From 4edcfb3fefe489715f26822bf06d46c5295bcf23 Mon Sep 17 00:00:00 2001
From: spicysama <122108331+AnyaCoder@users.noreply.github.com>
Date: Thu, 30 Nov 2023 12:10:44 +0800
Subject: [PATCH] text/init.py (#203)
* 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
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
all_process.py | 1378 +++++++++++++++++++++++++++++++++++++++++++++
asr_transcript.py | 102 ++++
clean_list.py | 48 ++
compress_model.py | 88 +++
config.py | 1 +
css/custom.css | 18 +
extract_list.py | 53 ++
resample.py | 4 +-
text/__init__.py | 7 +-
9 files changed, 1695 insertions(+), 4 deletions(-)
create mode 100644 all_process.py
create mode 100644 asr_transcript.py
create mode 100644 clean_list.py
create mode 100644 compress_model.py
create mode 100644 css/custom.css
create mode 100644 extract_list.py
diff --git a/all_process.py b/all_process.py
new file mode 100644
index 0000000..f5ef07b
--- /dev/null
+++ b/all_process.py
@@ -0,0 +1,1378 @@
+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("\\", "/")
+ 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(
+ """
+ ### 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(
+ """
+
+ ## +
+ """
+ )
+ 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="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)
diff --git a/asr_transcript.py b/asr_transcript.py
new file mode 100644
index 0000000..a237b74
--- /dev/null
+++ b/asr_transcript.py
@@ -0,0 +1,102 @@
+import argparse
+import concurrent.futures
+import os
+
+from loguru import logger
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from tqdm import tqdm
+
+os.environ["MODELSCOPE_CACHE"] = "./"
+
+
+def transcribe_worker(file_path: str, inference_pipeline, language):
+ """
+ Worker function for transcribing a segment of an audio file.
+ """
+ rec_result = inference_pipeline(audio_in=file_path)
+ text = str(rec_result.get("text", "")).strip()
+ text_without_spaces = text.replace(" ", "")
+ logger.info(file_path)
+ if language != "EN":
+ logger.info("text: " + text_without_spaces)
+ return text_without_spaces
+ else:
+ logger.info("text: " + text)
+ return text
+
+
+def transcribe_folder_parallel(folder_path, language, max_workers=4):
+ """
+ Transcribe all .wav files in the given folder using ThreadPoolExecutor.
+ """
+ logger.critical(f"parallel transcribe: {folder_path}|{language}|{max_workers}")
+ if language == "JP":
+ workers = [
+ pipeline(
+ task=Tasks.auto_speech_recognition,
+ model="damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline",
+ )
+ for _ in range(max_workers)
+ ]
+
+ elif language == "ZH":
+ workers = [
+ pipeline(
+ task=Tasks.auto_speech_recognition,
+ model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+ model_revision="v1.2.4",
+ )
+ for _ in range(max_workers)
+ ]
+ else:
+ workers = [
+ pipeline(
+ task=Tasks.auto_speech_recognition,
+ model="damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline",
+ )
+ for _ in range(max_workers)
+ ]
+
+ file_paths = []
+ langs = []
+ for root, _, files in os.walk(folder_path):
+ for file in files:
+ if file.lower().endswith(".wav"):
+ file_path = os.path.join(root, file)
+ lab_file_path = os.path.splitext(file_path)[0] + ".lab"
+ file_paths.append(file_path)
+ langs.append(language)
+
+ all_workers = (
+ workers * (len(file_paths) // max_workers)
+ + workers[: len(file_paths) % max_workers]
+ )
+
+ with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+ for i in tqdm(range(0, len(file_paths), max_workers), desc="转写进度: "):
+ l, r = i, min(i + max_workers, len(file_paths))
+ transcriptions = list(
+ executor.map(
+ transcribe_worker, file_paths[l:r], all_workers[l:r], langs[l:r]
+ )
+ )
+ for file_path, transcription in zip(file_paths[l:r], transcriptions):
+ if transcription:
+ lab_file_path = os.path.splitext(file_path)[0] + ".lab"
+ with open(lab_file_path, "w", encoding="utf-8") as lab_file:
+ lab_file.write(transcription)
+ logger.critical("已经将wav文件转写为同名的.lab文件")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "-f", "--filepath", default="./raw/lzy_zh", help="path of your model"
+ )
+ parser.add_argument("-l", "--language", default="ZH", help="language")
+ parser.add_argument("-w", "--workers", default="1", help="trans workers")
+ args = parser.parse_args()
+
+ transcribe_folder_parallel(args.filepath, args.language, int(args.workers))
+ print("转写结束！")
diff --git a/clean_list.py b/clean_list.py
new file mode 100644
index 0000000..5bead99
--- /dev/null
+++ b/clean_list.py
@@ -0,0 +1,48 @@
+import argparse
+import shutil
+from tempfile import NamedTemporaryFile
+
+from loguru import logger
+
+
+def remove_chars_from_file(chars_to_remove, input_file, output_file):
+ rm_cnt = 0
+ with open(input_file, "r", encoding="utf-8") as f_in, NamedTemporaryFile(
+ "w", delete=False, encoding="utf-8"
+ ) as f_tmp:
+ for line in f_in:
+ if any(char in line for char in chars_to_remove):
+ logger.info(f"删除了这一行:\n {line.strip()}")
+ rm_cnt += 1
+ else:
+ f_tmp.write(line)
+
+ shutil.move(f_tmp.name, output_file)
+ logger.critical(f"总计移除了: {rm_cnt} 行")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description="Remove lines from a file containing specified characters."
+ )
+
+ parser.add_argument(
+ "-c",
+ "--chars",
+ type=str,
+ required=True,
+ help="String of characters. If a line contains any of these characters, it will be removed.",
+ )
+ parser.add_argument(
+ "-i", "--input", type=str, required=True, help="Path to the input file."
+ )
+ parser.add_argument(
+ "-o", "--output", type=str, required=True, help="Path to the output file."
+ )
+
+ args = parser.parse_args()
+
+ # Setting up basic logging configuration for loguru
+ logger.add("removed_lines.log", rotation="1 MB")
+
+ remove_chars_from_file(args.chars, args.input, args.output)
diff --git a/compress_model.py b/compress_model.py
new file mode 100644
index 0000000..62a5a09
--- /dev/null
+++ b/compress_model.py
@@ -0,0 +1,88 @@
+from collections import OrderedDict
+from text.symbols import symbols
+import torch
+from tools.log import logger
+import utils
+from models import SynthesizerTrn
+import os
+
+
+def copyStateDict(state_dict):
+ if list(state_dict.keys())[0].startswith("module"):
+ start_idx = 1
+ else:
+ start_idx = 0
+ new_state_dict = OrderedDict()
+ for k, v in state_dict.items():
+ name = ",".join(k.split(".")[start_idx:])
+ new_state_dict[name] = v
+ return new_state_dict
+
+
+def removeOptimizer(config: str, input_model: str, ishalf: bool, output_model: str):
+ hps = utils.get_hparams_from_file(config)
+
+ net_g = SynthesizerTrn(
+ len(symbols),
+ hps.data.filter_length // 2 + 1,
+ hps.train.segment_size // hps.data.hop_length,
+ n_speakers=hps.data.n_speakers,
+ **hps.model,
+ )
+
+ optim_g = torch.optim.AdamW(
+ net_g.parameters(),
+ hps.train.learning_rate,
+ betas=hps.train.betas,
+ eps=hps.train.eps,
+ )
+
+ state_dict_g = torch.load(input_model, map_location="cpu")
+ new_dict_g = copyStateDict(state_dict_g)
+ keys = []
+ for k, v in new_dict_g["model"].items():
+ if "enc_q" in k:
+ continue # noqa: E701
+ keys.append(k)
+
+ new_dict_g = (
+ {k: new_dict_g["model"][k].half() for k in keys}
+ if ishalf
+ else {k: new_dict_g["model"][k] for k in keys}
+ )
+
+ torch.save(
+ {
+ "model": new_dict_g,
+ "iteration": 0,
+ "optimizer": optim_g.state_dict(),
+ "learning_rate": 0.0001,
+ },
+ output_model,
+ )
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("-c", "--config", type=str, default="configs/config.json")
+ parser.add_argument("-i", "--input", type=str)
+ parser.add_argument("-o", "--output", type=str, default=None)
+ parser.add_argument(
+ "-hf", "--half", action="store_true", default=False, help="Save as FP16"
+ )
+
+ args = parser.parse_args()
+
+ output = args.output
+
+ if output is None:
+ import os.path
+
+ filename, ext = os.path.splitext(args.input)
+ half = "_half" if args.half else ""
+ output = filename + "_release" + half + ext
+
+ removeOptimizer(args.config, args.input, args.half, output)
+ logger.info(f"压缩模型成功, 输出模型: {os.path.abspath(output)}")
diff --git a/config.py b/config.py
index ea930ee..01a5584 100644
--- a/config.py
+++ b/config.py
@@ -241,3 +241,4 @@ parser = argparse.ArgumentParser()
parser.add_argument("-y", "--yml_config", type=str, default="config.yml")
args, _ = parser.parse_known_args()
config = Config(args.yml_config)
+yml_config = args.yml_config
diff --git a/css/custom.css b/css/custom.css
new file mode 100644
index 0000000..5066448
--- /dev/null
+++ b/css/custom.css
@@ -0,0 +1,18 @@
+
+#yml_code {
+ height: 600px;
+ flex-grow: inherit;
+ overflow-y: auto;
+}
+
+#json_code {
+ height: 600px;
+ flex-grow: inherit;
+ overflow-y: auto;
+}
+
+#gpu_code {
+ height: 300px;
+ flex-grow: inherit;
+ overflow-y: auto;
+}
diff --git a/extract_list.py b/extract_list.py
new file mode 100644
index 0000000..1fdc6bf
--- /dev/null
+++ b/extract_list.py
@@ -0,0 +1,53 @@
+import argparse
+import os
+from loguru import logger
+
+
+def extract_list(folder_path, language, name, transcript_txt_file):
+ logger.info(f"extracting list: {folder_path}|{name}|{language}")
+ current_dir = os.getcwd()
+ relative_path = os.path.relpath(folder_path, current_dir)
+ print(relative_path)
+ os.makedirs(os.path.dirname(transcript_txt_file), exist_ok=True)
+ with open(transcript_txt_file, "w", encoding="utf-8") as f:
+ # 遍历 raw 文件夹下的所有子文件夹
+ for root, _, files in os.walk(relative_path):
+ for file in files:
+ if file.endswith(".lab"):
+ lab_file_path = os.path.join(root, file)
+ # 读取转写文本
+ with open(lab_file_path, "r", encoding="utf-8") as lab_file:
+ transcription = lab_file.read().strip()
+ if len(transcription) == 0:
+ continue
+ # 获取对应的 WAV 文件路径
+ # ./Data/宵宫/audios/raw
+ # ./Data/宵宫/audios/wavs
+ wav_file_path = os.path.splitext(lab_file_path)[0] + ".wav"
+ if os.path.isfile(wav_file_path):
+ wav_file_path = wav_file_path.replace("\\", "/").replace(
+ "/raw", "/wavs"
+ )
+ # 写入数据到总的转写文本文件
+ line = f"{wav_file_path}|{name}|{language}|{transcription}\n"
+ f.write(line)
+ else:
+ print("not exists!")
+ return f"转写文本 {transcript_txt_file} 生成完成"
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "-f",
+ "--filepath",
+ required=True,
+ help="path of your rawaudios, e.g. ./Data/xxx/audios/raw",
+ )
+ parser.add_argument("-l", "--language", default="ZH", help="language")
+ parser.add_argument("-n", "--name", required=True, help="name of the character")
+ parser.add_argument("-o", "--outfile", required=True, help="outfile")
+ args = parser.parse_args()
+
+ status_str = extract_list(args.filepath, args.language, args.name, args.outfile)
+ logger.critical(status_str)
diff --git a/resample.py b/resample.py
index 9ce599b..678c352 100644
--- a/resample.py
+++ b/resample.py
@@ -12,7 +12,7 @@ from config import config
def process(item):
spkdir, wav_name, args = item
wav_path = os.path.join(args.in_dir, spkdir, wav_name)
- if os.path.exists(wav_path) and ".wav" in wav_path:
+ if os.path.exists(wav_path) and wav_path.lower().endswith(".wav"):
wav, sr = librosa.load(wav_path, sr=args.sr)
soundfile.write(os.path.join(args.out_dir, spkdir, wav_name), wav, sr)
@@ -60,7 +60,7 @@ if __name__ == "__main__":
if not os.path.isdir(spk_dir_out):
os.makedirs(spk_dir_out, exist_ok=True)
for filename in filenames:
- if filename.endswith(".wav"):
+ if filename.lower().endswith(".wav"):
twople = (spk_dir, filename, args)
tasks.append(twople)
diff --git a/text/__init__.py b/text/__init__.py
index d297e9a..816ad01 100644
--- a/text/__init__.py
+++ b/text/__init__.py
@@ -49,9 +49,12 @@ def check_bert_models():
def init_openjtalk():
- import pyopenjtalk
+ import platform
- pyopenjtalk.g2p("こんにちは，世界。")
+ if platform.platform() == "Linux":
+ import pyopenjtalk
+
+ pyopenjtalk.g2p("こんにちは，世界。")
init_openjtalk()