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.HTML('''
  <h1 style="font-size: 25px;">A TTS GENERATOR</h1>
  <p style="margin-bottom: 10px; font-size: 100%">
   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❤️<br>
  </p>''')

    gr.Markdown("""* This space is based on the text-to-speech generation solution GPT-SoVITS . 
    You can visit the repo's github homepage to learn training and inference.<br>
    本空间基于文字转语音生成方案 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.<br>
    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。
    <br>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。 
    * The model's corresponding language is its native language, but in fact, 
    each model can speak three languages.<br>模型对应的语言是其母语，但实际上，
    每个模型都能说三种语言<br>モデルに対応する言語はその母国語ですが、実際には、各モデルは3つの言語を話すことができます。""")   
    gr.HTML('''<a href="https://colab.research.google.com/drive/1fTuPZ4tZsAjS-TrhQWMCb7KRdnU8aF6j" target="_blank"><img src="https://camo.githubusercontent.com/dd83d4a334eab7ada034c13747d9e2237182826d32e3fda6629740b6e02f18d8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6c61622d4639414230303f7374796c653d666f722d7468652d6261646765266c6f676f3d676f6f676c65636f6c616226636f6c6f723d353235323532" alt="colab"></a>
''')

    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)