import glob import json import logging import os import re import subprocess import sys import time import traceback from itertools import chain from pathlib import Path # os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt") import gradio as gr import librosa import numpy as np import soundfile import torch from compress_model import removeOptimizer from edgetts.tts_voices import SUPPORTED_LANGUAGES from inference.infer_tool import Svc from utils import mix_model logging.getLogger('numba').setLevel(logging.WARNING) logging.getLogger('markdown_it').setLevel(logging.WARNING) logging.getLogger('urllib3').setLevel(logging.WARNING) logging.getLogger('matplotlib').setLevel(logging.WARNING) logging.getLogger('multipart').setLevel(logging.WARNING) model = None spk = None debug = False local_model_root = './trained' cuda = {} if torch.cuda.is_available(): for i in range(torch.cuda.device_count()): device_name = torch.cuda.get_device_properties(i).name cuda[f"CUDA:{i} {device_name}"] = f"cuda:{i}" # 创建目录 ./trained/wdlm/ subprocess.run(["mkdir", "-p", "./trained/wdlm/"]) # 切换到目录 ./trained/wdlm/ os.chdir("./trained/wdlm/") subprocess.run(["wget", "-O", "G_38400.pth", "https://github.com/00000000002/render/releases/download/ddd/G_38400.pth"]) def upload_mix_append_file(files,sfiles): try: if(sfiles is 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 is 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,diff_model_path,diff_config_path,only_diffusion,use_spk_mix,local_model_enabled,local_model_selection): global model try: device = cuda[device] if "CUDA" in device else device cluster_filepath = os.path.split(cluster_model_path.name) if cluster_model_path is not None else "no_cluster" # get model and config path if (local_model_enabled): # local path model_path = glob.glob(os.path.join(local_model_selection, '*.pth'))[0] config_path = glob.glob(os.path.join(local_model_selection, '*.json'))[0] else: # upload from webpage model_path = model_path.name config_path = config_path.name fr = ".pkl" in cluster_filepath[1] model = Svc(model_path, config_path, device=device if device != "Auto" else None, cluster_model_path = cluster_model_path.name if cluster_model_path is not None else "", nsf_hifigan_enhance=enhance, diffusion_model_path = diff_model_path.name if diff_model_path is not None else "", diffusion_config_path = diff_config_path.name if diff_config_path is not None else "", shallow_diffusion = True if diff_model_path is not None else False, only_diffusion = only_diffusion, spk_mix_enable = use_spk_mix, feature_retrieval = fr ) spks = list(model.spk2id.keys()) device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev) msg = f"成功加载模型到设备{device_name}上\n" if cluster_model_path is None: msg += "未加载聚类模型或特征检索模型\n" elif fr: msg += f"特征检索模型{cluster_filepath[1]}加载成功\n" else: msg += f"聚类模型{cluster_filepath[1]}加载成功\n" if diff_model_path is None: msg += "未加载扩散模型\n" else: msg += f"扩散模型{diff_model_path.name}加载成功\n" msg += "当前模型的可用音色:\n" for i in spks: msg += i + " " return sid.update(choices = spks,value=spks[0]), msg except Exception as e: if debug: traceback.print_exc() raise gr.Error(e) def modelUnload(): global model if model is None: return sid.update(choices = [],value=""),"没有模型需要卸载!" else: model.unload_model() model = None torch.cuda.empty_cache() return sid.update(choices = [],value=""),"模型卸载完毕!" def vc_infer(output_format, sid, audio_path, truncated_basename, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment): global model _audio = model.slice_inference( audio_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment ) model.clear_empty() #构建保存文件的路径,并保存到results文件夹内 str(int(time.time())) if not os.path.exists("results"): os.makedirs("results") key = "auto" if auto_f0 else f"{int(vc_transform)}key" cluster = "_" if cluster_ratio == 0 else f"_{cluster_ratio}_" isdiffusion = "sovits" if model.shallow_diffusion: isdiffusion = "sovdiff" if model.only_diffusion: isdiffusion = "diff" output_file_name = 'result_'+truncated_basename+f'_{sid}_{key}{cluster}{isdiffusion}.{output_format}' output_file = os.path.join("results", output_file_name) soundfile.write(output_file, _audio, model.target_sample, format=output_format) return output_file def vc_fn(sid, input_audio, output_format, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment): global model try: if input_audio is None: return "You need to upload an audio", None if model is None: return "You need to upload an model", None if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False: if cluster_ratio != 0: return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None #print(input_audio) audio, sampling_rate = soundfile.read(input_audio) #print(audio.shape,sampling_rate) if np.issubdtype(audio.dtype, np.integer): audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) #print(audio.dtype) if len(audio.shape) > 1: audio = librosa.to_mono(audio.transpose(1, 0)) # 未知原因Gradio上传的filepath会有一个奇怪的固定后缀,这里去掉 truncated_basename = Path(input_audio).stem[:-6] processed_audio = os.path.join("raw", f"{truncated_basename}.wav") soundfile.write(processed_audio, audio, sampling_rate, format="wav") output_file = vc_infer(output_format, sid, processed_audio, truncated_basename, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment) return "Success", output_file except Exception as e: if debug: traceback.print_exc() raise gr.Error(e) def text_clear(text): return re.sub(r"[\n\,\(\) ]", "", text) def vc_fn2(_text, _lang, _gender, _rate, _volume, sid, output_format, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold, k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment): global model try: if model is None: return "You need to upload an model", None if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False: if cluster_ratio != 0: return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None _rate = f"+{int(_rate*100)}%" if _rate >= 0 else f"{int(_rate*100)}%" _volume = f"+{int(_volume*100)}%" if _volume >= 0 else f"{int(_volume*100)}%" if _lang == "Auto": _gender = "Male" if _gender == "男" else "Female" subprocess.run([sys.executable, "edgetts/tts.py", _text, _lang, _rate, _volume, _gender]) else: subprocess.run([sys.executable, "edgetts/tts.py", _text, _lang, _rate, _volume]) target_sr = 44100 y, sr = librosa.load("tts.wav") resampled_y = librosa.resample(y, orig_sr=sr, target_sr=target_sr) soundfile.write("tts.wav", resampled_y, target_sr, subtype = "PCM_16") input_audio = "tts.wav" #audio, _ = soundfile.read(input_audio) output_file_path = vc_infer(output_format, sid, input_audio, "tts", vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment) os.remove("tts.wav") return "Success", output_file_path except Exception as e: if debug: traceback.print_exc() # noqa: E701 raise gr.Error(e) def model_compression(_model): if _model == "": return "请先选择要压缩的模型" else: model_path = os.path.split(_model.name) filename, extension = os.path.splitext(model_path[1]) output_model_name = f"{filename}_compressed{extension}" output_path = os.path.join(os.getcwd(), output_model_name) removeOptimizer(_model.name, output_path) return f"模型已成功被保存在了{output_path}" def scan_local_models(): res = [] candidates = glob.glob(os.path.join(local_model_root, '**', '*.json'), recursive=True) candidates = set([os.path.dirname(c) for c in candidates]) for candidate in candidates: jsons = glob.glob(os.path.join(candidate, '*.json')) pths = glob.glob(os.path.join(candidate, '*.pth')) if (len(jsons) == 1 and len(pths) == 1): # must contain exactly one json and one pth file res.append(candidate) return res def local_model_refresh_fn(): choices = scan_local_models() return gr.Dropdown.update(choices=choices) def debug_change(): global debug debug = debug_button.value with gr.Blocks( theme=gr.themes.Base( primary_hue = gr.themes.colors.green, font=["Source Sans Pro", "Arial", "sans-serif"], font_mono=['JetBrains mono', "Consolas", 'Courier New'] ), ) as app: with gr.Tabs(): with gr.TabItem("推理"): gr.Markdown(value=""" So-vits-svc 4.0 推理 webui """) with gr.Row(variant="panel"): with gr.Column(): gr.Markdown(value=""" 模型设置 """) with gr.Tabs(): # invisible checkbox that tracks tab status local_model_enabled = gr.Checkbox(value=False, visible=False) with gr.TabItem('上传') as local_model_tab_upload: with gr.Row(): model_path = gr.File(label="选择模型文件") config_path = gr.File(label="选择配置文件") with gr.TabItem('本地') as local_model_tab_local: gr.Markdown(f'模型应当放置于{local_model_root}文件夹下') local_model_refresh_btn = gr.Button('刷新本地模型列表') local_model_selection = gr.Dropdown(label='选择模型文件夹', choices=[], interactive=True) with gr.Row(): diff_model_path = gr.File(label="选择扩散模型文件") diff_config_path = gr.File(label="选择扩散模型配置文件") cluster_model_path = gr.File(label="选择聚类模型或特征检索文件(没有可以不选)") device = gr.Dropdown(label="推理设备,默认为自动选择CPU和GPU", choices=["Auto",*cuda.keys(),"cpu"], value="Auto") enhance = gr.Checkbox(label="是否使用NSF_HIFIGAN增强,该选项对部分训练集少的模型有一定的音质增强效果,但是对训练好的模型有反面效果,默认关闭", value=False) only_diffusion = gr.Checkbox(label="是否使用全扩散推理,开启后将不使用So-VITS模型,仅使用扩散模型进行完整扩散推理,默认关闭", value=False) with gr.Column(): gr.Markdown(value=""" 左侧文件全部选择完毕后(全部文件模块显示download),点击“加载模型”进行解析: """) model_load_button = gr.Button(value="加载模型", variant="primary") model_unload_button = gr.Button(value="卸载模型", variant="primary") sid = gr.Dropdown(label="音色(说话人)") sid_output = gr.Textbox(label="Output Message") with gr.Row(variant="panel"): with gr.Column(): gr.Markdown(value=""" 推理设置 """) auto_f0 = gr.Checkbox(label="自动f0预测,配合聚类模型f0预测效果更好,会导致变调功能失效(仅限转换语音,歌声勾选此项会究极跑调)", value=False) f0_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,rmvpe,默认为pm(注意:crepe为原F0使用均值滤波器)", choices=["pm","dio","harvest","crepe","rmvpe"], value="pm") vc_transform = gr.Number(label="变调(整数,可以正负,半音数量,升高八度就是12)", value=0) cluster_ratio = gr.Number(label="聚类模型/特征检索混合比例,0-1之间,0即不启用聚类/特征检索。使用聚类/特征检索能提升音色相似度,但会导致咬字下降(如果使用建议0.5左右)", value=0) slice_db = gr.Number(label="切片阈值", value=-40) output_format = gr.Radio(label="音频输出格式", choices=["wav", "flac", "mp3"], value = "wav") noise_scale = gr.Number(label="noise_scale 建议不要动,会影响音质,玄学参数", value=0.4) k_step = gr.Slider(label="浅扩散步数,只有使用了扩散模型才有效,步数越大越接近扩散模型的结果", value=100, minimum = 1, maximum = 1000) with gr.Column(): pad_seconds = gr.Number(label="推理音频pad秒数,由于未知原因开头结尾会有异响,pad一小段静音段后就不会出现", value=0.5) cl_num = gr.Number(label="音频自动切片,0为不切片,单位为秒(s)", value=0) lg_num = gr.Number(label="两端音频切片的交叉淡入长度,如果自动切片后出现人声不连贯可调整该数值,如果连贯建议采用默认值0,注意,该设置会影响推理速度,单位为秒/s", value=0) lgr_num = gr.Number(label="自动音频切片后,需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例,范围0-1,左开右闭", value=0.75) enhancer_adaptive_key = gr.Number(label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0) cr_threshold = gr.Number(label="F0过滤阈值,只有启动crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率,但会增加哑音", value=0.05) loudness_envelope_adjustment = gr.Number(label="输入源响度包络替换输出响度包络融合比例,越靠近1越使用输出响度包络", value = 0) second_encoding = gr.Checkbox(label = "二次编码,浅扩散前会对原始音频进行二次编码,玄学选项,效果时好时差,默认关闭", value=False) use_spk_mix = gr.Checkbox(label = "动态声线融合", value = False, interactive = False) with gr.Tabs(): with gr.TabItem("音频转音频"): vc_input3 = gr.Audio(label="选择音频", type="filepath") vc_submit = gr.Button("音频转换", variant="primary") with gr.TabItem("文字转音频"): text2tts=gr.Textbox(label="在此输入要转译的文字。注意,使用该功能建议打开F0预测,不然会很怪") with gr.Row(): tts_gender = gr.Radio(label = "说话人性别", choices = ["男","女"], value = "男") tts_lang = gr.Dropdown(label = "选择语言,Auto为根据输入文字自动识别", choices=SUPPORTED_LANGUAGES, value = "Auto") tts_rate = gr.Slider(label = "TTS语音变速(倍速相对值)", minimum = -1, maximum = 3, value = 0, step = 0.1) tts_volume = gr.Slider(label = "TTS语音音量(相对值)", minimum = -1, maximum = 1.5, value = 0, step = 0.1) vc_submit2 = gr.Button("文字转换", variant="primary") with gr.Row(): with gr.Column(): vc_output1 = gr.Textbox(label="Output Message") with gr.Column(): vc_output2 = gr.Audio(label="Output Audio", interactive=False) 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 """) 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") 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.TabItem("模型压缩工具"): gr.Markdown(value=""" 该工具可以实现对模型的体积压缩,在**不影响模型推理功能**的情况下,将原本约600M的So-VITS模型压缩至约200M, 大大减少了硬盘的压力。 **注意:压缩后的模型将无法继续训练,请在确认封炉后再压缩。** """) model_to_compress = gr.File(label="模型上传") compress_model_btn = gr.Button("压缩模型", variant="primary") compress_model_output = gr.Textbox(label="输出信息", value="") compress_model_btn.click(model_compression, [model_to_compress], [compress_model_output]) 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) # refresh local model list local_model_refresh_btn.click(local_model_refresh_fn, outputs=local_model_selection) # set local enabled/disabled on tab switch local_model_tab_upload.select(lambda: False, outputs=local_model_enabled) local_model_tab_local.select(lambda: True, outputs=local_model_enabled) vc_submit.click(vc_fn, [sid, vc_input3, output_format, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2]) vc_submit2.click(vc_fn2, [text2tts, tts_lang, tts_gender, tts_rate, tts_volume, sid, output_format, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2]) debug_button.change(debug_change,[],[]) model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix,local_model_enabled,local_model_selection],[sid,sid_output]) model_unload_button.click(modelUnload,[],[sid,sid_output]) os.system("start http://127.0.0.1:7860") app.launch()