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@@ -0,0 +1,797 @@
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+from __future__ import annotations
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+
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+import html
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+import json
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+import os
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+import platform
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+import random
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+import shutil
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+import signal
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+import subprocess
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+import sys
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+from pathlib import Path
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+
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+import gradio as gr
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+import psutil
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+import yaml
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+from loguru import logger
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+from tqdm import tqdm
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+
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+from fish_speech.webui.launch_utils import Seafoam, versions_html
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+
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+PYTHON = os.path.join(os.environ.get("PYTHON_FOLDERPATH", ""), "python")
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+sys.path.insert(0, "")
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+print(sys.path)
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+cur_work_dir = Path(os.getcwd()).resolve()
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+print("You are in ", str(cur_work_dir))
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+config_path = cur_work_dir / "fish_speech" / "configs"
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+vqgan_yml_path = config_path / "vqgan_finetune.yaml"
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+llama_yml_path = config_path / "text2semantic_sft.yaml"
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+
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+env = os.environ.copy()
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+env["no_proxy"] = "127.0.0.1, localhost, 0.0.0.0"
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+
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+seafoam = Seafoam()
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+
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+
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+def build_html_error_message(error):
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+ return f"""
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+ <div style="color: red; font-weight: bold;">
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+ {html.escape(error)}
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+ </div>
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+ """
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+
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+
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+def build_html_ok_message(msg):
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+ return f"""
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+ <div style="color: green; font-weight: bold;">
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+ {html.escape(msg)}
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+ </div>
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+ """
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+
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+
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+def load_data_in_raw(path):
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+ with open(path, "r", encoding="utf-8") as file:
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+ data = file.read()
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+ return str(data)
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+
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+
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+def kill_proc_tree(pid, including_parent=True):
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+ try:
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+ parent = psutil.Process(pid)
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+ except psutil.NoSuchProcess:
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+ # Process already terminated
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+ return
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+
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+ children = parent.children(recursive=True)
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+ for child in children:
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+ try:
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+ os.kill(child.pid, signal.SIGTERM) # or signal.SIGKILL
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+ except OSError:
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+ pass
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+ if including_parent:
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+ try:
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+ os.kill(parent.pid, signal.SIGTERM) # or signal.SIGKILL
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+ except OSError:
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+ pass
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+
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+
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+system = platform.system()
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+p_label = None
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+p_infer = None
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+
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+
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+def kill_process(pid):
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+ if system == "Windows":
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+ cmd = "taskkill /t /f /pid %s" % pid
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+ # os.system(cmd)
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+ subprocess.run(cmd)
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+ else:
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+ kill_proc_tree(pid)
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+
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+
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+def change_label(if_label):
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+ global p_label
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+ if if_label == True and p_label == None:
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+ cmd = ["asr-label-win-x64.exe"]
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+ yield f"打标工具WebUI已开启, 访问：http://localhost:{3000}"
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+ p_label = subprocess.Popen(cmd, shell=True, env=env)
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+ elif if_label == False and p_label != None:
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+ kill_process(p_label.pid)
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+ p_label = None
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+ yield "打标工具WebUI已关闭"
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+
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+
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+def change_infer(
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+ if_infer, host, port, infer_vqgan_model, infer_llama_model, infer_compile
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+):
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+ global p_infer
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+ if if_infer == True and p_infer == None:
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+ env = os.environ.copy()
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+
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+ env["GRADIO_SERVER_NAME"] = host
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+ env["GRADIO_SERVER_PORT"] = port
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+ # 启动第二个进程
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+ yield build_html_ok_message(f"推理界面已开启, 访问 http://{host}:{port}")
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+ p_infer = subprocess.Popen(
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+ [
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+ PYTHON,
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+ "tools/webui.py",
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+ "--vqgan-checkpoint-path",
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+ infer_vqgan_model,
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+ "--llama-checkpoint-path",
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+ infer_llama_model,
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+ "--tokenizer",
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+ "checkpoints",
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+ ]
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+ + (["--compile"] if infer_compile == "Yes" else []),
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+ env=env,
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+ )
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+
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+ elif if_infer == False and p_infer != None:
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+ kill_process(p_infer.pid)
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+ p_infer = None
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+ yield build_html_error_message("推理界面已关闭")
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+
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+
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+js = load_data_in_raw("fish_speech/webui/js/animate.js")
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+css = load_data_in_raw("fish_speech/webui/css/style.css")
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+
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+data_pre_output = (cur_work_dir / "data").resolve()
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+default_model_output = (cur_work_dir / "results").resolve()
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+default_filelist = data_pre_output / "detect.list"
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+data_pre_output.mkdir(parents=True, exist_ok=True)
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+
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+items = []
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+dict_items = {}
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+
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+
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+def load_yaml_data_in_fact(yml_path):
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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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+ return yml
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+
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+
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+def write_yaml_data_in_fact(yml, yml_path):
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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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+ return yml
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+
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+
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+def generate_tree(directory, depth=0, max_depth=None, prefix=""):
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+ if max_depth is not None and depth > max_depth:
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+ return ""
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+
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+ tree_str = ""
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+ files = []
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+ directories = []
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+ for item in os.listdir(directory):
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+ if os.path.isdir(os.path.join(directory, item)):
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+ directories.append(item)
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+ else:
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+ files.append(item)
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+
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+ entries = directories + files
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+ for i, entry in enumerate(entries):
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+ connector = "├── " if i < len(entries) - 1 else "└── "
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+ tree_str += f"{prefix}{connector}{entry}<br />"
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+ if i < len(directories):
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+ extension = "│ " if i < len(entries) - 1 else " "
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+ tree_str += generate_tree(
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+ os.path.join(directory, entry),
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+ depth + 1,
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+ max_depth,
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+ prefix=prefix + extension,
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+ )
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+ return tree_str
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+
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+
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+def new_explorer(data_path, max_depth):
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+ return gr.Markdown(
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+ elem_classes=["scrollable-component"],
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+ value=generate_tree(data_path, max_depth=max_depth),
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+ )
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+
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+
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+def add_item(folder: str, method: str, filelist: str, label_lang: str):
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+ folder = folder.strip(" ").strip('"')
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+ filelist = filelist.strip(" ").strip('"')
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+
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+ folder_path = Path(folder)
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+ filelist_path = Path(filelist)
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+
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+ if folder and folder not in items and data_pre_output not in folder_path.parents:
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+ if folder_path.is_dir():
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+ items.append(folder)
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+ dict_items[folder] = dict(
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+ type="folder", method=method, label_lang=label_lang
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+ )
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+ elif folder:
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+ err = folder
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+ return gr.Checkboxgroup(choices=items), build_html_error_message(
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+ f"添加文件夹路径无效: {err}"
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+ )
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+
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+ if (
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+ filelist
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+ and filelist not in items
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+ and data_pre_output not in filelist_path.parents
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+ ):
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+ if filelist_path.is_file():
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+ items.append(filelist)
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+ dict_items[filelist] = dict(
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+ type="file", method=method, label_lang=label_lang
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+ )
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+ elif filelist:
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+ err = filelist
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+ return gr.Checkboxgroup(choices=items), build_html_error_message(
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+ f"添加文件路径无效: {err}"
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+ )
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+
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+ formatted_data = json.dumps(dict_items, ensure_ascii=False, indent=4)
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+ logger.info(formatted_data)
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+ return gr.Checkboxgroup(choices=items), build_html_ok_message("添加文件(夹)路径成功!")
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+
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+
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+def remove_items(selected_items):
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+ global items, dict_items
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+ to_remove = [item for item in items if item in selected_items]
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+ for item in to_remove:
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+ del dict_items[item]
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+ items = [item for item in items if item in dict_items.keys()]
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+ formatted_data = json.dumps(dict_items, ensure_ascii=False, indent=4)
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+ logger.info(formatted_data)
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+ return gr.Checkboxgroup(choices=items, value=[]), build_html_ok_message(
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+ "删除文件(夹)路径成功!"
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+ )
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+
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+
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+def show_selected(options):
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+ selected_options = ", ".join(options)
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+ return f"你选中了: {selected_options}" if options else "你没有选中任何选项"
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+
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+
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+def list_copy(list_file_path, method):
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+ wav_root = data_pre_output
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+ lst = []
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+ with list_file_path.open("r", encoding="utf-8") as file:
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+ for line in tqdm(file, desc="Processing audio/transcript"):
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+ wav_path, speaker_name, language, text = line.strip().split("|")
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+ original_wav_path = Path(wav_path)
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+ target_wav_path = (
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+ wav_root / original_wav_path.parent.name / original_wav_path.name
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+ )
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+ lst.append(f"{target_wav_path}|{speaker_name}|{language}|{text}")
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+ if target_wav_path.is_file():
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+ continue
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+ target_wav_path.parent.mkdir(parents=True, exist_ok=True)
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+ if method == "复制一份":
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+ shutil.copy(original_wav_path, target_wav_path)
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+ else:
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+ shutil.move(original_wav_path, target_wav_path.parent)
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+
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+ original_lab_path = original_wav_path.with_suffix(".lab")
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+ target_lab_path = (
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+ wav_root
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+ / original_wav_path.parent.name
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+ / original_wav_path.with_suffix(".lab").name
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+ )
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+ if target_lab_path.is_file():
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+ continue
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+ if method == "复制一份":
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+ shutil.copy(original_lab_path, target_lab_path)
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+ else:
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+ shutil.move(original_lab_path, target_lab_path.parent)
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+
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+ if method == "直接移动":
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+ with list_file_path.open("w", encoding="utf-8") as file:
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+ file.writelines("\n".join(lst))
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+
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+ del lst
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+ return build_html_ok_message("使用filelist")
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+
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+
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+def check_files(data_path: str, max_depth: int, label_model: str, label_device: str):
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+ dict_to_language = {"中文": "ZH", "英文": "EN", "日文": "JP", "不打标": "WTF"}
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+
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+ global dict_items
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+ data_path = Path(data_path)
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+ for item, content in dict_items.items():
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+ item_path = Path(item)
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+ tar_path = data_path / item_path.name
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+
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+ if content["type"] == "folder" and item_path.is_dir():
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+ cur_lang = dict_to_language[content["label_lang"]]
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+ if cur_lang != "WTF":
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+ try:
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+ subprocess.run(
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+ [
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+ PYTHON,
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+ "tools/whisper_asr.py",
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+ "--model-size",
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+ label_model,
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+ "--device",
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+ label_device,
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+ "--audio-dir",
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+ item_path,
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+ "--save-dir",
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+ item_path,
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+ "--language",
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+ cur_lang,
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+ ],
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+ env=env,
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+ )
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+ except Exception:
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+ print("Transcription error occurred")
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+
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+ if content["method"] == "复制一份":
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+ os.makedirs(tar_path, exist_ok=True)
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+ shutil.copytree(
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+ src=str(item_path), dst=str(tar_path), dirs_exist_ok=True
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+ )
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+ elif not tar_path.is_dir():
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+ shutil.move(src=str(item_path), dst=str(tar_path))
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+
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+ elif content["type"] == "file" and item_path.is_file():
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+ list_copy(item_path, content["method"])
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+
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+ return build_html_ok_message("文件移动完毕"), new_explorer(data_path, max_depth=max_depth)
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+
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+
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+def train_process(
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+ data_path: str,
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+ option: str,
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+ # vq-gan config
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+ vqgan_lr,
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+ vqgan_maxsteps,
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+ vqgan_data_num_workers,
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+ vqgan_data_batch_size,
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+ vqgan_data_val_batch_size,
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+ vqgan_precision,
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+ vqgan_check_interval,
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+ # llama config
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+ llama_lr,
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+ llama_maxsteps,
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+ llama_limit_val_batches,
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+ llama_data_num_workers,
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+ llama_data_batch_size,
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+ llama_data_max_length,
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+ llama_precision,
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+ llama_check_interval,
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+):
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+ backend = "nccl" if sys.platform == "linux" else "gloo"
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+ if option == "VQGAN" or option == "all":
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+ subprocess.run(
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+ [
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+ PYTHON,
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+ "tools/vqgan/create_train_split.py",
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+ str(data_pre_output.relative_to(cur_work_dir)),
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+ ]
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+ )
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+ train_cmd = [
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+ PYTHON,
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+ "fish_speech/train.py",
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+ "--config-name",
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+ "vqgan_finetune",
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+ f"trainer.strategy.process_group_backend={backend}",
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+ f"model.optimizer.lr={vqgan_lr}",
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+ f"trainer.max_steps={vqgan_maxsteps}",
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+ f"data.num_workers={vqgan_data_num_workers}",
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+ f"data.batch_size={vqgan_data_batch_size}",
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+ f"data.val_batch_size={vqgan_data_val_batch_size}",
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+ f"trainer.precision={vqgan_precision}",
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+ f"trainer.val_check_interval={vqgan_check_interval}",
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+ f"train_dataset.filelist={str(data_pre_output / 'vq_train_filelist.txt')}",
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+ f"val_dataset.filelist={str(data_pre_output / 'vq_val_filelist.txt')}",
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+ ]
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+ logger.info(train_cmd)
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+ subprocess.run(train_cmd)
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+
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+ if option == "LLAMA" or option == "all":
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+ subprocess.run(
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+ [
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+ PYTHON,
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+ "tools/vqgan/extract_vq.py",
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+ str(data_pre_output),
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+ "--num-workers",
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+ "1",
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+ "--batch-size",
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+ "16",
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+ "--config-name",
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+ "vqgan_pretrain",
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+ "--checkpoint-path",
|
|
|
+ "checkpoints/vq-gan-group-fsq-2x1024.pth",
|
|
|
+ ]
|
|
|
+ )
|
|
|
+
|
|
|
+ subprocess.run(
|
|
|
+ [
|
|
|
+ PYTHON,
|
|
|
+ "tools/llama/build_dataset.py",
|
|
|
+ "--input",
|
|
|
+ str(data_pre_output),
|
|
|
+ "--num-workers",
|
|
|
+ "16",
|
|
|
+ ]
|
|
|
+ )
|
|
|
+
|
|
|
+ protos_list = [
|
|
|
+ str(file) for file in Path("data/quantized-dataset-ft").glob("*.protos")
|
|
|
+ ]
|
|
|
+ train_cmd = [
|
|
|
+ PYTHON,
|
|
|
+ "fish_speech/train.py",
|
|
|
+ "--config-name",
|
|
|
+ "text2semantic_sft",
|
|
|
+ f"trainer.strategy.process_group_backend={backend}",
|
|
|
+ "model@model.model=dual_ar_2_codebook_medium",
|
|
|
+ "tokenizer.pretrained_model_name_or_path=checkpoints",
|
|
|
+ f"train_dataset.proto_files={str(protos_list)}",
|
|
|
+ f"val_dataset.proto_files={str(protos_list)}",
|
|
|
+ f"model.optimizer.lr={llama_lr}",
|
|
|
+ f"trainer.max_steps={llama_maxsteps}",
|
|
|
+ f"trainer.limit_val_batches={llama_limit_val_batches}",
|
|
|
+ f"data.num_workers={llama_data_num_workers}",
|
|
|
+ f"data.batch_size={llama_data_batch_size}",
|
|
|
+ f"max_length={llama_data_max_length}",
|
|
|
+ f"trainer.precision={llama_precision}",
|
|
|
+ f"trainer.val_check_interval={llama_check_interval}",
|
|
|
+ ]
|
|
|
+ logger.info(train_cmd)
|
|
|
+ subprocess.run(train_cmd)
|
|
|
+
|
|
|
+ return build_html_ok_message("训练终止")
|
|
|
+
|
|
|
+
|
|
|
+init_vqgan_yml = load_yaml_data_in_fact(vqgan_yml_path)
|
|
|
+init_llama_yml = load_yaml_data_in_fact(llama_yml_path)
|
|
|
+
|
|
|
+with gr.Blocks(
|
|
|
+ head="<style>\n" + css + "\n</style>",
|
|
|
+ js=js,
|
|
|
+ theme=seafoam,
|
|
|
+ analytics_enabled=False,
|
|
|
+ title="Fish-Speech 鱼语",
|
|
|
+) as demo:
|
|
|
+ with gr.Row():
|
|
|
+ with gr.Column():
|
|
|
+ with gr.Tab("\U0001F4D6 数据集准备"):
|
|
|
+ with gr.Row():
|
|
|
+ textbox = gr.Textbox(
|
|
|
+ label="\U0000270F 输入音频&转写源文件夹路径",
|
|
|
+ info="音频装在一个以说话人命名的文件夹内作为区分",
|
|
|
+ interactive=True,
|
|
|
+ )
|
|
|
+ transcript_path = gr.Textbox(
|
|
|
+ label="\U0001F4DD 转写文本filelist所在路径",
|
|
|
+ info="支持 Bert-Vits2 / GPT-SoVITS 格式",
|
|
|
+ interactive=True,
|
|
|
+ )
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ with gr.Column():
|
|
|
+ output_radio = gr.Radio(
|
|
|
+ label="\U0001F4C1 选择源文件(夹)处理方式",
|
|
|
+ choices=["复制一份", "直接移动"],
|
|
|
+ value="复制一份",
|
|
|
+ interactive=True,
|
|
|
+ )
|
|
|
+ with gr.Column():
|
|
|
+ error = gr.HTML(label="错误信息")
|
|
|
+ if_label = gr.Checkbox(
|
|
|
+ label="是否开启打标WebUI", scale=0, show_label=True
|
|
|
+ )
|
|
|
+ with gr.Row():
|
|
|
+ add_button = gr.Button("\U000027A1提交到处理区", variant="primary")
|
|
|
+ remove_button = gr.Button("\U000026D4 取消所选内容")
|
|
|
+
|
|
|
+ with gr.Row():
|
|
|
+ label_device = gr.Dropdown(
|
|
|
+ label="打标设备",
|
|
|
+ info="建议使用cuda, 实在是低配置再用cpu",
|
|
|
+ choices=["cpu", "cuda"],
|
|
|
+ value="cuda",
|
|
|
+ interactive=True,
|
|
|
+ )
|
|
|
+ label_model = gr.Dropdown(
|
|
|
+ label="打标模型大小",
|
|
|
+ info="显存10G以上用large, 5G用medium, 2G用small",
|
|
|
+ choices=["large", "medium", "small"],
|
|
|
+ value="small",
|
|
|
+ interactive=True,
|
|
|
+ )
|
|
|
+ label_radio = gr.Dropdown(
|
|
|
+ label="(可选)打标语言",
|
|
|
+ info="如果没有音频对应的文本，则进行辅助打标, 支持.txt或.lab格式",
|
|
|
+ choices=["中文", "日文", "英文", "不打标"],
|
|
|
+ value="不打标",
|
|
|
+ interactive=True,
|
|
|
+ )
|
|
|
+
|
|
|
+ with gr.Tab("\U0001F6E0 训练配置项"): # hammer
|
|
|
+ with gr.Column():
|
|
|
+ with gr.Row():
|
|
|
+ model_type_radio = gr.Radio(
|
|
|
+ label="选择要训练的模型类型",
|
|
|
+ interactive=True,
|
|
|
+ choices=["VQGAN", "LLAMA", "all"],
|
|
|
+ value="all",
|
|
|
+ )
|
|
|
+ with gr.Row():
|
|
|
+ with gr.Accordion("VQGAN配置项", open=False):
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ vqgan_lr_slider = gr.Slider(
|
|
|
+ label="初始学习率",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1e-5,
|
|
|
+ maximum=1e-4,
|
|
|
+ step=1e-5,
|
|
|
+ value=init_vqgan_yml["model"]["optimizer"]["lr"],
|
|
|
+ )
|
|
|
+ vqgan_maxsteps_slider = gr.Slider(
|
|
|
+ label="训练最大步数",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1000,
|
|
|
+ maximum=100000,
|
|
|
+ step=1000,
|
|
|
+ value=init_vqgan_yml["trainer"]["max_steps"],
|
|
|
+ )
|
|
|
+
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ vqgan_data_num_workers_slider = gr.Slider(
|
|
|
+ label="num_workers",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1,
|
|
|
+ maximum=16,
|
|
|
+ step=1,
|
|
|
+ value=init_vqgan_yml["data"]["num_workers"],
|
|
|
+ )
|
|
|
+
|
|
|
+ vqgan_data_batch_size_slider = gr.Slider(
|
|
|
+ label="batch_size",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1,
|
|
|
+ maximum=32,
|
|
|
+ step=1,
|
|
|
+ value=init_vqgan_yml["data"]["batch_size"],
|
|
|
+ )
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ vqgan_data_val_batch_size_slider = gr.Slider(
|
|
|
+ label="val_batch_size",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1,
|
|
|
+ maximum=32,
|
|
|
+ step=1,
|
|
|
+ value=init_vqgan_yml["data"]["val_batch_size"],
|
|
|
+ )
|
|
|
+ vqgan_precision_dropdown = gr.Dropdown(
|
|
|
+ label="训练精度",
|
|
|
+ interactive=True,
|
|
|
+ choices=["32", "bf16-true", "bf16-mixed"],
|
|
|
+ value=str(init_vqgan_yml["trainer"]["precision"]),
|
|
|
+ )
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ vqgan_check_interval_slider = gr.Slider(
|
|
|
+ label="每n步保存一个模型",
|
|
|
+ interactive=True,
|
|
|
+ minimum=500,
|
|
|
+ maximum=10000,
|
|
|
+ step=500,
|
|
|
+ value=init_vqgan_yml["trainer"][
|
|
|
+ "val_check_interval"
|
|
|
+ ],
|
|
|
+ )
|
|
|
+
|
|
|
+ with gr.Row():
|
|
|
+ with gr.Accordion("LLAMA配置项", open=False):
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ llama_lr_slider = gr.Slider(
|
|
|
+ label="初始学习率",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1e-5,
|
|
|
+ maximum=1e-4,
|
|
|
+ step=1e-5,
|
|
|
+ value=init_llama_yml["model"]["optimizer"]["lr"],
|
|
|
+ )
|
|
|
+ llama_maxsteps_slider = gr.Slider(
|
|
|
+ label="训练最大步数",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1000,
|
|
|
+ maximum=100000,
|
|
|
+ step=1000,
|
|
|
+ value=init_llama_yml["trainer"]["max_steps"],
|
|
|
+ )
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ llama_limit_val_batches_slider = gr.Slider(
|
|
|
+ label="limit_val_batches",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1,
|
|
|
+ maximum=20,
|
|
|
+ step=1,
|
|
|
+ value=init_llama_yml["trainer"][
|
|
|
+ "limit_val_batches"
|
|
|
+ ],
|
|
|
+ )
|
|
|
+ llama_data_num_workers_slider = gr.Slider(
|
|
|
+ label="num_workers",
|
|
|
+ minimum=0,
|
|
|
+ maximum=16,
|
|
|
+ step=1,
|
|
|
+ value=init_llama_yml["data"]["num_workers"]
|
|
|
+ if sys.platform == "linux"
|
|
|
+ else 0,
|
|
|
+ )
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ llama_data_batch_size_slider = gr.Slider(
|
|
|
+ label="batch_size",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1,
|
|
|
+ maximum=32,
|
|
|
+ step=1,
|
|
|
+ value=init_llama_yml["data"]["batch_size"],
|
|
|
+ )
|
|
|
+ llama_data_max_length_slider = gr.Slider(
|
|
|
+ label="max_length",
|
|
|
+ interactive=True,
|
|
|
+ minimum=1024,
|
|
|
+ maximum=4096,
|
|
|
+ step=128,
|
|
|
+ value=init_llama_yml["max_length"],
|
|
|
+ )
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ llama_precision_dropdown = gr.Dropdown(
|
|
|
+ label="训练精度",
|
|
|
+ interactive=True,
|
|
|
+ choices=["32", "bf16-true", "16-mixed"],
|
|
|
+ value="bf16-true",
|
|
|
+ )
|
|
|
+ llama_check_interval_slider = gr.Slider(
|
|
|
+ label="每n步保存一个模型",
|
|
|
+ interactive=True,
|
|
|
+ minimum=500,
|
|
|
+ maximum=10000,
|
|
|
+ step=500,
|
|
|
+ value=init_llama_yml["trainer"][
|
|
|
+ "val_check_interval"
|
|
|
+ ],
|
|
|
+ )
|
|
|
+
|
|
|
+ with gr.Tab("\U0001F9E0 进入推理界面"):
|
|
|
+ with gr.Column():
|
|
|
+ with gr.Row():
|
|
|
+ with gr.Accordion(label="\U0001F5A5 推理服务器配置", open=False):
|
|
|
+ with gr.Row():
|
|
|
+ infer_host_textbox = gr.Textbox(
|
|
|
+ label="Webui启动服务器地址", value="127.0.0.1"
|
|
|
+ )
|
|
|
+ infer_port_textbox = gr.Textbox(
|
|
|
+ label="Webui启动服务器端口", value="7862"
|
|
|
+ )
|
|
|
+ with gr.Row():
|
|
|
+ infer_vqgan_model = gr.Textbox(
|
|
|
+ label="VQGAN模型位置",
|
|
|
+ placeholder="填写pth/ckpt文件路径",
|
|
|
+ value="checkpoints/vq-gan-group-fsq-2x1024.pth",
|
|
|
+ )
|
|
|
+ with gr.Row():
|
|
|
+ infer_llama_model = gr.Textbox(
|
|
|
+ label="LLAMA模型位置",
|
|
|
+ placeholder="填写pth/ckpt文件路径",
|
|
|
+ value="checkpoints/text2semantic-medium-v1-2k.pth",
|
|
|
+ )
|
|
|
+ with gr.Row():
|
|
|
+ infer_compile = gr.Radio(
|
|
|
+ label="是否编译模型？", choices=["Yes", "No"], value="Yes"
|
|
|
+ )
|
|
|
+
|
|
|
+ with gr.Row():
|
|
|
+ infer_checkbox = gr.Checkbox(label="是否打开推理界面")
|
|
|
+ infer_error = gr.HTML(label="推理界面错误信息")
|
|
|
+
|
|
|
+ with gr.Column():
|
|
|
+ train_error = gr.HTML(label="训练时的报错信息")
|
|
|
+ checkbox_group = gr.CheckboxGroup(
|
|
|
+ label="\U0001F4CA 数据源列表",
|
|
|
+ info="左侧输入文件夹所在路径或filelist。无论是否勾选，在此列表中都会被用以后续训练。",
|
|
|
+ elem_classes=["data_src"],
|
|
|
+ )
|
|
|
+ train_box = gr.Textbox(
|
|
|
+ label="数据预处理文件夹路径", value=str(data_pre_output), interactive=False
|
|
|
+ )
|
|
|
+ model_box = gr.Textbox(
|
|
|
+ label="\U0001F4BE 模型输出路径",
|
|
|
+ value=str(default_model_output),
|
|
|
+ interactive=False,
|
|
|
+ )
|
|
|
+
|
|
|
+ with gr.Accordion(
|
|
|
+ "查看预处理文件夹状态 (滑块为显示深度大小)",
|
|
|
+ elem_classes=["scrollable-component"],
|
|
|
+ elem_id="file_accordion",
|
|
|
+ ):
|
|
|
+ tree_slider = gr.Slider(
|
|
|
+ minimum=0,
|
|
|
+ maximum=3,
|
|
|
+ value=0,
|
|
|
+ step=1,
|
|
|
+ show_label=False,
|
|
|
+ container=False,
|
|
|
+ )
|
|
|
+ file_markdown = new_explorer(str(data_pre_output), 0)
|
|
|
+ with gr.Row(equal_height=False):
|
|
|
+ admit_btn = gr.Button(
|
|
|
+ "\U00002705 文件预处理", scale=0, min_width=160, variant="primary"
|
|
|
+ )
|
|
|
+ fresh_btn = gr.Button("\U0001F503", scale=0, min_width=80)
|
|
|
+ help_button = gr.Button("\U00002753", scale=0, min_width=80) # question
|
|
|
+ train_btn = gr.Button("训练启动!", variant="primary")
|
|
|
+
|
|
|
+ footer = load_data_in_raw("fish_speech/webui/html/footer.html")
|
|
|
+ footer = footer.format(
|
|
|
+ versions=versions_html(),
|
|
|
+ api_docs="https://speech.fish.audio/inference/#http-api",
|
|
|
+ )
|
|
|
+ gr.HTML(footer, elem_id="footer")
|
|
|
+
|
|
|
+ add_button.click(
|
|
|
+ fn=add_item,
|
|
|
+ inputs=[textbox, output_radio, transcript_path, label_radio],
|
|
|
+ outputs=[checkbox_group, error],
|
|
|
+ )
|
|
|
+ remove_button.click(
|
|
|
+ fn=remove_items, inputs=[checkbox_group], outputs=[checkbox_group, error]
|
|
|
+ )
|
|
|
+ checkbox_group.change(fn=show_selected, inputs=checkbox_group, outputs=[error])
|
|
|
+ help_button.click(
|
|
|
+ fn=None,
|
|
|
+ js='() => { window.open("https://speech.fish.audio/", "newwindow", "height=100, width=400, '
|
|
|
+ 'toolbar=no, menubar=no, scrollbars=no, resizable=no, location=no, status=no")}',
|
|
|
+ )
|
|
|
+ if_label.change(fn=change_label, inputs=[if_label], outputs=[error])
|
|
|
+ train_btn.click(
|
|
|
+ fn=train_process,
|
|
|
+ inputs=[
|
|
|
+ train_box,
|
|
|
+ model_type_radio,
|
|
|
+ # vq-gan config
|
|
|
+ vqgan_lr_slider,
|
|
|
+ vqgan_maxsteps_slider,
|
|
|
+ vqgan_data_num_workers_slider,
|
|
|
+ vqgan_data_batch_size_slider,
|
|
|
+ vqgan_data_val_batch_size_slider,
|
|
|
+ vqgan_precision_dropdown,
|
|
|
+ vqgan_check_interval_slider,
|
|
|
+ # llama config
|
|
|
+ llama_lr_slider,
|
|
|
+ llama_maxsteps_slider,
|
|
|
+ llama_limit_val_batches_slider,
|
|
|
+ llama_data_num_workers_slider,
|
|
|
+ llama_data_batch_size_slider,
|
|
|
+ llama_data_max_length_slider,
|
|
|
+ llama_precision_dropdown,
|
|
|
+ llama_check_interval_slider,
|
|
|
+ ],
|
|
|
+ outputs=[train_error],
|
|
|
+ )
|
|
|
+ admit_btn.click(
|
|
|
+ fn=check_files,
|
|
|
+ inputs=[train_box, tree_slider, label_model, label_device],
|
|
|
+ outputs=[error, file_markdown],
|
|
|
+ )
|
|
|
+ fresh_btn.click(
|
|
|
+ fn=new_explorer, inputs=[train_box, tree_slider], outputs=[file_markdown]
|
|
|
+ )
|
|
|
+
|
|
|
+ infer_checkbox.change(
|
|
|
+ fn=change_infer,
|
|
|
+ inputs=[
|
|
|
+ infer_checkbox,
|
|
|
+ infer_host_textbox,
|
|
|
+ infer_port_textbox,
|
|
|
+ infer_vqgan_model,
|
|
|
+ infer_llama_model,
|
|
|
+ infer_compile,
|
|
|
+ ],
|
|
|
+ outputs=[infer_error],
|
|
|
+ )
|
|
|
+
|
|
|
+demo.launch(inbrowser=True)
|
spicysama