From 5c08bd92beaf6b1e7fb077541f43e3408f0c66a9 Mon Sep 17 00:00:00 2001
From: Erythrocyte3803 <2544390577@qq.com>
Date: Mon, 29 Jan 2024 14:00:39 +0900
Subject: [PATCH] =?UTF-8?q?=E6=8C=89=E7=85=A7=E6=A0=87=E7=82=B9=E7=AC=A6?=
=?UTF-8?q?=E5=8F=B7=E5=88=86=E5=8F=A5=EF=BC=8C=E4=B8=AD=E8=8B=B1=E6=96=87?=
=?UTF-8?q?=E9=80=9A=E7=94=A8?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
GPT_SoVITS/inference_webui.py | 260 ++++++++++++++++++++--------------
gweight.txt | 1 -
sweight.txt | 1 -
test.py | 17 ---
4 files changed, 151 insertions(+), 128 deletions(-)
delete mode 100644 gweight.txt
delete mode 100644 sweight.txt
delete mode 100644 test.py
diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index 3a1d62cd..37663e3e 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -1,4 +1,21 @@
-import os,re,logging
+import torch
+import librosa
+from tools.i18n.i18n import I18nAuto
+from my_utils import load_audio
+from module.mel_processing import spectrogram_torch
+from time import time as ttime
+from text.cleaner import clean_text
+from text import cleaned_text_to_sequence
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from module.models import SynthesizerTrn
+from feature_extractor import cnhubert
+import numpy as np
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import gradio as gr
+import pdb
+import os
+import re
+import logging
logging.getLogger("markdown_it").setLevel(logging.ERROR)
logging.getLogger("urllib3").setLevel(logging.ERROR)
logging.getLogger("httpcore").setLevel(logging.ERROR)
@@ -7,23 +24,23 @@ logging.getLogger("asyncio").setLevel(logging.ERROR)
logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
-import pdb
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()
gpt_path = os.environ.get(
- "gpt_path", gweight_data)
+ "gpt_path", gweight_data)
else:
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"):
- with open("./sweight.txt", 'r',encoding="utf-8") as file:
+ with open("./sweight.txt", 'r', encoding="utf-8") as file:
sweight_data = file.read()
sovits_path = os.environ.get("sovits_path", sweight_data)
else:
- sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
+ sovits_path = os.environ.get(
+ "sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
# gpt_path = os.environ.get(
# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
# )
@@ -37,28 +54,15 @@ bert_path = os.environ.get(
infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
infer_ttswebui = int(infer_ttswebui)
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:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
is_half = eval(os.environ.get("is_half", "True"))
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-import librosa,torch
-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 AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from text import cleaned_text_to_sequence
-from text.cleaner import clean_text
-from time import time as ttime
-from module.mel_processing import spectrogram_torch
-from my_utils import load_audio
-from tools.i18n.i18n import 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():
device = "cuda"
@@ -74,6 +78,7 @@ if is_half == True:
else:
bert_model = bert_model.to(device)
+
def get_bert_feature(text, word2ph):
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")
@@ -89,6 +94,7 @@ def get_bert_feature(text, word2ph):
phone_level_feature = torch.cat(phone_level_feature, dim=0)
return phone_level_feature.T
+
class DictToAttrRecursive(dict):
def __init__(self, input_dict):
super().__init__(input_dict)
@@ -123,10 +129,11 @@ if is_half == True:
else:
ssl_model = ssl_model.to(device)
+
def change_sovits_weights(sovits_path):
- global vq_model,hps
- dict_s2=torch.load(sovits_path,map_location="cpu")
- hps=dict_s2["config"]
+ global vq_model, hps
+ dict_s2 = torch.load(sovits_path, map_location="cpu")
+ hps = dict_s2["config"]
hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz"
vq_model = SynthesizerTrn(
@@ -135,7 +142,7 @@ def change_sovits_weights(sovits_path):
n_speakers=hps.data.n_speakers,
**hps.model
)
- if("pretrained"not in sovits_path):
+ if ("pretrained"not in sovits_path):
del vq_model.enc_q
if is_half == True:
vq_model = vq_model.half().to(device)
@@ -143,11 +150,15 @@ def change_sovits_weights(sovits_path):
vq_model = vq_model.to(device)
vq_model.eval()
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)
+
def change_gpt_weights(gpt_path):
- global hz,max_sec,t2s_model,config
+ global hz, max_sec, t2s_model, config
hz = 50
dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["config"]
@@ -160,9 +171,13 @@ def change_gpt_weights(gpt_path):
t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
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)
+
def get_spepc(hps, filename):
audio = load_audio(filename, int(hps.data.sampling_rate))
audio = torch.FloatTensor(audio)
@@ -179,10 +194,10 @@ def get_spepc(hps, filename):
return spec
-dict_language={
- i18n("中文"):"zh",
- i18n("英文"):"en",
- i18n("日文"):"ja"
+dict_language = {
+ i18n("中文"): "zh",
+ i18n("英文"): "en",
+ i18n("日文"): "ja"
}
@@ -246,6 +261,7 @@ def nonen_clean_text_inf(text, language):
return phones, word2ph, norm_text
+
def nonen_get_bert_inf(text, language):
textlist, langlist = splite_en_inf(text, language)
print(textlist)
@@ -261,25 +277,31 @@ def nonen_get_bert_inf(text, language):
return bert
-splits = {"，","。","？","！",",",".","?","!","~",":","：","—","…",}
+
+splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
+
+
def get_first(text):
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
text = re.split(pattern, text)[0].strip()
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()
prompt_text = prompt_text.strip("\n")
- if(prompt_text[-1]not in splits):prompt_text+="。"if prompt_text!="en"else "."
+ if (prompt_text[-1]not in splits):
+ prompt_text += "。"if prompt_text != "en"else "."
text = text.strip("\n")
- if(len(get_first(text))<4):text+="。"if text!="en"else "."
+ if (len(get_first(text)) < 4):
+ text += "。"if text != "en"else "."
zero_wav = np.zeros(
int(hps.data.sampling_rate * 0.3),
dtype=np.float16 if is_half == True else np.float32,
)
with torch.no_grad():
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！"))
wav16k = torch.from_numpy(wav16k)
zero_wav_torch = torch.from_numpy(zero_wav)
@@ -289,7 +311,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
else:
wav16k = wav16k.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))[
"last_hidden_state"
].transpose(
@@ -302,24 +324,32 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
text_language = dict_language[text_language]
if prompt_language == "en":
- phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
+ phones1, word2ph1, norm_text1 = clean_text_inf(
+ prompt_text, prompt_language)
else:
- 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)
- elif(how_to_cut==i18n("凑50字一切")):text=cut2(text)
- elif(how_to_cut==i18n("按中文句号。切")):text=cut3(text)
- elif(how_to_cut==i18n("按英文句号.切")):text=cut4(text)
- elif(how_to_cut==i18n("按中文标点分句切")):text=cut5(text)
- elif(how_to_cut==i18n("按英文标点分句切")):text=cut6(text)
- text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n")
- if(text[-1]not in splits):text+="。"if text_language!="en"else "."
- texts=text.split("\n")
+ 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)
+ elif (how_to_cut == i18n("凑50字一切")):
+ text = cut2(text)
+ elif (how_to_cut == i18n("按中文句号。切")):
+ text = cut3(text)
+ elif (how_to_cut == i18n("按英文句号.切")):
+ text = cut4(text)
+ elif (how_to_cut == i18n("按标点符号分句切")):
+ text = cut5(text)
+ text = text.replace("\n\n", "\n").replace(
+ "\n\n", "\n").replace("\n\n", "\n")
+ if (text[-1]not in splits):
+ text += "。"if text_language != "en"else "."
+ texts = text.split("\n")
audio_opt = []
if prompt_language == "en":
bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
else:
bert1 = nonen_get_bert_inf(prompt_text, prompt_language)
-
+
for text in texts:
# 解决输入目标文本的空行导致报错的问题
if (len(text.strip()) == 0):
@@ -327,7 +357,8 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
if text_language == "en":
phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
else:
- phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
+ phones2, word2ph2, norm_text2 = nonen_clean_text_inf(
+ text, text_language)
if text_language == "en":
bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
@@ -336,7 +367,8 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
bert = torch.cat([bert1, bert2], 1)
- all_phoneme_ids = torch.LongTensor(phones1 + 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)
@@ -365,12 +397,13 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
audio = (
vq_model.decode(
- pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
+ pred_semantic, torch.LongTensor(
+ phones2).to(device).unsqueeze(0), refer
)
.detach()
.cpu()
.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(zero_wav)
t4 = ttime()
@@ -379,6 +412,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
np.int16
)
+
def split(todo_text):
todo_text = todo_text.replace("……", "。").replace("——", "，")
if todo_text[-1] not in splits:
@@ -406,7 +440,7 @@ def cut1(inp):
if len(split_idx) > 1:
opts = []
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:
opts = [inp]
return "\n".join(opts)
@@ -430,7 +464,7 @@ def cut2(inp):
if tmp_str != "":
opts.append(tmp_str)
# 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 = opts[:-1]
return "\n".join(opts)
@@ -440,29 +474,22 @@ def cut3(inp):
inp = inp.strip("\n")
return "\n".join(["%s。" % item for item in inp.strip("。").split("。")])
+
def cut4(inp):
inp = inp.strip("\n")
return "\n".join(["%s." % item for item in inp.strip(".").split(".")])
-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 cut6(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 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):
# 使用正则表达式提取字符串中的数字部分和非数字部分
@@ -471,55 +498,71 @@ def custom_sort_key(s):
parts = [int(part) if part.isdigit() else part for part in parts]
return parts
+
def change_choices():
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():
SoVITS_names = [pretrained_sovits_name]
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]
for name in os.listdir(GPT_weight_root):
- if name.endswith(".ckpt"): GPT_names.append("%s/%s"%(GPT_weight_root,name))
- return SoVITS_names,GPT_names
-SoVITS_names,GPT_names = get_weights_names()
+ if name.endswith(".ckpt"):
+ GPT_names.append("%s/%s" % (GPT_weight_root, name))
+ return SoVITS_names, GPT_names
+
+
+SoVITS_names, GPT_names = get_weights_names()
with gr.Blocks(title="GPT-SoVITS WebUI") 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.
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.")
+ 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.
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.")
)
with gr.Group():
gr.Markdown(value=i18n("模型切换"))
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)
- SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_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)
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])
- SoVITS_dropdown.change(change_sovits_weights,[SoVITS_dropdown],[])
- GPT_dropdown.change(change_gpt_weights,[GPT_dropdown],[])
+ refresh_button.click(fn=change_choices, inputs=[], outputs=[
+ SoVITS_dropdown, GPT_dropdown])
+ SoVITS_dropdown.change(change_sovits_weights, [
+ SoVITS_dropdown], [])
+ GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
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")
+ 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_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():
text = gr.Textbox(label=i18n("需要合成的文本"), value="")
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(
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"),i18n("按中文标点分句切"),i18n("按英文标点分句切"),],
+ 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("按标点符号分句切")],
value=i18n("凑50字一切"),
interactive=True,
)
@@ -528,29 +571,28 @@ 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],
+ [inp_ref, prompt_text, prompt_language,
+ text, text_language, how_to_cut],
[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():
- 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="")
with gr.Row():
- button1 = gr.Button(i18n("凑四句一切"), variant="primary")
- button2 = gr.Button(i18n("凑50字一切"), variant="primary")
+ button1 = gr.Button(i18n("凑四句一切"), variant="primary")
+ button2 = gr.Button(i18n("凑50字一切"), variant="primary")
with gr.Row():
- button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
- button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
- with gr.Row():
- button5 = gr.Button(i18n("按中文标点分句切"), variant="primary")
- button6 = gr.Button(i18n("按英文标点分句切"), 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")
+ button5 = gr.Button(i18n("按标点符号分句切"), variant="primary")
text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
button1.click(cut1, [text_inp], [text_opt])
button2.click(cut2, [text_inp], [text_opt])
button3.click(cut3, [text_inp], [text_opt])
button4.click(cut4, [text_inp], [text_opt])
button5.click(cut5, [text_inp], [text_opt])
- button6.click(cut6, [text_inp], [text_opt])
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(
diff --git a/gweight.txt b/gweight.txt
deleted file mode 100644
index 6a330d58..00000000
--- a/gweight.txt
+++ /dev/null
@@ -1 +0,0 @@
-GPT_weights/na_xi_da-e50.ckpt
\ No newline at end of file
diff --git a/sweight.txt b/sweight.txt
deleted file mode 100644
index 3765f316..00000000
--- a/sweight.txt
+++ /dev/null
@@ -1 +0,0 @@
-SoVITS_weights/na_xi_da_e20_s2020.pth
\ No newline at end of file
diff --git a/test.py b/test.py
deleted file mode 100644
index 7ad88214..00000000
--- a/test.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import re
-
-def add_period(text):
- if not re.search(r'[^\w\s]', text[-1]):
- text += '。'
- return text
-
-def cut5(inp):
- inp = add_period(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
-
-print(cut5("测试"))
\ No newline at end of file