app.py

import argparse
import json
import os
import re
import tempfile
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
import ONNXVITS_infer
import librosa
import numpy as np
import torch
from torch import no_grad, LongTensor
import commons
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 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:
        return None

    if gr_utils.validate_url(y):
        file = gr_processing_utils.download_to_file(y, dir=self.temp_dir)
    elif isinstance(y, tuple):
        sample_rate, data = y
        file = tempfile.NamedTemporaryFile(
            suffix=".wav", dir=self.temp_dir, delete=False
        )
        gr_processing_utils.audio_to_file(sample_rate, data, file.name)
    else:
        file = gr_processing_utils.create_tmp_copy_of_file(y, dir=self.temp_dir)

    return gr_processing_utils.encode_url_or_file_to_base64(file.name)


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 text_to_phoneme(text, symbols, is_symbol):
  _symbol_to_id = {s: i for i, s in enumerate(symbols)}

  sequence = ""
  if not is_symbol:
      clean_text = japanese_cleaners(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

def get_text(text, hps, is_symbol):
    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
    if hps.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    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 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))

download_audio_js = """
() =>{{
    let root = document.querySelector("body > gradio-app");
    if (root.shadowRoot != null)
        root = root.shadowRoot;
    let audio = root.querySelector("#{audio_id}").querySelector("audio");
    if (audio == undefined)
        return;
    audio = audio.src;
    let oA = document.createElement("a");
    oA.download = Math.floor(Math.random()*100000000)+'.wav';
    oA.href = audio;
    document.body.appendChild(oA);
    oA.click();
    oA.remove();
}}
"""

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
    args = parser.parse_args()
    app = gr.Blocks()
    with app:
        gr.Markdown("# Umamusume voice synthesizer 赛马娘语音合成器\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"
                    "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], [],
                                  _js=f"""
                (i, symbols) => {{
                    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);
                    return [];
                }}""")
                # 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):
                    
                    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"
                   )
    app.queue(concurrency_count=3).launch(show_api=False, share=args.share)