Update app.py

app.py

@@ -5,7 +5,6 @@ import re
 import tempfile
 import logging
 logging.getLogger('numba').setLevel(logging.WARNING)
-import ONNXVITS_infer
 import librosa
 import numpy as np
 import torch
@@ -15,14 +14,12 @@ import utils
 import gradio as gr
 import gradio.utils as gr_utils
 import gradio.processing_utils as gr_processing_utils
-from models import SynthesizerTrn
+from ONNXVITS_infer import SynthesizerTrn
 from text import text_to_sequence, _clean_text
 from text.symbols import symbols
 from mel_processing import spectrogram_torch
-import translators.server as tss
 import psutil
 from datetime import datetime
-from text.cleaners import japanese_cleaners
 
 def audio_postprocess(self, y):
     if y is None:
@@ -42,53 +39,71 @@ def audio_postprocess(self, y):
     return gr_processing_utils.encode_url_or_file_to_base64(file.name)
 
 
+language_marks = {
+    "日本語": "[JA]",
+    "简体中文": "[ZH]",
+    "English": "[EN]",
+    "Mix": "",
+}
+
 gr.Audio.postprocess = audio_postprocess
 
 limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-languages = ['日本語', '简体中文', 'English']
-characters = ['0:特别周', '1:无声铃鹿', '2:东海帝王', '3:丸善斯基',
-              '4:富士奇迹', '5:小栗帽', '6:黄金船', '7:伏特加',
-              '8:大和赤骥', '9:大树快车', '10:草上飞', '11:菱亚马逊',
-              '12:目白麦昆', '13:神鹰', '14:好歌剧', '15:成田白仁',
-              '16:鲁道夫象征', '17:气槽', '18:爱丽数码', '19:青云天空',
-              '20:玉藻十字', '21:美妙姿势', '22:琵琶晨光', '23:重炮',
-              '24:曼城茶座', '25:美普波旁', '26:目白雷恩', '27:菱曙',
-              '28:雪之美人', '29:米浴', '30:艾尼斯风神', '31:爱丽速子',
-              '32:爱慕织姬', '33:稻荷一', '34:胜利奖券', '35:空中神宫',
-              '36:荣进闪耀', '37:真机伶', '38:川上公主', '39:黄金城市',
-              '40:樱花进王', '41:采珠', '42:新光风', '43:东商变革',
-              '44:超级小溪', '45:醒目飞鹰', '46:荒漠英雄', '47:东瀛佐敦',
-              '48:中山庆典', '49:成田大进', '50:西野花', '51:春乌拉拉',
-              '52:青竹回忆', '53:微光飞驹', '54:美丽周日', '55:待兼福来',
-              '56:Mr.C.B', '57:名将怒涛', '58:目白多伯', '59:优秀素质',
-              '60:帝王光环', '61:待兼诗歌剧', '62:生野狄杜斯', '63:目白善信',
-              '64:大拓太阳神', '65:双涡轮', '66:里见光钻', '67:北部玄驹',
-              '68:樱花千代王', '69:天狼星象征', '70:目白阿尔丹', '71:八重无敌',
-              '72:鹤丸刚志', '73:目白光明', '74:樱花桂冠', '75:成田路',
-              '76:也文摄辉', '77:吉兆', '78:谷野美酒', '79:第一红宝石',
-              '80:真弓快车', '81:骏川手纲', '82:凯斯奇迹', '83:小林历奇',
-              '84:北港火山', '85:奇锐骏', '86:秋川理事长']
-def show_memory_info(hint):
-    pid = os.getpid()
-    p = psutil.Process(pid)
-    info = p.memory_info()
-    memory = info.rss / 1024.0 / 1024
-    print("{} 内存占用: {} MB".format(hint, memory))
+def create_tts_fn(model, hps, speaker_ids):
+    def tts_fn(text, speaker, language, speed, is_symbol):
+        if limitation:
+            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
+            max_len = 150
+            if is_symbol:
+                max_len *= 3
+            if text_len > max_len:
+                return "Error: Text is too long", None
+        if language is not None:
+            text = language_marks[language] + text + language_marks[language]
+        speaker_id = speaker_ids[speaker]
+        stn_tst = get_text(text, hps, is_symbol)
+        with no_grad():
+            x_tst = stn_tst.unsqueeze(0).to(device)
+            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
+            sid = LongTensor([speaker_id]).to(device)
+            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
+                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
+        del stn_tst, x_tst, x_tst_lengths, sid
+        return "Success", (hps.data.sampling_rate, audio)
+
+    return tts_fn
 
-def text_to_phoneme(text, symbols, is_symbol):
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
+def create_vc_fn(model, hps, speaker_ids):
+    def vc_fn(original_speaker, target_speaker, input_audio):
+        if input_audio is None:
+            return "You need to upload an audio", None
+        sampling_rate, audio = input_audio
+        duration = audio.shape[0] / sampling_rate
+        if limitation and duration > 30:
+            return "Error: Audio is too long", None
+        original_speaker_id = speaker_ids[original_speaker]
+        target_speaker_id = speaker_ids[target_speaker]
 
-  sequence = ""
-  if not is_symbol:
-      clean_text = japanese_cleaners(text)
-  else:
-      clean_text = text
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += symbol
-  return sequence
+        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+        if len(audio.shape) > 1:
+            audio = librosa.to_mono(audio.transpose(1, 0))
+        if sampling_rate != hps.data.sampling_rate:
+            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
+        with no_grad():
+            y = torch.FloatTensor(audio)
+            y = y.unsqueeze(0)
+            spec = spectrogram_torch(y, hps.data.filter_length,
+                                     hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
+                                     center=False).to(device)
+            spec_lengths = LongTensor([spec.size(-1)]).to(device)
+            sid_src = LongTensor([original_speaker_id]).to(device)
+            sid_tgt = LongTensor([target_speaker_id]).to(device)
+            audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
+                0, 0].data.cpu().float().numpy()
+        del y, spec, spec_lengths, sid_src, sid_tgt
+        return "Success", (hps.data.sampling_rate, audio)
+
+    return vc_fn
 
 def get_text(text, hps, is_symbol):
     text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
@@ -97,120 +112,12 @@ def get_text(text, hps, is_symbol):
     text_norm = LongTensor(text_norm)
     return text_norm
 
-hps = utils.get_hparams_from_file("./configs/uma87.json")
-symbols = hps.symbols
-net_g = ONNXVITS_infer.SynthesizerTrn(
-    len(hps.symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("pretrained_models/G_1153000.pth", net_g)
+def create_to_symbol_fn(hps):
+    def to_symbol_fn(is_symbol_input, input_text, temp_text):
+        return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
+            else (temp_text, temp_text)
 
-def to_symbol_fn(is_symbol_input, input_text, temp_text):
-    return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
-        else (temp_text, temp_text)
-
-def infer(text_raw, character, language, duration, noise_scale, noise_scale_w, is_symbol):
-    # check character & duraction parameter
-    if language not in languages:
-        print("Error: No such language\n")
-        return "Error: No such language", None, None, None
-    if character not in characters:
-        print("Error: No such character\n")
-        return "Error: No such character", None, None, None
-    # check text length
-    if limitation:
-        text_len = len(text_raw) if is_symbol else len(re.sub("\[([A-Z]{2})\]", "", text_raw))
-        max_len = 150
-        if is_symbol:
-            max_len *= 3
-        if text_len > max_len:
-            print(f"Refused: Text too long ({text_len}).")
-            return "Error: Text is too long", None, None, None
-        if text_len == 0:
-            print("Refused: Text length is zero.")
-            return "Error: Please input text!", None, None, None
-    if is_symbol:
-        text = text_raw
-    elif language == '日本語':
-        text = text_raw
-    elif language == '简体中文':
-        text = tss.google(text_raw, from_language='zh', to_language='ja')
-    elif language == 'English':
-        text = tss.google(text_raw, from_language='en', to_language='ja')
-    char_id = int(character.split(':')[0])
-    stn_tst = get_text(text, hps, is_symbol)
-    with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-        sid = torch.LongTensor([char_id])
-        try:
-            jp2phoneme = text_to_phoneme(text, hps.symbols, is_symbol)
-            durations = net_g.predict_duration(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale,
-                                               noise_scale_w=noise_scale_w, length_scale=duration)
-            char_dur_list = []
-            for i, char in enumerate(jp2phoneme):
-                char_pos = i * 2 + 1
-                char_dur = durations[char_pos]
-                char_dur_list.append(char_dur)
-        except IndexError:
-            print("Refused: Phoneme input contains non-phoneme character.")
-            return "Error: You can only input phoneme under phoneme input model", None, None, None
-        char_spacing_dur_list = []
-        char_spacings = []
-        for i in range(len(durations)):
-            if i % 2 == 0:  # spacing
-                char_spacings.append("spacing")
-            elif i % 2 == 1:  # char
-                char_spacings.append(jp2phoneme[int((i - 1) / 2)])
-            char_spacing_dur_list.append(int(durations[i]))
-        # convert duration information to string
-        duration_info_str = ""
-        for i in range(len(char_spacings)):
-            if i == len(char_spacings) - 1:
-                duration_info_str += "(" + str(char_spacing_dur_list[i]) + ")"
-            elif char_spacings[i] == "spacing":
-                duration_info_str += "(" + str(char_spacing_dur_list[i]) + ")" + ", "
-            else:
-                duration_info_str += char_spacings[i] + ":" + str(char_spacing_dur_list[i])
-        audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=duration)[0][0,0].data.float().numpy()
-    currentDateAndTime = datetime.now()
-    print(f"\nCharacter {character} inference successful: {text}")
-    if language != '日本語':
-        print(f"translate from {language}: {text_raw}")
-    show_memory_info(str(currentDateAndTime) + " infer调用后")
-    return (text,(22050, audio), jp2phoneme, duration_info_str)
-
-def infer_from_phoneme_dur(duration_info_str, character, duration, noise_scale, noise_scale_w):
-    try:
-        phonemes = duration_info_str.split(", ")
-        recons_durs = []
-        recons_phonemes = ""
-        for i, item in enumerate(phonemes):
-            if i == 0:
-                recons_durs.append(int(item.strip("()")))
-            else:
-                phoneme_n_dur, spacing_dur = item.split("(")
-                recons_phonemes += phoneme_n_dur.split(":")[0]
-                recons_durs.append(int(phoneme_n_dur.split(":")[1]))
-                recons_durs.append(int(spacing_dur.strip(")")))
-    except ValueError:
-        return ("Error: Format must not be changed!", None)
-    except AssertionError:
-        return ("Error: Format must not be changed!", None)
-    char_id = int(character.split(':')[0])
-    stn_tst = get_text(recons_phonemes, hps, is_symbol=True)
-    with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-        sid = torch.LongTensor([char_id])
-        audio = net_g.infer_with_duration(x_tst, x_tst_lengths, w_ceil=recons_durs, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                            length_scale=duration)[0][0, 0].data.cpu().float().numpy()
-    print(f"\nCharacter {character} inference successful: {recons_phonemes}, from {duration_info_str}")
-    return (recons_phonemes, (22050, audio))
+    return to_symbol_fn
 
 download_audio_js = """
 () =>{{
@@ -230,134 +137,132 @@ download_audio_js = """
 }}
 """
 
+models_tts = []
+models_vc = []
+models_info = [
+    {
+        "title": "Japanese",
+        "langugages": ["日本語"],
+        "description": "",
+        "model_path": "./pretrained_models/G_1153000.pth",
+        "config_path": "./configs/uma87.json"
+        "examples": [['お疲れ様です，トレーナーさん。', 'Silence Suzuka', '日本語', 1, False],
+                        ['張り切っていこう！', 'Kitasan Black', '日本語', 1, False],
+                        ['何でこんなに慣れでんのよ，私のほが先に好きだっだのに。', 'Grass Wonder', '日本語', 1, False],
+                        ['授業中に出しだら，学校生活終わるですわ。', 'Mejiro Mcqueen', '日本語', 1, False],
+                        ['お帰りなさい，お兄様！', 'Rice Shower', '日本語', 1, False],
+                        ['私の処女をもらっでください！', 'Rice Shower', '日本語', 1, False]]
+    },
+    {
+        "title": "Japanese",
+        "langugages": ['日本語', '简体中文', 'English', 'Mix'],
+        "description": "",
+        "model_path": "./pretrained_models/G_1396000.pth",
+        "config_path": "./configs/uma_trilingual.json"
+        "examples": [['你好，训练员先生，很高兴见到你。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', '简体中文', 1, False],
+                        ['To be honest, I have no idea what to say as examples.', '派蒙 Paimon (Genshin Impact)', 'English', 1, False],
+                        ['授業中に出しだら，学校生活終わるですわ。', '綾地 寧々 Ayachi Nene (Sanoba Witch)', '日本語', 1, False]]
+    }
+]
+
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
     args = parser.parse_args()
+    for info in models_info:
+        name = info['title']
+        lang = info['languages']
+        examples = info['examples']
+        config_path = info['config_path']
+        model_path = info['model_path']
+        hps = utils.get_hparams_from_file(config_path)
+        model = SynthesizerTrn(
+            len(hps.symbols),
+            hps.data.filter_length // 2 + 1,
+            hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            **hps.model)
+        utils.load_checkpoint(model_path, model, None)
+        model.eval()
+        speaker_ids = hps.speakers
+        speakers = list(hps.speakers.keys())
+        models_tts.append((name, speakers, lang, example,
+                           hps.symbols, create_tts_fn(model, hps, speaker_ids),
+                           create_to_symbol_fn(hps)))
+        models_vc.append((name, speakers, create_vc_fn(model, hps, speaker_ids)))
     app = gr.Blocks()
     with app:
-        gr.Markdown("# Umamusume voice synthesizer 赛马娘语音合成器\n\n"
+        gr.Markdown("# English & Chinese & Japanese Anime TTS\n\n"
                     "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=Plachta.VITS-Umamusume-voice-synthesizer)\n\n"
-                    "This synthesizer is created based on [VITS](https://arxiv.org/abs/2106.06103) model, trained on voice data extracted from mobile game Umamusume Pretty Derby \n\n"
-                    "这个合成器是基于VITS文本到语音模型，在从手游《賽馬娘：Pretty Derby》解包的语音数据上训练得到。[Dataset Link](https://huggingface.co/datasets/Plachta/Umamusume-voice-text-pairs/tree/main)\n\n"
-                    "[introduction video / 模型介绍视频](https://www.bilibili.com/video/BV1T84y1e7p5/?vd_source=6d5c00c796eff1cbbe25f1ae722c2f9f#reply607277701)\n\n"
-                    "You may duplicate this space or [open in Colab](https://colab.research.google.com/drive/1J2Vm5dczTF99ckyNLXV0K-hQTxLwEaj5?usp=sharing) to run it privately and without any queue.\n\n"
-                    "您可以复制该空间至私人空间运行或打开[Google Colab](https://colab.research.google.com/drive/1J2Vm5dczTF99ckyNLXV0K-hQTxLwEaj5?usp=sharing)在线运行。\n\n"
+                    "Including Japanese TTS & Trilingual TTS, speakers are all anime characters. 包含一个纯日语TTS和一个中日英三语TTS模型，主要为二次元角色。"
                     "If you have any suggestions or bug reports, feel free to open discussion in [Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions).\n\n"
                     "若有bug反馈或建议，请在[Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions)下开启一个新的Discussion。 \n\n"
-                    "If your input language is not Japanese, it will be translated to Japanese by Google translator, but accuracy is not guaranteed.\n\n"
-                    "如果您的输入语言不是日语，则会由谷歌翻译自动翻译为日语，但是准确性不能保证。\n\n"
                     )
-        with gr.Row():
-            with gr.Column():
-                # We instantiate the Textbox class
-                textbox = gr.TextArea(label="Text", placeholder="Type your sentence here (Maximum 150 words)", value="こんにちわ。", elem_id=f"tts-input")
-                with gr.Accordion(label="Phoneme Input", open=False):
-                    temp_text_var = gr.Variable()
-                    symbol_input = gr.Checkbox(value=False, label="Symbol input")
-                    symbol_list = gr.Dataset(label="Symbol list", components=[textbox],
-                                             samples=[[x] for x in symbols],
-                                             elem_id=f"symbol-list")
-                    symbol_list_json = gr.Json(value=symbols, visible=False)
-                symbol_input.change(to_symbol_fn,
-                                    [symbol_input, textbox, temp_text_var],
-                                    [textbox, temp_text_var])
-                symbol_list.click(None, [symbol_list, symbol_list_json], textbox,
-                                  _js=f"""
-                (i, symbols, text) => {{
-                    let root = document.querySelector("body > gradio-app");
-                    if (root.shadowRoot != null)
-                        root = root.shadowRoot;
-                    let text_input = root.querySelector("#tts-input").querySelector("textarea");
-                    let startPos = text_input.selectionStart;
-                    let endPos = text_input.selectionEnd;
-                    let oldTxt = text_input.value;
-                    let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                    text_input.value = result;
-                    let x = window.scrollX, y = window.scrollY;
-                    text_input.focus();
-                    text_input.selectionStart = startPos + symbols[i].length;
-                    text_input.selectionEnd = startPos + symbols[i].length;
-                    text_input.blur();
-                    window.scrollTo(x, y);
-
-                    text = text_input.value;
-                    
-                    return text;
-                }}""")
-                # select character
-                char_dropdown = gr.Dropdown(choices=characters, value = "0:特别周", label='character')
-                language_dropdown = gr.Dropdown(choices=languages, value = "日本語", label='language')
-
-
-                duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, label='时长 Duration')
-                noise_scale_slider = gr.Slider(minimum=0.1, maximum=5, value=0.667, step=0.001, label='噪声比例 noise_scale')
-                noise_scale_w_slider = gr.Slider(minimum=0.1, maximum=5, value=0.8, step=0.1, label='噪声偏差 noise_scale_w')
-
-                
-                
-            with gr.Column():
-                text_output = gr.Textbox(label="Output Text")
-                phoneme_output = gr.Textbox(label="Output Phonemes", interactive=False)
-                audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
-                btn = gr.Button("Generate!")
-                cus_dur_gn_btn = gr.Button("Regenerate with custom phoneme durations")
-                
-                download = gr.Button("Download Audio")
-                download.click(None, [], [], _js=download_audio_js.format(audio_id="tts-audio"))
-                with gr.Accordion(label="Speaking Pace Control", open=True):
+        with gr.Tabs():
+            with gr.TabItem("TTS"):
+                with gr.Tabs():
+                    for i, (name, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate(models_tts):
+                        with gr.TabItem(name)
+                            with gr.Row():
+                                with gr.Column():
+                                    textbox = gr.TextArea(label="Text", placeholder="Type your sentence here (Maximum 150 words)", value="こんにちわ。", elem_id=f"tts-input")
+                                    with gr.Accordion(label="Phoneme Input", open=False):
+                                        temp_text_var = gr.Variable()
+                                        symbol_input = gr.Checkbox(value=False, label="Symbol input")
+                                        symbol_list = gr.Dataset(label="Symbol list", components=[textbox],
+                                                                 samples=[[x] for x in symbols],
+                                                                 elem_id=f"symbol-list")
+                                        symbol_list_json = gr.Json(value=symbols, visible=False)
+                                    symbol_input.change(to_symbol_fn,
+                                                        [symbol_input, textbox, temp_text_var],
+                                                        [textbox, temp_text_var])
+                                    symbol_list.click(None, [symbol_list, symbol_list_json], textbox,
+                                                      _js=f"""
+                                    (i, symbols, text) => {{
+                                        let root = document.querySelector("body > gradio-app");
+                                        if (root.shadowRoot != null)
+                                            root = root.shadowRoot;
+                                        let text_input = root.querySelector("#tts-input").querySelector("textarea");
+                                        let startPos = text_input.selectionStart;
+                                        let endPos = text_input.selectionEnd;
+                                        let oldTxt = text_input.value;
+                                        let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
+                                        text_input.value = result;
+                                        let x = window.scrollX, y = window.scrollY;
+                                        text_input.focus();
+                                        text_input.selectionStart = startPos + symbols[i].length;
+                                        text_input.selectionEnd = startPos + symbols[i].length;
+                                        text_input.blur();
+                                        window.scrollTo(x, y);
                     
-                    duration_output = gr.Textbox(label="Duration of each phoneme", placeholder="After you generate a sentence, the detailed information of each phoneme's duration will be presented here.",
-                                                interactive = True)
-                    gr.Markdown(
-                        "The number after the : mark represents the length of each phoneme in the generated audio, while the number inside ( ) represents the lenght of spacing between each phoneme and its next phoneme. "
-                        "You can manually change the numbers to adjust the length of each phoneme, so that speaking pace can be completely controlled. "
-                        "Note that these numbers should be integers only. \n\n(1 represents a length of 0.01161 seconds)\n\n"
-                        "音素冒号后的数字代表音素在生成音频中的长度，( )内的数字代表每个音素与下一个音素之间间隔的长度。"
-                        "您可以手动修改这些数字来控制每个音素以及间隔的长度，从而完全控制合成音频的说话节奏。"
-                        "注意这些数字只能是整数。 \n\n(1 代表 0.01161 秒的长度)\n\n"
-                    )
-                btn.click(infer, inputs=[textbox, char_dropdown, language_dropdown, duration_slider, noise_scale_slider, noise_scale_w_slider, symbol_input], 
-                  outputs=[text_output, audio_output, phoneme_output, duration_output])
-                cus_dur_gn_btn.click(infer_from_phoneme_dur, inputs=[duration_output, char_dropdown, duration_slider, noise_scale_slider, noise_scale_w_slider], 
-                          outputs=[phoneme_output, audio_output])
-                
-        examples = [['haa\u2193......haa\u2193......haa\u2193......haa\u2193......haa\u2193......haa\u2193......haa\u2193......haa\u2193......haa\u2193......haa\u2193......haa\u2193......haa\u2193......', '29:米浴', '日本語', 1, 0.667, 0.8, True],
-                    ['お疲れ様です，トレーナーさん。', '1:无声铃鹿', '日本語', 1, 0.667, 0.8, False],
-                    ['張り切っていこう！', '67:北部玄驹', '日本語', 1, 0.667, 0.8, False],
-                    ['何でこんなに慣れでんのよ，私のほが先に好きだっだのに。', '10:草上飞', '日本語', 1, 0.667, 0.8, False],
-                    ['授業中に出しだら，学校生活終わるですわ。', '12:目白麦昆', '日本語', 1, 0.667, 0.8, False],
-                    ['お帰りなさい，お兄様！', '29:米浴', '日本語', 1, 0.667, 0.8, False],
-                    ['私の処女をもらっでください！', '29:米浴', '日本語', 1, 0.667, 0.8, False]]
-        gr.Examples(
-            examples=examples,
-            inputs=[textbox, char_dropdown, language_dropdown,
-                    duration_slider, noise_scale_slider,noise_scale_w_slider, symbol_input],
-            outputs=[text_output, audio_output],
-            fn=infer
-        )
-        gr.Markdown("# Updates Logs 更新日志：\n\n"
-                   "2023/1/24：\n\n"
-                   "Improved the format of phoneme length control.\n\n"
-                   "改善了音素控制的格式。\n\n"
-                   "2023/1/24：\n\n"
-                   "Added more precise control on pace of speaking by modifying the duration of each phoneme.\n\n"
-                   "增加了对说话节奏的音素级控制。\n\n"
-                   "2023/1/13：\n\n"
-                   "Added one example of phoneme input.\n\n"
-                   "增加了音素输入的example（米浴喘气）\n\n"
-                   "2023/1/12：\n\n"
-                   "Added phoneme input, which enables more precise control on output audio.\n\n"
-                   "增加了音素输入的功能，可以对语气和语调做到一定程度的精细控制。\n\n"
-                   "Adjusted UI arrangements.\n\n"
-                   "调整了UI的布局。\n\n"
-                   "2023/1/10：\n\n"
-                   "Dataset used for training is now uploaded to [here](https://huggingface.co/datasets/Plachta/Umamusume-voice-text-pairs/tree/main)\n\n"
-                   "数据集已上传，您可以在[这里](https://huggingface.co/datasets/Plachta/Umamusume-voice-text-pairs/tree/main)下载。\n\n"
-                   "2023/1/9：\n\n"
-                   "Model inference has been fully converted to onnxruntime. There will be no more Runtime Error: Memory Limit Exceeded\n\n"
-                   "模型推理已全面转为onnxruntime，现在不会出现Runtime Error: Memory Limit Exceeded了。\n\n"
-                   "Now integrated to [Moe-tts](https://huggingface.co/spaces/skytnt/moe-tts) collection.\n\n"
-                   "现已加入[Moe-tts](https://huggingface.co/spaces/skytnt/moe-tts)模型大全。\n\n"
-                   )
+                                        text = text_input.value;
+                                        
+                                        return text;
+                                    }}""")
+                                    # select character
+                                    char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
+                                    language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
+                                    duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, label='时长 Duration')
+                                with gr.Column():
+                                    text_output = gr.Textbox(label="Message")
+                                    audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
+                                    btn = gr.Button("Generate!")
+                                    
+                                    download = gr.Button("Download Audio")
+                                    download.click(None, [], [], _js=download_audio_js.format(audio_id="tts-audio"))
+                                    if len(lang) == 1:
+                                        btn.click(tts_fn, inputs=[textbox, char_dropdown, None, duration_slider, symbol_input], 
+                                              outputs=[text_output, audio_output])
+                                    else:
+                                        btn.click(tts_fn, inputs=[textbox, char_dropdown, language_dropdown, duration_slider, symbol_input], 
+                                              outputs=[text_output, audio_output])
+                            gr.Examples(
+                                examples=example,
+                                inputs=[textbox, char_dropdown, language_dropdown,
+                                        duration_slider, symbol_input],
+                                outputs=[text_output, audio_output],
+                                fn=tts_fn
+                            )
     app.queue(concurrency_count=3).launch(show_api=False, share=args.share)