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 traceback import subprocess import edge_tts import asyncio from scipy.io import wavfile import librosa import torch import time 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()): cuda.append("cuda:{}".format(i)) 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_mean_pooling,enhancer_adaptive_key): 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 sampling_rate, audio = input_audio # print(audio.shape,sampling_rate) audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) 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_mean_pooling,enhancer_adaptive_key) 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 try: timestamp = str(int(time.time())) output_file = os.path.join("./results", sid + "_" + timestamp + ".wav") soundfile.write(output_file, _audio, model.target_sample, format="wav") return "Success", (model.target_sample, _audio) except Exception as e: if debug:traceback.print_exc() return "自动保存失败,请手动保存,音乐输出见下", (model.target_sample, _audio) except Exception as e: if debug:traceback.print_exc() return "异常信息:"+str(e)+"\n请排障后重试",None def tts_func(_text,_rate): #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 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 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,F0_mean_pooling,enhancer_adaptive_key): #使用edge-tts把文字转成音频 output_file=tts_func(text2tts,tts_rate) #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_mean_pooling,enhancer_adaptive_key) os.remove(output_file) os.remove(save_path2) return a,b app = gr.Blocks() with app: with gr.Tabs(): with gr.TabItem("Sovits4.0"): gr.Markdown(value=""" Sovits4.0 WebUI """) gr.Markdown(value=""" 下面是模型文件选择: """) model_path = gr.File(label="模型文件") gr.Markdown(value=""" 下面是配置文件选择: """) config_path = gr.File(label="配置文件") gr.Markdown(value=""" 下面是聚类模型文件选择,没有可以不填: """) cluster_model_path = gr.File(label="聚类模型文件") device = gr.Dropdown(label="推理设备,默认为自动选择cpu和gpu",choices=["Auto",*cuda,"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) gr.Markdown(value=""" 全部上传完毕后(全部文件模块显示download),点击模型解析进行解析: """) model_analysis_button = gr.Button(value="模型解析") model_unload_button = gr.Button(value="模型卸载") sid = gr.Dropdown(label="音色(说话人)") sid_output = gr.Textbox(label="Output Message") 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) vc_input3 = gr.Audio(label="上传音频") 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="聚类模型混合比例,0-1之间,默认为0不启用聚类,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) 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,歌声不要勾选此项会究极跑调)", value=False) F0_mean_pooling = gr.Checkbox(label="是否对F0使用均值滤波器(池化),对部分哑音有改善。注意,启动该选项会导致推理速度下降,默认关闭", value=False) 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) cl_num = gr.Number(label="音频自动切片,0为不切片,单位为秒/s", value=0) 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) 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,interactive=True) 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,interactive=True) vc_submit = gr.Button("音频直接转换", variant="primary") vc_submit2 = gr.Button("文字转音频+转换", variant="primary") vc_output1 = gr.Textbox(label="Output Message") vc_output2 = gr.Audio(label="Output Audio") def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance): global model try: 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) 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) return sid.update(choices = spks,value=spks[0]),"ok,模型被加载到了设备{}之上".format(device_name) except Exception as e: if debug:traceback.print_exc() return "","异常信息:"+str(e)+"\n请排障后重试" def modelUnload(): global model if model is None: return sid.update(choices = [],value=""),"没有模型需要卸载!" else: model = None torch.cuda.empty_cache() return sid.update(choices = [],value=""),"模型卸载完毕!" 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,F0_mean_pooling,enhancer_adaptive_key], [vc_output1, vc_output2]) model_analysis_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance],[sid,sid_output]) model_unload_button.click(modelUnload,[],[sid,sid_output]) app.launch()