Spaces:
Sleeping
Sleeping
| import io | |
| import os | |
| # os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt") | |
| import gradio as gr | |
| import gradio.processing_utils as gr_pu | |
| import librosa | |
| import numpy as np | |
| import soundfile | |
| from inference.infer_tool import Svc | |
| import logging | |
| import re | |
| import json | |
| import subprocess | |
| import edge_tts | |
| import asyncio | |
| from scipy.io import wavfile | |
| import librosa | |
| import torch | |
| import time | |
| import traceback | |
| from itertools import chain | |
| from utils import mix_model | |
| from compress_model import removeOptimizer | |
| 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 | |
| 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 == 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,diff_model_path,diff_config_path,only_diffusion,use_spk_mix): | |
| 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" | |
| fr = ".pkl" in cluster_filepath[1] | |
| #model = Svc(model_path.name, config_path.name, device=device if device!="Auto" else None, cluster_model_path = cluster_model_path.name if cluster_model_path != None else "",nsf_hifigan_enhance=enhance) | |
| model = Svc(model_path.name, | |
| config_path.name, | |
| 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_fn(sid, input_audio, 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 | |
| print(input_audio) | |
| sampling_rate, audio = input_audio | |
| print(audio.shape,sampling_rate) | |
| 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)) | |
| temp_path = "temp.wav" | |
| soundfile.write(temp_path, audio, sampling_rate, format="wav") | |
| _audio = model.slice_inference( | |
| temp_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() | |
| os.remove(temp_path) | |
| #构建保存文件的路径，并保存到results文件夹内 | |
| timestamp = str(int(time.time())) | |
| if not os.path.exists("results"): | |
| os.makedirs("results") | |
| output_file = os.path.join("results", sid + "_" + timestamp + ".wav") | |
| soundfile.write(output_file, _audio, model.target_sample, format="wav") | |
| return "Success", output_file | |
| except Exception as e: | |
| if debug: traceback.print_exc() | |
| raise gr.Error(e) | |
| def tts_func(_text,_rate,_voice): | |
| #使用edge-tts把文字转成音频 | |
| # voice = "zh-CN-XiaoyiNeural"#女性，较高音 | |
| # voice = "zh-CN-YunxiNeural"#男性 | |
| voice = "zh-CN-YunxiNeural"#男性 | |
| if ( _voice == "女" ) : voice = "zh-CN-XiaoyiNeural" | |
| output_file = _text[0:10]+".wav" | |
| # communicate = edge_tts.Communicate(_text, voice) | |
| # await communicate.save(output_file) | |
| if _rate>=0: | |
| ratestr="+{:.0%}".format(_rate) | |
| elif _rate<0: | |
| ratestr="{:.0%}".format(_rate)#减号自带 | |
| p=subprocess.Popen("edge-tts "+ | |
| " --text "+_text+ | |
| " --write-media "+output_file+ | |
| " --voice "+voice+ | |
| " --rate="+ratestr | |
| ,shell=True, | |
| stdout=subprocess.PIPE, | |
| stdin=subprocess.PIPE) | |
| p.wait() | |
| return output_file | |
| def text_clear(text): | |
| return re.sub(r"[\n\,\(\) ]", "", text) | |
| def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,f0_predictor,enhancer_adaptive_key,cr_threshold): | |
| #使用edge-tts把文字转成音频 | |
| text2tts=text_clear(text2tts) | |
| output_file=tts_func(text2tts,tts_rate,tts_voice) | |
| #调整采样率 | |
| sr2=44100 | |
| wav, sr = librosa.load(output_file) | |
| wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2) | |
| save_path2= text2tts[0:10]+"_44k"+".wav" | |
| wavfile.write(save_path2,sr2, | |
| (wav2 * np.iinfo(np.int16).max).astype(np.int16) | |
| ) | |
| #读取音频 | |
| sample_rate, data=gr_pu.audio_from_file(save_path2) | |
| vc_input=(sample_rate, data) | |
| a,b=vc_fn(sid, vc_input, 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) | |
| os.remove(output_file) | |
| os.remove(save_path2) | |
| return a,b | |
| 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 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.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.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,默认为pm(注意：crepe为原F0使用均值滤波器)", choices=["pm","dio","harvest","crepe"], 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) | |
| 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") | |
| 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") | |
| tts_rate = gr.Number(label="tts语速", value=0) | |
| tts_voice = gr.Radio(label="性别",choices=["男","女"], value="男") | |
| 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) | |
| 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_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, [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_predictor,enhancer_adaptive_key,cr_threshold], [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],[sid,sid_output]) | |
| model_unload_button.click(modelUnload,[],[sid,sid_output]) | |
| app.launch() | |