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 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 = {
    ("English"): "en"
    }


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")
        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
    
    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(text)
    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(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: {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: {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):
    parts = re.split('(\d+)', s)
    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"
    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")
        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")
            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")
        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('''
  <h1 style="font-size: 25px;">Text-to-Speech Generator</h1>
    <h1 style="font-size: 20px;">Supports English</h1>
    <p style="margin-bottom: 10px; font-size: 100%">
        Welcome to our Text-to-Speech generator! This tool converts written text into natural sounding audio in English. Perfect for presentations, educational content, or just having fun, it allows you to bring text to life effortlessly. Utilize the various voice options and tones to tailor your audio outputs according to your needs.
    </p>
  </p>''')


    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"]]
    
    
    with gr.Row():
        english_choice = gr.Radio(english_models, label="EN",value="Trump",scale=3)



    plsh='''
      Input any text
    '''
    limit='Max 70 words. Excess will be ignored.'

    gr.HTML('''
    <b>Input Text</b>''')
    with gr.Row():
        with gr.Column(scale=2): 
            model_name = gr.Textbox(label="Seleted Model", value=default_model_name, interactive=False,scale=1,) 
            text_language = gr.Textbox(
            label="Language for input text",
            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",
            choices=["tone1","tone2","tone3"],
            value="tone1",
            info='Tone influences the emotional expression ',scale=1)
        tone_sample=gr.Audio(label="🔊Preview tone ", 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=["English"], value=default_language, scale=1,interactive=False)
            dummy = gr.Radio(choices=["English"],visible=False)
     
    
    with gr.Accordion(label="Additional generation options", 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')
        
    
    gr.HTML('''
    <b>Generate Voice</b>''')
    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<br>
    If it generated silence, please try again.
    <br><br><br><br>
    <h1 style="font-size: 25px;">Clone custom Voice</h1>
    <p style="margin-bottom: 10px; font-size: 100%">
    <br>
    Requires 3-10 seconds of voice input. The cloned voice will have a similarity of 80% or above compared to the original.<br>
    
    
    </p>''')
    
    with gr.Row():
        user_voice = gr.Audio(type="filepath", label="（3~10s）Upload or Record audio",scale=3)
        with gr.Column(scale=7): 
            user_lang = gr.Textbox(label="Language",info='Automatic detection of input language type.',interactive=False)
            with gr.Row():
                user_text= gr.Textbox(label="Text for generation", 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('''<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,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=False).queue(api_open=False)