|
- 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}"
-
- 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="""
- <font size=2> 模型设置</font>
- """)
- 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="""
- <font size=3>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:</font>
- """)
- 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="""
- <font size=2> 推理设置</font>
- """)
- 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="""
- <font size=2> So-vits-svc 4.0 小工具/实验室特性</font>
- """)
- with gr.Tabs():
- with gr.TabItem("静态声线融合"):
- gr.Markdown(value="""
- <font size=2> 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
- </font>
- """)
- 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="""
- <font size=2> WebUI设置</font>
- """)
- 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()
-
-
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