mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-08-29 10:09:32 +02:00
2025 lines
74 KiB
Python
2025 lines
74 KiB
Python
import os
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import shutil
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os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
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os.environ.setdefault("no_proxy", "localhost, 127.0.0.1, ::1")
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os.environ.setdefault("weight_root", "assets/weights")
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os.environ.setdefault("weight_uvr5_root", "assets/uvr5_weights")
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os.environ.setdefault("index_root", "logs")
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os.environ.setdefault("outside_index_root", "assets/indices")
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os.environ.setdefault("rmvpe_root", "assets/rmvpe")
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now_dir = os.getcwd()
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tmp = os.path.join(now_dir, "TEMP")
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os.makedirs(tmp, exist_ok=True)
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os.environ["TEMP"] = tmp
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for name in os.listdir(tmp):
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if name == "jieba.cache":
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continue
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path = os.path.join(tmp, name)
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delete = (
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os.remove if os.path.isfile(path) or os.path.islink(path) else shutil.rmtree
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)
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try:
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delete(path)
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except Exception as error:
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print(str(error))
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from configs.config import Config, GPU_INDEX, GPU_INFOS, GPU_MEMORY, IS_GPU
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from infer.vc.modules import VC
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from tools.uvr5.webui import uvr
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from tools.file_io import read_text
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from train.process_ckpt import (
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change_info,
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extract_small_model,
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merge,
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show_info,
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)
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from i18n.i18n import I18nAuto
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import torch, platform
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import numpy as np
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import gradio as gr
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import pathlib
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import json
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from time import sleep
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from subprocess import Popen
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from random import shuffle
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import warnings
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import traceback
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import threading
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import logging
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import signal
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import socket
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import subprocess
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import time
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("httpx").setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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def find_available_port(start_port, host="0.0.0.0"):
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"""Return the first bindable TCP port at or above ``start_port``."""
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if not 1 <= start_port <= 65535:
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raise ValueError(f"Port must be between 1 and 65535, got {start_port}.")
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for port in range(start_port, 65536):
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try:
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
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sock.bind((host, port))
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return port
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except OSError:
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continue
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raise OSError(
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f"No available TCP port from {start_port} through 65535; WebUI was not started."
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)
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def is_gradio_port_in_use_error(error, port):
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"""Recognize Gradio's explicit-port conflict without hiding other launch errors."""
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return str(error).startswith(f"Port {port} is in use.")
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def launch_webui_with_port_fallback(app, config):
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"""Launch Gradio, increasing the requested port until startup succeeds."""
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next_port = config.listen_port
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queued_app = app.queue(concurrency_count=511, max_size=1022)
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while True:
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config.listen_port = find_available_port(next_port)
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if config.listen_port != next_port:
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logger.warning(
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"Port %s is occupied; trying port %s instead.",
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next_port,
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config.listen_port,
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)
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try:
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queued_app.launch(
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server_name="0.0.0.0",
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inbrowser=not config.noautoopen,
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server_port=config.listen_port,
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quiet=True,
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)
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return config.listen_port
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except OSError as error:
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if not is_gradio_port_in_use_error(error, config.listen_port):
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raise
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if config.listen_port == 65535:
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raise OSError(
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"No available TCP port through 65535; WebUI was not started."
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) from error
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logger.warning(
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"Port %s became occupied while Gradio was starting; trying the next port.",
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config.listen_port,
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)
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next_port = config.listen_port + 1
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runtime_dirs = (
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os.path.join(now_dir, "logs"),
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os.environ["weight_root"],
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os.environ["weight_uvr5_root"],
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os.environ["index_root"],
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os.environ["outside_index_root"],
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os.environ["rmvpe_root"],
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os.path.join(now_dir, "assets", "hubert_base"),
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os.path.join(now_dir, "assets", "pretrained"),
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os.path.join(now_dir, "assets", "pretrained_v2"),
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)
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for runtime_dir in runtime_dirs:
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os.makedirs(runtime_dir, exist_ok=True)
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warnings.filterwarnings("ignore")
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torch.manual_seed(114514)
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config = Config()
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vc = VC(config)
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i18n = I18nAuto()
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logger.info(i18n)
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print(
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i18n("当前设备：%s | 推理精度：%s") % (config.device, config.dtype),
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flush=True,
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)
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logger.info("RVC_CUDA_GRAPH=%s", os.environ.get("RVC_CUDA_GRAPH", "0"))
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# GPU filtering and precision rules are shared with inference/extraction/training.
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gpu_infos = list(GPU_INFOS)
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gpu_indices = sorted(GPU_INDEX)
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if_gpu_ok = IS_GPU
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if if_gpu_ok:
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gpu_info = "\n".join(gpu_infos)
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default_batch_size = max(1, int(min(GPU_MEMORY[i] for i in gpu_indices)) // 2)
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else:
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gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
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default_batch_size = 1
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gpus = "-".join(str(i) for i in gpu_indices)
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class ToolButton(gr.Button, gr.components.FormComponent):
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"""Small button with single emoji as text, fits inside gradio forms"""
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def __init__(self, **kwargs):
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super().__init__(variant="tool", **kwargs)
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def get_block_name(self):
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return "button"
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weight_root = os.getenv("weight_root")
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weight_uvr5_root = os.getenv("weight_uvr5_root")
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outside_index_root = os.getenv("outside_index_root")
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def weight_names():
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return sorted(
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name for name in os.listdir(weight_root) if name.endswith(".pth")
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)
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def refresh_weight_choices(previous_names=None, force=False):
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current_names = tuple(weight_names())
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if force or current_names != previous_names:
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return current_names, change_choices()
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return current_names, {"__type__": "update"}
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names = weight_names()
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uvr5_names = []
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for name in os.listdir(weight_uvr5_root):
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if name.endswith((".pth", ".ckpt")) or "onnx" in name:
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uvr5_names.append(name.replace(".pth", "").replace(".ckpt", ""))
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uvr5_names.sort()
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def change_choices():
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return {"choices": weight_names(), "__type__": "update"}
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def clean():
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return {"value": "", "__type__": "update"}
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sr_dict = {
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"32k": 32000,
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"40k": 40000,
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"48k": 48000,
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}
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TRAIN_TASK_LOCK = threading.Lock()
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TRAIN_TASK = None
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def button_update(value=None, variant=None, visible=None):
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update = {"__type__": "update"}
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if value is not None:
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update["value"] = value
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if variant is not None:
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update["variant"] = variant
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if visible is not None:
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update["visible"] = visible
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return update
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def format_status(title, state, detail=""):
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lines = ["【%s】" % i18n(title), "%s：%s" % (i18n("状态"), i18n(state))]
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if detail:
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lines.extend(["", detail.strip()])
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return "\n".join(lines)
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def format_workflow_status(step, detail="", completed_steps=None, state="运行中"):
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completed_steps = completed_steps or []
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detail = str(detail).strip()
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lines = []
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if completed_steps:
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lines.append("%s：" % i18n("已完成阶段"))
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lines.extend(
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"✓ %s：%s" % (i18n(completed_step), i18n("已成功"))
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for completed_step in completed_steps
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)
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if step:
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if lines:
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lines.append("")
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lines.append("%s：%s" % (i18n("当前阶段"), i18n(step)))
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if detail:
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lines.extend(["", detail])
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return format_status(
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"一键训练",
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state,
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"\n".join(lines),
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)
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def read_log(path, max_lines=40):
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try:
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lines = [line.rstrip() for line in read_text(path, errors="ignore").splitlines()]
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lines = [line for line in lines if line.strip()]
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if len(lines) > max_lines:
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tail_count = max(0, max_lines - 1)
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omitted = len(lines) - tail_count
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tail = lines[-tail_count:] if tail_count else []
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lines = [i18n("……已省略前%s行，仅显示最新状态") % omitted]
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lines.extend(tail)
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return "\n".join(lines)
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except FileNotFoundError:
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return ""
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def artifact_names(directory, suffix):
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if not os.path.isdir(directory):
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return set()
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return {
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name.split(".")[0]
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for name in os.listdir(directory)
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if name.lower().endswith(suffix)
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}
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def validate_preprocess_outputs(exp_dir):
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exp_path = os.path.join(now_dir, "logs", exp_dir)
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gt_names = artifact_names(os.path.join(exp_path, "0_gt_wavs"), ".wav")
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wav16_names = artifact_names(os.path.join(exp_path, "1_16k_wavs"), ".wav")
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if not gt_names:
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raise RuntimeError(i18n("数据切分没有生成有效训练音频，请检查训练集和数据切分日志"))
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if not wav16_names:
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raise RuntimeError(i18n("数据切分没有生成16k音频，已停止后续特征提取和训练"))
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if not gt_names & wav16_names:
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raise RuntimeError(i18n("数据切分输出文件不匹配，已停止后续特征提取和训练"))
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def validate_feature_outputs(exp_dir, version, if_f0):
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exp_path = os.path.join(now_dir, "logs", exp_dir)
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wav16_names = artifact_names(os.path.join(exp_path, "1_16k_wavs"), ".wav")
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feature_name = "3_feature256" if version == "v1" else "3_feature768"
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feature_names = artifact_names(os.path.join(exp_path, feature_name), ".npy")
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matched = wav16_names & feature_names
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if not feature_names or not matched:
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raise RuntimeError(i18n("HuBERT特征提取没有生成有效结果，已停止训练"))
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if if_f0:
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f0_names = artifact_names(os.path.join(exp_path, "2a_f0"), ".npy")
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f0nsf_names = artifact_names(os.path.join(exp_path, "2b-f0nsf"), ".npy")
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matched &= f0_names & f0nsf_names
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if not f0_names or not f0nsf_names or not matched:
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raise RuntimeError(i18n("F0提取没有生成有效结果，已停止训练"))
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return matched
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def kill_process(process, process_name=""):
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if process is None or process.poll() is not None:
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return
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pid = process.pid
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if platform.system() == "Windows":
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subprocess.run(
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"taskkill /t /f /pid %s" % pid,
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shell=True,
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stdout=subprocess.DEVNULL,
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stderr=subprocess.DEVNULL,
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)
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else:
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try:
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os.killpg(os.getpgid(pid), signal.SIGTERM)
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except (OSError, ProcessLookupError):
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try:
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os.kill(pid, signal.SIGTERM)
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except OSError:
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pass
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for _ in range(10):
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if process.poll() is not None:
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break
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time.sleep(0.1)
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if process.poll() is None:
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try:
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os.killpg(os.getpgid(pid), signal.SIGKILL)
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except (OSError, ProcessLookupError):
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pass
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logger.info(i18n("%s进程已终止") % i18n(process_name))
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def begin_train_task(name):
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global TRAIN_TASK
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with TRAIN_TASK_LOCK:
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if TRAIN_TASK is None:
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state = {
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"name": name,
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"processes": [],
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"stop_requested": False,
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||
}
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TRAIN_TASK = state
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return "start", state
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return "busy", TRAIN_TASK
|
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def stop_train_task(name):
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with TRAIN_TASK_LOCK:
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if TRAIN_TASK is None:
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return (
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format_status(name, "未运行"),
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button_update(visible=True),
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button_update(visible=False),
|
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)
|
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if TRAIN_TASK["name"] != name:
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return (
|
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format_status(
|
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name,
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"无法停止",
|
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i18n("%s运行中，请先停止该任务") % i18n(TRAIN_TASK["name"]),
|
||
),
|
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button_update(),
|
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button_update(),
|
||
)
|
||
state = TRAIN_TASK
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state["stop_requested"] = True
|
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processes = list(state["processes"])
|
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for process in processes:
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||
kill_process(process, name)
|
||
return (
|
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format_status(name, "已停止"),
|
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button_update(visible=True),
|
||
button_update(visible=False),
|
||
)
|
||
|
||
|
||
def finish_train_task(state):
|
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global TRAIN_TASK
|
||
with TRAIN_TASK_LOCK:
|
||
if TRAIN_TASK is state:
|
||
TRAIN_TASK = None
|
||
|
||
|
||
def train_task_stopped(state):
|
||
with TRAIN_TASK_LOCK:
|
||
return state["stop_requested"]
|
||
|
||
|
||
def start_train_process(state, cmd):
|
||
kwargs = {"shell": True, "cwd": now_dir}
|
||
if "train/train.py" in cmd.replace("\\", "/"):
|
||
training_env = os.environ.copy()
|
||
training_env["RVC_CUDA_GRAPH"] = "0"
|
||
kwargs["env"] = training_env
|
||
if platform.system() == "Windows":
|
||
kwargs["creationflags"] = subprocess.CREATE_NEW_PROCESS_GROUP
|
||
else:
|
||
kwargs["start_new_session"] = True
|
||
logger.info("%s: %s", i18n("执行命令"), cmd)
|
||
process = Popen(cmd, **kwargs)
|
||
with TRAIN_TASK_LOCK:
|
||
state["processes"].append(process)
|
||
stopped = state["stop_requested"]
|
||
if stopped:
|
||
kill_process(process, state["name"])
|
||
return process
|
||
|
||
|
||
def wait_train_processes(
|
||
state,
|
||
processes,
|
||
log_path=None,
|
||
title="任务",
|
||
format_output=True,
|
||
watch_weights=False,
|
||
):
|
||
last_snapshot = None
|
||
last_emit_time = 0
|
||
last_weight_names = tuple(weight_names()) if watch_weights else ()
|
||
while any(process.poll() is None for process in processes):
|
||
if train_task_stopped(state):
|
||
for process in processes:
|
||
kill_process(process, state["name"])
|
||
break
|
||
if log_path:
|
||
snapshot = read_log(log_path)
|
||
current_time = time.monotonic()
|
||
current_weight_names = tuple(weight_names()) if watch_weights else ()
|
||
weights_changed = watch_weights and current_weight_names != last_weight_names
|
||
if (
|
||
snapshot != last_snapshot
|
||
or weights_changed
|
||
or current_time - last_emit_time >= 5
|
||
):
|
||
yield (
|
||
format_status(title, "运行中", snapshot)
|
||
if format_output
|
||
else snapshot
|
||
)
|
||
last_snapshot = snapshot
|
||
last_emit_time = current_time
|
||
last_weight_names = current_weight_names
|
||
sleep(1)
|
||
with TRAIN_TASK_LOCK:
|
||
for process in processes:
|
||
if process in state["processes"]:
|
||
state["processes"].remove(process)
|
||
if log_path:
|
||
final_state = "已停止" if train_task_stopped(state) else "正在收尾"
|
||
snapshot = read_log(log_path)
|
||
yield (
|
||
format_status(title, final_state, snapshot)
|
||
if format_output
|
||
else snapshot
|
||
)
|
||
if not train_task_stopped(state):
|
||
failed = [process.returncode for process in processes if process.returncode != 0]
|
||
if failed:
|
||
raise RuntimeError(i18n("子进程执行失败，返回码：%s") % failed)
|
||
|
||
|
||
def run_preprocess_dataset(trainset_dir, exp_dir, sr, n_p, state, format_output=True):
|
||
sr = sr_dict[sr]
|
||
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
|
||
log_path = "%s/logs/%s/preprocess.log" % (now_dir, exp_dir)
|
||
with open(log_path, "w", encoding="utf8"):
|
||
pass
|
||
cmd = '"%s" train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
|
||
config.python_cmd,
|
||
trainset_dir,
|
||
sr,
|
||
n_p,
|
||
now_dir,
|
||
exp_dir,
|
||
config.noparallel,
|
||
config.preprocess_per,
|
||
)
|
||
process = start_train_process(state, cmd)
|
||
yield from wait_train_processes(
|
||
state, [process], log_path, "数据切分", format_output
|
||
)
|
||
if not train_task_stopped(state):
|
||
validate_preprocess_outputs(exp_dir)
|
||
|
||
|
||
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
|
||
action, state = begin_train_task("数据切分")
|
||
if action == "busy":
|
||
yield (
|
||
format_status(
|
||
"数据切分",
|
||
"等待中",
|
||
i18n("%s运行中，请先停止该任务") % i18n(state["name"]),
|
||
),
|
||
button_update(),
|
||
button_update(),
|
||
)
|
||
return
|
||
final_info = None
|
||
try:
|
||
yield (
|
||
format_status("数据切分", "正在启动"),
|
||
button_update(visible=False),
|
||
button_update(visible=True),
|
||
)
|
||
for info in run_preprocess_dataset(trainset_dir, exp_dir, sr, n_p, state):
|
||
yield info, button_update(visible=False), button_update(visible=True)
|
||
if train_task_stopped(state):
|
||
final_info = format_status("数据切分", "已停止")
|
||
except Exception:
|
||
final_info = format_status("数据切分", "失败", traceback.format_exc())
|
||
finally:
|
||
finish_train_task(state)
|
||
if final_info is None:
|
||
final_info = format_status("数据切分", "已完成")
|
||
yield final_info, button_update(visible=True), button_update(visible=False)
|
||
|
||
|
||
def stop_preprocess_dataset():
|
||
return stop_train_task("数据切分")
|
||
|
||
|
||
# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
|
||
def run_extract_f0_feature(
|
||
gpus,
|
||
n_p,
|
||
f0method,
|
||
if_f0,
|
||
exp_dir,
|
||
version19,
|
||
gpus_rmvpe,
|
||
state,
|
||
format_output=True,
|
||
):
|
||
if f0method not in ("pm", "rmvpe"):
|
||
raise ValueError(i18n("仅支持pm和rmvpe音高提取算法"))
|
||
log_path = "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir)
|
||
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
|
||
validate_preprocess_outputs(exp_dir)
|
||
with open(log_path, "w", encoding="utf8"):
|
||
pass
|
||
|
||
if if_f0:
|
||
processes = []
|
||
rmvpe_devices = [gpu for gpu in gpus_rmvpe.split("-") if gpu != ""]
|
||
if f0method == "pm" or (
|
||
f0method == "rmvpe" and not rmvpe_devices and not config.dml
|
||
):
|
||
cmd = (
|
||
'"%s" train/dataset/extract_f0.py cpu "%s/logs/%s" %s %s'
|
||
% (config.python_cmd, now_dir, exp_dir, n_p, f0method)
|
||
)
|
||
processes.append(start_train_process(state, cmd))
|
||
elif rmvpe_devices:
|
||
count = len(rmvpe_devices)
|
||
for index, gpu in enumerate(rmvpe_devices):
|
||
cmd = (
|
||
'"%s" train/dataset/extract_f0.py cuda %s %s %s "%s/logs/%s" %s'
|
||
% (
|
||
config.python_cmd,
|
||
count,
|
||
index,
|
||
gpu,
|
||
now_dir,
|
||
exp_dir,
|
||
config.is_half,
|
||
)
|
||
)
|
||
processes.append(start_train_process(state, cmd))
|
||
else:
|
||
cmd = (
|
||
'"%s" train/dataset/extract_f0.py dml "%s/logs/%s"'
|
||
% (config.python_cmd, now_dir, exp_dir)
|
||
)
|
||
processes.append(start_train_process(state, cmd))
|
||
yield from wait_train_processes(
|
||
state, processes, log_path, "F0提取", format_output
|
||
)
|
||
if train_task_stopped(state):
|
||
return
|
||
|
||
with open(log_path, "w", encoding="utf8"):
|
||
pass
|
||
|
||
feature_gpus = [gpu for gpu in gpus.split("-") if gpu != ""]
|
||
processes = []
|
||
if feature_gpus:
|
||
count = len(feature_gpus)
|
||
for index, gpu in enumerate(feature_gpus):
|
||
cmd = (
|
||
'"%s" train/dataset/extract_hubert_feature.py %s %s %s %s "%s/logs/%s" %s %s'
|
||
% (
|
||
config.python_cmd,
|
||
config.device,
|
||
count,
|
||
index,
|
||
gpu,
|
||
now_dir,
|
||
exp_dir,
|
||
version19,
|
||
config.is_half,
|
||
)
|
||
)
|
||
processes.append(start_train_process(state, cmd))
|
||
else:
|
||
cmd = (
|
||
'"%s" train/dataset/extract_hubert_feature.py %s 1 0 "%s/logs/%s" %s %s'
|
||
% (
|
||
config.python_cmd,
|
||
config.device,
|
||
now_dir,
|
||
exp_dir,
|
||
version19,
|
||
config.is_half,
|
||
)
|
||
)
|
||
processes.append(start_train_process(state, cmd))
|
||
yield from wait_train_processes(
|
||
state, processes, log_path, "HuBERT特征", format_output
|
||
)
|
||
if not train_task_stopped(state):
|
||
validate_feature_outputs(exp_dir, version19, if_f0)
|
||
|
||
|
||
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvpe):
|
||
action, state = begin_train_task("特征提取")
|
||
if action == "busy":
|
||
yield (
|
||
format_status(
|
||
"特征提取",
|
||
"等待中",
|
||
i18n("%s运行中，请先停止该任务") % i18n(state["name"]),
|
||
),
|
||
button_update(),
|
||
button_update(),
|
||
)
|
||
return
|
||
final_info = None
|
||
try:
|
||
yield (
|
||
format_status("特征提取", "正在启动"),
|
||
button_update(visible=False),
|
||
button_update(visible=True),
|
||
)
|
||
for info in run_extract_f0_feature(
|
||
gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvpe, state
|
||
):
|
||
yield info, button_update(visible=False), button_update(visible=True)
|
||
if train_task_stopped(state):
|
||
final_info = format_status("特征提取", "已停止")
|
||
except Exception:
|
||
final_info = format_status("特征提取", "失败", traceback.format_exc())
|
||
finally:
|
||
finish_train_task(state)
|
||
if final_info is None:
|
||
final_info = format_status("特征提取", "已完成")
|
||
yield final_info, button_update(visible=True), button_update(visible=False)
|
||
|
||
|
||
def stop_extract_f0_feature():
|
||
return stop_train_task("特征提取")
|
||
|
||
def get_pretrained_models(path_str, f0_str, sr2):
|
||
if_pretrained_generator_exist = os.access(
|
||
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
|
||
)
|
||
if_pretrained_discriminator_exist = os.access(
|
||
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
|
||
)
|
||
if not if_pretrained_generator_exist:
|
||
logger.warning(
|
||
i18n("生成器预训练模型不存在，将不使用：assets/pretrained%s/%sG%s.pth"),
|
||
path_str,
|
||
f0_str,
|
||
sr2,
|
||
)
|
||
if not if_pretrained_discriminator_exist:
|
||
logger.warning(
|
||
i18n("判别器预训练模型不存在，将不使用：assets/pretrained%s/%sD%s.pth"),
|
||
path_str,
|
||
f0_str,
|
||
sr2,
|
||
)
|
||
return (
|
||
(
|
||
"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
|
||
if if_pretrained_generator_exist
|
||
else ""
|
||
),
|
||
(
|
||
"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
|
||
if if_pretrained_discriminator_exist
|
||
else ""
|
||
),
|
||
)
|
||
|
||
|
||
def change_sr2(sr2, if_f0_3, version19):
|
||
path_str = "" if version19 == "v1" else "_v2"
|
||
f0_str = "f0" if if_f0_3 else ""
|
||
return get_pretrained_models(path_str, f0_str, sr2)
|
||
|
||
|
||
def change_version19(sr2, if_f0_3, version19):
|
||
path_str = "" if version19 == "v1" else "_v2"
|
||
if sr2 == "32k" and version19 == "v1":
|
||
sr2 = "40k"
|
||
to_return_sr2 = (
|
||
{"choices": ["40k", "48k"], "__type__": "update", "value": sr2}
|
||
if version19 == "v1"
|
||
else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
|
||
)
|
||
f0_str = "f0" if if_f0_3 else ""
|
||
return (
|
||
*get_pretrained_models(path_str, f0_str, sr2),
|
||
to_return_sr2,
|
||
)
|
||
|
||
|
||
def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
|
||
path_str = "" if version19 == "v1" else "_v2"
|
||
return (
|
||
{"visible": if_f0_3, "__type__": "update"},
|
||
{"visible": if_f0_3, "__type__": "update"},
|
||
*get_pretrained_models(path_str, "f0" if if_f0_3 == True else "", sr2),
|
||
)
|
||
|
||
|
||
# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
|
||
def run_train_model(
|
||
exp_dir1,
|
||
sr2,
|
||
if_f0_3,
|
||
spk_id5,
|
||
save_epoch10,
|
||
total_epoch11,
|
||
batch_size12,
|
||
if_save_latest13,
|
||
pretrained_G14,
|
||
pretrained_D15,
|
||
gpus16,
|
||
if_cache_gpu17,
|
||
if_save_every_weights18,
|
||
version19,
|
||
state,
|
||
format_output=True,
|
||
):
|
||
# 生成filelist
|
||
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
|
||
os.makedirs(exp_dir, exist_ok=True)
|
||
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
|
||
feature_dir = (
|
||
"%s/3_feature256" % (exp_dir)
|
||
if version19 == "v1"
|
||
else "%s/3_feature768" % (exp_dir)
|
||
)
|
||
if if_f0_3:
|
||
f0_dir = "%s/2a_f0" % (exp_dir)
|
||
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
|
||
names = (
|
||
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
|
||
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
|
||
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
|
||
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
|
||
)
|
||
else:
|
||
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
|
||
[name.split(".")[0] for name in os.listdir(feature_dir)]
|
||
)
|
||
if not names:
|
||
raise RuntimeError(i18n("没有可用于训练的有效音频，请先完成数据切分和特征提取"))
|
||
opt = []
|
||
for name in names:
|
||
if if_f0_3:
|
||
opt.append(
|
||
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
|
||
% (
|
||
gt_wavs_dir.replace("\\", "\\\\"),
|
||
name,
|
||
feature_dir.replace("\\", "\\\\"),
|
||
name,
|
||
f0_dir.replace("\\", "\\\\"),
|
||
name,
|
||
f0nsf_dir.replace("\\", "\\\\"),
|
||
name,
|
||
spk_id5,
|
||
)
|
||
)
|
||
else:
|
||
opt.append(
|
||
"%s/%s.wav|%s/%s.npy|%s"
|
||
% (
|
||
gt_wavs_dir.replace("\\", "\\\\"),
|
||
name,
|
||
feature_dir.replace("\\", "\\\\"),
|
||
name,
|
||
spk_id5,
|
||
)
|
||
)
|
||
fea_dim = 256 if version19 == "v1" else 768
|
||
if if_f0_3:
|
||
for _ in range(2):
|
||
opt.append(
|
||
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
|
||
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
|
||
)
|
||
else:
|
||
for _ in range(2):
|
||
opt.append(
|
||
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
|
||
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
|
||
)
|
||
shuffle(opt)
|
||
with open("%s/filelist.txt" % exp_dir, "w", encoding="utf8") as f:
|
||
f.write("\n".join(opt))
|
||
logger.debug(i18n("训练文件列表写入完成"))
|
||
# 生成config#无需生成config
|
||
# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
|
||
logger.info(i18n("使用显卡：%s"), str(gpus16))
|
||
if pretrained_G14 == "":
|
||
logger.info(i18n("未使用生成器预训练模型"))
|
||
if pretrained_D15 == "":
|
||
logger.info(i18n("未使用判别器预训练模型"))
|
||
if version19 == "v1" or sr2 == "40k":
|
||
config_path = "v1/%s.json" % sr2
|
||
else:
|
||
config_path = "v2/%s.json" % sr2
|
||
config_save_path = os.path.join(exp_dir, "config.json")
|
||
if not pathlib.Path(config_save_path).exists():
|
||
with open(config_save_path, "w", encoding="utf8") as f:
|
||
json.dump(
|
||
config.json_config[config_path],
|
||
f,
|
||
ensure_ascii=False,
|
||
indent=4,
|
||
sort_keys=True,
|
||
)
|
||
f.write("\n")
|
||
if gpus16:
|
||
cmd = (
|
||
'"%s" train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
|
||
% (
|
||
config.python_cmd,
|
||
exp_dir1,
|
||
sr2,
|
||
1 if if_f0_3 else 0,
|
||
batch_size12,
|
||
gpus16,
|
||
total_epoch11,
|
||
save_epoch10,
|
||
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
|
||
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
|
||
1 if if_save_latest13 == i18n("是") else 0,
|
||
1 if if_cache_gpu17 == i18n("是") else 0,
|
||
1 if if_save_every_weights18 == i18n("是") else 0,
|
||
version19,
|
||
)
|
||
)
|
||
else:
|
||
cmd = (
|
||
'"%s" train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
|
||
% (
|
||
config.python_cmd,
|
||
exp_dir1,
|
||
sr2,
|
||
1 if if_f0_3 else 0,
|
||
batch_size12,
|
||
total_epoch11,
|
||
save_epoch10,
|
||
"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
|
||
"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
|
||
1 if if_save_latest13 == i18n("是") else 0,
|
||
1 if if_cache_gpu17 == i18n("是") else 0,
|
||
1 if if_save_every_weights18 == i18n("是") else 0,
|
||
version19,
|
||
)
|
||
)
|
||
logger.info("%s: %s", i18n("执行命令"), cmd)
|
||
process = start_train_process(state, cmd)
|
||
yield from wait_train_processes(
|
||
state,
|
||
[process],
|
||
os.path.join(exp_dir, "train.log"),
|
||
"模型训练",
|
||
format_output,
|
||
True,
|
||
)
|
||
|
||
|
||
def click_train(
|
||
exp_dir1,
|
||
sr2,
|
||
if_f0_3,
|
||
spk_id5,
|
||
save_epoch10,
|
||
total_epoch11,
|
||
batch_size12,
|
||
if_save_latest13,
|
||
pretrained_G14,
|
||
pretrained_D15,
|
||
gpus16,
|
||
if_cache_gpu17,
|
||
if_save_every_weights18,
|
||
version19,
|
||
):
|
||
known_models = tuple(weight_names())
|
||
action, state = begin_train_task("模型训练")
|
||
if action == "busy":
|
||
yield (
|
||
format_status(
|
||
"模型训练",
|
||
"等待中",
|
||
i18n("%s运行中，请先停止该任务") % i18n(state["name"]),
|
||
),
|
||
button_update(),
|
||
button_update(),
|
||
button_update(),
|
||
)
|
||
return
|
||
final_info = None
|
||
try:
|
||
yield (
|
||
format_status("模型训练", "正在启动"),
|
||
button_update(visible=False),
|
||
button_update(visible=True),
|
||
button_update(),
|
||
)
|
||
for info in run_train_model(
|
||
exp_dir1,
|
||
sr2,
|
||
if_f0_3,
|
||
spk_id5,
|
||
save_epoch10,
|
||
total_epoch11,
|
||
batch_size12,
|
||
if_save_latest13,
|
||
pretrained_G14,
|
||
pretrained_D15,
|
||
gpus16,
|
||
if_cache_gpu17,
|
||
if_save_every_weights18,
|
||
version19,
|
||
state,
|
||
):
|
||
known_models, model_update = refresh_weight_choices(known_models)
|
||
yield (
|
||
info,
|
||
button_update(visible=False),
|
||
button_update(visible=True),
|
||
model_update,
|
||
)
|
||
if train_task_stopped(state):
|
||
final_info = format_status("模型训练", "已停止")
|
||
except Exception:
|
||
final_info = format_status("模型训练", "失败", traceback.format_exc())
|
||
finally:
|
||
finish_train_task(state)
|
||
if final_info is None:
|
||
final_info = format_status("模型训练", "已完成")
|
||
model_update = change_choices()
|
||
yield (
|
||
final_info,
|
||
button_update(visible=True),
|
||
button_update(visible=False),
|
||
model_update,
|
||
)
|
||
|
||
|
||
def stop_train_model():
|
||
return stop_train_task("模型训练")
|
||
|
||
|
||
# but4.click(train_index, [exp_dir1], info3)
|
||
def run_train_index(exp_dir1, version19, state, format_output=True):
|
||
exp_dir = os.path.join(now_dir, "logs", exp_dir1)
|
||
os.makedirs(exp_dir, exist_ok=True)
|
||
log_path = os.path.join(exp_dir, "train_index.log")
|
||
with open(log_path, "w", encoding="utf8"):
|
||
pass
|
||
cmd = (
|
||
'"%s" train/train_index.py "%s" %s "%s" %s'
|
||
% (
|
||
config.python_cmd,
|
||
exp_dir1,
|
||
version19,
|
||
outside_index_root,
|
||
config.n_cpu,
|
||
)
|
||
)
|
||
process = start_train_process(state, cmd)
|
||
yield from wait_train_processes(
|
||
state, [process], log_path, "索引训练", format_output
|
||
)
|
||
|
||
|
||
def train_index(exp_dir1, version19):
|
||
action, state = begin_train_task("索引训练")
|
||
if action == "busy":
|
||
yield (
|
||
format_status(
|
||
"索引训练",
|
||
"等待中",
|
||
i18n("%s运行中，请先停止该任务") % i18n(state["name"]),
|
||
),
|
||
button_update(),
|
||
button_update(),
|
||
)
|
||
return
|
||
final_info = None
|
||
try:
|
||
yield (
|
||
format_status("索引训练", "正在启动"),
|
||
button_update(visible=False),
|
||
button_update(visible=True),
|
||
)
|
||
for info in run_train_index(exp_dir1, version19, state):
|
||
yield info, button_update(visible=False), button_update(visible=True)
|
||
if train_task_stopped(state):
|
||
final_info = format_status("索引训练", "已停止")
|
||
except Exception:
|
||
final_info = format_status("索引训练", "失败", traceback.format_exc())
|
||
finally:
|
||
finish_train_task(state)
|
||
if final_info is None:
|
||
final_info = format_status("索引训练", "已完成")
|
||
yield final_info, button_update(visible=True), button_update(visible=False)
|
||
|
||
|
||
def stop_train_index():
|
||
return stop_train_task("索引训练")
|
||
|
||
# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
|
||
def train1key(
|
||
exp_dir1,
|
||
sr2,
|
||
if_f0_3,
|
||
trainset_dir4,
|
||
spk_id5,
|
||
np7,
|
||
f0method8,
|
||
save_epoch10,
|
||
total_epoch11,
|
||
batch_size12,
|
||
if_save_latest13,
|
||
pretrained_G14,
|
||
pretrained_D15,
|
||
gpus16,
|
||
if_cache_gpu17,
|
||
if_save_every_weights18,
|
||
version19,
|
||
gpus_rmvpe,
|
||
):
|
||
known_models = tuple(weight_names())
|
||
action, state = begin_train_task("一键训练")
|
||
if action == "busy":
|
||
yield (
|
||
format_status(
|
||
"一键训练",
|
||
"等待中",
|
||
i18n("%s运行中，请先停止该任务") % i18n(state["name"]),
|
||
),
|
||
button_update(),
|
||
button_update(),
|
||
button_update(),
|
||
)
|
||
return
|
||
|
||
completed_steps = []
|
||
step = ""
|
||
final_info = None
|
||
running = True
|
||
start_button = button_update(visible=False)
|
||
stop_button = button_update(visible=True)
|
||
try:
|
||
yield (
|
||
format_status("一键训练", "正在启动"),
|
||
start_button,
|
||
stop_button,
|
||
button_update(),
|
||
)
|
||
|
||
step = "数据切分"
|
||
yield (
|
||
format_workflow_status(step, completed_steps=completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
button_update(),
|
||
)
|
||
for info in run_preprocess_dataset(
|
||
trainset_dir4, exp_dir1, sr2, np7, state, False
|
||
):
|
||
yield (
|
||
format_workflow_status(step, info, completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
button_update(),
|
||
)
|
||
running = not train_task_stopped(state)
|
||
if running:
|
||
completed_steps.append(step)
|
||
|
||
if running:
|
||
step = "F0与HuBERT特征提取"
|
||
yield (
|
||
format_workflow_status(step, completed_steps=completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
button_update(),
|
||
)
|
||
for info in run_extract_f0_feature(
|
||
gpus16,
|
||
np7,
|
||
f0method8,
|
||
if_f0_3,
|
||
exp_dir1,
|
||
version19,
|
||
gpus_rmvpe,
|
||
state,
|
||
False,
|
||
):
|
||
yield (
|
||
format_workflow_status(step, info, completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
button_update(),
|
||
)
|
||
running = not train_task_stopped(state)
|
||
if running:
|
||
completed_steps.append(step)
|
||
|
||
if running:
|
||
step = "模型训练"
|
||
yield (
|
||
format_workflow_status(step, completed_steps=completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
button_update(),
|
||
)
|
||
for info in run_train_model(
|
||
exp_dir1,
|
||
sr2,
|
||
if_f0_3,
|
||
spk_id5,
|
||
save_epoch10,
|
||
total_epoch11,
|
||
batch_size12,
|
||
if_save_latest13,
|
||
pretrained_G14,
|
||
pretrained_D15,
|
||
gpus16,
|
||
if_cache_gpu17,
|
||
if_save_every_weights18,
|
||
version19,
|
||
state,
|
||
False,
|
||
):
|
||
known_models, model_update = refresh_weight_choices(known_models)
|
||
yield (
|
||
format_workflow_status(step, info, completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
model_update,
|
||
)
|
||
if not train_task_stopped(state):
|
||
yield (
|
||
format_workflow_status(step, completed_steps=completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
change_choices(),
|
||
)
|
||
running = not train_task_stopped(state)
|
||
if running:
|
||
completed_steps.append(step)
|
||
|
||
if running:
|
||
step = "索引训练"
|
||
yield (
|
||
format_workflow_status(step, completed_steps=completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
button_update(),
|
||
)
|
||
for info in run_train_index(exp_dir1, version19, state, False):
|
||
yield (
|
||
format_workflow_status(step, info, completed_steps),
|
||
start_button,
|
||
stop_button,
|
||
button_update(),
|
||
)
|
||
running = not train_task_stopped(state)
|
||
if running:
|
||
completed_steps.append(step)
|
||
|
||
if not running:
|
||
final_info = format_workflow_status(
|
||
step, completed_steps=completed_steps, state="已停止"
|
||
)
|
||
else:
|
||
final_info = format_workflow_status(
|
||
"", completed_steps=completed_steps, state="已完成"
|
||
)
|
||
except Exception:
|
||
final_info = format_workflow_status(
|
||
step,
|
||
traceback.format_exc(),
|
||
completed_steps,
|
||
"失败",
|
||
)
|
||
finally:
|
||
finish_train_task(state)
|
||
model_update = change_choices()
|
||
yield (
|
||
final_info,
|
||
button_update(visible=True),
|
||
button_update(visible=False),
|
||
model_update,
|
||
)
|
||
|
||
|
||
def stop_train1key():
|
||
return stop_train_task("一键训练")
|
||
|
||
# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
|
||
def change_info_(ckpt_path):
|
||
if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
|
||
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
||
try:
|
||
info = eval(
|
||
read_text(
|
||
ckpt_path.replace(os.path.basename(ckpt_path), "train.log")
|
||
)
|
||
.strip("\n")
|
||
.split("\n")[0]
|
||
.split("\t")[-1]
|
||
)
|
||
sr, f0 = info["sample_rate"], info["if_f0"]
|
||
version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
|
||
return sr, str(f0), version
|
||
except Exception:
|
||
traceback.print_exc()
|
||
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
||
|
||
|
||
F0GPUVisible = IS_GPU
|
||
|
||
|
||
def change_f0_method(f0method8):
|
||
if f0method8 == "rmvpe":
|
||
visible = F0GPUVisible
|
||
else:
|
||
visible = False
|
||
return {"visible": visible, "__type__": "update"}
|
||
|
||
|
||
with gr.Blocks(title="RVC WebUI") as app:
|
||
gr.Markdown("## RVC WebUI")
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>."
|
||
)
|
||
)
|
||
with gr.Tabs():
|
||
with gr.TabItem(i18n("模型推理")):
|
||
with gr.Row():
|
||
sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
|
||
with gr.Column():
|
||
refresh_button = gr.Button(
|
||
i18n("刷新音色列表"), variant="primary"
|
||
)
|
||
clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
|
||
spk_item = gr.Slider(
|
||
minimum=0,
|
||
maximum=2333,
|
||
step=1,
|
||
label=i18n("请选择说话人id"),
|
||
value=0,
|
||
visible=False,
|
||
interactive=True,
|
||
)
|
||
clean_button.click(
|
||
fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
|
||
)
|
||
with gr.TabItem(i18n("单次推理")):
|
||
with gr.Group():
|
||
with gr.Row():
|
||
with gr.Column():
|
||
with gr.Row(equal_height=True):
|
||
with gr.Column(scale=1, min_width=120):
|
||
vc_transform0 = gr.Number(
|
||
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
|
||
value=0,
|
||
)
|
||
with gr.Column(scale=2, min_width=200):
|
||
f0method0 = gr.Radio(
|
||
label=i18n("选择音高提取算法"),
|
||
choices=["pm", "rmvpe", "fcpe"],
|
||
value="rmvpe",
|
||
interactive=True,
|
||
)
|
||
input_audio0 = gr.Audio(
|
||
label=i18n("拖拽或点击上传待处理音频"),
|
||
source="upload",
|
||
type="filepath",
|
||
interactive=True,
|
||
)
|
||
|
||
with gr.Column():
|
||
resample_sr0 = gr.Slider(
|
||
minimum=0,
|
||
maximum=48000,
|
||
label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
|
||
value=0,
|
||
step=1,
|
||
interactive=True,
|
||
)
|
||
rms_mix_rate0 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n(
|
||
"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"
|
||
),
|
||
value=0.25,
|
||
interactive=True,
|
||
)
|
||
protect0 = gr.Slider(
|
||
minimum=0,
|
||
maximum=0.5,
|
||
label=i18n(
|
||
"保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
|
||
),
|
||
value=0.33,
|
||
step=0.01,
|
||
interactive=True,
|
||
)
|
||
index_rate1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n("检索特征占比"),
|
||
value=0.75,
|
||
interactive=True,
|
||
)
|
||
file_index1 = gr.Textbox(
|
||
label=i18n("特征检索库文件路径（选择模型后自动匹配，可手动修改）"),
|
||
placeholder="C:\\Users\\Desktop\\model_example.index",
|
||
interactive=True,
|
||
)
|
||
refresh_button.click(
|
||
fn=change_choices,
|
||
inputs=[],
|
||
outputs=sid0,
|
||
api_name="infer_refresh",
|
||
)
|
||
with gr.Group():
|
||
with gr.Column():
|
||
but0 = gr.Button(i18n("转换"), variant="primary")
|
||
with gr.Row():
|
||
vc_output1 = gr.Textbox(label=i18n("输出信息"))
|
||
vc_output2 = gr.Audio(
|
||
label=i18n("输出音频(右下角三个点,点了可以下载)")
|
||
)
|
||
|
||
but0.click(
|
||
vc.vc_single,
|
||
[
|
||
spk_item,
|
||
input_audio0,
|
||
vc_transform0,
|
||
f0method0,
|
||
file_index1,
|
||
index_rate1,
|
||
resample_sr0,
|
||
rms_mix_rate0,
|
||
protect0,
|
||
],
|
||
[vc_output1, vc_output2],
|
||
api_name="infer_convert",
|
||
)
|
||
with gr.TabItem(i18n("批量推理")):
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. "
|
||
)
|
||
)
|
||
with gr.Row():
|
||
with gr.Column():
|
||
vc_transform1 = gr.Number(
|
||
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
|
||
value=0,
|
||
)
|
||
opt_input = gr.Textbox(
|
||
label=i18n("指定输出文件夹"), value="opt"
|
||
)
|
||
file_index3 = gr.Textbox(
|
||
label=i18n("特征检索库文件路径（选择模型后自动匹配，可手动修改）"),
|
||
value="",
|
||
interactive=True,
|
||
)
|
||
f0method1 = gr.Radio(
|
||
label=i18n("选择音高提取算法"),
|
||
choices=["pm", "rmvpe", "fcpe"],
|
||
value="rmvpe",
|
||
interactive=True,
|
||
)
|
||
format1 = gr.Radio(
|
||
label=i18n("导出文件格式"),
|
||
choices=["wav", "flac", "mp3", "m4a"],
|
||
value="wav",
|
||
interactive=True,
|
||
)
|
||
|
||
with gr.Column():
|
||
resample_sr1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=48000,
|
||
label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
|
||
value=0,
|
||
step=1,
|
||
interactive=True,
|
||
)
|
||
rms_mix_rate1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n(
|
||
"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"
|
||
),
|
||
value=1,
|
||
interactive=True,
|
||
)
|
||
protect1 = gr.Slider(
|
||
minimum=0,
|
||
maximum=0.5,
|
||
label=i18n(
|
||
"保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
|
||
),
|
||
value=0.33,
|
||
step=0.01,
|
||
interactive=True,
|
||
)
|
||
index_rate2 = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n("检索特征占比"),
|
||
value=1,
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
dir_input = gr.Textbox(
|
||
label=i18n(
|
||
"输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"
|
||
),
|
||
placeholder="C:\\Users\\Desktop\\input_vocal_dir",
|
||
)
|
||
inputs = gr.File(
|
||
file_count="multiple",
|
||
label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),
|
||
)
|
||
|
||
with gr.Row():
|
||
but1 = gr.Button(i18n("转换"), variant="primary")
|
||
vc_output3 = gr.Textbox(label=i18n("输出信息"))
|
||
|
||
but1.click(
|
||
vc.vc_multi,
|
||
[
|
||
spk_item,
|
||
dir_input,
|
||
opt_input,
|
||
inputs,
|
||
vc_transform1,
|
||
f0method1,
|
||
file_index3,
|
||
index_rate2,
|
||
resample_sr1,
|
||
rms_mix_rate1,
|
||
protect1,
|
||
format1,
|
||
],
|
||
[vc_output3],
|
||
api_name="infer_convert_batch",
|
||
)
|
||
sid0.change(
|
||
fn=vc.get_vc,
|
||
inputs=[sid0, protect0, protect1],
|
||
outputs=[spk_item, protect0, protect1, file_index1, file_index3],
|
||
api_name="infer_change_voice",
|
||
)
|
||
with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
|
||
with gr.Group():
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"人声伴奏分离批量处理，使用UVR5模型。<br>可选择保留人声模型，或使用DeEcho、DeReverb模型去除延迟和混响。"
|
||
)
|
||
)
|
||
with gr.Row():
|
||
with gr.Column():
|
||
dir_wav_input = gr.Textbox(
|
||
label=i18n("输入待处理音频文件夹路径"),
|
||
placeholder="C:\\Users\\Desktop\\todo-songs",
|
||
)
|
||
wav_inputs = gr.File(
|
||
file_count="multiple",
|
||
label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),
|
||
)
|
||
with gr.Column():
|
||
model_choose = gr.Dropdown(
|
||
label=i18n("模型"), choices=uvr5_names
|
||
)
|
||
agg = gr.Slider(
|
||
minimum=0,
|
||
maximum=20,
|
||
step=1,
|
||
label=i18n("人声提取激进程度"),
|
||
value=10,
|
||
interactive=True,
|
||
visible=False, # 先不开放调整
|
||
)
|
||
opt_vocal_root = gr.Textbox(
|
||
label=i18n("指定输出主人声文件夹"), value="opt"
|
||
)
|
||
opt_ins_root = gr.Textbox(
|
||
label=i18n("指定输出非主人声文件夹"), value="opt"
|
||
)
|
||
format0 = gr.Radio(
|
||
label=i18n("导出文件格式"),
|
||
choices=["wav", "flac", "mp3", "m4a"],
|
||
value="flac",
|
||
interactive=True,
|
||
)
|
||
but2 = gr.Button(i18n("转换"), variant="primary")
|
||
vc_output4 = gr.Textbox(label=i18n("输出信息"))
|
||
but2.click(
|
||
uvr,
|
||
[
|
||
model_choose,
|
||
dir_wav_input,
|
||
opt_vocal_root,
|
||
wav_inputs,
|
||
opt_ins_root,
|
||
agg,
|
||
format0,
|
||
],
|
||
[vc_output4],
|
||
api_name="uvr_convert",
|
||
)
|
||
with gr.TabItem(i18n("训练")):
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. "
|
||
)
|
||
)
|
||
with gr.Row():
|
||
exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test")
|
||
sr2 = gr.Radio(
|
||
label=i18n("目标采样率"),
|
||
choices=["40k", "48k"],
|
||
value="40k",
|
||
interactive=True,
|
||
)
|
||
if_f0_3 = gr.Radio(
|
||
label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"),
|
||
choices=[True, False],
|
||
value=True,
|
||
interactive=True,
|
||
)
|
||
version19 = gr.Radio(
|
||
label=i18n("版本"),
|
||
choices=["v1", "v2"],
|
||
value="v2",
|
||
interactive=True,
|
||
visible=True,
|
||
)
|
||
np7 = gr.Slider(
|
||
minimum=0,
|
||
maximum=config.n_cpu,
|
||
step=1,
|
||
label=i18n("提取音高和处理数据使用的CPU进程数"),
|
||
value=int(np.ceil(config.n_cpu / 1.5)),
|
||
interactive=True,
|
||
)
|
||
with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. "
|
||
)
|
||
)
|
||
with gr.Row():
|
||
trainset_dir4 = gr.Textbox(
|
||
label=i18n("输入训练文件夹路径"),
|
||
value=i18n("E:\\语音音频+标注\\米津玄师\\src"),
|
||
)
|
||
spk_id5 = gr.Slider(
|
||
minimum=0,
|
||
maximum=4,
|
||
step=1,
|
||
label=i18n("请指定说话人id"),
|
||
value=0,
|
||
interactive=True,
|
||
)
|
||
but1 = gr.Button(i18n("处理数据"), variant="primary")
|
||
stop_but1 = gr.Button(
|
||
i18n("停止处理数据"), variant="stop", visible=False
|
||
)
|
||
info1 = gr.Textbox(label=i18n("输出信息"), value="")
|
||
but1.click(
|
||
preprocess_dataset,
|
||
[trainset_dir4, exp_dir1, sr2, np7],
|
||
[info1, but1, stop_but1],
|
||
api_name="train_preprocess",
|
||
)
|
||
stop_but1.click(
|
||
stop_preprocess_dataset,
|
||
[],
|
||
[info1, but1, stop_but1],
|
||
queue=False,
|
||
)
|
||
with gr.Group():
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)"
|
||
)
|
||
)
|
||
with gr.Row():
|
||
with gr.Column():
|
||
gpus6 = gr.Textbox(
|
||
label=i18n(
|
||
"以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"
|
||
),
|
||
value=gpus,
|
||
interactive=True,
|
||
visible=F0GPUVisible,
|
||
)
|
||
gpu_info9 = gr.Textbox(
|
||
label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible
|
||
)
|
||
with gr.Column():
|
||
f0method8 = gr.Radio(
|
||
label=i18n("选择音高提取算法"),
|
||
choices=["pm", "rmvpe"],
|
||
value="rmvpe",
|
||
interactive=True,
|
||
)
|
||
gpus_rmvpe = gr.Textbox(
|
||
label=i18n(
|
||
"rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程"
|
||
),
|
||
value="%s-%s" % (gpus, gpus),
|
||
interactive=True,
|
||
visible=F0GPUVisible,
|
||
)
|
||
with gr.Row():
|
||
but2 = gr.Button(i18n("特征提取"), variant="primary")
|
||
stop_but2 = gr.Button(
|
||
i18n("停止特征提取"), variant="stop", visible=False
|
||
)
|
||
info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
f0method8.change(
|
||
fn=change_f0_method,
|
||
inputs=[f0method8],
|
||
outputs=[gpus_rmvpe],
|
||
)
|
||
but2.click(
|
||
extract_f0_feature,
|
||
[
|
||
gpus6,
|
||
np7,
|
||
f0method8,
|
||
if_f0_3,
|
||
exp_dir1,
|
||
version19,
|
||
gpus_rmvpe,
|
||
],
|
||
[info2, but2, stop_but2],
|
||
api_name="train_extract_f0_feature",
|
||
)
|
||
stop_but2.click(
|
||
stop_extract_f0_feature,
|
||
[],
|
||
[info2, but2, stop_but2],
|
||
queue=False,
|
||
)
|
||
with gr.Group():
|
||
gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
|
||
with gr.Row():
|
||
save_epoch10 = gr.Slider(
|
||
minimum=1,
|
||
maximum=50,
|
||
step=1,
|
||
label=i18n("保存频率save_every_epoch"),
|
||
value=5,
|
||
interactive=True,
|
||
)
|
||
total_epoch11 = gr.Slider(
|
||
minimum=2,
|
||
maximum=1200,
|
||
step=1,
|
||
label=i18n("总训练轮数total_epoch"),
|
||
value=20,
|
||
interactive=True,
|
||
)
|
||
batch_size12 = gr.Slider(
|
||
minimum=1,
|
||
maximum=40,
|
||
step=1,
|
||
label=i18n("每张显卡的batch_size"),
|
||
value=default_batch_size,
|
||
interactive=True,
|
||
)
|
||
if_save_latest13 = gr.Radio(
|
||
label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),
|
||
choices=[i18n("是"), i18n("否")],
|
||
value=i18n("否"),
|
||
interactive=True,
|
||
)
|
||
if_cache_gpu17 = gr.Radio(
|
||
label=i18n(
|
||
"是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速"
|
||
),
|
||
choices=[i18n("是"), i18n("否")],
|
||
value=i18n("否"),
|
||
interactive=True,
|
||
)
|
||
if_save_every_weights18 = gr.Radio(
|
||
label=i18n(
|
||
"是否在每次保存时间点将最终小模型保存至weights文件夹"
|
||
),
|
||
choices=[i18n("是"), i18n("否")],
|
||
value=i18n("否"),
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
pretrained_G14 = gr.Textbox(
|
||
label=i18n("加载预训练底模G路径"),
|
||
value="assets/pretrained_v2/f0G40k.pth",
|
||
interactive=True,
|
||
)
|
||
pretrained_D15 = gr.Textbox(
|
||
label=i18n("加载预训练底模D路径"),
|
||
value="assets/pretrained_v2/f0D40k.pth",
|
||
interactive=True,
|
||
)
|
||
sr2.change(
|
||
change_sr2,
|
||
[sr2, if_f0_3, version19],
|
||
[pretrained_G14, pretrained_D15],
|
||
)
|
||
version19.change(
|
||
change_version19,
|
||
[sr2, if_f0_3, version19],
|
||
[pretrained_G14, pretrained_D15, sr2],
|
||
)
|
||
if_f0_3.change(
|
||
change_f0,
|
||
[if_f0_3, sr2, version19],
|
||
[f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15],
|
||
)
|
||
gpus16 = gr.Textbox(
|
||
label=i18n(
|
||
"以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"
|
||
),
|
||
value=gpus,
|
||
interactive=True,
|
||
)
|
||
but3 = gr.Button(i18n("训练模型"), variant="primary")
|
||
stop_but3 = gr.Button(
|
||
i18n("停止训练模型"), variant="stop", visible=False
|
||
)
|
||
but4 = gr.Button(i18n("训练特征索引"), variant="primary")
|
||
stop_but4 = gr.Button(
|
||
i18n("停止训练索引"), variant="stop", visible=False
|
||
)
|
||
but5 = gr.Button(i18n("一键训练"), variant="primary")
|
||
stop_but5 = gr.Button(
|
||
i18n("停止一键训练"), variant="stop", visible=False
|
||
)
|
||
info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10)
|
||
but3.click(
|
||
click_train,
|
||
[
|
||
exp_dir1,
|
||
sr2,
|
||
if_f0_3,
|
||
spk_id5,
|
||
save_epoch10,
|
||
total_epoch11,
|
||
batch_size12,
|
||
if_save_latest13,
|
||
pretrained_G14,
|
||
pretrained_D15,
|
||
gpus16,
|
||
if_cache_gpu17,
|
||
if_save_every_weights18,
|
||
version19,
|
||
],
|
||
[info3, but3, stop_but3, sid0],
|
||
api_name="train_start",
|
||
)
|
||
stop_but3.click(
|
||
stop_train_model,
|
||
[],
|
||
[info3, but3, stop_but3],
|
||
queue=False,
|
||
)
|
||
but4.click(
|
||
train_index,
|
||
[exp_dir1, version19],
|
||
[info3, but4, stop_but4],
|
||
)
|
||
stop_but4.click(
|
||
stop_train_index,
|
||
[],
|
||
[info3, but4, stop_but4],
|
||
queue=False,
|
||
)
|
||
but5.click(
|
||
train1key,
|
||
[
|
||
exp_dir1,
|
||
sr2,
|
||
if_f0_3,
|
||
trainset_dir4,
|
||
spk_id5,
|
||
np7,
|
||
f0method8,
|
||
save_epoch10,
|
||
total_epoch11,
|
||
batch_size12,
|
||
if_save_latest13,
|
||
pretrained_G14,
|
||
pretrained_D15,
|
||
gpus16,
|
||
if_cache_gpu17,
|
||
if_save_every_weights18,
|
||
version19,
|
||
gpus_rmvpe,
|
||
],
|
||
[info3, but5, stop_but5, sid0],
|
||
api_name="train_start_all",
|
||
)
|
||
stop_but5.click(
|
||
stop_train1key,
|
||
[],
|
||
[info3, but5, stop_but5],
|
||
queue=False,
|
||
)
|
||
|
||
with gr.TabItem(i18n("ckpt处理")):
|
||
with gr.Group():
|
||
gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
|
||
with gr.Row():
|
||
ckpt_a = gr.Textbox(
|
||
label=i18n("A模型路径"), value="", interactive=True
|
||
)
|
||
ckpt_b = gr.Textbox(
|
||
label=i18n("B模型路径"), value="", interactive=True
|
||
)
|
||
alpha_a = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
label=i18n("A模型权重"),
|
||
value=0.5,
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
sr_ = gr.Radio(
|
||
label=i18n("目标采样率"),
|
||
choices=["40k", "48k"],
|
||
value="40k",
|
||
interactive=True,
|
||
)
|
||
if_f0_ = gr.Radio(
|
||
label=i18n("模型是否带音高指导"),
|
||
choices=[i18n("是"), i18n("否")],
|
||
value=i18n("是"),
|
||
interactive=True,
|
||
)
|
||
info__ = gr.Textbox(
|
||
label=i18n("要置入的模型信息"),
|
||
value="",
|
||
max_lines=8,
|
||
interactive=True,
|
||
)
|
||
name_to_save0 = gr.Textbox(
|
||
label=i18n("保存的模型名不带后缀"),
|
||
value="",
|
||
max_lines=1,
|
||
interactive=True,
|
||
)
|
||
version_2 = gr.Radio(
|
||
label=i18n("模型版本型号"),
|
||
choices=["v1", "v2"],
|
||
value="v1",
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
but6 = gr.Button(i18n("融合"), variant="primary")
|
||
info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
but6.click(
|
||
merge,
|
||
[
|
||
ckpt_a,
|
||
ckpt_b,
|
||
alpha_a,
|
||
sr_,
|
||
if_f0_,
|
||
info__,
|
||
name_to_save0,
|
||
version_2,
|
||
],
|
||
info4,
|
||
api_name="ckpt_merge",
|
||
) # def merge(path1,path2,alpha1,sr,f0,info):
|
||
with gr.Group():
|
||
gr.Markdown(
|
||
value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)")
|
||
)
|
||
with gr.Row():
|
||
ckpt_path0 = gr.Textbox(
|
||
label=i18n("模型路径"), value="", interactive=True
|
||
)
|
||
info_ = gr.Textbox(
|
||
label=i18n("要改的模型信息"),
|
||
value="",
|
||
max_lines=8,
|
||
interactive=True,
|
||
)
|
||
name_to_save1 = gr.Textbox(
|
||
label=i18n("保存的文件名, 默认空为和源文件同名"),
|
||
value="",
|
||
max_lines=8,
|
||
interactive=True,
|
||
)
|
||
with gr.Row():
|
||
but7 = gr.Button(i18n("修改"), variant="primary")
|
||
info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
but7.click(
|
||
change_info,
|
||
[ckpt_path0, info_, name_to_save1],
|
||
info5,
|
||
api_name="ckpt_modify",
|
||
)
|
||
with gr.Group():
|
||
gr.Markdown(
|
||
value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")
|
||
)
|
||
with gr.Row():
|
||
ckpt_path1 = gr.Textbox(
|
||
label=i18n("模型路径"), value="", interactive=True
|
||
)
|
||
but8 = gr.Button(i18n("查看"), variant="primary")
|
||
info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
|
||
with gr.Group():
|
||
gr.Markdown(
|
||
value=i18n(
|
||
"模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况"
|
||
)
|
||
)
|
||
with gr.Row():
|
||
ckpt_path2 = gr.Textbox(
|
||
label=i18n("模型路径"),
|
||
value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
|
||
interactive=True,
|
||
)
|
||
save_name = gr.Textbox(
|
||
label=i18n("保存名"), value="", interactive=True
|
||
)
|
||
sr__ = gr.Radio(
|
||
label=i18n("目标采样率"),
|
||
choices=["32k", "40k", "48k"],
|
||
value="40k",
|
||
interactive=True,
|
||
)
|
||
if_f0__ = gr.Radio(
|
||
label=i18n("模型是否带音高指导,1是0否"),
|
||
choices=["1", "0"],
|
||
value="1",
|
||
interactive=True,
|
||
)
|
||
version_1 = gr.Radio(
|
||
label=i18n("模型版本型号"),
|
||
choices=["v1", "v2"],
|
||
value="v2",
|
||
interactive=True,
|
||
)
|
||
info___ = gr.Textbox(
|
||
label=i18n("要置入的模型信息"),
|
||
value="",
|
||
max_lines=8,
|
||
interactive=True,
|
||
)
|
||
but9 = gr.Button(i18n("提取"), variant="primary")
|
||
info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
|
||
ckpt_path2.change(
|
||
change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
|
||
)
|
||
but9.click(
|
||
extract_small_model,
|
||
[ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
|
||
info7,
|
||
api_name="ckpt_extract",
|
||
)
|
||
|
||
tab_faq = i18n("常见问题解答")
|
||
with gr.TabItem(tab_faq):
|
||
try:
|
||
if tab_faq == "常见问题解答":
|
||
info = read_text("docs/cn/faq.md")
|
||
else:
|
||
info = read_text("docs/en/faq_en.md")
|
||
gr.Markdown(value=info)
|
||
except Exception:
|
||
gr.Markdown(traceback.format_exc())
|
||
|
||
if config.iscolab:
|
||
app.queue(concurrency_count=511, max_size=1022).launch(share=True)
|
||
else:
|
||
launch_webui_with_port_fallback(app, config)
|