'''
按中英混合识别
按日英混合识别
多语种启动切分识别语种
全部按中文识别
全部按英文识别
全部按日文识别
'''
import logging
import traceback
logging.getLogger("markdown_it").setLevel(logging.ERROR)
logging.getLogger("urllib3").setLevel(logging.ERROR)
logging.getLogger("httpcore").setLevel(logging.ERROR)
logging.getLogger("httpx").setLevel(logging.ERROR)
logging.getLogger("asyncio").setLevel(logging.ERROR)
logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
import LangSegment, os, re, sys, json
import pdb
import torch
version=os.environ.get("version","v2")
pretrained_sovits_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth", "GPT_SoVITS/pretrained_models/s2G488k.pth"]
pretrained_gpt_name=["GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"]
_ =[[],[]]
for i in range(2):
if os.path.exists(pretrained_gpt_name[i]):
_[0].append(pretrained_gpt_name[i])
if os.path.exists(pretrained_sovits_name[i]):
_[-1].append(pretrained_sovits_name[i])
pretrained_gpt_name,pretrained_sovits_name = _
if os.path.exists(f"./weight.json"):
pass
else:
with open(f"./weight.json", 'w', encoding="utf-8") as file:json.dump({'GPT':{},'SoVITS':{}},file)
with open(f"./weight.json", 'r', encoding="utf-8") as file:
weight_data = file.read()
weight_data=json.loads(weight_data)
gpt_path = os.environ.get(
"gpt_path", weight_data.get('GPT',{}).get(version,pretrained_gpt_name))
sovits_path = os.environ.get(
"sovits_path", weight_data.get('SoVITS',{}).get(version,pretrained_sovits_name))
if isinstance(gpt_path,list):
gpt_path = gpt_path[0]
if isinstance(sovits_path,list):
sovits_path = sovits_path[0]
# gpt_path = os.environ.get(
# "gpt_path", pretrained_gpt_name
# )
# sovits_path = os.environ.get("sovits_path", pretrained_sovits_name)
cnhubert_base_path = os.environ.get(
"cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
)
bert_path = os.environ.get(
"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
)
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)
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")) and torch.cuda.is_available()
punctuation = set(['!', '?', '…', ',', '.', '-'," "])
import gradio as gr
from transformers import AutoModelForMaskedLM, AutoTokenizer
import numpy as np
import librosa
from feature_extractor import cnhubert
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 tools.my_utils import load_audio
from tools.i18n.i18n import I18nAuto, scan_language_list
language=os.environ.get("language","Auto")
language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
i18n = I18nAuto(language=language)
# 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"
else:
device = "cpu"
dict_language_v1 = {
i18n("中文"): "all_zh",#全部按中文识别
i18n("英文"): "en",#全部按英文识别#######不变
i18n("日文"): "all_ja",#全部按日文识别
i18n("中英混合"): "zh",#按中英混合识别####不变
i18n("日英混合"): "ja",#按日英混合识别####不变
i18n("多语种混合"): "auto",#多语种启动切分识别语种
}
dict_language_v2 = {
i18n("中文"): "all_zh",#全部按中文识别
i18n("英文"): "en",#全部按英文识别#######不变
i18n("日文"): "all_ja",#全部按日文识别
i18n("粤语"): "all_yue",#全部按中文识别
i18n("韩文"): "all_ko",#全部按韩文识别
i18n("中英混合"): "zh",#按中英混合识别####不变
i18n("日英混合"): "ja",#按日英混合识别####不变
i18n("粤英混合"): "yue",#按粤英混合识别####不变
i18n("韩英混合"): "ko",#按韩英混合识别####不变
i18n("多语种混合"): "auto",#多语种启动切分识别语种
i18n("多语种混合(粤语)"): "auto_yue",#多语种启动切分识别语种
}
dict_language = dict_language_v1 if version =='v1' else dict_language_v2
tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
if is_half == True:
bert_model = bert_model.half().to(device)
else:
bert_model = bert_model.to(device)
def get_bert_feature(text, word2ph):
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(device)
res = bert_model(**inputs, output_hidden_states=True)
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
assert len(word2ph) == len(text)
phone_level_feature = []
for i in range(len(word2ph)):
repeat_feature = res[i].repeat(word2ph[i], 1)
phone_level_feature.append(repeat_feature)
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)
for key, value in input_dict.items():
if isinstance(value, dict):
value = DictToAttrRecursive(value)
self[key] = value
setattr(self, key, value)
def __getattr__(self, item):
try:
return self[item]
except KeyError:
raise AttributeError(f"Attribute {item} not found")
def __setattr__(self, key, value):
if isinstance(value, dict):
value = DictToAttrRecursive(value)
super(DictToAttrRecursive, self).__setitem__(key, value)
super().__setattr__(key, value)
def __delattr__(self, item):
try:
del self[item]
except KeyError:
raise AttributeError(f"Attribute {item} not found")
ssl_model = cnhubert.get_model()
if is_half == True:
ssl_model = ssl_model.half().to(device)
else:
ssl_model = ssl_model.to(device)
def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
global vq_model, hps, version, dict_language
dict_s2 = torch.load(sovits_path, map_location="cpu")
hps = dict_s2["config"]
hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz"
if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
hps.model.version = "v1"
else:
hps.model.version = "v2"
version = hps.model.version
# print("sovits版本:",hps.model.version)
vq_model = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model
)
if ("pretrained" not in sovits_path):
del vq_model.enc_q
if is_half == True:
vq_model = vq_model.half().to(device)
else:
vq_model = vq_model.to(device)
vq_model.eval()
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
dict_language = dict_language_v1 if version =='v1' else dict_language_v2
with open("./weight.json")as f:
data=f.read()
data=json.loads(data)
data["SoVITS"][version]=sovits_path
with open("./weight.json","w")as f:f.write(json.dumps(data))
if prompt_language is not None and text_language is not None:
if prompt_language in list(dict_language.keys()):
prompt_text_update, prompt_language_update = {'__type__':'update'}, {'__type__':'update', 'value':prompt_language}
else:
prompt_text_update = {'__type__':'update', 'value':''}
prompt_language_update = {'__type__':'update', 'value':i18n("中文")}
if text_language in list(dict_language.keys()):
text_update, text_language_update = {'__type__':'update'}, {'__type__':'update', 'value':text_language}
else:
text_update = {'__type__':'update', 'value':''}
text_language_update = {'__type__':'update', 'value':i18n("中文")}
return {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update
change_sovits_weights(sovits_path)
def change_gpt_weights(gpt_path):
global hz, max_sec, t2s_model, config
hz = 50
dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["config"]
max_sec = config["data"]["max_sec"]
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
if is_half == True:
t2s_model = t2s_model.half()
t2s_model = t2s_model.to(device)
t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))
with open("./weight.json")as f:
data=f.read()
data=json.loads(data)
data["GPT"][version]=gpt_path
with open("./weight.json","w")as f:f.write(json.dumps(data))
change_gpt_weights(gpt_path)
def get_spepc(hps, filename):
audio = load_audio(filename, int(hps.data.sampling_rate))
audio = torch.FloatTensor(audio)
maxx=audio.abs().max()
if(maxx>1):audio/=min(2,maxx)
audio_norm = audio
audio_norm = audio_norm.unsqueeze(0)
spec = spectrogram_torch(
audio_norm,
hps.data.filter_length,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
center=False,
)
return spec
def clean_text_inf(text, language, version):
phones, word2ph, norm_text = clean_text(text, language, version)
phones = cleaned_text_to_sequence(phones, version)
return phones, word2ph, norm_text
dtype=torch.float16 if is_half == True else torch.float32
def get_bert_inf(phones, word2ph, norm_text, language):
language=language.replace("all_","")
if language == "zh":
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
else:
bert = torch.zeros(
(1024, len(phones)),
dtype=torch.float16 if is_half == True else torch.float32,
).to(device)
return bert
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
def get_first(text):
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
text = re.split(pattern, text)[0].strip()
return text
from text import chinese
def get_phones_and_bert(text,language,version):
if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
language = language.replace("all_","")
if language == "en":
LangSegment.setfilters(["en"])
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
else:
# 因无法区别中日韩文汉字,以用户输入为准
formattext = text
while " " in formattext:
formattext = formattext.replace(" ", " ")
if language == "zh":
if re.search(r'[A-Za-z]', formattext):
formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
formattext = chinese.mix_text_normalize(formattext)
return get_phones_and_bert(formattext,"zh",version)
else:
phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
bert = get_bert_feature(norm_text, word2ph).to(device)
elif language == "yue" and re.search(r'[A-Za-z]', formattext):
formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
formattext = chinese.mix_text_normalize(formattext)
return get_phones_and_bert(formattext,"yue",version)
else:
phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
bert = torch.zeros(
(1024, len(phones)),
dtype=torch.float16 if is_half == True else torch.float32,
).to(device)
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
textlist=[]
langlist=[]
LangSegment.setfilters(["zh","ja","en","ko"])
if language == "auto":
for tmp in LangSegment.getTexts(text):
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
elif language == "auto_yue":
for tmp in LangSegment.getTexts(text):
if tmp["lang"] == "zh":
tmp["lang"] = "yue"
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
else:
for tmp in LangSegment.getTexts(text):
if tmp["lang"] == "en":
langlist.append(tmp["lang"])
else:
# 因无法区别中日韩文汉字,以用户输入为准
langlist.append(language)
textlist.append(tmp["text"])
print(textlist)
print(langlist)
phones_list = []
bert_list = []
norm_text_list = []
for i in range(len(textlist)):
lang = langlist[i]
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
bert = get_bert_inf(phones, word2ph, norm_text, lang)
phones_list.append(phones)
norm_text_list.append(norm_text)
bert_list.append(bert)
bert = torch.cat(bert_list, dim=1)
phones = sum(phones_list, [])
norm_text = ''.join(norm_text_list)
return phones,bert.to(dtype),norm_text
def merge_short_text_in_array(texts, threshold):
if (len(texts)) < 2:
return texts
result = []
text = ""
for ele in texts:
text += ele
if len(text) >= threshold:
result.append(text)
text = ""
if (len(text) > 0):
if len(result) == 0:
result.append(text)
else:
result[len(result) - 1] += text
return result
##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
# cache_tokens={}#暂未实现清理机制
cache= {}
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,speed=1,if_freeze=False,inp_refs=123):
global cache
if ref_wav_path:pass
else:gr.Warning(i18n('请上传参考音频'))
if text:pass
else:gr.Warning(i18n('请填入推理文本'))
t = []
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]
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("实际输入的目标文本:"), text)
zero_wav = np.zeros(
int(hps.data.sampling_rate * 0.3),
dtype=np.float16 if is_half == True else np.float32,
)
if not ref_free:
with torch.no_grad():
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
gr.Warning(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)
zero_wav_torch = torch.from_numpy(zero_wav)
if is_half == True:
wav16k = wav16k.half().to(device)
zero_wav_torch = zero_wav_torch.half().to(device)
else:
wav16k = wav16k.to(device)
zero_wav_torch = zero_wav_torch.to(device)
wav16k = torch.cat([wav16k, zero_wav_torch])
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
"last_hidden_state"
].transpose(
1, 2
) # .float()
codes = vq_model.extract_latent(ssl_content)
prompt_semantic = codes[0, 0]
prompt = prompt_semantic.unsqueeze(0).to(device)
t1 = ttime()
t.append(t1-t0)
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("按标点符号切")):
text = cut5(text)
while "\n\n" in text:
text = text.replace("\n\n", "\n")
print(i18n("实际输入的目标文本(切句后):"), text)
texts = text.split("\n")
texts = process_text(texts)
texts = merge_short_text_in_array(texts, 5)
audio_opt = []
if not ref_free:
phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language, version)
for i_text,text in enumerate(texts):
# 解决输入目标文本的空行导致报错的问题
if (len(text.strip()) == 0):
continue
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
print(i18n("实际输入的目标文本(每句):"), text)
phones2,bert2,norm_text2=get_phones_and_bert(text, text_language, version)
print(i18n("前端处理后的文本(每句):"), norm_text2)
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)
bert = bert.to(device).unsqueeze(0)
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
t2 = ttime()
# cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
# print(cache.keys(),if_freeze)
if(i_text in cache and if_freeze==True):pred_semantic=cache[i_text]
else:
with torch.no_grad():
pred_semantic, idx = t2s_model.model.infer_panel(
all_phoneme_ids,
all_phoneme_len,
None if ref_free else prompt,
bert,
# prompt_phone_len=ph_offset,
top_k=top_k,
top_p=top_p,
temperature=temperature,
early_stop_num=hz * max_sec,
)
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
cache[i_text]=pred_semantic
t3 = ttime()
refers=[]
if(inp_refs):
for path in inp_refs:
try:
refer = get_spepc(hps, path.name).to(dtype).to(device)
refers.append(refer)
except:
traceback.print_exc()
if(len(refers)==0):refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
audio = (vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers,speed=speed).detach().cpu().numpy()[0, 0])
max_audio=np.abs(audio).max()#简单防止16bit爆音
if max_audio>1:audio/=max_audio
audio_opt.append(audio)
audio_opt.append(zero_wav)
t4 = ttime()
t.extend([t2 - t1,t3 - t2, t4 - t3])
t1 = ttime()
print("%.3f\t%.3f\t%.3f\t%.3f" %
(t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3]))
)
yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
np.int16
)
def split(todo_text):
todo_text = todo_text.replace("……", "。").replace("——", "，")
if todo_text[-1] not in splits:
todo_text += "。"
i_split_head = i_split_tail = 0
len_text = len(todo_text)
todo_texts = []
while 1:
if i_split_head >= len_text:
break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
if todo_text[i_split_head] in splits:
i_split_head += 1
todo_texts.append(todo_text[i_split_tail:i_split_head])
i_split_tail = i_split_head
else:
i_split_head += 1
return todo_texts
def cut1(inp):
inp = inp.strip("\n")
inps = split(inp)
split_idx = list(range(0, len(inps), 4))
split_idx[-1] = None
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]]))
else:
opts = [inp]
opts = [item for item in opts if not set(item).issubset(punctuation)]
return "\n".join(opts)
def cut2(inp):
inp = inp.strip("\n")
inps = split(inp)
if len(inps) < 2:
return inp
opts = []
summ = 0
tmp_str = ""
for i in range(len(inps)):
summ += len(inps[i])
tmp_str += inps[i]
if summ > 50:
summ = 0
opts.append(tmp_str)
tmp_str = ""
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
opts[-2] = opts[-2] + opts[-1]
opts = opts[:-1]
opts = [item for item in opts if not set(item).issubset(punctuation)]
return "\n".join(opts)
def cut3(inp):
inp = inp.strip("\n")
opts = ["%s" % item for item in inp.strip("。").split("。")]
opts = [item for item in opts if not set(item).issubset(punctuation)]
return "\n".join(opts)
def cut4(inp):
inp = inp.strip("\n")
opts = ["%s" % item for item in inp.strip(".").split(".")]
opts = [item for item in opts if not set(item).issubset(punctuation)]
return "\n".join(opts)
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
def cut5(inp):
inp = inp.strip("\n")
punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
mergeitems = []
items = []
for i, char in enumerate(inp):
if char in punds:
if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
items.append(char)
else:
items.append(char)
mergeitems.append("".join(items))
items = []
else:
items.append(char)
if items:
mergeitems.append("".join(items))
opt = [item for item in mergeitems if not set(item).issubset(punds)]
return "\n".join(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
parts = re.split('(\d+)', s)
# 将数字部分转换为整数，非数字部分保持不变
parts = [int(part) if part.isdigit() else part for part in parts]
return parts
def process_text(texts):
_text=[]
if all(text in [None, " ", "\n",""] for text in texts):
raise ValueError(i18n("请输入有效文本"))
for text in texts:
if text in [None, " ", ""]:
pass
else:
_text.append(text)
return _text
def change_choices():
SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
SoVITS_weight_root=["SoVITS_weights_v2","SoVITS_weights"]
GPT_weight_root=["GPT_weights_v2","GPT_weights"]
for path in SoVITS_weight_root+GPT_weight_root:
os.makedirs(path,exist_ok=True)
def get_weights_names(GPT_weight_root, SoVITS_weight_root):
SoVITS_names = [i for i in pretrained_sovits_name]
for path in SoVITS_weight_root:
for name in os.listdir(path):
if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (path, name))
GPT_names = [i for i in pretrained_gpt_name]
for path in GPT_weight_root:
for name in os.listdir(path):
if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (path, name))
return SoVITS_names, GPT_names
SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
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}>
</div>"""
def html_left(text, label='p'):
return f"""<div style="text-align: left; margin: 0; padding: 0;">
<{label} style="margin: 0; padding: 0;">{text}</{label}>
</div>"""
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. <br>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<b>LICENSE</b>.")
)
with gr.Group():
gr.Markdown(html_center(i18n("模型切换"),'h3'))
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, scale=14)
SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True, scale=14)
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=14)
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
gr.Markdown(html_center(i18n("*请上传并填写参考信息"),'h3'))
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", scale=13)
with gr.Column(scale=13):
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(html_left(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开。<br>开启后无视填写的参考文本。")))
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=3, max_lines=3)
prompt_language = gr.Dropdown(
label=i18n("参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文"), scale=14
)
inp_refs = gr.File(label=i18n("可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"),file_count="multiple",scale=13)
gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"),'h3'))
with gr.Row():
with gr.Column(scale=13):
text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=26, max_lines=26)
with gr.Column(scale=7):
text_language = gr.Dropdown(
label=i18n("需要合成的语种")+i18n(".限制范围越小判别效果越好。"), choices=list(dict_language.keys()), value=i18n("中文"), scale=1
)
how_to_cut = gr.Dropdown(
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("按标点符号切"), ],
value=i18n("凑四句一切"),
interactive=True, scale=1
)
gr.Markdown(value=html_center(i18n("语速调整，高为更快")))
if_freeze=gr.Checkbox(label=i18n("是否直接对上次合成结果调整语速和音色。防止随机性。"), value=False, interactive=True,show_label=True, scale=1)
speed = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label=i18n("语速"),value=1,interactive=True, scale=1)
gr.Markdown(html_center(i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")))
top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=15,interactive=True, scale=1)
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True, scale=1)
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True, scale=1)
# with gr.Column():
# gr.Markdown(value=i18n("手工调整音素。当音素框不为空时使用手工音素输入推理，无视目标文本框。"))
# phoneme=gr.Textbox(label=i18n("音素框"), value="")
# get_phoneme_button = gr.Button(i18n("目标文本转音素"), variant="primary")
with gr.Row():
inference_button = gr.Button(i18n("合成语音"), variant="primary", size='lg', scale=25)
output = gr.Audio(label=i18n("输出的语音"), scale=14)
inference_button.click(
get_tts_wav,
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free,speed,if_freeze,inp_refs],
[output],
)
SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown,prompt_language,text_language], [prompt_language,text_language,prompt_text,prompt_language,text,text_language])
GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
# 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="")
# button1 = gr.Button(i18n("凑四句一切"), 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")
# 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="")
# 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])
# gr.Markdown(html_center(i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")))
if __name__ == '__main__':
app.queue().launch(
share=False,
show_error=True,
)