"),
interactive=True, interactive=True,
) )
inference_button = gr.Button(i18n("合成语音"), variant="primary") inference_button = gr.Button(i18n("合成语音"), variant="primary")
@ -508,22 +553,24 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
inference_button.click( inference_button.click(
get_tts_wav, get_tts_wav,
[inp_ref, prompt_text, prompt_language, text, text_language,how_to_cut], [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut],
[output], [output],
) )
gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")) gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
with gr.Row(): with gr.Row():
text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"),value="") text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
button1 = gr.Button(i18n("凑四句一切"), variant="primary") button1 = gr.Button(i18n("凑四句一切"), variant="primary")
button2 = gr.Button(i18n("凑50字一切"), variant="primary") button2 = gr.Button(i18n("凑50字一切"), variant="primary")
button3 = gr.Button(i18n("按中文句号。切"), variant="primary") button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
button4 = gr.Button(i18n("按英文句号.切"), variant="primary") button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
button5 = gr.Button(i18n("按标点符号切"), variant="primary")
text_opt = gr.Textbox(label=i18n("切分后文本"), value="") text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
button1.click(cut1, [text_inp], [text_opt]) button1.click(cut1, [text_inp], [text_opt])
button2.click(cut2, [text_inp], [text_opt]) button2.click(cut2, [text_inp], [text_opt])
button3.click(cut3, [text_inp], [text_opt]) button3.click(cut3, [text_inp], [text_opt])
button4.click(cut4, [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("后续将支持混合语种编码文本输入。"))
app.queue(concurrency_count=511, max_size=1022).launch( app.queue(concurrency_count=511, max_size=1022).launch(