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import os |
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import sys |
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from dotenv import load_dotenv |
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|
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now_dir = os.getcwd() |
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sys.path.append(now_dir) |
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load_dotenv() |
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from infer.modules.vc.modules import VC |
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from infer.modules.uvr5.modules import uvr |
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from infer.lib.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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from configs.config import Config |
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from sklearn.cluster import MiniBatchKMeans |
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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 faiss |
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import fairseq |
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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 shutil |
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import logging |
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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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tmp = os.path.join(now_dir, "TEMP") |
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shutil.rmtree(tmp, ignore_errors=True) |
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shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) |
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shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True) |
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os.makedirs(tmp, exist_ok=True) |
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os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True) |
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os.makedirs(os.path.join(now_dir, "assets/weights"), exist_ok=True) |
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os.environ["TEMP"] = tmp |
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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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if config.dml == True: |
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def forward_dml(ctx, x, scale): |
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ctx.scale = scale |
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res = x.clone().detach() |
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return res |
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fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml |
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i18n = I18nAuto() |
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logger.info(i18n) |
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ngpu = torch.cuda.device_count() |
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gpu_infos = [] |
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mem = [] |
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if_gpu_ok = False |
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if torch.cuda.is_available() or ngpu != 0: |
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for i in range(ngpu): |
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gpu_name = torch.cuda.get_device_name(i) |
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if any( |
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value in gpu_name.upper() |
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for value in [ |
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"10", |
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"16", |
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"20", |
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"30", |
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"40", |
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"A2", |
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"A3", |
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"A4", |
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"P4", |
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"A50", |
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"500", |
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"A60", |
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"70", |
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"80", |
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"90", |
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"M4", |
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"T4", |
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"TITAN", |
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"4060", |
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"L", |
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"6000", |
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] |
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): |
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if_gpu_ok = True |
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gpu_infos.append("%s\t%s" % (i, gpu_name)) |
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mem.append( |
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int( |
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torch.cuda.get_device_properties(i).total_memory |
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/ 1024 |
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/ 1024 |
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/ 1024 |
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+ 0.4 |
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) |
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) |
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if if_gpu_ok and len(gpu_infos) > 0: |
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gpu_info = "\n".join(gpu_infos) |
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default_batch_size = min(mem) // 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([i[0] for i in gpu_infos]) |
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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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|
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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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index_root = os.getenv("index_root") |
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outside_index_root = os.getenv("outside_index_root") |
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|
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names = [] |
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for name in os.listdir(weight_root): |
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if name.endswith(".pth"): |
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names.append(name) |
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index_paths = [] |
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|
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def lookup_indices(index_root): |
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global index_paths |
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for root, dirs, files in os.walk(index_root, topdown=False): |
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for name in files: |
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if name.endswith(".index") and "trained" not in name: |
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index_paths.append("%s/%s" % (root, name)) |
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|
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lookup_indices(index_root) |
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lookup_indices(outside_index_root) |
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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") or "onnx" in name: |
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uvr5_names.append(name.replace(".pth", "")) |
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|
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def change_choices(): |
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names = [] |
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for name in os.listdir(weight_root): |
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if name.endswith(".pth"): |
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names.append(name) |
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index_paths = [] |
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for root, dirs, files in os.walk(index_root, topdown=False): |
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for name in files: |
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if name.endswith(".index") and "trained" not in name: |
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index_paths.append("%s/%s" % (root, name)) |
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return {"choices": sorted(names), "__type__": "update"}, { |
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"choices": sorted(index_paths), |
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"__type__": "update", |
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} |
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|
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def clean(): |
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return {"value": "", "__type__": "update"} |
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|
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def export_onnx(ModelPath, ExportedPath): |
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from infer.modules.onnx.export import export_onnx as eo |
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eo(ModelPath, ExportedPath) |
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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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def if_done(done, p): |
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while 1: |
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if p.poll() is None: |
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sleep(0.5) |
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else: |
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break |
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done[0] = True |
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|
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def if_done_multi(done, ps): |
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while 1: |
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flag = 1 |
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for p in ps: |
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if p.poll() is None: |
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flag = 0 |
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sleep(0.5) |
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break |
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if flag == 1: |
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break |
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done[0] = True |
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|
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def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): |
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sr = sr_dict[sr] |
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os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) |
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f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w") |
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f.close() |
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cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % ( |
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config.python_cmd, |
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trainset_dir, |
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sr, |
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n_p, |
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now_dir, |
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exp_dir, |
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config.noparallel, |
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config.preprocess_per, |
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) |
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logger.info("Execute: " + cmd) |
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p = Popen(cmd, shell=True) |
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done = [False] |
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threading.Thread( |
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target=if_done, |
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args=( |
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done, |
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p, |
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), |
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).start() |
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while 1: |
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with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: |
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yield (f.read()) |
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sleep(1) |
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if done[0]: |
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break |
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with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: |
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log = f.read() |
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logger.info(log) |
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yield log |
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def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvpe): |
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gpus = gpus.split("-") |
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os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) |
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f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w") |
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f.close() |
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if if_f0: |
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if f0method != "rmvpe_gpu": |
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cmd = ( |
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'"%s" infer/modules/train/extract/extract_f0_print.py "%s/logs/%s" %s %s' |
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% ( |
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config.python_cmd, |
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now_dir, |
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exp_dir, |
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n_p, |
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f0method, |
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) |
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) |
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logger.info("Execute: " + cmd) |
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p = Popen( |
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cmd, shell=True, cwd=now_dir |
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) |
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done = [False] |
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threading.Thread( |
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target=if_done, |
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args=( |
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done, |
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p, |
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), |
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).start() |
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else: |
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if gpus_rmvpe != "-": |
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gpus_rmvpe = gpus_rmvpe.split("-") |
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leng = len(gpus_rmvpe) |
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ps = [] |
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for idx, n_g in enumerate(gpus_rmvpe): |
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cmd = ( |
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'"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s ' |
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% ( |
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config.python_cmd, |
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leng, |
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idx, |
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n_g, |
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now_dir, |
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exp_dir, |
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config.is_half, |
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) |
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) |
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logger.info("Execute: " + cmd) |
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p = Popen( |
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cmd, shell=True, cwd=now_dir |
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) |
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ps.append(p) |
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|
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done = [False] |
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threading.Thread( |
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target=if_done_multi, |
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args=( |
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done, |
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ps, |
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), |
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).start() |
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else: |
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cmd = ( |
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config.python_cmd |
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+ ' infer/modules/train/extract/extract_f0_rmvpe_dml.py "%s/logs/%s" ' |
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% ( |
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now_dir, |
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exp_dir, |
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) |
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) |
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logger.info("Execute: " + cmd) |
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p = Popen( |
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cmd, shell=True, cwd=now_dir |
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) |
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p.wait() |
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done = [True] |
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while 1: |
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with open( |
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"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" |
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) as f: |
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yield (f.read()) |
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sleep(1) |
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if done[0]: |
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break |
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: |
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log = f.read() |
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logger.info(log) |
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yield log |
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|
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""" |
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n_part=int(sys.argv[1]) |
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i_part=int(sys.argv[2]) |
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i_gpu=sys.argv[3] |
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exp_dir=sys.argv[4] |
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os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) |
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""" |
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leng = len(gpus) |
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ps = [] |
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for idx, n_g in enumerate(gpus): |
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cmd = ( |
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'"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s %s' |
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% ( |
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config.python_cmd, |
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config.device, |
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leng, |
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idx, |
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n_g, |
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now_dir, |
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exp_dir, |
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version19, |
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config.is_half, |
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) |
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) |
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logger.info("Execute: " + cmd) |
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p = Popen( |
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cmd, shell=True, cwd=now_dir |
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) |
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ps.append(p) |
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|
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done = [False] |
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threading.Thread( |
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target=if_done_multi, |
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args=( |
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done, |
|
ps, |
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), |
|
).start() |
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while 1: |
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: |
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yield (f.read()) |
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sleep(1) |
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if done[0]: |
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break |
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: |
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log = f.read() |
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logger.info(log) |
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yield log |
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|
|
|
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def get_pretrained_models(path_str, f0_str, sr2): |
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if_pretrained_generator_exist = os.access( |
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK |
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) |
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if_pretrained_discriminator_exist = os.access( |
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK |
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) |
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if not if_pretrained_generator_exist: |
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logger.warning( |
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"assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model", |
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path_str, |
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f0_str, |
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sr2, |
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) |
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if not if_pretrained_discriminator_exist: |
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logger.warning( |
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"assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model", |
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path_str, |
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f0_str, |
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sr2, |
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) |
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return ( |
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( |
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2) |
|
if if_pretrained_generator_exist |
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else "" |
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), |
|
( |
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"assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2) |
|
if if_pretrained_discriminator_exist |
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else "" |
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), |
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) |
|
|
|
|
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def change_sr2(sr2, if_f0_3, version19): |
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path_str = "" if version19 == "v1" else "_v2" |
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f0_str = "f0" if if_f0_3 else "" |
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return get_pretrained_models(path_str, f0_str, sr2) |
|
|
|
|
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def change_version19(sr2, if_f0_3, version19): |
|
path_str = "" if version19 == "v1" else "_v2" |
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if sr2 == "32k" and version19 == "v1": |
|
sr2 = "40k" |
|
to_return_sr2 = ( |
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{"choices": ["40k", "48k"], "__type__": "update", "value": sr2} |
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if version19 == "v1" |
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else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2} |
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) |
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f0_str = "f0" if if_f0_3 else "" |
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return ( |
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*get_pretrained_models(path_str, f0_str, sr2), |
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to_return_sr2, |
|
) |
|
|
|
|
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def change_f0(if_f0_3, sr2, version19): |
|
path_str = "" if version19 == "v1" else "_v2" |
|
return ( |
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{"visible": if_f0_3, "__type__": "update"}, |
|
{"visible": if_f0_3, "__type__": "update"}, |
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*get_pretrained_models(path_str, "f0" if if_f0_3 == True else "", sr2), |
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) |
|
|
|
|
|
|
|
def click_train( |
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exp_dir1, |
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sr2, |
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if_f0_3, |
|
spk_id5, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
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if_cache_gpu17, |
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if_save_every_weights18, |
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version19, |
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): |
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|
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exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) |
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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" |
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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)] |
|
) |
|
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") as f: |
|
f.write("\n".join(opt)) |
|
logger.debug("Write filelist done") |
|
|
|
|
|
logger.info("Use gpus: %s", str(gpus16)) |
|
if pretrained_G14 == "": |
|
logger.info("No pretrained Generator") |
|
if pretrained_D15 == "": |
|
logger.info("No pretrained Discriminator") |
|
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="utf-8") 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" infer/modules/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" infer/modules/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("Execute: " + cmd) |
|
p = Popen(cmd, shell=True, cwd=now_dir) |
|
p.wait() |
|
return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log" |
|
|
|
|
|
|
|
def train_index(exp_dir1, version19): |
|
|
|
exp_dir = "logs/%s" % (exp_dir1) |
|
os.makedirs(exp_dir, exist_ok=True) |
|
feature_dir = ( |
|
"%s/3_feature256" % (exp_dir) |
|
if version19 == "v1" |
|
else "%s/3_feature768" % (exp_dir) |
|
) |
|
if not os.path.exists(feature_dir): |
|
return "请先进行特征提取!" |
|
listdir_res = list(os.listdir(feature_dir)) |
|
if len(listdir_res) == 0: |
|
return "请先进行特征提取！" |
|
infos = [] |
|
npys = [] |
|
for name in sorted(listdir_res): |
|
phone = np.load("%s/%s" % (feature_dir, name)) |
|
npys.append(phone) |
|
big_npy = np.concatenate(npys, 0) |
|
big_npy_idx = np.arange(big_npy.shape[0]) |
|
np.random.shuffle(big_npy_idx) |
|
big_npy = big_npy[big_npy_idx] |
|
if big_npy.shape[0] > 2e5: |
|
infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]) |
|
yield "\n".join(infos) |
|
try: |
|
big_npy = ( |
|
MiniBatchKMeans( |
|
n_clusters=10000, |
|
verbose=True, |
|
batch_size=256 * config.n_cpu, |
|
compute_labels=False, |
|
init="random", |
|
) |
|
.fit(big_npy) |
|
.cluster_centers_ |
|
) |
|
except: |
|
info = traceback.format_exc() |
|
logger.info(info) |
|
infos.append(info) |
|
yield "\n".join(infos) |
|
|
|
np.save("%s/total_fea.npy" % exp_dir, big_npy) |
|
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) |
|
infos.append("%s,%s" % (big_npy.shape, n_ivf)) |
|
yield "\n".join(infos) |
|
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) |
|
|
|
infos.append("training") |
|
yield "\n".join(infos) |
|
index_ivf = faiss.extract_index_ivf(index) |
|
index_ivf.nprobe = 1 |
|
index.train(big_npy) |
|
faiss.write_index( |
|
index, |
|
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
) |
|
infos.append("adding") |
|
yield "\n".join(infos) |
|
batch_size_add = 8192 |
|
for i in range(0, big_npy.shape[0], batch_size_add): |
|
index.add(big_npy[i : i + batch_size_add]) |
|
faiss.write_index( |
|
index, |
|
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
) |
|
infos.append( |
|
"成功构建索引 added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (n_ivf, index_ivf.nprobe, exp_dir1, version19) |
|
) |
|
try: |
|
link = os.link if platform.system() == "Windows" else os.symlink |
|
link( |
|
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
"%s/%s_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% ( |
|
outside_index_root, |
|
exp_dir1, |
|
n_ivf, |
|
index_ivf.nprobe, |
|
exp_dir1, |
|
version19, |
|
), |
|
) |
|
infos.append("链接索引到外部-%s" % (outside_index_root)) |
|
except: |
|
infos.append("链接索引到外部-%s失败" % (outside_index_root)) |
|
|
|
|
|
|
|
yield "\n".join(infos) |
|
|
|
|
|
|
|
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, |
|
): |
|
infos = [] |
|
|
|
def get_info_str(strr): |
|
infos.append(strr) |
|
return "\n".join(infos) |
|
|
|
|
|
yield get_info_str(i18n("step1:正在处理数据")) |
|
[get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)] |
|
|
|
|
|
yield get_info_str(i18n("step2:正在提取音高&正在提取特征")) |
|
[ |
|
get_info_str(_) |
|
for _ in extract_f0_feature( |
|
gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe |
|
) |
|
] |
|
|
|
|
|
yield get_info_str(i18n("step3a:正在训练模型")) |
|
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, |
|
) |
|
yield get_info_str( |
|
i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log") |
|
) |
|
|
|
|
|
[get_info_str(_) for _ in train_index(exp_dir1, version19)] |
|
yield get_info_str(i18n("全流程结束！")) |
|
|
|
|
|
|
|
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: |
|
with open( |
|
ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r" |
|
) as f: |
|
info = eval(f.read().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: |
|
traceback.print_exc() |
|
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} |
|
|
|
|
|
F0GPUVisible = config.dml == False |
|
|
|
|
|
def change_f0_method(f0method8): |
|
if f0method8 == "rmvpe_gpu": |
|
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("刷新音色列表和索引路径"), 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(): |
|
vc_transform0 = gr.Number( |
|
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), |
|
value=0, |
|
) |
|
input_audio0 = gr.Textbox( |
|
label=i18n( |
|
"输入待处理音频文件路径(默认是正确格式示例)" |
|
), |
|
placeholder="C:\\Users\\Desktop\\audio_example.wav", |
|
) |
|
file_index1 = gr.Textbox( |
|
label=i18n( |
|
"特征检索库文件路径,为空则使用下拉的选择结果" |
|
), |
|
placeholder="C:\\Users\\Desktop\\model_example.index", |
|
interactive=True, |
|
) |
|
file_index2 = gr.Dropdown( |
|
label=i18n("自动检测index路径,下拉式选择(dropdown)"), |
|
choices=sorted(index_paths), |
|
interactive=True, |
|
) |
|
f0method0 = gr.Radio( |
|
label=i18n( |
|
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" |
|
), |
|
choices=( |
|
["pm", "harvest", "crepe", "rmvpe"] |
|
if config.dml == False |
|
else ["pm", "harvest", "rmvpe"] |
|
), |
|
value="rmvpe", |
|
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, |
|
) |
|
filter_radius0 = gr.Slider( |
|
minimum=0, |
|
maximum=7, |
|
label=i18n( |
|
">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音" |
|
), |
|
value=3, |
|
step=1, |
|
interactive=True, |
|
) |
|
index_rate1 = gr.Slider( |
|
minimum=0, |
|
maximum=1, |
|
label=i18n("检索特征占比"), |
|
value=0.75, |
|
interactive=True, |
|
) |
|
f0_file = gr.File( |
|
label=i18n( |
|
"F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调" |
|
), |
|
visible=False, |
|
) |
|
|
|
refresh_button.click( |
|
fn=change_choices, |
|
inputs=[], |
|
outputs=[sid0, file_index2], |
|
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, |
|
f0_file, |
|
f0method0, |
|
file_index1, |
|
file_index2, |
|
|
|
index_rate1, |
|
filter_radius0, |
|
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,为空则使用下拉的选择结果"), |
|
value="", |
|
interactive=True, |
|
) |
|
file_index4 = gr.Dropdown( |
|
label=i18n("自动检测index路径,下拉式选择(dropdown)"), |
|
choices=sorted(index_paths), |
|
interactive=True, |
|
) |
|
f0method1 = gr.Radio( |
|
label=i18n( |
|
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU" |
|
), |
|
choices=( |
|
["pm", "harvest", "crepe", "rmvpe"] |
|
if config.dml == False |
|
else ["pm", "harvest", "rmvpe"] |
|
), |
|
value="rmvpe", |
|
interactive=True, |
|
) |
|
format1 = gr.Radio( |
|
label=i18n("导出文件格式"), |
|
choices=["wav", "flac", "mp3", "m4a"], |
|
value="wav", |
|
interactive=True, |
|
) |
|
|
|
refresh_button.click( |
|
fn=lambda: change_choices()[1], |
|
inputs=[], |
|
outputs=file_index4, |
|
api_name="infer_refresh_batch", |
|
) |
|
|
|
|
|
|
|
|
|
|
|
|
|
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, |
|
) |
|
filter_radius1 = gr.Slider( |
|
minimum=0, |
|
maximum=7, |
|
label=i18n( |
|
">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音" |
|
), |
|
value=3, |
|
step=1, |
|
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, |
|
file_index4, |
|
|
|
index_rate2, |
|
filter_radius1, |
|
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_index2, file_index4], |
|
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>合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。 <br>模型分为三类： <br>1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点； <br>2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型； <br> 3、去混响、去延迟模型（by FoxJoy）：<br> (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；<br> (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。<br>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" |
|
) |
|
) |
|
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="人声提取激进程度", |
|
value=10, |
|
interactive=True, |
|
visible=False, |
|
) |
|
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(): |
|
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") |
|
info1 = gr.Textbox(label=i18n("输出信息"), value="") |
|
but1.click( |
|
preprocess_dataset, |
|
[trainset_dir4, exp_dir1, sr2, np7], |
|
[info1], |
|
api_name="train_preprocess", |
|
) |
|
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:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU" |
|
), |
|
choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"], |
|
value="rmvpe_gpu", |
|
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, |
|
) |
|
but2 = gr.Button(i18n("特征提取"), variant="primary") |
|
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], |
|
api_name="train_extract_f0_feature", |
|
) |
|
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=1000, |
|
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") |
|
but4 = gr.Button(i18n("训练特征索引"), variant="primary") |
|
but5 = gr.Button(i18n("一键训练"), variant="primary") |
|
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, |
|
api_name="train_start", |
|
) |
|
but4.click(train_index, [exp_dir1, version19], info3) |
|
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, |
|
api_name="train_start_all", |
|
) |
|
|
|
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", |
|
) |
|
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", |
|
) |
|
|
|
with gr.TabItem(i18n("Onnx导出")): |
|
with gr.Row(): |
|
ckpt_dir = gr.Textbox( |
|
label=i18n("RVC模型路径"), value="", interactive=True |
|
) |
|
with gr.Row(): |
|
onnx_dir = gr.Textbox( |
|
label=i18n("Onnx输出路径"), value="", interactive=True |
|
) |
|
with gr.Row(): |
|
infoOnnx = gr.Label(label="info") |
|
with gr.Row(): |
|
butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") |
|
butOnnx.click( |
|
export_onnx, [ckpt_dir, onnx_dir], infoOnnx, api_name="export_onnx" |
|
) |
|
|
|
tab_faq = i18n("常见问题解答") |
|
with gr.TabItem(tab_faq): |
|
try: |
|
if tab_faq == "常见问题解答": |
|
with open("docs/cn/faq.md", "r", encoding="utf8") as f: |
|
info = f.read() |
|
else: |
|
with open("docs/en/faq_en.md", "r", encoding="utf8") as f: |
|
info = f.read() |
|
gr.Markdown(value=info) |
|
except: |
|
gr.Markdown(traceback.format_exc()) |
|
|
|
if config.iscolab: |
|
app.queue(concurrency_count=511, max_size=1022).launch(share=True) |
|
else: |
|
app.queue(concurrency_count=511, max_size=1022).launch( |
|
server_name="0.0.0.0", |
|
inbrowser=not config.noautoopen, |
|
server_port=config.listen_port, |
|
quiet=True, |
|
) |
|
|
