app.py

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)