import json import os import platform import shutil import signal import subprocess import webbrowser import GPUtil import gradio as gr import psutil import torch import yaml from config import yml_config from tools.log import logger bert_model_paths = [ "./bert/chinese-roberta-wwm-ext-large/pytorch_model.bin", "./bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin", "./bert/deberta-v3-large/pytorch_model.bin", "./bert/deberta-v3-large/spm.model", ] emo_model_paths = [ "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/pytorch_model.bin" ] train_base_model_paths = ["D_0.pth", "G_0.pth", "DUR_0.pth"] default_yaml_path = "default_config.yml" default_config_path = "configs/config.json" def load_yaml_data_in_raw(yml_path=yml_config): with open(yml_path, "r", encoding="utf-8") as file: # data = yaml.safe_load(file) data = file.read() return str(data) def load_json_data_in_raw(json_path): with open(json_path, "r", encoding="utf-8") as file: json_data = json.load(file) formatted_json_data = json.dumps(json_data, ensure_ascii=False, indent=2) return formatted_json_data def load_json_data_in_fact(json_path): with open(json_path, "r", encoding="utf-8") as file: json_data = json.load(file) return json_data def load_yaml_data_in_fact(yml_path=yml_config): with open(yml_path, "r", encoding="utf-8") as file: yml = yaml.safe_load(file) # data = file.read() return yml def fill_openi_token(token: str): yml = load_yaml_data_in_fact() yml["mirror"] = "openi" yml["openi_token"] = token write_yaml_data_in_fact(yml) msg = "openi 令牌已填写完成" logger.info(msg) return gr.Textbox(value=msg), gr.Code(value=load_yaml_data_in_raw()) def load_train_param(cfg_path): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] train_json_path = os.path.join(data_path, cfg_path).replace("\\", "/") json_data = load_json_data_in_fact(train_json_path) bs = json_data["train"]["batch_size"] nc = json_data["train"].get("keep_ckpts", 5) li = json_data["train"]["log_interval"] ei = json_data["train"]["eval_interval"] ep = json_data["train"]["epochs"] lr = json_data["train"]["learning_rate"] ver = json_data["version"] msg = f"加载训练配置文件: {train_json_path}" logger.info(msg) return ( gr.Textbox(value=msg), gr.Code(label=train_json_path, value=load_yaml_data_in_raw(train_json_path)), gr.Slider(value=bs), gr.Slider(value=nc), gr.Slider(value=li), gr.Slider(value=ei), gr.Slider(value=ep), gr.Slider(value=lr), gr.Dropdown(value=ver), ) def write_yaml_data_in_fact(yml, yml_path=yml_config): with open(yml_path, "w", encoding="utf-8") as file: yaml.safe_dump(yml, file, allow_unicode=True) # data = file.read() return yml def write_json_data_in_fact(json_path, json_data): with open(json_path, "w", encoding="utf-8") as file: json.dump(json_data, file, ensure_ascii=False, indent=2) def check_if_exists_model(paths: list[str]): check_results = { path: os.path.exists(path) and os.path.isfile(path) for path in paths } val = [path for path, exists in check_results.items() if exists] return val def check_bert_models(): return gr.CheckboxGroup(value=check_if_exists_model(bert_model_paths)) def check_emo_models(): return gr.CheckboxGroup(value=check_if_exists_model(emo_model_paths)) def check_base_models(): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] models_dir = yml["train_ms"]["model"] model_paths = [ os.path.join(data_path, models_dir, p).replace("\\", "/") for p in train_base_model_paths ] return gr.CheckboxGroup( label="检测底模状态", info="最好去下载底模进行训练", choices=model_paths, value=check_if_exists_model(model_paths), interactive=False, ) def modify_data_path(data_path): yml = load_yaml_data_in_fact() yml["dataset_path"] = data_path write_yaml_data_in_fact(yml) txt_box = gr.Textbox(value=data_path) return ( gr.Dropdown(value=data_path), txt_box, txt_box, txt_box, gr.Code(value=load_yaml_data_in_raw()), check_base_models(), ) def modify_preprocess_param(trans_path, cfg_path, val_per_spk, max_val_total): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] yml["preprocess_text"]["transcription_path"] = trans_path yml["preprocess_text"]["config_path"] = cfg_path yml["preprocess_text"]["val_per_spk"] = val_per_spk yml["preprocess_text"]["max_val_total"] = max_val_total write_yaml_data_in_fact(yml) whole_path = os.path.join(data_path, cfg_path).replace("\\", "/") logger.info("预处理配置: ", whole_path) if not os.path.exists(whole_path): os.makedirs(os.path.dirname(whole_path), exist_ok=True) shutil.copy(default_config_path, os.path.dirname(whole_path)) return gr.Dropdown(value=trans_path), gr.Code(value=load_yaml_data_in_raw()) def modify_resample_path(in_dir, out_dir, sr): yml = load_yaml_data_in_fact() yml["resample"]["in_dir"] = in_dir yml["resample"]["out_dir"] = out_dir yml["resample"]["sampling_rate"] = int(sr) write_yaml_data_in_fact(yml) msg = f"重采样参数已更改: [{in_dir}, {out_dir}, {sr}]\n" logger.info(msg) return ( gr.Textbox(value=in_dir), gr.Textbox(value=out_dir), gr.Textbox(value=msg), gr.Dropdown(value=sr), gr.Code(value=load_yaml_data_in_raw()), ) def modify_bert_config(cfg_path, nps, dev, multi): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] yml["bert_gen"]["config_path"] = cfg_path yml["bert_gen"]["num_processes"] = int(nps) yml["bert_gen"]["device"] = dev yml["bert_gen"]["use_multi_device"] = multi write_yaml_data_in_fact(yml) whole_path = os.path.join(data_path, cfg_path).replace("\\", "/") logger.info("bert配置路径: ", whole_path) if not os.path.exists(whole_path): os.makedirs(os.path.dirname(whole_path), exist_ok=True) shutil.copy(default_config_path, os.path.dirname(whole_path)) return ( gr.Textbox(value=cfg_path), gr.Slider(value=int(nps)), gr.Dropdown(value=dev), gr.Radio(value=multi), gr.Code(value=load_yaml_data_in_raw()), ) def modify_train_path(model, cfg_path): yml = load_yaml_data_in_fact() yml["train_ms"]["config_path"] = cfg_path yml["train_ms"]["model"] = model write_yaml_data_in_fact(yml) logger.info(f"训练配置文件路径: {cfg_path}\n") logger.info(f"训练模型文件夹路径: {model}") return ( gr.Textbox(value=model), gr.Textbox(value=cfg_path), gr.Code(value=load_yaml_data_in_raw()), check_base_models(), ) def modify_train_param(bs, nc, li, ei, ep, lr, ver): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] cfg_path = yml["train_ms"]["config_path"] ok = False whole_path = os.path.join(data_path, cfg_path).replace("\\", "/") logger.info("config_path: ", whole_path) if not os.path.exists(whole_path): os.makedirs(os.path.dirname(whole_path), exist_ok=True) shutil.copy(default_config_path, os.path.dirname(whole_path)) if os.path.exists(whole_path) and os.path.isfile(whole_path): ok = True with open(whole_path, "r", encoding="utf-8") as file: json_data = json.load(file) json_data["train"]["batch_size"] = bs json_data["train"]["keep_ckpts"] = nc json_data["train"]["log_interval"] = li json_data["train"]["eval_interval"] = ei json_data["train"]["epochs"] = ep json_data["train"]["learning_rate"] = lr json_data["version"] = ver with open(whole_path, "w", encoding="utf-8") as file: json.dump(json_data, file, ensure_ascii=False, indent=2) msg = f"成功更改训练参数! [{bs},{nc},{li},{ei},{ep},{lr}]" logger.info(msg) else: msg = f"打开训练配置文件时出现错误: {whole_path}\n" f"该文件不存在或损坏，现在打开默认配置文件" logger.error(msg) return gr.Textbox(value=msg), gr.Code( label=whole_path if ok else default_config_path, value=load_json_data_in_raw(whole_path) if ok else load_json_data_in_raw(default_config_path), ) def modify_infer_param(model_path, config_path, port, share, debug, ver): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] yml["webui"]["model"] = os.path.relpath(model_path, start=data_path) yml["webui"]["config_path"] = os.path.relpath(config_path, start=data_path) port = int(port) port = port if 0 <= port <= 65535 else 10086 yml["webui"]["port"] = port yml["webui"]["share"] = share yml["webui"]["debug"] = debug write_yaml_data_in_fact(yml) json_data = load_json_data_in_fact(config_path) json_data["version"] = ver write_json_data_in_fact(config_path, json_data) msg = f"修改推理配置文件成功: [{model_path}, {config_path}, {port}, {ver}]" logger.info(msg) return ( gr.Textbox(value=msg), gr.Code(value=load_yaml_data_in_raw()), gr.Code( label=config_path, value=load_json_data_in_raw(config_path) if os.path.exists(config_path) else load_json_data_in_raw(default_config_path), ), ) def get_status(): """获取电脑运行状态""" cpu_percent = psutil.cpu_percent(interval=1) memory_info = psutil.virtual_memory() memory_total = memory_info.total memory_available = memory_info.available memory_used = memory_info.used memory_percent = memory_info.percent gpuInfo = [] devices = ["cpu"] for i in range(torch.cuda.device_count()): devices.append(f"cuda:{i}") if torch.cuda.device_count() > 0: gpus = GPUtil.getGPUs() for gpu in gpus: gpuInfo.append( { "GPU编号": gpu.id, "GPU负载": f"{gpu.load} %", "专用GPU内存": { "总内存": f"{gpu.memoryTotal} MB", "已使用": f"{gpu.memoryUsed} MB", "空闲": f"{gpu.memoryFree} MB", }, } ) status_data = { "devices": devices, "CPU占用率": f"{cpu_percent} %", "总内存": f"{memory_total // (1024 * 1024)} MB", "可用内存": f"{memory_available // (1024 * 1024)} MB", "已使用内存": f"{memory_used // (1024 * 1024)} MB", "百分数": f"{memory_percent} %", "gpu信息": gpuInfo, } formatted_json_data = json.dumps(status_data, ensure_ascii=False, indent=2) logger.info(formatted_json_data) return str(formatted_json_data) def get_gpu_status(): return gr.Code(value=get_status()) def list_infer_models(): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] inf_models, json_files = [], [] for root, dirs, files in os.walk(data_path): for file in files: filepath = os.path.join(root, file).replace("\\", "/") if file.startswith("G_") and file.lower().endswith(".pth"): inf_models.append(filepath) elif file.lower().endswith(".json"): json_files.append(filepath) logger.info("找到推理模型文件: " + str(inf_models)) logger.info("找到推理配置文件: " + str(json_files)) return gr.Dropdown(choices=inf_models), gr.Dropdown(choices=json_files) def do_resample(nps): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] in_dir = yml["resample"]["in_dir"] comp_in_dir = os.path.join(os.path.abspath(data_path), in_dir).replace("\\", "/") logger.info(f"\n重采样路径: {comp_in_dir}") cmd = f"python resample.py --processes {nps}" logger.info(cmd) subprocess.run(cmd, shell=True) return gr.Textbox(value="重采样完成!") def do_transcript(lang, workers): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] in_dir = yml["resample"]["in_dir"] comp_in_dir = os.path.join(os.path.abspath(data_path), in_dir).replace("\\", "/") logger.info(f"\n转写文件夹路径: {comp_in_dir}") cmd = f'python asr_transcript.py -f "{comp_in_dir}" -l {lang} -w {workers}' logger.info(cmd) subprocess.run(cmd, shell=True) return gr.Textbox(value=f"\n转写文件夹路径: {comp_in_dir}\n转写到.lab完成!") def do_extract(raw_path, lang, unclean, char_name): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] lab_path = os.path.join(os.path.abspath(data_path), raw_path).replace("\\", "/") unclean_path = os.path.join( data_path, os.path.splitext(unclean)[0] + ".txt" ).replace("\\", "/") logger.info(f"\n提取转写文本路径: {lab_path}") lab_ok = False for root, _, files in os.walk(lab_path): for f_name in files: if str(f_name).lower().endswith(".lab"): lab_ok = True break if lab_ok: break if os.path.exists(lab_path) and os.path.isdir(lab_path): if lab_ok: cmd = f'python extract_list.py -f "{lab_path}" -l {lang} -n "{char_name}" -o "{unclean_path}"' logger.info(cmd) subprocess.run(cmd, shell=True) msg = f"提取完成!生成如下文件: {unclean_path}" logger.info(msg) else: msg = "未找到提取转写文本路径下的.lab文件!" logger.warning(msg) else: msg = "路径未选择正确!" logger.error(msg) return gr.Textbox(value=msg) def do_clean_list(ban_chars, unclean, clean): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] unclean_path = os.path.join(data_path, unclean) clean_path = os.path.join(data_path, clean) if os.path.exists(unclean_path) and os.path.isfile(unclean_path): cmd = f'python clean_list.py -c "{ban_chars}" -i "{unclean_path}" -o "{clean_path}"' logger.info(cmd) subprocess.run(cmd, shell=True) msg = "清洗标注文本完成!" logger.info(msg) else: msg = "未找到可清洗标注文本，请到2.2节重新生成!" logger.warning(msg) return gr.Textbox(value=msg) def do_preprocess_text(): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] trans_path = yml["preprocess_text"]["transcription_path"] comp_trans_path = os.path.join(os.path.abspath(data_path), trans_path).replace( "\\", "/" ) logger.info(f"\n清洗后标注文本文件路径: {comp_trans_path}") if os.path.exists(comp_trans_path) and os.path.isfile(comp_trans_path): cmd = "python preprocess_text.py" logger.info(cmd) subprocess.run(cmd, shell=True) msg = "文本预处理完成!" else: msg = "\n清洗后标注文本文件不存在或失效！" logger.info(msg) return gr.Textbox(value=msg) def do_bert_gen(): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] train_list_path = yml["preprocess_text"]["train_path"] val_list_path = yml["preprocess_text"]["val_path"] comp_t_path = os.path.join(os.path.abspath(data_path), train_list_path).replace( "\\", "/" ) comp_v_path = os.path.join(os.path.abspath(data_path), val_list_path).replace( "\\", "/" ) if os.path.exists(comp_t_path) and os.path.isfile(comp_t_path): subprocess.run("python bert_gen.py", shell=True) msg = "bert文件生成完成!" logger.info(msg) else: msg = f"未找到训练集和验证集文本!\ntrain: {comp_t_path}\nval:{comp_v_path}" logger.error(msg) return gr.Textbox(value=msg) def modify_emo_gen(emo_cfg, emo_nps, emo_device): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] yml["emo_gen"]["config_path"] = emo_cfg yml["emo_gen"]["num_processes"] = emo_nps yml["emo_gen"]["device"] = emo_device write_yaml_data_in_fact(yml) comp_emo_cfg = os.path.join(os.path.abspath(data_path), emo_cfg).replace("\\", "/") if not os.path.exists(comp_emo_cfg): os.makedirs(os.path.dirname(comp_emo_cfg), exist_ok=True) shutil.copy(default_config_path, os.path.dirname(comp_emo_cfg)) msg = f"修改emo配置参数: [配置路径:{comp_emo_cfg}, 处理数:{emo_nps}, 设备:{emo_device}]" logger.info(msg) return gr.Textbox(value=msg), gr.Code(value=load_yaml_data_in_raw()) def do_emo_gen(): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] emo_config_path = yml["emo_gen"]["config_path"] comp_emo_path = os.path.join(os.path.abspath(data_path), emo_config_path).replace( "\\", "/" ) if os.path.exists(comp_emo_path) and os.path.isfile(comp_emo_path): subprocess.run("python emo_gen.py", shell=True) msg = "emo.npy文件生成完成!" logger.info(msg) else: msg = f"选定路径下未找到配置文件!\n需要的config路径 : {comp_emo_path}" logger.error(msg) return gr.Textbox(value=msg) def do_my_train(): yml = load_yaml_data_in_fact() n_gpus = torch.cuda.device_count() # subprocess.run(f'python train_ms.py', shell=True) if os.path.exists(r"..\vits\python.exe") and os.path.isfile(r"..\vits\python.exe"): cmd = ( r"..\vits\python ..\vits\Scripts\torchrun.exe " f"--nproc_per_node={n_gpus} train_ms.py" ) else: cmd = f"torchrun --nproc_per_node={n_gpus} train_ms.py" subprocess.Popen(cmd, shell=True) train_port = yml["train_ms"]["env"]["MASTER_PORT"] train_addr = yml["train_ms"]["env"]["MASTER_ADDR"] url = f"env://{train_addr}:{train_port}" msg = f"训练开始!\nMASTER_URL: {url}\n使用gpu数:{n_gpus}\n推荐按下终止训练按钮来结束!" logger.info(msg) return gr.Textbox(value=msg) def do_tensorboard(): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] train_model_dir = yml["train_ms"]["model"] whole_dir = os.path.join(data_path, train_model_dir).replace("\\", "/") if os.path.exists(r"..\vits\python.exe") and os.path.isfile(r"..\vits\python.exe"): first_cmd = r"..\vits\python ..\vits\Scripts\tensorboard.exe " else: first_cmd = "tensorboard " tb_cmd = ( first_cmd + f"--logdir={whole_dir} " f"--port={11451} " f'--window_title="训练情况一览" ' f"--reload_interval={120}" ) subprocess.Popen(tb_cmd, shell=True) url = f"http://localhost:{11451}" webbrowser.open(url=url) msg = tb_cmd + "\n" + url logger.info(msg) return gr.Textbox(value=msg) def do_webui_infer(): yml = load_yaml_data_in_fact() data_path = yml["dataset_path"] model_path = yml["webui"]["model"] config_path = yml["webui"]["config_path"] comp_m_path = os.path.join(os.path.abspath(data_path), model_path) comp_c_path = os.path.join(os.path.abspath(data_path), config_path) if os.path.exists(comp_c_path) and os.path.exists(comp_m_path): webui_port = yml["webui"]["port"] subprocess.Popen("python webui.py", shell=True) url = f"http://localhost:{webui_port} | http://127.0.0.1:{webui_port}" msg = f"推理端已开启, 到控制台中复制网址打开页面\n{url}\n选择的模型:{model_path}" logger.info(msg) else: msg = f"未找到有效的模型或配置文件!\n模型路径:{comp_m_path}\n配置路径:{comp_c_path}" logger.error(msg) return gr.Textbox(value=msg) def compress_model(cfg_path, in_path, out_path): subprocess.Popen( "python compress_model.py" f" -c {cfg_path}" f" -i {in_path}", shell=True ) msg = "到控制台中查看压缩结果" logger.info(msg) return gr.Textbox(value=msg) def kill_specific_process_linux(cmd): try: output = subprocess.check_output(["pgrep", "-f", cmd], text=True) pids = output.strip().split("\n") for pid in pids: if pid: logger.critical(f"终止进程: {pid}") os.kill(int(pid), signal.SIGTERM) # os.kill(int(pid), signal.SIGKILL) except subprocess.CalledProcessError: logger.error("没有找到匹配的进程。") except Exception as e: logger.error(f"发生错误: {e}") def kill_specific_process_windows(cmd): try: # 使用tasklist和findstr来找到匹配特定命令行模式的进程 output = subprocess.check_output( f'tasklist /FO CSV /V | findstr /C:"{cmd}"', shell=True, text=True ) lines = output.strip().split("\n") for line in lines: if line: pid = line.split(",")[1].strip('"') logger.critical(f"终止进程: {pid}") subprocess.run(["taskkill", "/PID", pid, "/F"], shell=True) # 强制终止 except subprocess.CalledProcessError: logger.error(f"没有找到匹配的{cmd}进程。") except Exception as e: logger.error(f"发生错误: {e}") def stop_train_ms(): yml = load_yaml_data_in_fact() train_port = yml["train_ms"]["env"]["MASTER_PORT"] train_addr = yml["train_ms"]["env"]["MASTER_ADDR"] if platform.system() == "Windows": kill_specific_process_windows("torchrun") else: kill_specific_process_linux("torchrun") url = f"env://{train_addr}:{train_port}" msg = f"训练结束!\nMASTER_URL: {url}" logger.critical(msg) return gr.Textbox(value=msg) def stop_tensorboard(): if platform.system() == "Windows": kill_specific_process_windows("tensorboard") else: kill_specific_process_linux("tensorboard") msg = "关闭tensorboard!\n" logger.critical(msg) return gr.Textbox(value=msg) def stop_webui_infer(): yml = load_yaml_data_in_fact() webui_port = yml["webui"]["port"] if platform.system() == "Linux": kill_specific_process_linux("python webui.py") else: kill_specific_process_windows("python webui.py") msg = f"尝试终止推理进程，请到控制台查看情况\nport={webui_port}" logger.critical(msg) return gr.Textbox(value=msg) if __name__ == "__main__": init_yml = load_yaml_data_in_fact() with gr.Blocks( title="Bert-VITS-2-v2.0-管理器", theme=gr.themes.Soft(), css=os.path.abspath("./css/custom.css"), ) as app: with gr.Row(): with gr.Tabs(): with gr.TabItem("首页"): gr.Markdown( """ ## Bert-VITS2-v2.0 可视化界面 #### Copyright/Powered by 怕吃辣滴辣子酱 #### 许可： [AGPL 3.0 Licence](https://github.com/AnyaCoder/Bert-VITS2/blob/master/LICENSE) #### 请订阅我的频道: 1. Bilibili： [spicysama](https://space.bilibili.com/47278440) 2. github： [AnyaCoder](https://github.com/AnyaCoder) ### 严禁将此项目用于一切违反《中华人民共和国宪法》，《中华人民共和国刑法》，《中华人民共和国治安管理处罚法》和《中华人民共和国民法典》之用途。 ### 严禁用于任何政治相关用途。 ## References + [anyvoiceai/MassTTS](https://github.com/anyvoiceai/MassTTS) + [jaywalnut310/vits](https://github.com/jaywalnut310/vits) + [p0p4k/vits2_pytorch](https://github.com/p0p4k/vits2_pytorch) + [svc-develop-team/so-vits-svc](https://github.com/svc-develop-team/so-vits-svc) + [PaddlePaddle/PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech) ## 感谢所有贡献者作出的努力 Made with [contrib.rocks](https://contrib.rocks). """ ) with gr.TabItem("填入openi token"): with gr.Row(): gr.Markdown( """ ### 为了后续步骤中能够方便地自动下载模型，强烈推荐完成这一步骤! ### 去openi官网注册并登录后: ### [点击此处跳转到openi官网](https://openi.pcl.ac.cn/) ### , 点击右上角`个人头像`-> `设置` -> `应用`, 生成令牌(token) ### 复制token, 粘贴到下面的框框, 点击确认 """ ) with gr.Row(): openi_token_box = gr.Textbox( label="填入openi token", value=init_yml["openi_token"] ) with gr.Row(): openi_token_btn = gr.Button(value="确认填写", variant="primary") with gr.Row(): openi_token_status = gr.Textbox(label="状态信息") with gr.TabItem("模型检测"): CheckboxGroup_bert_models = gr.CheckboxGroup( label="检测bert模型状态", info="对应文件夹下必须有对应的模型文件(填入openi token后，则后续步骤中会自动下载)", choices=bert_model_paths, value=check_if_exists_model(bert_model_paths), interactive=False, ) check_pth_btn1 = gr.Button(value="检查bert模型状态") CheckboxGroup_emo_models = gr.CheckboxGroup( label="检测emo模型状态", info="对应文件夹下必须有对应的模型文件", choices=emo_model_paths, value=check_if_exists_model(emo_model_paths), interactive=False, ) check_pth_btn2 = gr.Button(value="检查emo模型状态") with gr.TabItem("数据处理"): with gr.Row(): dropdown_data_path = gr.Dropdown( label="选择数据集存放路径 (右侧的dataset_path)", info="详细说明可见右侧带注释的yaml文件", interactive=True, allow_custom_value=True, choices=[init_yml["dataset_path"]], value=init_yml["dataset_path"], ) with gr.Row(): data_path_btn = gr.Button(value="确认更改存放路径", variant="primary") with gr.Tabs(): with gr.TabItem("1. 音频重采样"): with gr.Row(): resample_in_box = gr.Textbox( label="输入音频文件夹in_dir", value=init_yml["resample"]["in_dir"], lines=1, interactive=True, ) resample_out_box = gr.Textbox( label="输出音频文件夹out_dir", lines=1, value=init_yml["resample"]["out_dir"], interactive=True, ) with gr.Row(): dropdown_resample_sr = gr.Dropdown( label="输出采样率(Hz)", choices=["16000", "22050", "44100", "48000"], value="44100", ) slider_resample_nps = gr.Slider( label="采样用的CPU核心数", minimum=1, maximum=64, step=1, value=2, ) with gr.Row(): resample_config_btn = gr.Button( value="确认重采样配置", variant="secondary", ) resample_btn = gr.Button( value="1. 音频重采样", variant="primary", ) with gr.Row(): resample_status = gr.Textbox( label="重采样结果", placeholder="执行重采样后可查看", lines=3, interactive=False, ) with gr.TabItem("2. 转写文本生成"): with gr.Row(): dropdown_lang = gr.Dropdown( label="选择语言", info="ZH中文，JP日语，EN英语", choices=["ZH", "JP", "EN"], value="ZH", ) slider_transcribe = gr.Slider( label="转写进程数", info="目的路径与前一节一致\n 重采样的输入路径", minimum=1, maximum=10, step=1, value=1, interactive=True, ) clean_txt_box = gr.Textbox( label="非法字符集", info="在此文本框内出现的字符都会被整行删除", lines=1, value="{}<>", interactive=True, ) with gr.Row(): unclean_box = gr.Textbox( label="未清洗的文本", info="仅将.lab提取到这个文件里, 请保持txt格式", lines=1, value=os.path.splitext( init_yml["preprocess_text"][ "transcription_path" ] )[0] + ".txt", interactive=True, ) clean_box = gr.Textbox( label="已清洗的文本", info="将未清洗的文本做去除非法字符集处理后的文本", lines=1, value=init_yml["preprocess_text"][ "transcription_path" ], interactive=True, ) char_name_box = gr.Textbox( label="输入角色名", info="区分说话人用", lines=1, placeholder="填入一个名称", interactive=True, ) with gr.Row(): transcribe_btn = gr.Button( value="2.1 转写文本", interactive=True ) extract_list_btn = gr.Button( value="2.2 合成filelist", ) clean_trans_btn = gr.Button(value="2.3 清洗标注") with gr.Row(): preprocess_status_box = gr.Textbox(label="标注状态") with gr.TabItem("3. 文本预处理"): with gr.Row(): slider_val_per_spk = gr.Slider( label="每个speaker的验证集条数", info="TensorBoard里的eval音频展示条目", minimum=1, maximum=20, step=1, value=init_yml["preprocess_text"]["val_per_spk"], ) slider_max_val_total = gr.Slider( label="验证集最大条数", info="多于此项的会被截断并放到训练集中", minimum=8, maximum=160, step=8, value=init_yml["preprocess_text"]["max_val_total"], ) with gr.Row(): dropdown_filelist_path = gr.Dropdown( interactive=True, label="输入filelist路径", allow_custom_value=True, choices=[ init_yml["preprocess_text"][ "transcription_path" ] ], value=init_yml["preprocess_text"][ "transcription_path" ], ) preprocess_config_box = gr.Textbox( label="预处理配置文件路径", value=init_yml["preprocess_text"]["config_path"], ) with gr.Row(): preprocess_config_btn = gr.Button(value="更新预处理配置文件") preprocess_text_btn = gr.Button( value="标注文本预处理", variant="primary" ) with gr.Row(): label_status = gr.Textbox(label="转写状态") with gr.TabItem("4. bert_gen"): with gr.Row(): bert_dataset_box = gr.Textbox( label="数据集存放路径", text_align="right", value=str(init_yml["dataset_path"]).rstrip("/"), lines=1, interactive=False, scale=10, ) gr.Markdown( """

## + """ ) bert_config_box = gr.Textbox( label="bert_gen配置文件路径", text_align="left", value=init_yml["bert_gen"]["config_path"], lines=1, interactive=True, scale=10, ) with gr.Row(): slider_bert_nps = gr.Slider( label="bert_gen并行处理数", minimum=1, maximum=12, step=1, value=init_yml["bert_gen"]["num_processes"], ) dropdown_bert_dev = gr.Dropdown( label="bert_gen处理设备", choices=["cuda", "cpu"], value=init_yml["bert_gen"]["device"], ) radio_bert_multi = gr.Radio( label="使用多卡推理", choices=[True, False], value=False ) with gr.Row(): bert_config_btn = gr.Button(value="确认更改bert配置项") bert_gen_btn = gr.Button( value="Go! Bert Gen!", variant="primary" ) with gr.Row(): bert_status = gr.Textbox(label="状态信息") with gr.TabItem("5. emo_gen"): with gr.Row(): emo_config_box = gr.Textbox( label="emo_gen配置文件路径", info="找一找你的config.json路径,相对于数据集路径", value=init_yml["emo_gen"]["config_path"], lines=1, interactive=True, scale=10, ) with gr.Row(): slider_emo_nps = gr.Slider( label="emo_gen并行处理数", info="最好预留2个以上的核数空闲，防卡死", minimum=1, maximum=32, step=1, value=init_yml["emo_gen"]["num_processes"], ) dropdown_emo_device = gr.Dropdown( label="emo_gen使用设备", info="可选cpu或cuda", choices=["cpu", "cuda"], value="cuda", ) with gr.Row(): emo_config_btn = gr.Button(value="更新emo配置") emo_gen_btn = gr.Button( value="Emo Gen!", variant="primary" ) with gr.Row(): emo_status = gr.Textbox(label="状态信息") with gr.TabItem("训练界面"): with gr.Tabs(): with gr.TabItem("训练配置文件路径"): with gr.Row(): train_dataset_box_1 = gr.Textbox( label="数据集存放路径", text_align="right", value=str(init_yml["dataset_path"]).rstrip("/"), lines=1, interactive=False, scale=20, ) gr.Markdown( """

## + """ ) train_config_box = gr.Textbox( label="train_ms配置文件路径", text_align="left", value=init_yml["train_ms"]["config_path"], lines=1, interactive=True, scale=20, ) with gr.Row(): train_dataset_box_2 = gr.Textbox( label="数据集存放路径", text_align="right", value=str(init_yml["dataset_path"]).rstrip("/"), lines=1, interactive=False, scale=20, ) gr.Markdown( """

## + """ ) train_model_box = gr.Textbox( label="train_ms模型文件夹路径", value=init_yml["train_ms"]["model"], lines=1, interactive=True, scale=20, ) with gr.Row(): train_ms_path_btn = gr.Button(value="更改训练路径配置") CheckboxGroup_train_models = check_base_models() check_pth_btn3 = gr.Button(value="检查训练底模状态") with gr.TabItem("训练参数设置"): with gr.Row(): slider_batch_size = gr.Slider( minimum=1, maximum=40, value=4, step=1, label="batch_size 批处理大小", ) slider_keep_ckpts = gr.Slider( minimum=1, maximum=20, value=5, step=1, label="keep_ckpts 最多保存n个最新模型", info="若超过，则删除最早的" ) with gr.Row(): slider_log_interval = gr.Slider( minimum=50, maximum=3000, value=200, step=50, label="log_interval 打印日志步数间隔", ) slider_eval_interval = gr.Slider( minimum=100, maximum=5000, value=1000, step=50, label="eval_interval 保存模型步数间隔", ) with gr.Row(): slider_epochs = gr.Slider( minimum=50, maximum=2000, value=100, step=50, label="epochs 训练轮数", ) slider_lr = gr.Slider( minimum=0.0001, maximum=0.0010, value=0.0003, step=0.0001, label="learning_rate 初始学习率", ) with gr.Row(): dropdown_version = gr.Dropdown( label="模型版本选择", info="推荐使用最新版底模和版本训练", choices=["2.1", "2.0.2", "2.0.1", "2.0", "1.1.1", "1.1.0", "1.0.1"], value="2.1", ) with gr.Row(): train_ms_load_btn = gr.Button( value="加载训练参数配置", variant="primary" ) train_ms_param_btn = gr.Button( value="更改训练参数配置", variant="primary" ) with gr.Row(): train_btn = gr.Button( value="3.1 点击开始训练", variant="primary" ) train_btn_2 = gr.Button( value="3.2 继续训练", variant="primary" ) stop_train_btn = gr.Button( value="终止训练", variant="secondary" ) with gr.Row(): train_output_box = gr.Textbox( label="状态信息", lines=1, autoscroll=True ) with gr.TabItem("TensorBoard"): with gr.Row(): gr.Markdown( """ ### Tensorboard的logdir 默认为训练的models路径 ### 请在前一节 `训练配置文件路径` 查看 """ ) with gr.Row(): open_tb_btn = gr.Button("开启Tensorboard") stop_tb_btn = gr.Button("关闭Tensorboard") with gr.Row(): tb_output_box = gr.Textbox( label="状态信息", lines=1, autoscroll=True ) with gr.TabItem("推理界面"): with gr.Tabs(): with gr.TabItem("模型选择"): with gr.Row(): dropdown_infer_model = gr.Dropdown( label="选择推理模型", info="默认选择预处理阶段配置的文件夹内容; 也可以自己输入路径。", interactive=True, allow_custom_value=True, ) dropdown_infer_config = gr.Dropdown( label="选择配置文件", info="默认选择预处理阶段配置的文件夹内容; 也可以自己输入路径。", interactive=True, allow_custom_value=True, ) with gr.Row(): dropdown_model_fresh_btn = gr.Button(value="刷新推理模型列表") with gr.Row(): webui_port_box = gr.Textbox( label="WebUI推理的端口号", placeholder="范围:[0, 65535]", max_lines=1, lines=1, value=init_yml["webui"]["port"], interactive=True, ) infer_ver_box = gr.Dropdown( label="更改推理版本", info="已经实现兼容推理，请选择合适的版本", choices=["2.1", "2.0.2", "2.0.1", "2.0", "1.1.1", "1.1.0", "1.0.1"], value="2.1", ) with gr.Row(): radio_webui_share = gr.Radio( label="公开", info="是否公开部署，对外网开放", choices=[True, False], value=init_yml["webui"]["share"], ) radio_webui_debug = gr.Radio( label="调试模式", info="是否开启debug模式", choices=[True, False], value=init_yml["webui"]["debug"], ) with gr.Row(): infer_config_btn = gr.Button(value="更新推理配置文件") stop_infer_btn = gr.Button(value="结束WebUI推理") with gr.Row(): infer_webui_btn = gr.Button( value="开启WebUI推理", variant="primary" ) with gr.Row(): infer_webui_box = gr.Textbox( label="提示信息", interactive=False ) with gr.TabItem("模型压缩"): with gr.Row(): compress_config = gr.Textbox( label="压缩配置文件", info="模型对应的config.json" ) with gr.Row(): compress_input_path = gr.Textbox( label="待压缩模型路径", info="所谓的模型是：G_{步数}.pth" ) with gr.Row(): compress_output_path = gr.Textbox( label="输出模型路径", info="输出为：G_{步数}_release.pth", value="在待压缩模型路径的同一文件夹下", interactive=False, ) with gr.Row(): compress_btn = gr.Button( value="压缩模型", variant="primary" ) with gr.Row(): compress_status = gr.Textbox(label="状态信息") with gr.Tabs(): with gr.TabItem("yaml配置文件状态"): code_config_yml = gr.Code( interactive=False, label=yml_config, value=load_yaml_data_in_raw(), language="yaml", elem_id="yml_code", ) with gr.TabItem("带注释的yaml配置文件"): code_default_yml = gr.Code( interactive=False, label=default_yaml_path, value=load_yaml_data_in_raw(default_yaml_path), language="yaml", elem_id="yml_code", ) with gr.TabItem("训练的json配置文件"): code_train_config_json = gr.Code( interactive=False, label=default_config_path, value=load_json_data_in_raw(default_config_path), language="json", elem_id="json_code", ) with gr.TabItem("推理的json配置文件"): code_infer_config_json = gr.Code( interactive=False, label=default_config_path, value=load_json_data_in_raw(default_config_path), language="json", elem_id="json_code", ) with gr.TabItem("其他状态"): code_gpu_json = gr.Code( label="本机资源使用情况", interactive=False, value=get_status(), language="json", elem_id="gpu_code", ) gpu_json_btn = gr.Button(value="刷新本机状态") openi_token_btn.click( fn=fill_openi_token, inputs=[openi_token_box], outputs=[openi_token_status, code_config_yml], ) check_pth_btn1.click( fn=check_bert_models, inputs=[], outputs=[CheckboxGroup_bert_models] ) check_pth_btn2.click( fn=check_emo_models, inputs=[], outputs=[CheckboxGroup_emo_models] ) check_pth_btn3.click( fn=check_base_models, inputs=[], outputs=[CheckboxGroup_train_models] ) data_path_btn.click( fn=modify_data_path, inputs=[dropdown_data_path], outputs=[ dropdown_data_path, bert_dataset_box, train_dataset_box_1, train_dataset_box_2, code_config_yml, CheckboxGroup_train_models, ], ) preprocess_config_btn.click( fn=modify_preprocess_param, inputs=[ dropdown_filelist_path, preprocess_config_box, slider_val_per_spk, slider_max_val_total, ], outputs=[dropdown_filelist_path, code_config_yml], ) preprocess_text_btn.click( fn=do_preprocess_text, inputs=[], outputs=[label_status] ) resample_config_btn.click( fn=modify_resample_path, inputs=[resample_in_box, resample_out_box, dropdown_resample_sr], outputs=[ resample_in_box, resample_out_box, resample_status, dropdown_resample_sr, code_config_yml, ], ) resample_btn.click( fn=do_resample, inputs=[slider_resample_nps], outputs=[resample_status] ) transcribe_btn.click( fn=do_transcript, inputs=[dropdown_lang, slider_transcribe], outputs=[preprocess_status_box], ) extract_list_btn.click( fn=do_extract, inputs=[resample_in_box, dropdown_lang, unclean_box, char_name_box], outputs=[preprocess_status_box], ) clean_trans_btn.click( fn=do_clean_list, inputs=[clean_txt_box, unclean_box, clean_box], outputs=[preprocess_status_box], ) bert_config_btn.click( fn=modify_bert_config, inputs=[ bert_config_box, slider_bert_nps, dropdown_bert_dev, radio_bert_multi, ], outputs=[ bert_config_box, slider_bert_nps, dropdown_bert_dev, radio_bert_multi, code_config_yml, ], ) bert_gen_btn.click(fn=do_bert_gen, inputs=[], outputs=[bert_status]) emo_config_btn.click( fn=modify_emo_gen, inputs=[emo_config_box, slider_emo_nps, dropdown_emo_device], outputs=[emo_status, code_config_yml], ) emo_gen_btn.click(fn=do_emo_gen, inputs=[], outputs=[emo_status]) train_ms_load_btn.click( fn=load_train_param, inputs=[train_config_box], outputs=[ train_output_box, code_train_config_json, slider_batch_size, slider_keep_ckpts, slider_log_interval, slider_eval_interval, slider_epochs, slider_lr, dropdown_version, ], ) train_ms_path_btn.click( fn=modify_train_path, inputs=[train_model_box, train_config_box], outputs=[ train_model_box, train_config_box, code_config_yml, CheckboxGroup_train_models, ], ) train_ms_param_btn.click( fn=modify_train_param, inputs=[ slider_batch_size, slider_keep_ckpts, slider_log_interval, slider_eval_interval, slider_epochs, slider_lr, dropdown_version, ], outputs=[train_output_box, code_train_config_json], ) train_btn.click(fn=do_my_train, inputs=[], outputs=[train_output_box]) train_btn_2.click(fn=do_my_train, inputs=[], outputs=[train_output_box]) stop_train_btn.click(fn=stop_train_ms, inputs=[], outputs=[train_output_box]) open_tb_btn.click(fn=do_tensorboard, inputs=[], outputs=[tb_output_box]) stop_tb_btn.click(fn=stop_tensorboard, inputs=[], outputs=[tb_output_box]) dropdown_model_fresh_btn.click( fn=list_infer_models, inputs=[], outputs=[dropdown_infer_model, dropdown_infer_config], ) infer_config_btn.click( fn=modify_infer_param, inputs=[ dropdown_infer_model, dropdown_infer_config, webui_port_box, radio_webui_share, radio_webui_debug, infer_ver_box, ], outputs=[infer_webui_box, code_config_yml, code_infer_config_json], ) infer_webui_btn.click(fn=do_webui_infer, inputs=[], outputs=[infer_webui_box]) compress_btn.click( fn=compress_model, inputs=[compress_config, compress_input_path, compress_output_path], outputs=[compress_status], ) stop_infer_btn.click(fn=stop_webui_infer, inputs=[], outputs=[infer_webui_box]) gpu_json_btn.click(fn=get_gpu_status, inputs=[], outputs=[code_gpu_json]) os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0" webbrowser.open("http://127.0.0.1:6006") app.launch(share=False, server_port=6006)