From b2d41240c64ea57531373605b6fb3a60024e0367 Mon Sep 17 00:00:00 2001 From: luoye2333 <32596596+luoye2333@users.noreply.github.com> Date: Wed, 29 Mar 2023 17:03:32 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=96=87=E5=AD=97=E8=BD=AC?= =?UTF-8?q?=E8=AF=AD=E9=9F=B3=E5=8A=9F=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- webUI.py | 86 ++++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 71 insertions(+), 15 deletions(-) diff --git a/webUI.py b/webUI.py index 47e99fe..785df22 100644 --- a/webUI.py +++ b/webUI.py @@ -3,12 +3,18 @@ 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 torch + +import subprocess +import edge_tts +import asyncio +from scipy.io import wavfile +import librosa logging.getLogger('numba').setLevel(logging.WARNING) logging.getLogger('markdown_it').setLevel(logging.WARNING) @@ -18,10 +24,6 @@ logging.getLogger('multipart').setLevel(logging.WARNING) model = None spk = None -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): global model @@ -36,13 +38,56 @@ def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise 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") + soundfile.write(temp_path, audio, model.target_sample, 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) model.clear_empty() os.remove(temp_path) return "Success", (model.target_sample, _audio) except Exception as e: 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 + 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): + #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) + return a,b app = gr.Blocks() with app: @@ -64,13 +109,17 @@ with app: 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") + device = gr.Dropdown(label="推理设备，留白则为自动选择cpu和gpu",choices=[None,"cuda","cpu"],value=None) 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") 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") + 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) @@ -81,19 +130,26 @@ with app: 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) - vc_submit = gr.Button("转换", variant="primary") + 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): - try: - global model - 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 "") + global model + debug=False + if debug: + model = Svc(model_path.name, config_path.name,device=device if device!="" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "") 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: - return "","异常信息:"+str(e)+"\n请排障后重试" + return sid.update(choices = spks,value=spks[0]),"ok" + else: + try: + model = Svc(model_path.name, config_path.name,device=device if device!="" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "") + spks = list(model.spk2id.keys()) + return sid.update(choices = spks,value=spks[0]),"ok" + except Exception as e: + return "","异常信息:"+str(e)+"\n请排障后重试" 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], [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], [vc_output1, vc_output2]) model_analysis_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device],[sid,sid_output]) app.launch()