377454be50
* Create all_process.py * Create asr_transcript.py * Update config.py * Create extract_list.py * Create clean_list.py * Create custom.css * Create compress_model.py * Update all_process.py * Update resample.py * Update resample.py * configs/config.json copy utils * mirror: openi + token bert models optimize * text/__init__.py platform compatibility compress_model.py output * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * default_config.yml fix 'config_path' all_process.py fix 'config_path' * default_config.yml fix 'config_path' all_process.py fix 'config_path' * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
1382 lines
59 KiB
Python
1382 lines
59 KiB
Python
import json
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import os
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import platform
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import shutil
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import signal
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import subprocess
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import webbrowser
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import GPUtil
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import gradio as gr
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import psutil
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import torch
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import yaml
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from config import yml_config
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from tools.log import logger
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bert_model_paths = [
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"./bert/chinese-roberta-wwm-ext-large/pytorch_model.bin",
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"./bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin",
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"./bert/deberta-v3-large/pytorch_model.bin",
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"./bert/deberta-v3-large/spm.model",
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]
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emo_model_paths = [
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"./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/pytorch_model.bin"
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]
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train_base_model_paths = ["D_0.pth", "G_0.pth", "DUR_0.pth"]
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default_yaml_path = "default_config.yml"
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default_config_path = "configs/config.json"
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def load_yaml_data_in_raw(yml_path=yml_config):
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with open(yml_path, "r", encoding="utf-8") as file:
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# data = yaml.safe_load(file)
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data = file.read()
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return str(data)
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def load_json_data_in_raw(json_path):
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with open(json_path, "r", encoding="utf-8") as file:
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json_data = json.load(file)
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formatted_json_data = json.dumps(json_data, ensure_ascii=False, indent=2)
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return formatted_json_data
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def load_json_data_in_fact(json_path):
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with open(json_path, "r", encoding="utf-8") as file:
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json_data = json.load(file)
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return json_data
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def load_yaml_data_in_fact(yml_path=yml_config):
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with open(yml_path, "r", encoding="utf-8") as file:
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yml = yaml.safe_load(file)
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# data = file.read()
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return yml
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def fill_openi_token(token: str):
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yml = load_yaml_data_in_fact()
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yml["mirror"] = "openi"
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yml["openi_token"] = token
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write_yaml_data_in_fact(yml)
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msg = "openi 令牌已填写完成"
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logger.info(msg)
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return gr.Textbox(value=msg), gr.Code(value=load_yaml_data_in_raw())
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def load_train_param(cfg_path):
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yml = load_yaml_data_in_fact()
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data_path = yml["dataset_path"]
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train_json_path = os.path.join(data_path, cfg_path).replace("\\", "/")
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json_data = load_json_data_in_fact(train_json_path)
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bs = json_data["train"]["batch_size"]
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nc = json_data["train"].get("keep_ckpts", 5)
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li = json_data["train"]["log_interval"]
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ei = json_data["train"]["eval_interval"]
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ep = json_data["train"]["epochs"]
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lr = json_data["train"]["learning_rate"]
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ver = json_data["version"]
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msg = f"加载训练配置文件: {train_json_path}"
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logger.info(msg)
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return (
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gr.Textbox(value=msg),
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gr.Code(label=train_json_path, value=load_yaml_data_in_raw(train_json_path)),
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gr.Slider(value=bs),
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gr.Slider(value=nc),
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gr.Slider(value=li),
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gr.Slider(value=ei),
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gr.Slider(value=ep),
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gr.Slider(value=lr),
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gr.Dropdown(value=ver),
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)
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def write_yaml_data_in_fact(yml, yml_path=yml_config):
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with open(yml_path, "w", encoding="utf-8") as file:
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yaml.safe_dump(yml, file, allow_unicode=True)
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# data = file.read()
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return yml
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def write_json_data_in_fact(json_path, json_data):
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with open(json_path, "w", encoding="utf-8") as file:
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json.dump(json_data, file, ensure_ascii=False, indent=2)
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def check_if_exists_model(paths: list[str]):
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check_results = {
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path: os.path.exists(path) and os.path.isfile(path) for path in paths
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}
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val = [path for path, exists in check_results.items() if exists]
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return val
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def check_bert_models():
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return gr.CheckboxGroup(value=check_if_exists_model(bert_model_paths))
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def check_emo_models():
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return gr.CheckboxGroup(value=check_if_exists_model(emo_model_paths))
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def check_base_models():
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yml = load_yaml_data_in_fact()
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data_path = yml["dataset_path"]
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models_dir = yml["train_ms"]["model"]
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model_paths = [
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os.path.join(data_path, models_dir, p).replace("\\", "/")
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for p in train_base_model_paths
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]
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return gr.CheckboxGroup(
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label="检测底模状态",
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info="最好去下载底模进行训练",
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choices=model_paths,
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value=check_if_exists_model(model_paths),
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interactive=False,
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)
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def modify_data_path(data_path):
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yml = load_yaml_data_in_fact()
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yml["dataset_path"] = data_path
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write_yaml_data_in_fact(yml)
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txt_box = gr.Textbox(value=data_path)
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return (
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gr.Dropdown(value=data_path),
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txt_box,
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txt_box,
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txt_box,
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gr.Code(value=load_yaml_data_in_raw()),
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check_base_models(),
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)
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def modify_preprocess_param(trans_path, cfg_path, val_per_spk, max_val_total):
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yml = load_yaml_data_in_fact()
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data_path = yml["dataset_path"]
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yml["preprocess_text"]["transcription_path"] = trans_path
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yml["preprocess_text"]["config_path"] = cfg_path
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yml["preprocess_text"]["val_per_spk"] = val_per_spk
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yml["preprocess_text"]["max_val_total"] = max_val_total
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write_yaml_data_in_fact(yml)
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whole_path = os.path.join(data_path, cfg_path).replace("\\", "/")
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logger.info("预处理配置: ", whole_path)
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if not os.path.exists(whole_path):
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os.makedirs(os.path.dirname(whole_path), exist_ok=True)
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shutil.copy(default_config_path, os.path.dirname(whole_path))
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return gr.Dropdown(value=trans_path), gr.Code(value=load_yaml_data_in_raw())
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def modify_resample_path(in_dir, out_dir, sr):
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yml = load_yaml_data_in_fact()
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yml["resample"]["in_dir"] = in_dir
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yml["resample"]["out_dir"] = out_dir
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yml["resample"]["sampling_rate"] = int(sr)
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write_yaml_data_in_fact(yml)
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msg = f"重采样参数已更改: [{in_dir}, {out_dir}, {sr}]\n"
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logger.info(msg)
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return (
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gr.Textbox(value=in_dir),
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gr.Textbox(value=out_dir),
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gr.Textbox(value=msg),
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gr.Dropdown(value=sr),
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gr.Code(value=load_yaml_data_in_raw()),
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)
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def modify_bert_config(cfg_path, nps, dev, multi):
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yml = load_yaml_data_in_fact()
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data_path = yml["dataset_path"]
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yml["bert_gen"]["config_path"] = cfg_path
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yml["bert_gen"]["num_processes"] = int(nps)
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yml["bert_gen"]["device"] = dev
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yml["bert_gen"]["use_multi_device"] = multi
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write_yaml_data_in_fact(yml)
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whole_path = os.path.join(data_path, cfg_path).replace("\\", "/")
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logger.info("bert配置路径: ", whole_path)
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if not os.path.exists(whole_path):
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os.makedirs(os.path.dirname(whole_path), exist_ok=True)
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shutil.copy(default_config_path, os.path.dirname(whole_path))
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return (
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gr.Textbox(value=cfg_path),
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gr.Slider(value=int(nps)),
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gr.Dropdown(value=dev),
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gr.Radio(value=multi),
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gr.Code(value=load_yaml_data_in_raw()),
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)
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def modify_train_path(model, cfg_path):
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yml = load_yaml_data_in_fact()
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yml["train_ms"]["config_path"] = cfg_path
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yml["train_ms"]["model"] = model
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write_yaml_data_in_fact(yml)
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logger.info(f"训练配置文件路径: {cfg_path}\n")
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logger.info(f"训练模型文件夹路径: {model}")
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return (
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gr.Textbox(value=model),
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gr.Textbox(value=cfg_path),
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gr.Code(value=load_yaml_data_in_raw()),
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check_base_models(),
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)
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def modify_train_param(bs, nc, li, ei, ep, lr, ver):
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yml = load_yaml_data_in_fact()
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data_path = yml["dataset_path"]
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cfg_path = yml["train_ms"]["config_path"]
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ok = False
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whole_path = os.path.join(data_path, cfg_path).replace("\\", "/")
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logger.info("config_path: ", whole_path)
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if not os.path.exists(whole_path):
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os.makedirs(os.path.dirname(whole_path), exist_ok=True)
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shutil.copy(default_config_path, os.path.dirname(whole_path))
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if os.path.exists(whole_path) and os.path.isfile(whole_path):
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ok = True
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with open(whole_path, "r", encoding="utf-8") as file:
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json_data = json.load(file)
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json_data["train"]["batch_size"] = bs
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json_data["train"]["keep_ckpts"] = nc
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json_data["train"]["log_interval"] = li
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json_data["train"]["eval_interval"] = ei
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json_data["train"]["epochs"] = ep
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json_data["train"]["learning_rate"] = lr
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json_data["version"] = ver
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with open(whole_path, "w", encoding="utf-8") as file:
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json.dump(json_data, file, ensure_ascii=False, indent=2)
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msg = f"成功更改训练参数! [{bs},{nc},{li},{ei},{ep},{lr}]"
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logger.info(msg)
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else:
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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"
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logger.error(msg)
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return gr.Textbox(value=msg), gr.Code(
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label=whole_path if ok else default_config_path,
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value=load_json_data_in_raw(whole_path)
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if ok
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else load_json_data_in_raw(default_config_path),
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)
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def modify_infer_param(model_path, config_path, port, share, debug, ver):
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yml = load_yaml_data_in_fact()
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data_path = yml["dataset_path"]
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yml["webui"]["model"] = os.path.relpath(model_path, start=data_path)
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yml["webui"]["config_path"] = os.path.relpath(config_path, start=data_path)
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port = int(port)
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port = port if 0 <= port <= 65535 else 10086
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yml["webui"]["port"] = port
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yml["webui"]["share"] = share
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yml["webui"]["debug"] = debug
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write_yaml_data_in_fact(yml)
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json_data = load_json_data_in_fact(config_path)
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json_data["version"] = ver
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write_json_data_in_fact(config_path, json_data)
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msg = f"修改推理配置文件成功: [{model_path}, {config_path}, {port}, {ver}]"
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logger.info(msg)
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return (
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gr.Textbox(value=msg),
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gr.Code(value=load_yaml_data_in_raw()),
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gr.Code(
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label=config_path,
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value=load_json_data_in_raw(config_path)
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if os.path.exists(config_path)
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else load_json_data_in_raw(default_config_path),
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),
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)
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def get_status():
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"""获取电脑运行状态"""
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cpu_percent = psutil.cpu_percent(interval=1)
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memory_info = psutil.virtual_memory()
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memory_total = memory_info.total
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memory_available = memory_info.available
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memory_used = memory_info.used
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memory_percent = memory_info.percent
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gpuInfo = []
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devices = ["cpu"]
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for i in range(torch.cuda.device_count()):
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devices.append(f"cuda:{i}")
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if torch.cuda.device_count() > 0:
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gpus = GPUtil.getGPUs()
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for gpu in gpus:
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gpuInfo.append(
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{
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"GPU编号": gpu.id,
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"GPU负载": f"{gpu.load} %",
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"专用GPU内存": {
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"总内存": f"{gpu.memoryTotal} MB",
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"已使用": f"{gpu.memoryUsed} MB",
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"空闲": f"{gpu.memoryFree} MB",
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},
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}
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)
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status_data = {
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"devices": devices,
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"CPU占用率": f"{cpu_percent} %",
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"总内存": f"{memory_total // (1024 * 1024)} MB",
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"可用内存": f"{memory_available // (1024 * 1024)} MB",
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"已使用内存": f"{memory_used // (1024 * 1024)} MB",
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"百分数": f"{memory_percent} %",
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"gpu信息": gpuInfo,
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}
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formatted_json_data = json.dumps(status_data, ensure_ascii=False, indent=2)
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logger.info(formatted_json_data)
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return str(formatted_json_data)
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def get_gpu_status():
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return gr.Code(value=get_status())
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def list_infer_models():
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yml = load_yaml_data_in_fact()
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data_path = yml["dataset_path"]
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inf_models, json_files = [], []
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for root, dirs, files in os.walk(data_path):
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for file in files:
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filepath = os.path.join(root, file).replace("\\", "/")
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if file.startswith("G_") and file.lower().endswith(".pth"):
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inf_models.append(filepath)
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elif file.lower().endswith(".json"):
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json_files.append(filepath)
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logger.info("找到推理模型文件: " + str(inf_models))
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logger.info("找到推理配置文件: " + str(json_files))
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return gr.Dropdown(choices=inf_models), gr.Dropdown(choices=json_files)
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def do_resample(nps):
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yml = load_yaml_data_in_fact()
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data_path = yml["dataset_path"]
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in_dir = yml["resample"]["in_dir"]
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comp_in_dir = os.path.join(os.path.abspath(data_path), in_dir).replace("\\", "/")
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logger.info(f"\n重采样路径: {comp_in_dir}")
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cmd = f"python resample.py --processes {nps}"
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logger.info(cmd)
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subprocess.run(cmd, shell=True)
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return gr.Textbox(value="重采样完成!")
|
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|
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|
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def do_transcript(lang, workers):
|
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yml = load_yaml_data_in_fact()
|
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data_path = yml["dataset_path"]
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in_dir = yml["resample"]["in_dir"]
|
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comp_in_dir = os.path.join(os.path.abspath(data_path), in_dir).replace("\\", "/")
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logger.info(f"\n转写文件夹路径: {comp_in_dir}")
|
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cmd = f'python asr_transcript.py -f "{comp_in_dir}" -l {lang} -w {workers}'
|
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logger.info(cmd)
|
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subprocess.run(cmd, shell=True)
|
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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"]
|
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lab_path = os.path.join(os.path.abspath(data_path), raw_path).replace("\\", "/")
|
||
unclean_path = os.path.join(
|
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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
|
||
<a href="https://github.com/AnyaCoder/Bert-VITS2/graphs/contributors">
|
||
<img src="https://contrib.rocks/image?repo=AnyaCoder/Bert-VITS2" />
|
||
</a>
|
||
|
||
Made with [contrib.rocks](https://contrib.rocks).
|
||
|
||
"""
|
||
)
|
||
with gr.TabItem("填入openi token"):
|
||
with gr.Row():
|
||
gr.Markdown(
|
||
"""
|
||
### 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)
|
||
"""
|
||
)
|
||
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(
|
||
"""
|
||
<br></br>
|
||
## +
|
||
"""
|
||
)
|
||
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(
|
||
"""
|
||
<br></br>
|
||
## +
|
||
"""
|
||
)
|
||
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(
|
||
"""
|
||
<br></br>
|
||
## +
|
||
"""
|
||
)
|
||
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="最多保存n个最新模型，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=["2.0", "1.1.1", "1.1.0", "1.0.1"],
|
||
value="2.0",
|
||
)
|
||
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=["2.0", "1.1.1", "1.1.0", "1.0.1"],
|
||
value="2.0",
|
||
)
|
||
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)
|