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| # flake8: noqa: E402 | |
| import logging | |
| logging.getLogger("numba").setLevel(logging.WARNING) | |
| logging.getLogger("markdown_it").setLevel(logging.WARNING) | |
| logging.getLogger("urllib3").setLevel(logging.WARNING) | |
| logging.getLogger("matplotlib").setLevel(logging.WARNING) | |
| logging.basicConfig( | |
| level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s" | |
| ) | |
| logger = logging.getLogger(__name__) | |
| import datetime | |
| import numpy as np | |
| import torch | |
| from ebooklib import epub | |
| import PyPDF2 | |
| from PyPDF2 import PdfReader | |
| import zipfile | |
| import shutil | |
| import sys, os | |
| import json | |
| from bs4 import BeautifulSoup | |
| import argparse | |
| import commons | |
| import utils | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import cleaned_text_to_sequence, get_bert | |
| from text.cleaner import clean_text | |
| import gradio as gr | |
| import webbrowser | |
| import re | |
| from scipy.io.wavfile import write | |
| from datetime import datetime | |
| net_g = None | |
| BandList = { | |
| "PoppinParty":["香澄","有咲","たえ","りみ","沙綾"], | |
| "Afterglow":["蘭","モカ","ひまり","巴","つぐみ"], | |
| "HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"], | |
| "PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"], | |
| "Roselia":["友希那","紗夜","リサ","燐子","あこ"], | |
| "RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"], | |
| "Morfonica":["ましろ","瑠唯","つくし","七深","透子"], | |
| "MyGo&AveMujica(Part)":["燈","愛音","そよ","立希","楽奈","祥子","睦","海鈴"], | |
| } | |
| if sys.platform == "darwin" and torch.backends.mps.is_available(): | |
| device = "mps" | |
| os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" | |
| else: | |
| device = "cuda" | |
| def is_japanese(string): | |
| for ch in string: | |
| if ord(ch) > 0x3040 and ord(ch) < 0x30FF: | |
| return True | |
| return False | |
| def extrac(text): | |
| text = re.sub("<[^>]*>","",text) | |
| result_list = re.split(r'\n', text) | |
| final_list = [] | |
| for i in result_list: | |
| i = i.replace('\n','').replace(' ','') | |
| #Current length of single sentence: 20 | |
| if len(i)>1: | |
| if len(i) > 20: | |
| try: | |
| cur_list = re.split(r'。|！', i) | |
| for i in cur_list: | |
| if len(i)>1: | |
| final_list.append(i+'。') | |
| except: | |
| pass | |
| else: | |
| final_list.append(i) | |
| ''' | |
| final_list.append(i) | |
| ''' | |
| final_list = [x for x in final_list if x != ''] | |
| return final_list | |
| def get_text(text, language_str, hps): | |
| norm_text, phone, tone, word2ph = clean_text(text, language_str) | |
| phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) | |
| if hps.data.add_blank: | |
| phone = commons.intersperse(phone, 0) | |
| tone = commons.intersperse(tone, 0) | |
| language = commons.intersperse(language, 0) | |
| for i in range(len(word2ph)): | |
| word2ph[i] = word2ph[i] * 2 | |
| word2ph[0] += 1 | |
| bert = get_bert(norm_text, word2ph, language_str, device) | |
| del word2ph | |
| assert bert.shape[-1] == len(phone), phone | |
| if language_str == "ZH": | |
| bert = bert | |
| ja_bert = torch.zeros(768, len(phone)) | |
| elif language_str == "JA": | |
| ja_bert = bert | |
| bert = torch.zeros(1024, len(phone)) | |
| else: | |
| bert = torch.zeros(1024, len(phone)) | |
| ja_bert = torch.zeros(768, len(phone)) | |
| assert bert.shape[-1] == len( | |
| phone | |
| ), f"Bert seq len {bert.shape[-1]} != {len(phone)}" | |
| phone = torch.LongTensor(phone) | |
| tone = torch.LongTensor(tone) | |
| language = torch.LongTensor(language) | |
| return bert, ja_bert, phone, tone, language | |
| def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language): | |
| global net_g | |
| bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps) | |
| with torch.no_grad(): | |
| x_tst = phones.to(device).unsqueeze(0) | |
| tones = tones.to(device).unsqueeze(0) | |
| lang_ids = lang_ids.to(device).unsqueeze(0) | |
| bert = bert.to(device).unsqueeze(0) | |
| ja_bert = ja_bert.to(device).unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) | |
| del phones | |
| speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) | |
| audio = ( | |
| net_g.infer( | |
| x_tst, | |
| x_tst_lengths, | |
| speakers, | |
| tones, | |
| lang_ids, | |
| bert, | |
| ja_bert, | |
| sdp_ratio=sdp_ratio, | |
| noise_scale=noise_scale, | |
| noise_scale_w=noise_scale_w, | |
| length_scale=length_scale, | |
| )[0][0, 0] | |
| .data.cpu() | |
| .float() | |
| .numpy() | |
| ) | |
| current_time = datetime.now() | |
| print(str(current_time)+':'+str(sid)) | |
| del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers | |
| return audio | |
| def tts_fn( | |
| text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,LongSentence | |
| ): | |
| if not LongSentence: | |
| with torch.no_grad(): | |
| audio = infer( | |
| text, | |
| sdp_ratio=sdp_ratio, | |
| noise_scale=noise_scale, | |
| noise_scale_w=noise_scale_w, | |
| length_scale=length_scale, | |
| sid=speaker, | |
| language= "JP" if is_japanese(text) else "ZH", | |
| ) | |
| torch.cuda.empty_cache() | |
| return (hps.data.sampling_rate, audio) | |
| else: | |
| audiopath = 'voice.wav' | |
| a = ['【','[','(','（'] | |
| b = ['】',']',')','）'] | |
| for i in a: | |
| text = text.replace(i,'<') | |
| for i in b: | |
| text = text.replace(i,'>') | |
| final_list = extrac(text.replace('“','').replace('”','')) | |
| audio_fin = [] | |
| for sentence in final_list: | |
| with torch.no_grad(): | |
| audio = infer( | |
| sentence, | |
| sdp_ratio=sdp_ratio, | |
| noise_scale=noise_scale, | |
| noise_scale_w=noise_scale_w, | |
| length_scale=length_scale, | |
| sid=speaker, | |
| language= "JP" if is_japanese(text) else "ZH", | |
| ) | |
| audio_fin.append(audio) | |
| return (hps.data.sampling_rate, np.concatenate(audio_fin)) | |
| def split_into_sentences(text): | |
| """将文本分割为句子，基于中文的标点符号""" | |
| sentences = re.split(r'(?<=[。！？…\n])', text) | |
| return [sentence.strip() for sentence in sentences if sentence] | |
| def seconds_to_ass_time(seconds): | |
| """将秒数转换为ASS时间格式""" | |
| hours = int(seconds / 3600) | |
| minutes = int((seconds % 3600) / 60) | |
| seconds = int(seconds) % 60 | |
| milliseconds = int((seconds - int(seconds)) * 1000) | |
| return "{:01d}:{:02d}:{:02d}.{:02d}".format(hours, minutes, seconds, int(milliseconds / 10)) | |
| def generate_audio_and_srt_for_group(group, outputPath, group_index, sampling_rate, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime): | |
| audio_fin = [] | |
| ass_entries = [] | |
| start_time = 0 | |
| ass_header = """[Script Info] | |
| ; Script generated by OpenAI Assistant | |
| Title: Audiobook | |
| ScriptType: v4.00+ | |
| WrapStyle: 0 | |
| PlayResX: 640 | |
| PlayResY: 360 | |
| ScaledBorderAndShadow: yes | |
| [V4+ Styles] | |
| Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding | |
| Style: Default,Arial,20,&H00FFFFFF,&H000000FF,&H00000000,&H00000000,0,0,0,0,100,100,0,0,1,1,1,2,10,10,10,1 | |
| [Events] | |
| Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text | |
| """ | |
| for sentence in group: | |
| try: | |
| print(sentence) | |
| FakeSpeaker = sentence.split("|")[0] | |
| print(FakeSpeaker) | |
| SpeakersList = re.split('\n', spealerList) | |
| if FakeSpeaker in list(hps.data.spk2id.keys()): | |
| speaker = FakeSpeaker | |
| for i in SpeakersList: | |
| if FakeSpeaker == i.split("|")[1]: | |
| speaker = i.split("|")[0] | |
| speaker_ids = hps.data.spk2id | |
| _, audio = tts_fn(sentence.split("|")[-1], speaker=speaker, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, LongSentence=True) | |
| silence_frames = int(silenceTime * 44010) | |
| silence_data = np.zeros((silence_frames,), dtype=audio.dtype) | |
| audio_fin.append(audio) | |
| audio_fin.append(silence_data) | |
| duration = len(audio) / sampling_rate | |
| end_time = start_time + duration + silenceTime | |
| ass_entries.append("Dialogue: 0,{},{},".format(seconds_to_ass_time(start_time), seconds_to_ass_time(end_time)) + "Default,,0,0,0,,{}".format(sentence.replace("|","："))) | |
| start_time = end_time | |
| except: | |
| pass | |
| wav_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.wav') | |
| ass_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.ass') | |
| write(wav_filename, sampling_rate, np.concatenate(audio_fin)) | |
| with open(ass_filename, 'w', encoding='utf-8') as f: | |
| f.write(ass_header + '\n'.join(ass_entries)) | |
| return (hps.data.sampling_rate, np.concatenate(audio_fin)) | |
| def extract_text_from_epub(file_path): | |
| book = epub.read_epub(file_path) | |
| content = [] | |
| for item in book.items: | |
| if isinstance(item, epub.EpubHtml): | |
| soup = BeautifulSoup(item.content, 'html.parser') | |
| content.append(soup.get_text()) | |
| return '\n'.join(content) | |
| def extract_text_from_pdf(file_path): | |
| with open(file_path, 'rb') as file: | |
| reader = PdfReader(file) | |
| content = [page.extract_text() for page in reader.pages] | |
| return '\n'.join(content) | |
| def extract_text_from_game2(data): | |
| current_content = [] | |
| def _extract(data, current_data=None): | |
| nonlocal current_content | |
| if current_data is None: | |
| current_data = {} | |
| if isinstance(data, dict): | |
| if 'name' in data and 'body' in data: | |
| current_name = data['name'] | |
| current_body = data['body'].replace('\n', '') | |
| current_content.append(f"{current_name}|{current_body}") | |
| for key, value in data.items(): | |
| _extract(value, dict(current_data)) | |
| elif isinstance(data, list): | |
| for item in data: | |
| _extract(item, dict(current_data)) | |
| _extract(data) | |
| return '\n'.join(current_content) | |
| def extract_text_from_file(inputFile): | |
| file_extension = os.path.splitext(inputFile)[1].lower() | |
| if file_extension == ".epub": | |
| return extract_text_from_epub(inputFile) | |
| elif file_extension == ".pdf": | |
| return extract_text_from_pdf(inputFile) | |
| elif file_extension == ".txt": | |
| with open(inputFile, 'r', encoding='utf-8') as f: | |
| return f.read() | |
| elif file_extension == ".asset": | |
| with open(inputFile, 'r', encoding='utf-8') as f: | |
| content = json.load(f) | |
| return extract_text_from_game2(content) if extract_text_from_game2(content) != '' else extract_text_from_game2(content) | |
| else: | |
| raise ValueError(f"Unsupported file format: {file_extension}") | |
| def audiobook(inputFile, groupsize, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime): | |
| directory_path = "books" | |
| output_path = "books/audiobook_part_1.wav" | |
| if os.path.exists(directory_path): | |
| shutil.rmtree(directory_path) | |
| os.makedirs(directory_path) | |
| text = extract_text_from_file(inputFile.name) | |
| sentences = split_into_sentences(text) | |
| GROUP_SIZE = groupsize | |
| for i in range(0, len(sentences), GROUP_SIZE): | |
| group = sentences[i:i+GROUP_SIZE] | |
| if spealerList == "": | |
| spealerList = "无" | |
| result = generate_audio_and_srt_for_group(group,directory_path, i//GROUP_SIZE + 1, 44100, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime) | |
| if not torch.cuda.is_available(): | |
| return result | |
| return result | |
| def loadmodel(model): | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True) | |
| return "success" | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "-m", "--model", default="./logs/BangDream/G_45000.pth", help="path of your model" | |
| ) | |
| parser.add_argument( | |
| "-c", | |
| "--config", | |
| default="configs/config.json", | |
| help="path of your config file", | |
| ) | |
| parser.add_argument( | |
| "--share", default=True, help="make link public", action="store_true" | |
| ) | |
| parser.add_argument( | |
| "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log" | |
| ) | |
| args = parser.parse_args() | |
| if args.debug: | |
| logger.info("Enable DEBUG-LEVEL log") | |
| logging.basicConfig(level=logging.DEBUG) | |
| device = ( | |
| "cuda:0" | |
| if torch.cuda.is_available() | |
| else ( | |
| "mps" | |
| if sys.platform == "darwin" and torch.backends.mps.is_available() | |
| else "cpu" | |
| ) | |
| ) | |
| hps = utils.get_hparams_from_file(args.config) | |
| net_g = SynthesizerTrn( | |
| len(symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model, | |
| ).to(device) | |
| loadmodel(args.model) | |
| speaker_ids = hps.data.spk2id | |
| speakers = list(speaker_ids.keys()) | |
| languages = ["ZH", "JP"] | |
| examples = [ | |
| ["filelist/Scenarioband6-018.asset", 500, "つくし", "ましろ|真白\n七深|七深\n透子|透子\nつくし|筑紫\n瑠唯|瑠唯\nそよ|素世\n祥子|祥子", "扩展功能"], | |
| ] | |
| modelPaths = [] | |
| for dirpath, dirnames, filenames in os.walk("./logs/BangDream/"): | |
| for filename in filenames: | |
| modelPaths.append(os.path.join(dirpath, filename)) | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| f"少歌邦邦全员TTS,使用本模型请严格遵守法律法规!\n 发布二创作品请注明项目和本模型作者<a href='https://space.bilibili.com/19874615/'>B站@Mahiroshi</a>及项目链接\n从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看使用说明</a>" | |
| ) | |
| for band in BandList: | |
| with gr.TabItem(band): | |
| for name in BandList[band]: | |
| with gr.TabItem(name): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown( | |
| '<div align="center">' | |
| f'<img style="width:auto;height:400px;" src="file/image/{name}.png">' | |
| '</div>' | |
| ) | |
| length_scale = gr.Slider( | |
| minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节" | |
| ) | |
| with gr.Accordion(label="切换模型(合成中文建议切换为早期模型)", open=False): | |
| modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value") | |
| btnMod = gr.Button("载入模型") | |
| statusa = gr.TextArea() | |
| btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa]) | |
| with gr.Column(): | |
| text = gr.TextArea( | |
| label="输入纯日语或者中文", | |
| placeholder="输入纯日语或者中文", | |
| value="有个人躺在地上，哀嚎......\n有个人睡着了，睡在盒子里。\n我要把它打开，看看他的梦是什么。", | |
| ) | |
| btn = gr.Button("点击生成", variant="primary") | |
| audio_output = gr.Audio(label="Output Audio") | |
| with gr.Accordion(label="其它参数设定", open=False): | |
| sdp_ratio = gr.Slider( | |
| minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比" | |
| ) | |
| noise_scale = gr.Slider( | |
| minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节" | |
| ) | |
| noise_scale_w = gr.Slider( | |
| minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度" | |
| ) | |
| LongSentence = gr.Checkbox(value=True, label="Generate LongSentence") | |
| speaker = gr.Dropdown( | |
| choices=speakers, value=name, label="说话人" | |
| ) | |
| btn.click( | |
| tts_fn, | |
| inputs=[ | |
| text, | |
| speaker, | |
| sdp_ratio, | |
| noise_scale, | |
| noise_scale_w, | |
| length_scale, | |
| LongSentence, | |
| ], | |
| outputs=[audio_output], | |
| ) | |
| for i in examples: | |
| with gr.Tab(i[-1]): | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| f"从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看自制galgame使用说明\n</a>" | |
| ) | |
| inputFile = gr.inputs.File(label="上传txt(可设置角色对应表)、epub或mobi文件") | |
| groupSize = gr.Slider( | |
| minimum=10, maximum=1000,value = i[1], step=1, label="当个音频文件包含的最大字数" | |
| ) | |
| silenceTime = gr.Slider( | |
| minimum=0, maximum=1, value=0.5, step=0.1, label="句子的间隔" | |
| ) | |
| spealerList = gr.TextArea( | |
| label="角色对应表", | |
| placeholder="左边是你想要在每一句话合成中用到的speaker(见角色清单)右边是你上传文本时分隔符左边设置的说话人:{ChoseSpeakerFromConfigList1}|{SeakerInUploadText1}\n{ChoseSpeakerFromConfigList2}|{SeakerInUploadText2}\n{ChoseSpeakerFromConfigList3}|{SeakerInUploadText3}\n", | |
| value = i[3], | |
| ) | |
| speaker = gr.Dropdown( | |
| choices=speakers, value = i[2], label="选择默认说话人" | |
| ) | |
| with gr.Column(): | |
| sdp_ratio = gr.Slider( | |
| minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比" | |
| ) | |
| noise_scale = gr.Slider( | |
| minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节" | |
| ) | |
| noise_scale_w = gr.Slider( | |
| minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度" | |
| ) | |
| length_scale = gr.Slider( | |
| minimum=0.1, maximum=2, value=1, step=0.01, label="生成长度" | |
| ) | |
| LastAudioOutput = gr.Audio(label="当用cuda在本地运行时才能在book文件夹下浏览全部合成内容") | |
| btn2 = gr.Button("点击生成", variant="primary") | |
| btn2.click( | |
| audiobook, | |
| inputs=[ | |
| inputFile, | |
| groupSize, | |
| speaker, | |
| sdp_ratio, | |
| noise_scale, | |
| noise_scale_w, | |
| length_scale, | |
| spealerList, | |
| silenceTime | |
| ], | |
| outputs=[LastAudioOutput], | |
| ) | |
| app.launch() | |