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import argparse |
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import json |
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import os |
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import re |
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import tempfile |
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import logging |
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logging.getLogger('numba').setLevel(logging.WARNING) |
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import ONNXVITS_infer |
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import librosa |
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import numpy as np |
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import torch |
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from torch import no_grad, LongTensor |
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import commons |
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import utils |
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import gradio as gr |
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import gradio.utils as gr_utils |
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import gradio.processing_utils as gr_processing_utils |
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from models import SynthesizerTrn |
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from text import text_to_sequence, _clean_text |
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from text.symbols import symbols |
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from mel_processing import spectrogram_torch |
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import translators.server as tss |
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import psutil |
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from datetime import datetime |
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from text.cleaners import japanese_cleaners |
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def audio_postprocess(self, y): |
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if y is None: |
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return None |
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|
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if gr_utils.validate_url(y): |
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file = gr_processing_utils.download_to_file(y, dir=self.temp_dir) |
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elif isinstance(y, tuple): |
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sample_rate, data = y |
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file = tempfile.NamedTemporaryFile( |
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suffix=".wav", dir=self.temp_dir, delete=False |
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) |
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gr_processing_utils.audio_to_file(sample_rate, data, file.name) |
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else: |
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file = gr_processing_utils.create_tmp_copy_of_file(y, dir=self.temp_dir) |
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return gr_processing_utils.encode_url_or_file_to_base64(file.name) |
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gr.Audio.postprocess = audio_postprocess |
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limitation = os.getenv("SYSTEM") == "spaces" |
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languages = ['日本語', '简体中文', 'English', 'English2Katakana'] |
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characters = ['0:特别周', '1:无声铃鹿', '2:东海帝王', '3:丸善斯基', |
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'4:富士奇迹', '5:小栗帽', '6:黄金船', '7:伏特加', |
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'8:大和赤骥', '9:大树快车', '10:草上飞', '11:菱亚马逊', |
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'12:目白麦昆', '13:神鹰', '14:好歌剧', '15:成田白仁', |
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'16:鲁道夫象征', '17:气槽', '18:爱丽数码', '19:青云天空', |
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'20:玉藻十字', '21:美妙姿势', '22:琵琶晨光', '23:重炮', |
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'24:曼城茶座', '25:美普波旁', '26:目白雷恩', '27:菱曙', |
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'28:雪之美人', '29:米浴', '30:艾尼斯风神', '31:爱丽速子', |
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'32:爱慕织姬', '33:稻荷一', '34:胜利奖券', '35:空中神宫', |
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'36:荣进闪耀', '37:真机伶', '38:川上公主', '39:黄金城市', |
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'40:樱花进王', '41:采珠', '42:新光风', '43:东商变革', |
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'44:超级小溪', '45:醒目飞鹰', '46:荒漠英雄', '47:东瀛佐敦', |
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'48:中山庆典', '49:成田大进', '50:西野花', '51:春乌拉拉', |
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'52:青竹回忆', '53:微光飞驹', '54:美丽周日', '55:待兼福来', |
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'56:Mr.C.B', '57:名将怒涛', '58:目白多伯', '59:优秀素质', |
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'60:帝王光环', '61:待兼诗歌剧', '62:生野狄杜斯', '63:目白善信', |
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'64:大拓太阳神', '65:双涡轮', '66:里见光钻', '67:北部玄驹', |
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'68:樱花千代王', '69:天狼星象征', '70:目白阿尔丹', '71:八重无敌', |
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'72:鹤丸刚志', '73:目白光明', '74:樱花桂冠', '75:成田路', |
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'76:也文摄辉', '77:吉兆', '78:谷野美酒', '79:第一红宝石', |
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'80:真弓快车', '81:骏川手纲', '82:凯斯奇迹', '83:小林历奇', |
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'84:北港火山', '85:奇锐骏', '86:秋川理事长'] |
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def show_memory_info(hint): |
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pid = os.getpid() |
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p = psutil.Process(pid) |
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info = p.memory_info() |
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memory = info.rss / 1024.0 / 1024 |
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print("{} 内存占用: {} MB".format(hint, memory)) |
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def get_text(text, hps, is_symbol): |
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text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = LongTensor(text_norm) |
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return text_norm |
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hps = utils.get_hparams_from_file("./configs/uma87.json") |
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symbols = hps.symbols |
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net_g = ONNXVITS_infer.SynthesizerTrn( |
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len(hps.symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint("pretrained_models/G_1153000.pth", net_g) |
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def to_symbol_fn(is_symbol_input, input_text, temp_text): |
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return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \ |
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else (temp_text, temp_text) |
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def infer(text_raw, character, language, duration, noise_scale, noise_scale_w, is_symbol): |
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if language not in languages: |
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print("Error: No such language\n") |
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return "Error: No such language", None |
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if character not in characters: |
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print("Error: No such character\n") |
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return "Error: No such character", None |
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if limitation: |
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text_len = len(text_raw) if is_symbol else len(re.sub("\[([A-Z]{2})\]", "", text_raw)) |
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max_len = 150 |
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if is_symbol: |
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max_len *= 3 |
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if text_len > max_len: |
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print(f"Refused: Text too long ({text_len}).") |
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return "Error: Text is too long", None |
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if text_len == 0: |
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print("Refused: Text length is zero.") |
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return "Error: Please input text!", None |
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if is_symbol: |
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text = text_raw |
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elif language == '日本語': |
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text = text_raw |
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elif language == '简体中文': |
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text = tss.google(text_raw, from_language='zh', to_language='ja') |
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elif language == 'English': |
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text = tss.google(text_raw, from_language='en', to_language='ja') |
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elif language == "English2Katakana": |
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text = romajitable.to_kana(text_raw).katakana |
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char_id = int(character.split(':')[0]) |
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stn_tst = get_text(text, hps, is_symbol) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) |
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sid = torch.LongTensor([char_id]) |
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jp2phoneme = japanese_cleaners(text) |
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durations = net_g.predict_duration(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, length_scale=duration) |
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char_dur_list = [] |
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for i, char in enumerate(jp2phoneme): |
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char_pos = i * 2 + 1 |
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char_dur = durations[char_pos] |
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char_dur_list.append(char_dur) |
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char_spacing_dur_list = [] |
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char_spacings = [] |
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for i in range(len(durations)): |
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if i % 2 == 0: |
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char_spacings.append("spacing") |
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elif i % 2 == 1: |
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char_spacings.append(jp2phoneme[int((i - 1) / 2)]) |
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char_spacing_dur_list.append(int(durations[i])) |
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duration_info_str = "" |
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for i in range(len(char_spacings)): |
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if char_spacings[i] == "spacing": |
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duration_info_str += str(char_spacing_dur_list[i]) |
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else: |
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duration_info_str += "{" + char_spacings[i] + ":" + str(char_spacing_dur_list[i]) + "}" |
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if i != len(char_spacings)-1: |
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duration_info_str += ", " |
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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() |
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currentDateAndTime = datetime.now() |
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print(f"Character {character} inference successful: {text}\n") |
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if language != '日本語': |
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print(f"translate from {language}: {text_raw}") |
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show_memory_info(str(currentDateAndTime) + " infer调用后") |
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return (text,(22050, audio), jp2phoneme, duration_info_str) |
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def infer_from_phoneme_dur(duration_info_str, character, duration, noise_scale, noise_scale_w): |
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try: |
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phonemes = duration_info_str.split(", ") |
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recons_durs = [] |
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recons_phonemes = "" |
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for item in phonemes: |
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if "{" not in item: |
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recons_durs.append(int(item)) |
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else: |
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recons_phonemes += item.strip("{}").split(":")[0] |
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recons_durs.append(int(item.strip("{}").split(":")[1])) |
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except ValueError: |
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return ("Error: Format must not be changed!", None) |
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except AssertionError: |
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return ("Error: Format must not be changed!", None) |
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char_id = int(character.split(':')[0]) |
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stn_tst = get_text(recons_phonemes, hps, is_symbol=True) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) |
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sid = torch.LongTensor([char_id]) |
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print(len(recons_durs)) |
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print(x_tst.shape[1]) |
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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, |
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length_scale=duration)[0][0, 0].data.cpu().float().numpy() |
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return (recons_phonemes, (22050, audio)) |
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download_audio_js = """ |
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() =>{{ |
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let root = document.querySelector("body > gradio-app"); |
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if (root.shadowRoot != null) |
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root = root.shadowRoot; |
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let audio = root.querySelector("#{audio_id}").querySelector("audio"); |
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if (audio == undefined) |
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return; |
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audio = audio.src; |
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let oA = document.createElement("a"); |
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oA.download = Math.floor(Math.random()*100000000)+'.wav'; |
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oA.href = audio; |
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document.body.appendChild(oA); |
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oA.click(); |
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oA.remove(); |
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}} |
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""" |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--share", action="store_true", default=False, help="share gradio app") |
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args = parser.parse_args() |
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app = gr.Blocks() |
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with app: |
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gr.Markdown("# Umamusume voice synthesizer 赛马娘语音合成器\n\n" |
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"\n\n" |
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"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" |
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"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" |
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"[introduction video / 模型介绍视频](https://www.bilibili.com/video/BV1T84y1e7p5/?vd_source=6d5c00c796eff1cbbe25f1ae722c2f9f#reply607277701)\n\n" |
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"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" |
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"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" |
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"This model has been integrated to the model collections of [Moe-tts](https://huggingface.co/spaces/skytnt/moe-tts).\n\n" |
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"现已加入[Moe-tts](https://huggingface.co/spaces/skytnt/moe-tts)模型大全。\n\n" |
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"If you have any suggestions or bug reports, feel free to open discussion in Community.\n\n" |
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"若有bug反馈或建议，请在Community下开启一个新的Discussion。 \n\n" |
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"If your input language is not Japanese, it will be translated to Japanese by Google translator, but accuracy is not guaranteed.\n\n" |
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"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" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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textbox = gr.TextArea(label="Text", placeholder="Type your sentence here (Maximum 150 words)", value="こんにちわ。", elem_id=f"tts-input") |
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with gr.Accordion(label="Phoneme Input", open=False): |
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temp_text_var = gr.Variable() |
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symbol_input = gr.Checkbox(value=False, label="Symbol input") |
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symbol_list = gr.Dataset(label="Symbol list", components=[textbox], |
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samples=[[x] for x in symbols], |
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elem_id=f"symbol-list") |
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symbol_list_json = gr.Json(value=symbols, visible=False) |
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symbol_input.change(to_symbol_fn, |
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[symbol_input, textbox, temp_text_var], |
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[textbox, temp_text_var]) |
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symbol_list.click(None, [symbol_list, symbol_list_json], [], |
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_js=f""" |
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(i, symbols) => {{ |
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let root = document.querySelector("body > gradio-app"); |
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if (root.shadowRoot != null) |
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root = root.shadowRoot; |
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let text_input = root.querySelector("#tts-input").querySelector("textarea"); |
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let startPos = text_input.selectionStart; |
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let endPos = text_input.selectionEnd; |
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let oldTxt = text_input.value; |
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let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos); |
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text_input.value = result; |
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let x = window.scrollX, y = window.scrollY; |
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text_input.focus(); |
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text_input.selectionStart = startPos + symbols[i].length; |
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text_input.selectionEnd = startPos + symbols[i].length; |
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text_input.blur(); |
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window.scrollTo(x, y); |
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return []; |
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}}""") |
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|
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char_dropdown = gr.Dropdown(choices=characters, value = "0:特别周", label='character') |
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language_dropdown = gr.Dropdown(choices=languages, value = "日本語", label='language') |
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|
|
|
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duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, label='时长 Duration') |
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noise_scale_slider = gr.Slider(minimum=0.1, maximum=5, value=0.667, step=0.001, label='噪声比例 noise_scale') |
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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') |
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|
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|
|
|
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with gr.Column(): |
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text_output = gr.Textbox(label="Output Text") |
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phoneme_output = gr.Textbox(label="Output Phonemes", interactive=False) |
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audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio") |
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btn = gr.Button("Generate!") |
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cus_dur_gn_btn = gr.Button("Regenerate with custom phoneme durations") |
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btn.click(infer, inputs=[textbox, char_dropdown, language_dropdown, duration_slider, noise_scale_slider, noise_scale_w_slider, symbol_input], |
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outputs=[text_output, audio_output, phoneme_output, duration_output]) |
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download = gr.Button("Download Audio") |
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download.click(None, [], [], _js=download_audio_js.format(audio_id="tts-audio")) |
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with gr.Accordion(label="Speaking Pace Control", open=True): |
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|
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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.", |
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interactive = True) |
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cus_dur_gn_btn.click(infer_from_phoneme_dur, inputs=[duration_output, char_dropdown, duration_slider, noise_scale_slider, noise_scale_w_slider], |
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outputs=[phoneme_output, audio_output]) |
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gr.Markdown( |
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"\{ \}内的数字代表每个音素在生成的音频中的长度，\{ \}外的数字代表音素之间间隔的长度。" |
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"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。" |
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"注意这些数字只能是整数。 \n\n(1 代表 0.01161 秒的长度)\n\n" |
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"The numbers inside \{ \} represent the length for each phoneme in the generated audio, while the numbers out of \{ \} represent the length of spacings between phonemes." |
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"You can manually change the numbers to adjust the length of each phoneme, so that speaking pace can be completely controlled." |
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"Note that these numbers should be integers only. \n\n(1 represents a length of 0.01161 seconds)\n\n" |
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) |
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|
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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], |
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['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], |
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['張り切っていこう！', '67:北部玄驹', '日本語', 1, 0.667, 0.8, False], |
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['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], |
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['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], |
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['お帰りなさい，お兄様！', '29:米浴', '日本語', 1, 0.667, 0.8, False], |
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['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]] |
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gr.Examples( |
|
examples=examples, |
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inputs=[textbox, char_dropdown, language_dropdown, |
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duration_slider, noise_scale_slider,noise_scale_w_slider, symbol_input], |
|
outputs=[text_output, audio_output], |
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fn=infer |
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) |
|
gr.Markdown("# Updates Logs 更新日志：\n\n" |
|
"2023/1/24：\n\n" |
|
"增加了对说话节奏的音素级控制。\n\n" |
|
"Added more precise control on pace of speaking by modifying the duration of each phoneme.\n\n" |
|
"2023/1/13：\n\n" |
|
"增加了音素输入的example（米浴喘气）\n\n" |
|
"Added one example of phoneme input.\n\n" |
|
"2023/1/12：\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" |
|
"Added phoneme input, which enables more precise control on output audio.\n\n" |
|
"调整了UI的布局。\n\n" |
|
"Adjusted UI arrangements.\n\n" |
|
"2023/1/10：\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" |
|
"Dataset used for training is now uploaded to [here](https://huggingface.co/datasets/Plachta/Umamusume-voice-text-pairs/tree/main)\n\n" |
|
"2023/1/9：\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" |
|
"Model inference has been fully converted to onnxruntime. There will be no more Runtime Error: Memory Limit Exceeded\n\n" |
|
"现已加入[Moe-tts](https://huggingface.co/spaces/skytnt/moe-tts)模型大全。\n\n" |
|
"Now integrated to [Moe-tts](https://huggingface.co/spaces/skytnt/moe-tts) collection.\n\n" |
|
) |
|
app.queue(concurrency_count=3).launch(show_api=False, share=args.share) |
