GPT-SoVITS-Trilingual

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Update app.py

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	import gradio as gr
	import numpy as np
	import soundfile as sf
	from datetime import datetime
	from time import time as ttime
	from my_utils import load_audio
	from transformers import pipeline
	from text.cleaner import clean_text
	from feature_extractor import cnhubert
	from timeit import default_timer as timer
	from text import cleaned_text_to_sequence
	from module.models import SynthesizerTrn
	import os,re,sys,LangSegment,librosa,pdb,torch,pytz
	from module.mel_processing import spectrogram_torch
	from transformers.pipelines.audio_utils import ffmpeg_read
	from transformers import AutoModelForMaskedLM, AutoTokenizer
	from AR.models.t2s_lightning_module import Text2SemanticLightningModule


	import logging
	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").setLevel(logging.WARNING)
	from download import *
	download()


	if "_CUDA_VISIBLE_DEVICES" in os.environ:
	os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
	tz = pytz.timezone('Asia/Singapore')
	device = "cuda" if torch.cuda.is_available() else "cpu"

	def abs_path(dir):
	global_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
	return(os.path.join(global_dir, dir))
	gpt_path = abs_path("MODELS/22/22.ckpt")
	sovits_path=abs_path("MODELS/22/22.pth")
	cnhubert_base_path = os.environ.get("cnhubert_base_path", "pretrained_models/chinese-hubert-base")
	bert_path = os.environ.get("bert_path", "pretrained_models/chinese-roberta-wwm-ext-large")

	if not os.path.exists(cnhubert_base_path):
	cnhubert_base_path = "TencentGameMate/chinese-hubert-base"
	if not os.path.exists(bert_path):
	bert_path = "hfl/chinese-roberta-wwm-ext-large"
	cnhubert.cnhubert_base_path = cnhubert_base_path

	whisper_path = os.environ.get("whisper_path", "pretrained_models/whisper-tiny")
	if not os.path.exists(whisper_path):
	whisper_path = "openai/whisper-tiny"

	pipe = pipeline(
	task="automatic-speech-recognition",
	model=whisper_path,
	chunk_length_s=30,
	device=device,)


	is_half = eval(
	os.environ.get("is_half", "True" if torch.cuda.is_available() else "False")
	)

	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):
	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(
	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))
	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
	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("./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)
	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


	dict_language = {
	("中文1"): "all_zh",#全部按中文识别
	("English"): "en",#全部按英文识别#######不变
	("日文1"): "all_ja",#全部按日文识别
	("中文"): "zh",#按中英混合识别####不变
	("日本語"): "ja",#按日英混合识别####不变
	("混合"): "auto",#多语种启动切分识别语种
	}


	def splite_en_inf(sentence, language):
	pattern = re.compile(r'[a-zA-Z ]+')
	textlist = []
	langlist = []
	pos = 0
	for match in pattern.finditer(sentence):
	start, end = match.span()
	if start > pos:
	textlist.append(sentence[pos:start])
	langlist.append(language)
	textlist.append(sentence[start:end])
	langlist.append("en")
	pos = end
	if pos < len(sentence):
	textlist.append(sentence[pos:])
	langlist.append(language)
	# Merge punctuation into previous word
	for i in range(len(textlist)-1, 0, -1):
	if re.match(r'^[\W_]+$', textlist[i]):
	textlist[i-1] += textlist[i]
	del textlist[i]
	del langlist[i]
	# Merge consecutive words with the same language tag
	i = 0
	while i < len(langlist) - 1:
	if langlist[i] == langlist[i+1]:
	textlist[i] += textlist[i+1]
	del textlist[i+1]
	del langlist[i+1]
	else:
	i += 1

	return textlist, langlist


	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:
	formattext = formattext.replace(" ", " ")
	phones, word2ph, norm_text = clean_text(formattext, language)
	phones = cleaned_text_to_sequence(phones)
	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


	def nonen_clean_text_inf(text, language):
	if(language!="auto"):
	textlist, langlist = splite_en_inf(text, language)
	else:
	textlist=[]
	langlist=[]
	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 = []
	for i in range(len(textlist)):
	lang = langlist[i]
	phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
	phones_list.append(phones)
	if lang == "zh":
	word2ph_list.append(word2ph)
	norm_text_list.append(norm_text)
	print(word2ph_list)
	phones = sum(phones_list, [])
	word2ph = sum(word2ph_list, [])
	norm_text = ' '.join(norm_text_list)

	return phones, word2ph, norm_text


	def nonen_get_bert_inf(text, language):
	if(language!="auto"):
	textlist, langlist = splite_en_inf(text, language)
	else:
	textlist=[]
	langlist=[]
	for tmp in LangSegment.getTexts(text):
	langlist.append(tmp["lang"])
	textlist.append(tmp["text"])
	print(textlist)
	print(langlist)
	bert_list = []
	for i in range(len(textlist)):
	lang = langlist[i]
	phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
	bert = get_bert_inf(phones, word2ph, norm_text, lang)
	bert_list.append(bert)
	bert = torch.cat(bert_list, dim=1)

	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


	def get_cleaned_text_final(text,language):
	if language in {"en","all_zh","all_ja"}:
	phones, word2ph, norm_text = clean_text_inf(text, language)
	elif language in {"zh", "ja","auto"}:
	phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
	return phones, word2ph, norm_text

	def get_bert_final(phones, word2ph, text,language,device):
	if language == "en":
	bert = get_bert_inf(phones, word2ph, text, language)
	elif language in {"zh", "ja","auto"}:
	bert = nonen_get_bert_inf(text, language)
	elif language == "all_zh":
	bert = get_bert_feature(text, word2ph).to(device)
	else:
	bert = torch.zeros((1024, len(phones))).to(device)
	return bert

	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


	def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=("Do not split"), volume_scale=1.0):
	if not duration(ref_wav_path):
	return None
	if text == '':
	wprint("Please enter text to generate/请输入生成文字")
	return None
	t0 = ttime()
	startTime=timer()
	text=trim_text(text,text_language)
	change_sovits_weights(sovits_path)
	tprint(f'🏕️LOADED SoVITS Model: {sovits_path}')
	change_gpt_weights(gpt_path)
	tprint(f'🏕️LOADED GPT Model: {gpt_path}')

	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 "."
	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(("实际输入的参考文本:"), prompt_text)
	#print(("📝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,
	)
	with torch.no_grad():
	wav16k, sr = librosa.load(ref_wav_path, sr=16000)
	if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
	errinfo='参考音频在3~10秒范围外，请更换！'
	raise OSError((errinfo))
	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]
	t1 = ttime()

	phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)

	if (how_to_cut == ("Split into groups of 4 sentences")):
	text = cut1(text)
	elif (how_to_cut == ("Split every 50 characters")):
	text = cut2(text)
	elif (how_to_cut == ("Split at CN/JP periods (。)")):
	text = cut3(text)
	elif (how_to_cut == ("Split at English periods (.)")):
	text = cut4(text)
	elif (how_to_cut == ("Split at punctuation marks")):
	text = cut5(text)
	while "\n\n" in text:
	text = text.replace("\n\n", "\n")
	print(f"🧨实际输入的目标文本(切句后):{text}\n")
	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)

	for text in texts:
	if (len(text.strip()) == 0):
	continue
	if (text[-1] not in splits): text += "。" if text_language != "en" else "."
	print(("\n🎈实际输入的目标文本(每句):"), text)
	phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
	try:
	bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
	except RuntimeError as e:
	wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}")
	return None
	bert = torch.cat([bert1, bert2], 1)

	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)
	t2 = ttime()
	with torch.no_grad():
	# pred_semantic = t2s_model.model.infer(
	pred_semantic, idx = t2s_model.model.infer_panel(
	all_phoneme_ids,
	all_phoneme_len,
	prompt,
	bert,
	# prompt_phone_len=ph_offset,
	top_k=config["inference"]["top_k"],
	early_stop_num=hz * max_sec,
	)
	t3 = ttime()
	# print(pred_semantic.shape,idx)
	pred_semantic = pred_semantic[:, -idx:].unsqueeze(
	0
	) # .unsqueeze(0)#mq要多unsqueeze一次
	refer = get_spepc(hps, ref_wav_path) # .to(device)
	if is_half == True:
	refer = refer.half().to(device)
	else:
	refer = refer.to(device)
	# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
	try:
	audio = (
	vq_model.decode(
	pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
	)
	.detach()
	.cpu()
	.numpy()[0, 0]
	)
	except RuntimeError as e:
	wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}")
	return None

	max_audio=np.abs(audio).max()
	if max_audio>1:audio/=max_audio
	audio_opt.append(audio)
	audio_opt.append(zero_wav)
	t4 = ttime()
	print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
	#yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
	audio_data = (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)

	audio_data = (audio_data.astype(np.float32) * volume_scale).astype(np.int16)
	output_wav = "output_audio.wav"
	sf.write(output_wav, audio_data, hps.data.sampling_rate)
	endTime=timer()
	tprint(f'🆗TTS COMPLETE,{round(endTime-startTime,4)}s')
	return output_wav

	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
	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]
	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:
	opts[-2] = opts[-2] + opts[-1]
	opts = opts[:-1]
	return "\n".join(opts)


	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(".")])


	# 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)
	mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
	if len(items)%2 == 1:
	mergeitems.append(items[-1])
	opt = "\n".join(mergeitems)
	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
	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 tprint(text):
	now=datetime.now(tz).strftime('%H:%M:%S')
	print(f'UTC+8 - {now} - {text}')

	def wprint(text):
	tprint(text)
	gr.Warning(text)

	#裁切文本
	def trim_text(text,language):
	limit_cj = 120 #character
	limit_en = 60 #words
	search_limit_cj = limit_cj+30
	search_limit_en = limit_en +30
	text = text.replace('\n', '').strip()

	if language =='English':
	words = text.split()
	if len(words) <= limit_en:
	return text
	# English
	for i in range(limit_en, -1, -1):
	if any(punct in words[i] for punct in splits):
	return ' '.join(words[:i+1])
	for i in range(limit_en, min(len(words), search_limit_en)):
	if any(punct in words[i] for punct in splits):
	return ' '.join(words[:i+1])
	return ' '.join(words[:limit_en])

	else:#中文日文
	if len(text) <= limit_cj:
	return text
	for i in range(limit_cj, -1, -1):
	if text[i] in splits:
	return text[:i+1]
	for i in range(limit_cj, min(len(text), search_limit_cj)):
	if text[i] in splits:
	return text[:i+1]
	return text[:limit_cj]

	def duration(audio_file_path):
	try:
	audio_duration = librosa.get_duration(filename=audio_file_path)
	if not 3 < audio_duration < 10:
	wprint("The audio length must be between 3~10 seconds/音频时长须在3~10秒之间")
	return False
	return True
	except FileNotFoundError:
	wprint("Failed to obtain uploaded audio/未找到音频文件")
	return False

	def update_model(choice):
	global gpt_path, sovits_path
	model_info = models[choice]
	gpt_path = abs_path(model_info["gpt_weight"])
	sovits_path = abs_path(model_info["sovits_weight"])
	model_name = choice
	tone_info = model_info["tones"]["tone1"]
	tone_sample_path = abs_path(tone_info["sample"])
	tprint(f'✅SELECT MODEL：{choice}')
	# 返回默认tone“tone1”
	return (
	tone_info["example_voice_wav"],
	tone_info["example_voice_wav_words"],
	model_info["default_language"],
	model_info["default_language"],
	model_name,
	"tone1" ,
	tone_sample_path
	)

	def update_tone(model_choice, tone_choice):
	model_info = models[model_choice]
	tone_info = model_info["tones"][tone_choice]
	example_voice_wav = abs_path(tone_info["example_voice_wav"])
	example_voice_wav_words = tone_info["example_voice_wav_words"]
	tone_sample_path = abs_path(tone_info["sample"])
	return example_voice_wav, example_voice_wav_words,tone_sample_path

	def transcribe(voice):
	time1=timer()
	tprint('⚡Start Clone - transcribe')
	task="transcribe"
	if voice is None:
	wprint("No audio file submitted! Please upload or record an audio file before submitting your request.")
	R = pipe(voice, batch_size=8, generate_kwargs={"task": task}, return_timestamps=True,return_language=True)
	text=R['text']
	lang=R['chunks'][0]['language']
	if lang=='english':
	language='English'
	elif lang =='chinese':
	language='中文'
	elif lang=='japanese':
	language = '日本語'

	time2=timer()
	tprint(f'transcribe COMPLETE,{round(time2-time1,4)}s')
	tprint(f'\n🔣转录结果：\n 🔣Language：{language} \n 🔣Text：{text}' )
	return text,language

	def clone_voice(user_voice,user_text,user_lang):
	if not duration(user_voice):
	return None
	if user_text == '':
	wprint("Please enter text to generate/请输入生成文字")
	return None
	tprint('⚡Start clone')
	user_text=trim_text(user_text,user_lang)
	time1=timer()
	global gpt_path, sovits_path
	gpt_path = abs_path("pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
	#tprint(f'Model loaded:{gpt_path}')
	sovits_path = abs_path("pretrained_models/s2G488k.pth")
	#tprint(f'Model loaded:{sovits_path}')
	prompt_text, prompt_language = transcribe(user_voice)
	output_wav = get_tts_wav(
	user_voice,
	prompt_text,
	prompt_language,
	user_text,
	user_lang,
	how_to_cut="Do not split",
	volume_scale=1.0)
	time2=timer()
	tprint(f'🆗CLONE COMPLETE,{round(time2-time1,4)}s')
	return output_wav


	from info import models
	models_by_language = {
	"English": [],
	"中文": [],
	"日本語": []
	}
	for model_name, model_info in models.items():
	language = model_info["default_language"]
	models_by_language[language].append((model_name, model_info))

	##########GRADIO###########

	with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
	gr.Markdown("# <center>🥳💕🎶 GPT-SoVITS 1分钟完美声音克隆，再次升级！</center>")
	gr.Markdown("## <center>🌟 支持中日英三语自动标注及训练 + 中文方言训练！Powered by [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)</center>")
	gr.Markdown("### <center>🌊 更多精彩应用，尽在[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>")


	default_voice_wav, default_voice_wav_words, default_language, _, default_model_name, _, default_tone_sample_path = update_model("Trump")
	english_models = [name for name, _ in models_by_language["English"]]
	chinese_models = [name for name, _ in models_by_language["中文"]]
	japanese_models = [name for name, _ in models_by_language["日本語"]]
	with gr.Row():
	english_choice = gr.Radio(english_models, label="EN\|English Model",value="Trump",scale=3)
	chinese_choice = gr.Radio(chinese_models, label="CN\|中文模型",scale=2)
	japanese_choice = gr.Radio(japanese_models, label="JP\|日本語モデル",scale=4)

	plsh='Text must match the selected language option to prevent errors, for example, if English is input but Chinese is selected for generation.\n文字一定要和语言选项匹配，不然要报错，比如输入的是英文，生成语言选中文'
	limit='Max 70 words. Excess will be ignored./单次最多处理120字左右，多余的会被忽略'

	gr.HTML('''
	<b>输入文字</b>''')
	with gr.Row():
	model_name = gr.Textbox(label="Seleted Model/已选模型", value=default_model_name, scale=1)
	text = gr.Textbox(label="Input some text for voice generation/输入想要生成语音的文字", lines=5,scale=8,
	placeholder=plsh,info=limit)


	with gr.Row():
	with gr.Column(scale=2):
	tone_select = gr.Radio(
	label="Select Tone/选择语气",
	choices=["tone1","tone2","tone3"],
	value="tone1",
	info='Tone influences the emotional expression ',scale=1)

	text_language = gr.Radio(
	label="Select language for input text/输入的文字对应语言",
	choices=["中文","English","日本語"],
	value=default_language,
	info='Input text and language must match.',scale=1,
	)

	tone_sample=gr.Audio(label="🔊Preview tone/试听语气 ", scale=5)


	with gr.Accordion(label="prpt voice", open=True, visible=True):
	with gr.Row(visible=True):
	inp_ref = gr.Audio(label="Reference audio", type="filepath", value=default_voice_wav, scale=3)
	prompt_text = gr.Textbox(label="Reference text", value=default_voice_wav_words, scale=3)
	prompt_language = gr.Dropdown(label="Language of the reference audio", choices=["中文", "English", "日本語"], value=default_language, scale=1,interactive=False)



	with gr.Accordion(label="Additional generation options/附加生成选项", open=False):
	how_to_cut = gr.Dropdown(
	label=("How to split?"),
	choices=[("Do not split"), ("Split into groups of 4 sentences"), ("Split every 50 characters"),
	("Split at CN/JP periods (。)"), ("Split at English periods (.)"), ("Split at punctuation marks"), ],
	value=("Split into groups of 4 sentences"),
	interactive=True,
	info='A suitable splitting method can achieve better generation results'
	)
	volume = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.01, label='Volume/音量')


	gr.HTML('''
	<b>开始生成</b>''')
	with gr.Row():
	main_button = gr.Button("✨Generate Voice", variant="primary", scale=1)
	output = gr.Audio(label="💾Download it by clicking ⬇️", scale=3)
	#info = gr.Textbox(label="INFO", visible=True, readonly=True, scale=1)

	gr.HTML('''
	Generation is slower, please be patient and wait/合成比较慢，请耐心等待<br>
	If it generated silence, please try again./如果生成了空白声音，请重试
	<br><br><br><br>
	<h1 style="font-size: 25px;">Clone custom Voice/克隆自定义声音</h1>
	<p style="margin-bottom: 10px; font-size: 100%">Need 3~10s audio.This involves voice-to-text conversion followed by text-to-voice conversion, so it takes longer time<br>
	需要3~10秒语音，这个会涉及语音转文字，之后再转语音，所以耗时比较久
	</p>''')

	with gr.Row():
	user_voice = gr.Audio(type="filepath", label="（3~10s）Upload or Record audio/上传或录制声音",scale=3)
	user_lang = gr.Dropdown(label="Language/生成语言", choices=["中文", "English", "日本語"],scale=1,value='English')
	user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=5,scale=5,
	placeholder=plsh,info=limit)

	user_button = gr.Button("✨Clone Voice", variant="primary")
	user_output = gr.Audio(label="💾Download it by clicking ⬇️")

	gr.HTML('''<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=Ailyth/DLMP9" /></div>''')

	english_choice.change(update_model, inputs=[english_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select, tone_sample])
	chinese_choice.change(update_model, inputs=[chinese_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select, tone_sample])
	japanese_choice.change(update_model, inputs=[japanese_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select, tone_sample])
	tone_select.change(update_tone, inputs=[model_name, tone_select], outputs=[inp_ref, prompt_text, tone_sample])

	main_button.click(
	get_tts_wav,
	inputs=[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut,volume],
	outputs=[output])

	user_button.click(
	clone_voice,
	inputs=[user_voice,user_text,user_lang],
	outputs=[user_output])

	app.launch(share=True)