From 237f351089278760ba772a113211f6d442a9d87e Mon Sep 17 00:00:00 2001 From: ylzz1997 Date: Sat, 15 Apr 2023 02:59:35 +0800 Subject: [PATCH] Add mix model --- utils.py | 15 +++++++++- webUI.py | 89 ++++++++++++++++++++++++++++++++++++++++++++++++++++---- 2 files changed, 97 insertions(+), 7 deletions(-) diff --git a/utils.py b/utils.py index 548be1c..613d09b 100644 --- a/utils.py +++ b/utils.py @@ -205,7 +205,7 @@ def f0_to_coarse(f0): f0_mel[f0_mel <= 1] = 1 f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1 - f0_coarse = (f0_mel + 0.5).int() if is_torch else np.rint(f0_mel).astype(np.int) + f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int) assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min()) return f0_coarse @@ -500,6 +500,19 @@ def repeat_expand_2d(content, target_len): return target +def mix_model(model_paths,mix_rate,mode): + mix_rate = torch.FloatTensor(mix_rate)/100 + model_tem = torch.load(model_paths[0]) + models = [torch.load(path)["model"] for path in model_paths] + if mode == 0: + mix_rate = F.softmax(mix_rate,dim=0) + for k in model_tem["model"].keys(): + model_tem["model"][k] = torch.zeros_like(model_tem["model"][k]) + for i,model in enumerate(models): + model_tem["model"][k] += model[k]*mix_rate[i] + torch.save(model_tem,os.path.join(os.path.curdir,"output.pth")) + return os.path.join(os.path.curdir,"output.pth") + class HParams(): def __init__(self, **kwargs): for k, v in kwargs.items(): diff --git a/webUI.py b/webUI.py index af2aa72..402bc8d 100644 --- a/webUI.py +++ b/webUI.py @@ -9,6 +9,8 @@ import numpy as np import soundfile from inference.infer_tool import Svc import logging +import re +import json import subprocess import edge_tts @@ -18,6 +20,8 @@ import librosa import torch import time import traceback +from itertools import chain +from utils import mix_model logging.getLogger('numba').setLevel(logging.WARNING) logging.getLogger('markdown_it').setLevel(logging.WARNING) @@ -35,6 +39,41 @@ if torch.cuda.is_available(): device_name = torch.cuda.get_device_properties(i).name cuda[f"CUDA:{i} {device_name}"] = f"cuda:{i}" +def upload_mix_append_file(files,sfiles): + try: + if(sfiles == None): + file_paths = [file.name for file in files] + else: + file_paths = [file.name for file in chain(files,sfiles)] + p = {file:100 for file in file_paths} + return file_paths,mix_model_output1.update(value=json.dumps(p,indent=2)) + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def mix_submit_click(js,mode): + try: + assert js.lstrip()!="" + modes = {"凸组合":0, "线性组合":1} + mode = modes[mode] + data = json.loads(js) + data = list(data.items()) + model_path,mix_rate = zip(*data) + path = mix_model(model_path,mix_rate,mode) + return f"成功，文件被保存在了{path}" + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def updata_mix_info(files): + try: + if files == None : return mix_model_output1.update(value="") + p = {file.name:100 for file in files} + return mix_model_output1.update(value=json.dumps(p,indent=2)) + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance): global model try: @@ -162,7 +201,7 @@ with gr.Blocks( ), ) as app: with gr.Tabs(): - with gr.TabItem("Inference"): + with gr.TabItem("推理"): gr.Markdown(value=""" So-vits-svc 4.0 推理 webui """) @@ -217,12 +256,49 @@ with gr.Blocks( vc_output1 = gr.Textbox(label="Output Message") with gr.Column(): vc_output2 = gr.Audio(label="Output Audio", interactive=False) - with gr.Row(variant="panel"): - with gr.Column(): - gr.Markdown(value=""" - WebUI设置 + + with gr.TabItem("小工具/实验室特性"): + gr.Markdown(value=""" + So-vits-svc 4.0 小工具/实验室特性 """) - debug_button = gr.Checkbox(label="Debug模式，如果向社区反馈BUG需要打开，打开后控制台可以显示具体错误提示", value=debug) + with gr.Tabs(): + with gr.TabItem("静态声线融合"): + gr.Markdown(value=""" + 介绍:该功能可以将多个声音模型合成为一个声音模型(多个模型参数的凸组合或线性组合)，从而制造出现实中不存在的声线 + 注意： + 1.该功能仅支持单说话人的模型 + 2.如果强行使用多说话人模型，需要保证多个模型的说话人数量相同，这样可以混合同一个SpaekerID下的声音 + 3.保证所有待混合模型的config.json中的model字段是相同的 + 4.输出的混合模型可以使用待合成模型的任意一个config.json，但聚类模型将不能使用 + 5.批量上传模型的时候最好把模型放到一个文件夹选中后一起上传 + 6.混合比例调整建议大小在0-100之间，也可以调为其他数字，但在线性组合模式下会出现未知的效果 + 7.混合完毕后，文件将会保存在项目根目录中，文件名为output.pth + 8.凸组合模式会将混合比例执行Softmax使混合比例相加为1，而线性组合模式不会 + + """) + mix_model_path = gr.Files(label="选择需要混合模型文件") + mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple", variant="primary") + mix_model_output1 = gr.Textbox( + label="混合比例调整，单位/%", + interactive = True + ) + mix_mode = gr.Radio(choices=["凸组合", "线性组合"], label="融合模式",value="凸组合",interactive = True) + mix_submit = gr.Button("声线融合启动", variant="primary") + mix_model_output2 = gr.Textbox( + label="Output Message" + ) + mix_model_path.change(updata_mix_info,[mix_model_path],[mix_model_output1]) + mix_model_upload_button.upload(upload_mix_append_file, [mix_model_upload_button,mix_model_path], [mix_model_path,mix_model_output1]) + mix_submit.click(mix_submit_click, [mix_model_output1,mix_mode], [mix_model_output2]) + + + with gr.Tabs(): + with gr.Row(variant="panel"): + with gr.Column(): + gr.Markdown(value=""" + WebUI设置 + """) + debug_button = gr.Checkbox(label="Debug模式，如果向社区反馈BUG需要打开，打开后控制台可以显示具体错误提示", value=debug) vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key], [vc_output1, vc_output2]) vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,F0_mean_pooling,enhancer_adaptive_key], [vc_output1, vc_output2]) debug_button.change(debug_change,[],[]) @@ -231,3 +307,4 @@ with gr.Blocks( app.launch() + \ No newline at end of file