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liujing04
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@ -45,7 +45,8 @@ class FeatureInput(object):
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f0, t = pyworld.harvest(
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x.astype(np.double),
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fs=sr,
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f0_ceil=1100,
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f0_ceil=self.f0_max,
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f0_floor=self.f0_min,
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frame_period=1000 * self.hop / sr,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
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@ -53,7 +54,8 @@ class FeatureInput(object):
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f0, t = pyworld.dio(
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x.astype(np.double),
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fs=sr,
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f0_ceil=1100,
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f0_ceil=self.f0_max,
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f0_floor=self.f0_min,
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frame_period=1000 * self.hop / sr,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
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@ -1,5 +1,5 @@
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import os,sys,traceback
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device=sys.argv[1]
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# device=sys.argv[1]
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n_part=int(sys.argv[2])
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i_part=int(sys.argv[3])
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if len(sys.argv) == 5:
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@ -14,6 +14,7 @@ import torch.nn.functional as F
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import soundfile as sf
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import numpy as np
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from fairseq import checkpoint_utils
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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f = open("%s/extract_f0_feature.log"%exp_dir, "a+")
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def printt(strr):
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@ -48,7 +49,7 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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)
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model = models[0]
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model = model.to(device)
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printt("move model to "+device)
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printt("move model to %s"%device)
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if device != "cpu": model = model.half()
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model.eval()
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@ -0,0 +1 @@
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runtime\python.exe infer-web.py --pycmd runtime\python.exe
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30
infer-web.py
30
infer-web.py
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@ -5,6 +5,16 @@ from subprocess import Popen
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from time import sleep
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import torch, os,traceback,sys,warnings,shutil,numpy as np
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import faiss
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now_dir=os.getcwd()
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sys.path.append(now_dir)
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tmp=os.path.join(now_dir,"TEMP")
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shutil.rmtree(tmp,ignore_errors=True)
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os.makedirs(tmp,exist_ok=True)
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os.makedirs(os.path.join(now_dir,"logs"),exist_ok=True)
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os.makedirs(os.path.join(now_dir,"weights"),exist_ok=True)
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os.environ["TEMP"]=tmp
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warnings.filterwarnings("ignore")
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torch.manual_seed(114514)
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from webui_locale import I18nAuto
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i18n = I18nAuto()
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#判断是否有能用来训练和加速推理的N卡
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@ -22,16 +32,6 @@ else:
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gpu_infos.append("%s\t%s"%(i,gpu_name))
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gpu_info="\n".join(gpu_infos)if if_gpu_ok==True and len(gpu_infos)>0 else "很遗憾您这没有能用的显卡来支持您训练"
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gpus="-".join([i[0]for i in gpu_infos])
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now_dir=os.getcwd()
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sys.path.append(now_dir)
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tmp=os.path.join(now_dir,"TEMP")
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shutil.rmtree(tmp,ignore_errors=True)
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os.makedirs(tmp,exist_ok=True)
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os.makedirs(os.path.join(now_dir,"logs"),exist_ok=True)
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os.makedirs(os.path.join(now_dir,"weights"),exist_ok=True)
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os.environ["TEMP"]=tmp
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warnings.filterwarnings("ignore")
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torch.manual_seed(114514)
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from scipy.io import wavfile
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from fairseq import checkpoint_utils
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@ -563,7 +563,7 @@ with gr.Blocks() as app:
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total_epoch11 = gr.Slider(minimum=0, maximum=1000, step=1, label=i18n("总训练轮数total_epoch"), value=20,interactive=True)
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batch_size12 = gr.Slider(minimum=0, maximum=32, step=1, label='每张显卡的batch_size', value=4,interactive=True)
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if_save_latest13 = gr.Radio(label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), choices=["是", "否"], value="否", interactive=True)
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if_cache_gpu17 = gr.Radio(label=i18n("是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速"), choices=["是", "否"], value="否", interactive=True)
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if_cache_gpu17 = gr.Radio(label=i18n("是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速"), choices=["是", "否"], value="是", interactive=True)
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with gr.Row():
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pretrained_G14 = gr.Textbox(label=i18n("加载预训练底模G路径"), value="pretrained/f0G40k.pth",interactive=True)
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pretrained_D15 = gr.Textbox(label=i18n("加载预训练底模D路径"), value="pretrained/f0D40k.pth",interactive=True)
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@ -624,10 +624,10 @@ with gr.Blocks() as app:
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ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
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but9.click(extract_small_model, [ckpt_path2,save_name,sr__,if_f0__,info___], info7)
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with gr.TabItem(i18n("招募音高曲线前端编辑器")):
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gr.Markdown(value=i18n("加开发群联系我xxxxx"))
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with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
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gr.Markdown(value=i18n("xxxxx"))
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# with gr.TabItem(i18n("招募音高曲线前端编辑器")):
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# gr.Markdown(value=i18n("加开发群联系我xxxxx"))
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# with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
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# gr.Markdown(value=i18n("xxxxx"))
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if iscolab:
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app.queue(concurrency_count=511, max_size=1022).launch(share=True)
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@ -15,6 +15,13 @@ import multiprocessing
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from my_utils import load_audio
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mutex = multiprocessing.Lock()
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f = open("%s/preprocess.log"%exp_dir, "a+")
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def println(strr):
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mutex.acquire()
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print(strr)
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f.write("%s\n" % strr)
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f.flush()
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mutex.release()
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class PreProcess():
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def __init__(self,sr,exp_dir):
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@ -35,18 +42,10 @@ class PreProcess():
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self.exp_dir=exp_dir
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self.gt_wavs_dir="%s/0_gt_wavs"%exp_dir
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self.wavs16k_dir="%s/1_16k_wavs"%exp_dir
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self.f = open("%s/preprocess.log"%exp_dir, "a+")
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os.makedirs(self.exp_dir,exist_ok=True)
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os.makedirs(self.gt_wavs_dir,exist_ok=True)
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os.makedirs(self.wavs16k_dir,exist_ok=True)
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def println(self, strr):
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mutex.acquire()
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print(strr)
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self.f.write("%s\n" % strr)
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self.f.flush()
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mutex.release()
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def norm_write(self,tmp_audio,idx0,idx1):
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tmp_audio = (tmp_audio / np.abs(tmp_audio).max() * (self.max * self.alpha)) + (1 - self.alpha) * tmp_audio
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wavfile.write("%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1), self.sr, (tmp_audio*32768).astype(np.int16))
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@ -70,9 +69,9 @@ class PreProcess():
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tmp_audio = audio[start:]
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break
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self.norm_write(tmp_audio, idx0, idx1)
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self.println("%s->Suc."%path)
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println("%s->Suc."%path)
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except:
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self.println("%s->%s"%(path,traceback.format_exc()))
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println("%s->%s"%(path,traceback.format_exc()))
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def pipeline_mp(self,infos):
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for path, idx0 in infos:
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@ -91,14 +90,14 @@ class PreProcess():
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ps.append(p)
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for p in ps:p.join()
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except:
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self.println("Fail. %s"%traceback.format_exc())
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println("Fail. %s"%traceback.format_exc())
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def preprocess_trainset(inp_root, sr, n_p, exp_dir):
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pp=PreProcess(sr,exp_dir)
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pp.println("start preprocess")
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pp.println(sys.argv)
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println("start preprocess")
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println(sys.argv)
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pp.pipeline_mp_inp_dir(inp_root,n_p)
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pp.println("end preprocess")
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println("end preprocess")
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if __name__=='__main__':
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preprocess_trainset(inp_root, sr, n_p, exp_dir)
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@ -35,6 +35,7 @@ class VC(object):
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x.astype(np.double),
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fs=self.sr,
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f0_ceil=f0_max,
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f0_floor=f0_min,
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frame_period=10,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
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@ -13,7 +13,7 @@ class I18nAuto:
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if language == 'auto':
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language = locale.getdefaultlocale()[0]
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self.language = language
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print("Use Languane:", language)
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print("Use Language:", language)
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self.language_map = load_language_list(language)
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def __call__(self, key):
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