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 polyglot.detect import Detector
from feature_extractor import cnhubert
from timeit import default_timer as timer
from text import cleaned_text_to_sequence
from module.models import SynthesizerTrn
from module.mel_processing import spectrogram_torch
from transformers.pipelines.audio_utils import ffmpeg_read
import os,re,sys,LangSegment,librosa,pdb,torch,pytz,random
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]
try:
text_language = dict_language[text_language]
except KeyError as e:
wprint(f"Unsupported language type: {e}")
return None
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
#==========custom functions============
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)
def lang_detector(text):
min_chars = 5
if len(text) < min_chars:
return "Input text too short/输入文本太短"
try:
detector = Detector(text).language
lang_info = str(detector)
code = re.search(r"name: (\w+)", lang_info).group(1)
if code == 'Japanese':
return "日本語"
elif code == 'Chinese':
return "中文"
elif code == 'English':
return 'English'
else:
return code
except Exception as e:
return f"ERROR：{str(e)}"
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):
if not audio_file_path:
wprint("Failed to obtain uploaded audio/未找到音频文件")
return False
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:
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'\nTRANSCRIBE RESULT：\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
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}')
try:
prompt_text, prompt_language = transcribe(user_voice)
except UnboundLocalError as e:
wprint(f"The language in the audio cannot be recognized ：{str(e)}")
return None
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
with open('dummy') as f:
dummy_txt = f.read().strip().splitlines()
def dice():
return random.choice(dummy_txt), '🎲'
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.HTML('''
TEXT TO SPEECH
Support English/Chinese/Japanese
If you like this space, please click the ❤️ at the top of the page..如喜欢，请点一下页面顶部的❤️
''')
gr.Markdown("""* This space is based on the text-to-speech generation solution [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) .
You can visit the repo's github homepage to learn training and inference.
本空间基于文字转语音生成方案 [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS). 你可以前往项目的github主页学习如何推理和训练。
* ⚠️Generating voice is very slow due to using HuggingFace's free CPU in this space.
For faster generation, click the Colab icon below to use this space in Colab,
which will significantly improve the speed.
由于本空间使用huggingface的免费CPU进行推理，因此速度很慢，如想快速生成，请点击下方的Colab图标，
前往Colab使用已获得更快的生成速度。
Colabの使用を強くお勧めします。より速い生成速度が得られます。
* each model can speak three languages.
每个模型都能说三种语言
各モデルは3つの言語を話すことができます。""")
gr.HTML('''
''')
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",value="Trump",scale=3)
chinese_choice = gr.Radio(chinese_models, label="ZH",scale=2)
japanese_choice = gr.Radio(japanese_models, label="JA",scale=4)
plsh='''
Support【English/中文/日本語】，Input text here / 在这輸入文字 /ここにテキストを入力する。
If you don't know what to input, you can click the dice on the right, and random text will appear.
如果你不知道输入什么，可以点击右边的骰子，会出现随机文本。
入力するものがわからない場合は、右側のサイコロをクリックすると、ランダムなテキストが表示されます。
'''
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('''
Input Text/输入文字''')
with gr.Row():
with gr.Column(scale=2):
model_name = gr.Textbox(label="Seleted Model/已选模型", value=default_model_name, interactive=False,scale=1,)
text_language = gr.Textbox(
label="Language for input text/生成语言",
info='Automatic detection of input language type.',scale=1,interactive=False
)
text = gr.Textbox(label="INPUT TEXT", lines=5,placeholder=plsh,info=limit,scale=10,min_width=0)
ddice= gr.Button('🎲', variant='tool',min_width=0,scale=0)
ddice.click(dice, outputs=[text, ddice])
text.change( lang_detector, text, text_language)
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)
tone_sample=gr.Audio(label="🔊Preview tone/试听语气 ", scale=8)
with gr.Accordion(label="prpt voice", open=False,visible=False):
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)
dummy = gr.Radio(choices=["中文","English","日本語"],visible=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('''
Generate Voice/生成''')
with gr.Row():
main_button = gr.Button("✨Generate Voice", variant="primary", scale=2)
output = gr.Audio(label="💾Download it by clicking ⬇️", scale=6)
#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
If it generated silence, please try again./如果生成了空白声音，请重试
Clone custom Voice/克隆自定义声音
需要3~10秒语音，克隆后的声音和原音相似度80%以上
Requires 3-10 seconds of voice input. The cloned voice will have a similarity of 80% or above compared to the original.
3~10秒の音声入力が必要です。クローンされた音声は、オリジナルと80%以上の類似性があります。
''')
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)
with gr.Column(scale=7):
user_lang = gr.Textbox(label="Language/生成语言",info='Automatic detection of input language type.',interactive=False)
with gr.Row():
user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=5,placeholder=plsh,info=limit)
dddice= gr.Button('🎲', variant='tool',min_width=0,scale=0)
dddice.click(dice, outputs=[user_text, dddice])
user_text.change( lang_detector, user_text, user_lang)
user_button = gr.Button("✨Clone Voice", variant="primary")
user_output = gr.Audio(label="💾Download it by clicking ⬇️")
gr.HTML('''''')
english_choice.change(update_model, inputs=[english_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample])
chinese_choice.change(update_model, inputs=[chinese_choice], outputs=[inp_ref, prompt_text, prompt_language, dummy,model_name, tone_select, tone_sample])
japanese_choice.change(update_model, inputs=[japanese_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,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, show_api=True).queue(api_open=True)