Add files via upload

extract_f0_print.py

@@ -45,7 +45,8 @@ class FeatureInput(object):
             f0, t = pyworld.harvest(
                 x.astype(np.double),
                 fs=sr,
-                f0_ceil=1100,
+                f0_ceil=self.f0_max,
+                f0_floor=self.f0_min,
                 frame_period=1000 * self.hop / sr,
             )
             f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
@@ -53,7 +54,8 @@ class FeatureInput(object):
             f0, t = pyworld.dio(
                 x.astype(np.double),
                 fs=sr,
-                f0_ceil=1100,
+                f0_ceil=self.f0_max,
+                f0_floor=self.f0_min,
                 frame_period=1000 * self.hop / sr,
             )
             f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)

extract_feature_print.py

@@ -1,5 +1,5 @@
 import os,sys,traceback
-device=sys.argv[1]
+# device=sys.argv[1]
 n_part=int(sys.argv[2])
 i_part=int(sys.argv[3])
 if len(sys.argv) == 5:
@@ -14,6 +14,7 @@ import torch.nn.functional as F
 import soundfile as sf
 import numpy as np
 from fairseq import checkpoint_utils
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 f = open("%s/extract_f0_feature.log"%exp_dir, "a+")
 def printt(strr):
@@ -48,7 +49,7 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
 )
 model = models[0]
 model = model.to(device)
-printt("move model to "+device)
+printt("move model to %s"%device)
 if device != "cpu": model = model.half()
 model.eval()
 

go-web.bat

@@ -0,0 +1 @@
+runtime\python.exe infer-web.py --pycmd runtime\python.exe

infer-web.py

@@ -5,6 +5,16 @@ from subprocess import Popen
 from time import sleep
 import torch, os,traceback,sys,warnings,shutil,numpy as np
 import faiss
+now_dir=os.getcwd()
+sys.path.append(now_dir)
+tmp=os.path.join(now_dir,"TEMP")
+shutil.rmtree(tmp,ignore_errors=True)
+os.makedirs(tmp,exist_ok=True)
+os.makedirs(os.path.join(now_dir,"logs"),exist_ok=True)
+os.makedirs(os.path.join(now_dir,"weights"),exist_ok=True)
+os.environ["TEMP"]=tmp
+warnings.filterwarnings("ignore")
+torch.manual_seed(114514)
 from webui_locale import I18nAuto
 i18n = I18nAuto()
 #判断是否有能用来训练和加速推理的N卡
@@ -22,16 +32,6 @@ else:
             gpu_infos.append("%s\t%s"%(i,gpu_name))
 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"
 gpus="-".join([i[0]for i in gpu_infos])
-now_dir=os.getcwd()
-sys.path.append(now_dir)
-tmp=os.path.join(now_dir,"TEMP")
-shutil.rmtree(tmp,ignore_errors=True)
-os.makedirs(tmp,exist_ok=True)
-os.makedirs(os.path.join(now_dir,"logs"),exist_ok=True)
-os.makedirs(os.path.join(now_dir,"weights"),exist_ok=True)
-os.environ["TEMP"]=tmp
-warnings.filterwarnings("ignore")
-torch.manual_seed(114514)
 from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
 from scipy.io import wavfile
 from fairseq import checkpoint_utils
@@ -563,7 +563,7 @@ with gr.Blocks() as app:
                     total_epoch11 = gr.Slider(minimum=0, maximum=1000, step=1, label=i18n("总训练轮数total_epoch"), value=20,interactive=True)
                     batch_size12 = gr.Slider(minimum=0, maximum=32, step=1, label='每张显卡的batch_size', value=4,interactive=True)
                     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)
-                    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)
+                    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)
                 with gr.Row():
                     pretrained_G14 = gr.Textbox(label=i18n("加载预训练底模G路径"), value="pretrained/f0G40k.pth",interactive=True)
                     pretrained_D15 = gr.Textbox(label=i18n("加载预训练底模D路径"), value="pretrained/f0D40k.pth",interactive=True)
@@ -624,10 +624,10 @@ with gr.Blocks() as app:
                     ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
                 but9.click(extract_small_model, [ckpt_path2,save_name,sr__,if_f0__,info___], info7)
 
-        with gr.TabItem(i18n("招募音高曲线前端编辑器")):
+        # with gr.TabItem(i18n("招募音高曲线前端编辑器")):
-            gr.Markdown(value=i18n("加开发群联系我xxxxx"))
+        #     gr.Markdown(value=i18n("加开发群联系我xxxxx"))
-        with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
+        # with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
-            gr.Markdown(value=i18n("xxxxx"))
+        #     gr.Markdown(value=i18n("xxxxx"))
 
     if iscolab:
         app.queue(concurrency_count=511, max_size=1022).launch(share=True)

trainset_preprocess_pipeline_print.py

@@ -15,6 +15,13 @@ import multiprocessing
 from my_utils import load_audio
 
 mutex = multiprocessing.Lock()
+f = open("%s/preprocess.log"%exp_dir, "a+")
+def println(strr):
+    mutex.acquire()
+    print(strr)
+    f.write("%s\n" % strr)
+    f.flush()
+    mutex.release()
 
 class PreProcess():
     def __init__(self,sr,exp_dir):
@@ -35,18 +42,10 @@ class PreProcess():
         self.exp_dir=exp_dir
         self.gt_wavs_dir="%s/0_gt_wavs"%exp_dir
         self.wavs16k_dir="%s/1_16k_wavs"%exp_dir
-        self.f = open("%s/preprocess.log"%exp_dir, "a+")
         os.makedirs(self.exp_dir,exist_ok=True)
         os.makedirs(self.gt_wavs_dir,exist_ok=True)
         os.makedirs(self.wavs16k_dir,exist_ok=True)
 
-    def println(self, strr):
-        mutex.acquire()
-        print(strr)
-        self.f.write("%s\n" % strr)
-        self.f.flush()
-        mutex.release()
-
     def norm_write(self,tmp_audio,idx0,idx1):
         tmp_audio = (tmp_audio / np.abs(tmp_audio).max() * (self.max * self.alpha)) + (1 - self.alpha) * tmp_audio
         wavfile.write("%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1), self.sr, (tmp_audio*32768).astype(np.int16))
@@ -70,9 +69,9 @@ class PreProcess():
                         tmp_audio = audio[start:]
                         break
                 self.norm_write(tmp_audio, idx0, idx1)
-            self.println("%s->Suc."%path)
+            println("%s->Suc."%path)
         except:
-            self.println("%s->%s"%(path,traceback.format_exc()))
+            println("%s->%s"%(path,traceback.format_exc()))
 
     def pipeline_mp(self,infos):
         for path, idx0 in infos:
@@ -91,14 +90,14 @@ class PreProcess():
                     ps.append(p)
                     for p in ps:p.join()
         except:
-            self.println("Fail. %s"%traceback.format_exc())
+            println("Fail. %s"%traceback.format_exc())
 
 def preprocess_trainset(inp_root, sr, n_p, exp_dir):
     pp=PreProcess(sr,exp_dir)
-    pp.println("start preprocess")
+    println("start preprocess")
-    pp.println(sys.argv)
+    println(sys.argv)
     pp.pipeline_mp_inp_dir(inp_root,n_p)
-    pp.println("end preprocess")
+    println("end preprocess")
 
 if __name__=='__main__':
     preprocess_trainset(inp_root, sr, n_p, exp_dir)

vc_infer_pipeline.py

@@ -35,6 +35,7 @@ class VC(object):
                 x.astype(np.double),
                 fs=self.sr,
                 f0_ceil=f0_max,
+                f0_floor=f0_min,
                 frame_period=10,
             )
             f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)

webui_locale.py

@@ -13,7 +13,7 @@ class I18nAuto:
         if language == 'auto':
             language = locale.getdefaultlocale()[0]
         self.language = language
-        print("Use Languane:", language)
+        print("Use Language:", language)
         self.language_map = load_language_list(language)
 
     def __call__(self, key):