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| import subprocess | |
| import random | |
| import os | |
| from pathlib import Path | |
| import librosa | |
| from scipy.io import wavfile | |
| import numpy as np | |
| import torch | |
| import csv | |
| import whisper | |
| import gradio as gr | |
| import soundfile as sf | |
| os.system("pip install --upgrade Cython==0.29.35") | |
| os.system("pip install pysptk --no-build-isolation") | |
| os.system("pip install kantts -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html") | |
| os.system("pip install tts-autolabel -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html") | |
| import sox | |
| def split_long_audio(model, filepaths, save_dir="data_dir", out_sr=44100): | |
| if isinstance(filepaths, str): | |
| filepaths = [filepaths] | |
| for file_idx, filepath in enumerate(filepaths): | |
| save_path = Path(save_dir) | |
| save_path.mkdir(exist_ok=True, parents=True) | |
| print(f"Transcribing file {file_idx}: '{filepath}' to segments...") | |
| result = model.transcribe(filepath, word_timestamps=True, task="transcribe", beam_size=5, best_of=5) | |
| segments = result['segments'] | |
| wav, sr = librosa.load(filepath, sr=None, offset=0, duration=None, mono=True) | |
| wav, _ = librosa.effects.trim(wav, top_db=20) | |
| peak = np.abs(wav).max() | |
| if peak > 1.0: | |
| wav = 0.98 * wav / peak | |
| wav2 = librosa.resample(wav, orig_sr=sr, target_sr=out_sr) | |
| wav2 /= max(wav2.max(), -wav2.min()) | |
| for i, seg in enumerate(segments): | |
| start_time = seg['start'] | |
| end_time = seg['end'] | |
| wav_seg = wav2[int(start_time * out_sr):int(end_time * out_sr)] | |
| wav_seg_name = f"{file_idx}_{i}.wav" | |
| out_fpath = save_path / wav_seg_name | |
| wavfile.write(out_fpath, rate=out_sr, data=(wav_seg * np.iinfo(np.int16).max).astype(np.int16)) | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| whisper_size = "medium" | |
| whisper_model = whisper.load_model(whisper_size).to(device) | |
| from modelscope.tools import run_auto_label | |
| from modelscope.models.audio.tts import SambertHifigan | |
| from modelscope.pipelines import pipeline | |
| from modelscope.utils.constant import Tasks | |
| from modelscope.metainfo import Trainers | |
| from modelscope.trainers import build_trainer | |
| from modelscope.utils.audio.audio_utils import TtsTrainType | |
| pretrained_model_id = 'damo/speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k' | |
| dataset_id = "/home/user/app/output_training_data/" | |
| pretrain_work_dir = "/home/user/app/pretrain_work_dir/" | |
| def auto_label(Voicetoclone, VoiceMicrophone): | |
| if VoiceMicrophone is not None: | |
| audio = VoiceMicrophone | |
| else: | |
| audio = Voicetoclone | |
| try: | |
| split_long_audio(whisper_model, audio, "/home/user/app/test_wavs/") | |
| input_wav = "/home/user/app/test_wavs/" | |
| output_data = "/home/user/app/output_training_data/" | |
| ret, report = run_auto_label(input_wav=input_wav, work_dir=output_data, resource_revision="v1.0.7") | |
| except Exception as e: | |
| print(e) | |
| return "标注成功" | |
| def train(train_step): | |
| try: | |
| train_info = { | |
| TtsTrainType.TRAIN_TYPE_SAMBERT: { # 配置训练AM（sambert）模型 | |
| 'train_steps': int(train_step / 20) * 20 + 2, # 训练多少个step | |
| 'save_interval_steps': int(train_step / 20) * 20, # 每训练多少个step保存一次checkpoint | |
| 'log_interval': int(train_step / 20) * 20 # 每训练多少个step打印一次训练日志 | |
| } | |
| } | |
| kwargs = dict( | |
| model=pretrained_model_id, # 指定要finetune的模型 | |
| model_revision = "v1.0.6", | |
| work_dir=pretrain_work_dir, # 指定临时工作目录 | |
| train_dataset=dataset_id, # 指定数据集id | |
| train_type=train_info # 指定要训练类型及参数 | |
| ) | |
| trainer = build_trainer(Trainers.speech_kantts_trainer, | |
| default_args=kwargs) | |
| trainer.train() | |
| except Exception as e: | |
| print(e) | |
| return "训练完成" | |
| # 保存模型 | |
| import shutil | |
| import datetime | |
| def save_model(worked_dir,dest_dir): | |
| worked_dir = "/home/user/app/pretrain_work_dir" | |
| dest_dir = "/home/user/app/trained_model" | |
| if os.listdir(worked_dir): | |
| now = datetime.datetime.now() | |
| date_str = now.strftime("%Y%m%d%H%M%S") | |
| dest_folder = os.path.join(dest_dir, date_str) | |
| shutil.copytree(worked_dir, dest_folder) | |
| # List of files and directories to delete | |
| files_to_delete = [ | |
| "tmp_voc", | |
| "tmp_am/ckpt/checkpoint_2400000.pth", | |
| "orig_model/description", | |
| "orig_model/.mdl", | |
| "orig_model/.msc", | |
| "orig_model/README.md", | |
| "orig_model/resource", | |
| "orig_model/description", | |
| "orig_model/basemodel_16k/sambert", | |
| "orig_model/basemodel_16k/speaker_embedding", | |
| "data/duration", | |
| "data/energy", | |
| "data/f0", | |
| "data/frame_energy", | |
| "data/frame_f0", | |
| "data/frame_uv", | |
| "data/mel", | |
| "data/raw_duration", | |
| "data/wav", | |
| "data/am_train.lst", | |
| "data/am_valid.lst", | |
| "data/badlist.txt", | |
| "data/raw_metafile.txt", | |
| "data/Script.xml", | |
| "data/train.lst", | |
| "data/valid.lst", | |
| "data/se/0_*" | |
| ] | |
| for item in files_to_delete: | |
| item_path = os.path.join(dest_folder, item) | |
| if os.path.exists(item_path): | |
| if os.path.isdir(item_path): | |
| shutil.rmtree(item_path) | |
| else: | |
| os.remove(item_path) | |
| shutil.rmtree("/home/user/app/output_training_data") | |
| shutil.rmtree("/home/user/app/pretrain_work_dir") | |
| shutil.rmtree("/home/user/app/test_wavs") | |
| os.mkdir("/home/user/app/output_training_data") | |
| os.mkdir("/home/user/app/pretrain_work_dir") | |
| os.mkdir("/home/user/app/test_wavs") | |
| return f"模型已成功保存为 {date_str}" | |
| else: | |
| return "保存失败，模型已保存或已被清除" | |
| import random | |
| def infer(text): | |
| model_dir = "/home/user/app/pretrain_work_dir/" | |
| test_infer_abs = { | |
| 'voice_name': | |
| 'F7', | |
| 'am_ckpt': | |
| os.path.join(model_dir, 'tmp_am', 'ckpt'), | |
| 'am_config': | |
| os.path.join(model_dir, 'tmp_am', 'config.yaml'), | |
| 'voc_ckpt': | |
| os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan', 'ckpt'), | |
| 'voc_config': | |
| os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan', | |
| 'config.yaml'), | |
| 'audio_config': | |
| os.path.join(model_dir, 'data', 'audio_config.yaml'), | |
| 'se_file': | |
| os.path.join(model_dir, 'data', 'se', 'se.npy') | |
| } | |
| kwargs = {'custom_ckpt': test_infer_abs} | |
| model_id = SambertHifigan(os.path.join(model_dir, "orig_model"), **kwargs) | |
| inference = pipeline(task=Tasks.text_to_speech, model=model_id) | |
| output = inference(input=text) | |
| now = datetime.datetime.now() | |
| date_str = now.strftime("%Y%m%d%H%M%S") | |
| rand_num = random.randint(1000, 9999) | |
| filename = date_str + str(rand_num) | |
| with open(filename + "0.wav", mode='bx') as f: | |
| f.write(output["output_wav"]) | |
| y, sr = librosa.load(filename + "0.wav") | |
| S = librosa.stft(y) | |
| noise = S[np.abs(S) < np.percentile(S, 95)] | |
| noise_mean, noise_std = np.mean(noise), np.std(noise) | |
| filter_ = np.ones_like(S) | |
| filter_[np.abs(S) < noise_mean + 2 * noise_std] = 0 | |
| filtered_S = filter_ * S | |
| filtered_y = librosa.istft(filtered_S) | |
| sf.write(filename + "testfile.wav", filtered_y, sr) | |
| os.remove(filename + "0.wav") | |
| return filename + "testfile.wav" | |
| def infer_custom(model_name, text, noise_level): | |
| custom_model_dir = os.path.join("/home/user/app/trained_model/", model_name) | |
| custom_infer_abs = { | |
| 'voice_name': | |
| 'F7', | |
| 'am_ckpt': | |
| os.path.join(custom_model_dir, 'tmp_am', 'ckpt'), | |
| 'am_config': | |
| os.path.join(custom_model_dir, 'tmp_am', 'config.yaml'), | |
| 'voc_ckpt': | |
| os.path.join(custom_model_dir, 'orig_model', 'basemodel_16k', 'hifigan', 'ckpt'), | |
| 'voc_config': | |
| os.path.join(custom_model_dir, 'orig_model', 'basemodel_16k', 'hifigan', | |
| 'config.yaml'), | |
| 'audio_config': | |
| os.path.join(custom_model_dir, 'data', 'audio_config.yaml'), | |
| 'se_file': | |
| os.path.join(custom_model_dir, 'data', 'se', 'se.npy') | |
| } | |
| kwargs = {'custom_ckpt': custom_infer_abs} | |
| model_id = SambertHifigan(os.path.join(custom_model_dir, "orig_model"), **kwargs) | |
| inference = pipeline(task=Tasks.text_to_speech, model=model_id) | |
| output = inference(input=text) | |
| now = datetime.datetime.now() | |
| date_str = now.strftime("%Y%m%d%H%M%S") | |
| rand_num = random.randint(1000, 9999) | |
| filename = date_str + str(rand_num) | |
| with open(filename + ".wav", mode='bx') as f: | |
| f.write(output["output_wav"]) | |
| y, sr = librosa.load(filename + ".wav") | |
| S = librosa.stft(y) | |
| noise = S[np.abs(S) < np.percentile(S, 95)] | |
| noise_mean, noise_std = np.mean(noise), np.std(noise) | |
| filter_ = np.ones_like(S) | |
| filter_[np.abs(S) < noise_mean + noise_level * noise_std] = 0 | |
| filtered_S = filter_ * S | |
| filtered_y = librosa.istft(filtered_S) | |
| sf.write(filename + "customfile.wav", filtered_y, sr) | |
| os.remove(filename + ".wav") | |
| return filename + "customfile.wav" | |
| trained_model = "/home/user/app/trained_model/" | |
| def update_model_dropdown(inp3): | |
| model_list = os.listdir(trained_model) | |
| return gr.Dropdown(choices=model_list, value=inp3) | |
| def rename_model(old_name, new_name): | |
| if not os.path.isdir(os.path.join(trained_model, old_name)): | |
| return "模型名称不存在，请重新输入！" | |
| else: | |
| try: | |
| os.rename(os.path.join(trained_model, old_name), os.path.join(trained_model, new_name)) | |
| return "模型重命名成功！" | |
| except OSError: | |
| return "新名称已经存在，请重新输入！" | |
| # 清除训练缓存 | |
| def clear_cache(a): | |
| shutil.rmtree("/home/user/app/output_training_data") | |
| shutil.rmtree("/home/user/app/pretrain_work_dir") | |
| shutil.rmtree("/home/user/app/test_wavs") | |
| os.mkdir("/home/user/app/output_training_data") | |
| os.mkdir("/home/user/app/pretrain_work_dir") | |
| os.mkdir("/home/user/app/test_wavs") | |
| return "已清除缓存，请返回训练页面重新训练" | |
| from textwrap import dedent | |
| def FRCRN_De_Noise(noise_wav, noisemic_wav): | |
| if noisemic_wav is not None: | |
| noise_audio = noisemic_wav | |
| else: | |
| noise_audio = noise_wav | |
| ans = pipeline( | |
| Tasks.acoustic_noise_suppression, | |
| model='damo/speech_frcrn_ans_cirm_16k') | |
| now = datetime.datetime.now() | |
| date_str = now.strftime("%Y%m%d%H%M%S") | |
| rand_num = random.randint(1000, 9999) | |
| filename = date_str + str(rand_num) | |
| result = ans( | |
| noise_audio, | |
| output_path= filename + "AIdenoise.wav" ) | |
| return filename + "AIdenoise.wav" | |
| def Normal_De_Noise(noise_wav, noisemic_wav, noise_level): | |
| if noisemic_wav is not None: | |
| noise_audio = noisemic_wav | |
| else: | |
| noise_audio = noise_wav | |
| now = datetime.datetime.now() | |
| date_str = now.strftime("%Y%m%d%H%M%S") | |
| rand_num = random.randint(1000, 9999) | |
| filename = date_str + str(rand_num) | |
| y, sr = librosa.load(noise_audio) | |
| S = librosa.stft(y) | |
| noise = S[np.abs(S) < np.percentile(S, 95)] | |
| noise_mean, noise_std = np.mean(noise), np.std(noise) | |
| filter_ = np.ones_like(S) | |
| filter_[np.abs(S) < noise_mean + noise_level * noise_std] = 0 | |
| filtered_S = filter_ * S | |
| filtered_y = librosa.istft(filtered_S) | |
| sf.write(filename + "denoise.wav", filtered_y, sr) | |
| return filename + "denoise.wav" | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# <center>🥳🎶🎡 - Sambert中文声音克隆</center>") | |
| gr.Markdown("## <center>🌟 - 训练3分钟，推理10秒钟，中英真实拟声 </center>") | |
| gr.Markdown("### <center>🌊 - 基于SambertHifiGan项目修改而来，添加两种降噪功能、模型管理功能等") | |
| with gr.Tabs(): | |
| with gr.TabItem("一键训练"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| inp1 = gr.Audio(type="filepath", sources="upload", label="方案一：请从本地上传一段语音") | |
| inp_micro = gr.Audio(type="filepath", sources="microphone", label="方案二：请用麦克风录制您的声音") | |
| with gr.Column(): | |
| out1 = gr.Textbox(label="标注情况", lines=1, interactive=False) | |
| out2 = gr.Textbox(label="训练情况", lines=1, interactive=False) | |
| inp2 = gr.Slider(label="训练步数(需要为20的倍数)", minimum=200, maximum=4000, value=400, min_width=40) | |
| inp3 = gr.Textbox(label="请在这里填写您想合成的文本", placeholder="想说却还没说的 还很多...", lines=3, interactive=True) | |
| with gr.Column(): | |
| out3 = gr.Audio(type="filepath", label="为您合成的专属音频") | |
| out4 = gr.Textbox(label="保存情况", lines=1, interactive=False) | |
| with gr.Row(): | |
| btn1 = gr.Button("1.标注数据") | |
| btn2 = gr.Button("2.开始训练") | |
| btn3 = gr.Button("3.一键推理", variant="primary") | |
| btn4 = gr.Button("4.保存模型", variant="primary") | |
| btn1.click(auto_label, [inp1, inp_micro], out1) | |
| btn2.click(train, inp2, out2) | |
| btn3.click(infer, inp3, out3) | |
| btn4.click(save_model, out1, out4) | |
| with gr.Accordion("📒 训练教程", open=True): | |
| _ = f""" 如何开始训练: | |
| * 第一步，选择 [方案一] 或 [方案二] 上传一分钟左右的音频，注意要吐字清晰、感情饱满、音色纯净不含杂音 | |
| * 第二步，点击“标注数据”，等到提示标注成功后，选择合适的训练步数，点击“开始训练”等待训练完成 | |
| * 第三步，耐心等待训练成功后，在文本框内输入想要生成的文字，点击“一键生成”按钮，生成克隆后的语音 | |
| * ！！注意！！ 不要生成会对个人以及组织造成侵害的内容 | |
| * 如果您的训练素材比较嘈杂，您可以在[AI降噪]选项卡上传或录制训练音频，降噪后再上传到训练界面 | |
| * 如果您需要用方案二录制您的声音，以下是一段长度合适的文本，供您朗读并录制： | |
| 记得春天的时候，小草就转出地面，树上的叶子也抽出来了，大地一片绿色，就像穿上了一件绿衣裳。我就与小孩子一起到田野去捉蜻蜓，玩游戏，比如老鹰合作小鸡或是捉迷藏，又或是跳格子。到了夏天，天气热了，我就会与小孩子到水库里面游泳，那时候水库的安全系数还不是很高，几乎每年都会有事故发生，所以父母都不会让我去游泳的，被发现之后当然就是处罚或是责骂了。可是那时候自己真的很叛逆，也不知道什么是危险，被处罚之后下一次还是回去的。到了秋天，田野一片金黄，山上的野果也成熟了，我就会与自己的伙伴拿着篮子到上山去采，采回来了还要跟自己的好朋友一起分享。 | |
| """ | |
| gr.Markdown(dedent(_)) | |
| with gr.TabItem("声音合成"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| inp21 = gr.Dropdown(label="请选择一个模型", choices=os.listdir(trained_model)) | |
| inp22 = gr.Slider(label="降噪强度(为0时不降噪)", minimum=0, maximum=3, value=2) | |
| with gr.Column(): | |
| inp23 = gr.Textbox(label="请在这里填写您想合成的文本", placeholder="想说却还没说的 还很多...", lines=3, interactive=True) | |
| with gr.Column(): | |
| out21 = gr.Audio(type="filepath", label="为您合成的专属音频", interactive=False) | |
| with gr.Row(): | |
| btn21 = gr.Button("刷新模型列表") | |
| btn22 = gr.Button("一键推理", variant="primary") | |
| btn21.click(update_model_dropdown, inp21, inp21) | |
| btn22.click(infer_custom, [inp21, inp23, inp22], out21) | |
| with gr.Accordion("📒 推理教程", open=True): | |
| _ = f""" 如何推理声音: | |
| * 第一步，选择一个你想要使用的模型，如果训练后保存的模型无法找到请点击“刷新模型列表” | |
| * 第二步，在文本框处输入你想要生成的文本，选择降噪强度，如果无需降噪请将强度设为0 | |
| * 第三步，点击“一键生成”按钮，生成克隆后的语音 | |
| * ！！注意！！ 不要生成会对个人以及组织造成侵害的内容 | |
| * 此处使用的降噪算法为机械降噪，非AI降噪，如需AI降噪可以将生成的音频下载后转到“AI降噪”选项卡进行AI降噪 | |
| """ | |
| gr.Markdown(dedent(_)) | |
| with gr.TabItem("模型修改"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| inp31 = gr.Dropdown(label="选择重命名的模型", choices=os.listdir(trained_model)) | |
| with gr.Column(): | |
| inp32 = gr.Textbox(label="输入模型命名", placeholder="新名称", lines=1, interactive=True) | |
| with gr.Column(): | |
| out31 = gr.Textbox(label="保存情况", lines=1, interactive=False) | |
| with gr.Row(): | |
| btn31 = gr.Button("刷新模型列表") | |
| btn32 = gr.Button("重命名", variant="primary") | |
| btn31.click(update_model_dropdown, inp31, inp31) | |
| btn32.click(rename_model, [inp31, inp32], out31) | |
| with gr.Accordion("📒 推理教程", open=True): | |
| _ = f""" 如何修改模型名称: | |
| * 第一步，选择一个你想要修改的模型，如果训练后保存的模型无法找到请点击“刷新模型列表” | |
| * 第二步，在文本框处输入你想要修改的模型名称，推荐以“[训练步数]时间-名称”来命名 | |
| * 第三步，点击“重命名”按钮对模型重命名 | |
| """ | |
| gr.Markdown(dedent(_)) | |
| with gr.TabItem("AI降噪"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| inp41 = gr.Audio(type="filepath", sources="upload", label="方案一：请从本地上传一段语音") | |
| inp_micro42 = gr.Audio(type="filepath", sources="microphone", label="方案二：请用麦克风录制您的声音") | |
| with gr.Column(): | |
| out41 = gr.Audio(type="filepath", label="降噪后的音频", interactive=False) | |
| inp43 = gr.Slider(label="机械降噪强度(非AI降噪)", minimum=0, maximum=3, value=2) | |
| btn41 = gr.Button("机械降噪") | |
| btn42 = gr.Button("一键AI降噪", variant="primary") | |
| btn41.click(Normal_De_Noise, [inp41, inp_micro42, inp43], out41) | |
| btn42.click(FRCRN_De_Noise, [inp41, inp_micro42], out41) | |
| with gr.Accordion("📒 AI降噪", open=True): | |
| _ = f""" 如何使用AI降噪: | |
| * 第一步，在[方案一]上传你想要降噪的音频，或者在[方案二]录制音频 | |
| * 第二步，点击“一键AI降噪”进行降噪 | |
| * 第三步，下载降噪后的音频 | |
| * 如果您的训练素材比较嘈杂，您可以在此处上传或录制训练音频，降噪后再上传到训练界面 | |
| * 如果您需要用方案二录制您的声音，以下是一段长度合适的文本，供您朗读并录制： | |
| 记得春天的时候，小草就转出地面，树上的叶子也抽出来了，大地一片绿色，就像穿上了一件绿衣裳。我就与小孩子一起到田野去捉蜻蜓，玩游戏，比如老鹰合作小鸡或是捉迷藏，又或是跳格子。到了夏天，天气热了，我就会与小孩子到水库里面游泳，那时候水库的安全系数还不是很高，几乎每年都会有事故发生，所以父母都不会让我去游泳的，被发现之后当然就是处罚或是责骂了。可是那时候自己真的很叛逆，也不知道什么是危险，被处罚之后下一次还是回去的。到了秋天，田野一片金黄，山上的野果也成熟了，我就会与自己的伙伴拿着篮子到上山去采，采回来了还要跟自己的好朋友一起分享。 | |
| * AI降噪与机械降噪的不同：机械降噪主要是移除声音的激波，会对人声造成一定的破坏，而AI降噪主要是移除声音中的非人声部分，可以处理复杂的背景音频环境，但是对人声本身质量问题处理的效果一般 | |
| """ | |
| gr.Markdown(dedent(_)) | |
| with gr.TabItem("缓存清理"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("### <center>注意，这会清除[一键训练]界面生成的所有数据") | |
| gr.Markdown("### <center>包括标注数据，训练数据，及最终模型") | |
| gr.Markdown("### <center>如需保存模型请点击保存当前模型按钮") | |
| with gr.Column(): | |
| out97 = gr.Textbox(label="", lines=1, interactive=False) | |
| btn91 = gr.Button("保存当前模型", ) | |
| btn92 = gr.Button("清空缓存数据", variant="primary") | |
| btn91.click(save_model, out1, out97) | |
| btn92.click(clear_cache, out1, out97) | |
| with gr.Accordion("📒 使用指南", open=False): | |
| _ = f""" 如何使用此程序: | |
| * [一键训练] ： 上传或录制音频，程序会自动标注音频，一键训练模型，支持训练后推理试听，支持模型保存 | |
| * [声音合成] ： 在这里可以选择已保存的模型进行推理，自带可调机械降噪，可以任意选择已训练的音频进行推理 | |
| * [模型修改] ： 在这里可以选择已保存的模型进行重命名，方便日后推理使用 | |
| * [ AI降噪 ] : 在这里可以上传音频进行AI降噪，一键去除噪音杂声 | |
| * [缓存清理] ： 如果训练时出现报错可以尝试缓存清理，每次保存模型会自动清理缓存，如果未保存就重新开始训练需要清理缓存 | |
| * ！！注意！！ 不要生成会对个人以及组织造成侵害的内容 | |
| * 如果您需要录制您的声音，以下是一段长度合适的文本，供您朗读并录制： | |
| 记得春天的时候，小草就转出地面，树上的叶子也抽出来了，大地一片绿色，就像穿上了一件绿衣裳。我就与小孩子一起到田野去捉蜻蜓，玩游戏，比如老鹰合作小鸡或是捉迷藏，又或是跳格子。到了夏天，天气热了，我就会与小孩子到水库里面游泳，那时候水库的安全系数还不是很高，几乎每年都会有事故发生，所以父母都不会让我去游泳的，被发现之后当然就是处罚或是责骂了。可是那时候自己真的很叛逆，也不知道什么是危险，被处罚之后下一次还是回去的。到了秋天，田野一片金黄，山上的野果也成熟了，我就会与自己的伙伴拿着篮子到上山去采，采回来了还要跟自己的好朋友一起分享。 | |
| """ | |
| gr.Markdown(dedent(_)) | |
| gr.Markdown("### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>") | |
| gr.HTML(''' | |
| <div class="footer"> | |
| <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘 | |
| </p> | |
| </div> | |
| ''') | |
| app.launch(show_error=True, share=False) | |