diff --git a/extract_f0_print.py b/extract_f0_print.py
index d2fef0f..fc689f7 100644
--- a/extract_f0_print.py
+++ b/extract_f0_print.py
@@ -1,5 +1,7 @@
 import os, traceback, sys, parselmouth
-import librosa
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from my_utils import load_audio
 import pyworld
 from scipy.io import wavfile
 import numpy as np, logging
@@ -33,17 +35,14 @@ class FeatureInput(object):
         self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
 
     def compute_f0(self, path, f0_method):
-        # default resample type of librosa.resample is "soxr_hq".
-        # Quality: soxr_vhq > soxr_hq
-        x, sr = librosa.load(path, self.fs)  # , res_type='soxr_vhq'
+        x=load_audio(path,self.fs)
         p_len = x.shape[0] // self.hop
-        assert sr == self.fs
         if f0_method == "pm":
             time_step = 160 / 16000 * 1000
             f0_min = 50
             f0_max = 1100
             f0 = (
-                parselmouth.Sound(x, sr)
+                parselmouth.Sound(x, self.fs)
                 .to_pitch_ac(
                     time_step=time_step / 1000,
                     voicing_threshold=0.6,
@@ -60,19 +59,19 @@ class FeatureInput(object):
         elif f0_method == "harvest":
             f0, t = pyworld.harvest(
                 x.astype(np.double),
-                fs=sr,
+                fs=self.fs,
                 f0_ceil=self.f0_max,
                 f0_floor=self.f0_min,
-                frame_period=1000 * self.hop / sr,
+                frame_period=1000 * self.hop / self.fs,
             )
             f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
         elif f0_method == "dio":
             f0, t = pyworld.dio(
                 x.astype(np.double),
-                fs=sr,
+                fs=self.fs,
                 f0_ceil=self.f0_max,
                 f0_floor=self.f0_min,
-                frame_period=1000 * self.hop / sr,
+                frame_period=1000 * self.hop / self.fs,
             )
             f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
         return f0
diff --git a/extract_feature_print.py b/extract_feature_print.py
index f1c4f9a..37cc4e2 100644
--- a/extract_feature_print.py
+++ b/extract_feature_print.py
@@ -9,7 +9,7 @@ else:
     i_gpu = sys.argv[4]
     exp_dir = sys.argv[5]
     os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
-
+version = sys.argv[6]
 import torch
 import torch.nn.functional as F
 import soundfile as sf
@@ -18,12 +18,9 @@ from fairseq import checkpoint_utils
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-if torch.cuda.is_available():
-    device = "cuda"
-elif torch.backends.mps.is_available():
-    device = "mps"
-else:
-    device = "cpu"
+if torch.cuda.is_available():device="cuda"
+elif torch.backends.mps.is_available():device="mps"
+else:device="cpu"
 
 f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
 
@@ -39,7 +36,7 @@ model_path = "hubert_base.pt"
 
 printt(exp_dir)
 wavPath = "%s/1_16k_wavs" % exp_dir
-outPath = "%s/3_feature256" % exp_dir
+outPath = "%s/3_feature256" % exp_dir if version=="v1"else "%s/3_feature768" % exp_dir
 os.makedirs(outPath, exist_ok=True)
 
 
@@ -67,7 +64,7 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
 model = models[0]
 model = model.to(device)
 printt("move model to %s" % device)
-if device not in ["mps", "cpu"]:
+if device not in ["mps","cpu"]:
     model = model.half()
 model.eval()
 
@@ -93,11 +90,11 @@ else:
                     if device not in ["mps", "cpu"]
                     else feats.to(device),
                     "padding_mask": padding_mask.to(device),
-                    "output_layer": 9,  # layer 9
+                    "output_layer": 9 if version=="v1"else 12,  # layer 9
                 }
                 with torch.no_grad():
                     logits = model.extract_features(**inputs)
-                    feats = model.final_proj(logits[0])
+                    feats = model.final_proj(logits[0])if version=="v1"else logits[0]
 
                 feats = feats.squeeze(0).float().cpu().numpy()
                 if np.isnan(feats).sum() == 0:
diff --git a/infer-web.py b/infer-web.py
index 089eab4..200f2c1 100644
--- a/infer-web.py
+++ b/infer-web.py
@@ -1,9 +1,9 @@
+import torch, os, traceback, sys, warnings, shutil, numpy as np
+os.environ["no_proxy"]="localhost, 127.0.0.1, ::1"
 from multiprocessing import cpu_count
 import threading
 from time import sleep
 from subprocess import Popen
-from time import sleep
-import torch, os, traceback, sys, warnings, shutil, numpy as np
 import faiss
 from random import shuffle
 
@@ -11,8 +11,8 @@ now_dir = os.getcwd()
 sys.path.append(now_dir)
 tmp = os.path.join(now_dir, "TEMP")
 shutil.rmtree(tmp, ignore_errors=True)
-shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
-shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
+shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack"%(now_dir), ignore_errors=True)
+shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack"%(now_dir) , 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)
@@ -70,7 +70,7 @@ else:
     gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
     default_batch_size = 1
 gpus = "-".join([i[0] for i in gpu_infos])
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
+from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono,SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
 from scipy.io import wavfile
 from fairseq import checkpoint_utils
 import gradio as gr
@@ -121,11 +121,11 @@ names = []
 for name in os.listdir(weight_root):
     if name.endswith(".pth"):
         names.append(name)
-index_paths = []
+index_paths=[]
 for root, dirs, files in os.walk(index_root, topdown=False):
     for name in files:
         if name.endswith(".index") and "trained" not in name:
-            index_paths.append("%s/%s" % (root, name))
+            index_paths.append("%s/%s"%(root,name))
 uvr5_names = []
 for name in os.listdir(weight_uvr5_root):
     if name.endswith(".pth"):
@@ -144,29 +144,29 @@ def vc_single(
     index_rate,
     filter_radius,
     resample_sr,
+    rms_mix_rate
 ):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
-    global tgt_sr, net_g, vc, hubert_model
+    global tgt_sr, net_g, vc, hubert_model,version
     if input_audio_path is None:
         return "You need to upload an audio", None
     f0_up_key = int(f0_up_key)
     try:
         audio = load_audio(input_audio_path, 16000)
+        audio_max=np.abs(audio).max()/0.95
+        if(audio_max>1):
+            audio/=audio_max
         times = [0, 0, 0]
         if hubert_model == None:
             load_hubert()
         if_f0 = cpt.get("f0", 1)
         file_index = (
-            (
-                file_index.strip(" ")
-                .strip('"')
-                .strip("\n")
-                .strip('"')
-                .strip(" ")
-                .replace("trained", "added")
-            )
-            if file_index != ""
-            else file_index2
-        )  # 防止小白写错，自动帮他替换掉
+            file_index.strip(" ")
+            .strip('"')
+            .strip("\n")
+            .strip('"')
+            .strip(" ")
+            .replace("trained", "added")
+        )if file_index!=""else file_index2  # 防止小白写错，自动帮他替换掉
         # file_big_npy = (
         #     file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
         # )
@@ -186,21 +186,14 @@ def vc_single(
             filter_radius,
             tgt_sr,
             resample_sr,
+            rms_mix_rate,
+            version,
             f0_file=f0_file,
         )
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            tgt_sr = resample_sr
-        index_info = (
-            "Using index:%s." % file_index
-            if os.path.exists(file_index)
-            else "Index not used."
-        )
-        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
-            index_info,
-            times[0],
-            times[1],
-            times[2],
-        ), (tgt_sr, audio_opt)
+        if(resample_sr>=16000 and tgt_sr!=resample_sr):
+            tgt_sr=resample_sr
+        index_info="Using index:%s."%file_index if os.path.exists(file_index)else"Index not used."
+        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss"%(index_info,times[0],times[1],times[2]), (tgt_sr, audio_opt)
     except:
         info = traceback.format_exc()
         print(info)
@@ -220,6 +213,7 @@ def vc_multi(
     index_rate,
     filter_radius,
     resample_sr,
+    rms_mix_rate
 ):
     try:
         dir_path = (
@@ -249,8 +243,9 @@ def vc_multi(
                 index_rate,
                 filter_radius,
                 resample_sr,
+                rms_mix_rate
             )
-            if "Success" in info:
+            if "Success"in info:
                 try:
                     tgt_sr, audio_opt = opt
                     wavfile.write(
@@ -335,8 +330,8 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
 
 # 一个选项卡全局只能有一个音色
 def get_vc(sid):
-    global n_spk, tgt_sr, net_g, vc, cpt
-    if sid == "" or sid == []:
+    global n_spk, tgt_sr, net_g, vc, cpt,version
+    if sid == ""or sid==[]:
         global hubert_model
         if hubert_model != None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
             print("clean_empty_cache")
@@ -346,12 +341,17 @@ def get_vc(sid):
                 torch.cuda.empty_cache()
             ###楼下不这么折腾清理不干净
             if_f0 = cpt.get("f0", 1)
-            if if_f0 == 1:
-                net_g = SynthesizerTrnMs256NSFsid(
-                    *cpt["config"], is_half=config.is_half
-                )
-            else:
-                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            version = cpt.get("version", "v1")
+            if (version == "v1"):
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+                else:
+                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            elif (version == "v2"):
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+                else:
+                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
             del net_g, cpt
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
@@ -363,12 +363,19 @@ def get_vc(sid):
     tgt_sr = cpt["config"][-1]
     cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
     if_f0 = cpt.get("f0", 1)
-    if if_f0 == 1:
-        net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-    else:
-        net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    version = cpt.get("version", "v1")
+    if(version=="v1"):
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    elif(version=="v2"):
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
     del net_g.enc_q
-    print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净, 真奇葩
+    print(net_g.load_state_dict(cpt["weight"], strict=False))
     net_g.eval().to(config.device)
     if config.is_half:
         net_g = net_g.half()
@@ -384,37 +391,17 @@ def change_choices():
     for name in os.listdir(weight_root):
         if name.endswith(".pth"):
             names.append(name)
-    index_paths = []
+    index_paths=[]
     for root, dirs, files in os.walk(index_root, topdown=False):
         for name in files:
             if name.endswith(".index") and "trained" not in name:
                 index_paths.append("%s/%s" % (root, name))
-    return {"choices": sorted(names), "__type__": "update"}, {
-        "choices": sorted(index_paths),
-        "__type__": "update",
-    }
+    return {"choices": sorted(names), "__type__": "update"},{"choices": sorted(index_paths), "__type__": "update"}
 
 
 def clean():
     return {"value": "", "__type__": "update"}
 
-
-def change_f0(if_f0_3, sr2):  # np7, f0method8,pretrained_G14,pretrained_D15
-    if if_f0_3:
-        return (
-            {"visible": True, "__type__": "update"},
-            {"visible": True, "__type__": "update"},
-            "pretrained/f0G%s.pth" % sr2,
-            "pretrained/f0D%s.pth" % sr2,
-        )
-    return (
-        {"visible": False, "__type__": "update"},
-        {"visible": False, "__type__": "update"},
-        "pretrained/G%s.pth" % sr2,
-        "pretrained/D%s.pth" % sr2,
-    )
-
-
 sr_dict = {
     "32k": 32000,
     "40k": 40000,
@@ -481,7 +468,7 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
 
 
 # but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
-def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
+def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
     gpus = gpus.split("-")
     os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
     f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
@@ -527,13 +514,14 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
     leng = len(gpus)
     ps = []
     for idx, n_g in enumerate(gpus):
-        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
+        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" % (
             config.device,
             leng,
             idx,
             n_g,
             now_dir,
             exp_dir,
+            version19,
         )
         print(cmd)
         p = Popen(
@@ -561,12 +549,33 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
     yield log
 
 
-def change_sr2(sr2, if_f0_3):
-    if if_f0_3:
-        return "pretrained/f0G%s.pth" % sr2, "pretrained/f0D%s.pth" % sr2
-    else:
-        return "pretrained/G%s.pth" % sr2, "pretrained/D%s.pth" % sr2
+def change_sr2(sr2, if_f0_3,version19):
+    vis_v=True if sr2=="40k"else False
+    if(sr2!="40k"):version19="v1"
+    path_str=""if version19=="v1"else "_v2"
+    version_state={"visible": vis_v, "__type__": "update"}
+    if(vis_v==False):version_state["value"]="v1"
+    f0_str="f0"if if_f0_3 else""
+    return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2),version_state
 
+def change_version19(sr2,if_f0_3,version19):
+    path_str=""if version19=="v1"else "_v2"
+    f0_str="f0"if if_f0_3 else""
+    return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2)
+
+def change_f0(if_f0_3, sr2,version19):  # f0method8,pretrained_G14,pretrained_D15
+    path_str=""if version19=="v1"else "_v2"
+    if if_f0_3:
+        return (
+            {"visible": True, "__type__": "update"},
+            "pretrained%s/f0G%s.pth" % (path_str,sr2),
+            "pretrained%s/f0D%s.pth" % (path_str,sr2),
+        )
+    return (
+        {"visible": False, "__type__": "update"},
+        "pretrained%s/G%s.pth" % (path_str,sr2),
+        "pretrained%s/D%s.pth" % (path_str,sr2),
+    )
 
 # but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
 def click_train(
@@ -582,24 +591,26 @@ def click_train(
     pretrained_D15,
     gpus16,
     if_cache_gpu17,
+    if_save_every_weights18,
+    version19,
 ):
     # 生成filelist
     exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
     os.makedirs(exp_dir, exist_ok=True)
     gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
-    co256_dir = "%s/3_feature256" % (exp_dir)
+    feature_dir = "%s/3_feature256" % (exp_dir)if version19=="v1"else "%s/3_feature768" % (exp_dir)
     if if_f0_3:
         f0_dir = "%s/2a_f0" % (exp_dir)
         f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
         names = (
             set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
-            & set([name.split(".")[0] for name in os.listdir(co256_dir)])
+            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
             & set([name.split(".")[0] for name in os.listdir(f0_dir)])
             & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
         )
     else:
         names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
-            [name.split(".")[0] for name in os.listdir(co256_dir)]
+            [name.split(".")[0] for name in os.listdir(feature_dir)]
         )
     opt = []
     for name in names:
@@ -609,7 +620,7 @@ def click_train(
                 % (
                     gt_wavs_dir.replace("\\", "\\\\"),
                     name,
-                    co256_dir.replace("\\", "\\\\"),
+                    feature_dir.replace("\\", "\\\\"),
                     name,
                     f0_dir.replace("\\", "\\\\"),
                     name,
@@ -624,22 +635,23 @@ def click_train(
                 % (
                     gt_wavs_dir.replace("\\", "\\\\"),
                     name,
-                    co256_dir.replace("\\", "\\\\"),
+                    feature_dir.replace("\\", "\\\\"),
                     name,
                     spk_id5,
                 )
             )
+    fea_dim = 256 if version19 == "v1"else 768
     if if_f0_3:
         for _ in range(2):
             opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
-                % (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+                % (now_dir, sr2, now_dir,fea_dim, now_dir, now_dir, spk_id5)
             )
     else:
         for _ in range(2):
             opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
-                % (now_dir, sr2, now_dir, spk_id5)
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
+                % (now_dir, sr2, now_dir,fea_dim, spk_id5)
             )
     shuffle(opt)
     with open("%s/filelist.txt" % exp_dir, "w") as f:
@@ -651,7 +663,7 @@ def click_train(
     if gpus16:
         cmd = (
             config.python_cmd
-            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
             % (
                 exp_dir1,
                 sr2,
@@ -664,12 +676,14 @@ def click_train(
                 pretrained_D15,
                 1 if if_save_latest13 == i18n("是") else 0,
                 1 if if_cache_gpu17 == i18n("是") else 0,
+                1 if if_save_every_weights18 == i18n("是") else 0,
+                version19,
             )
         )
     else:
         cmd = (
             config.python_cmd
-            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
             % (
                 exp_dir1,
                 sr2,
@@ -681,6 +695,8 @@ def click_train(
                 pretrained_D15,
                 1 if if_save_latest13 == i18n("是") else 0,
                 1 if if_cache_gpu17 == i18n("是") else 0,
+                1 if if_save_every_weights18 == i18n("是") else 0,
+                version19,
             )
         )
     print(cmd)
@@ -690,10 +706,10 @@ def click_train(
 
 
 # but4.click(train_index, [exp_dir1], info3)
-def train_index(exp_dir1):
+def train_index(exp_dir1,version19):
     exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
     os.makedirs(exp_dir, exist_ok=True)
-    feature_dir = "%s/3_feature256" % (exp_dir)
+    feature_dir = "%s/3_feature256" % (exp_dir)if version19=="v1"else "%s/3_feature768" % (exp_dir)
     if os.path.exists(feature_dir) == False:
         return "请先进行特征提取!"
     listdir_res = list(os.listdir(feature_dir))
@@ -713,8 +729,8 @@ def train_index(exp_dir1):
     infos = []
     infos.append("%s,%s" % (big_npy.shape, n_ivf))
     yield "\n".join(infos)
-    index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
-    # index = faiss.index_factory(256, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
+    index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,Flat" % n_ivf)
+    # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
     infos.append("training")
     yield "\n".join(infos)
     index_ivf = faiss.extract_index_ivf(index)  #
@@ -722,9 +738,9 @@ def train_index(exp_dir1):
     index.train(big_npy)
     faiss.write_index(
         index,
-        "%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
+        "%s/trained_IVF%s_Flat_nprobe_%s_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe,version19),
     )
-    # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
+    # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
     infos.append("adding")
     yield "\n".join(infos)
     batch_size_add = 8192
@@ -734,9 +750,9 @@ def train_index(exp_dir1):
         index,
         "%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
     )
-    infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe))
-    # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
-    # infos.append("成功构建索引，added_IVF%s_Flat_FastScan.index"%(n_ivf))
+    infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s_%s.index" % (n_ivf, index_ivf.nprobe,version19))
+    # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
+    # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
     yield "\n".join(infos)
 
 
@@ -757,6 +773,8 @@ def train1key(
     pretrained_D15,
     gpus16,
     if_cache_gpu17,
+    if_save_every_weights18,
+    version19,
 ):
     infos = []
 
@@ -768,7 +786,7 @@ def train1key(
     preprocess_log_path = "%s/preprocess.log" % model_log_dir
     extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
     gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
-    feature256_dir = "%s/3_feature256" % model_log_dir
+    feature_dir = "%s/3_feature256" % model_log_dir if version19=="v1"else "%s/3_feature768" % model_log_dir
 
     os.makedirs(model_log_dir, exist_ok=True)
     #########step1:处理数据
@@ -807,12 +825,12 @@ def train1key(
     leng = len(gpus)
     ps = []
     for idx, n_g in enumerate(gpus):
-        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s" % (
+        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % (
             config.device,
             leng,
             idx,
             n_g,
-            model_log_dir,
+            model_log_dir,version19,
         )
         yield get_info_str(cmd)
         p = Popen(
@@ -831,13 +849,13 @@ def train1key(
         f0nsf_dir = "%s/2b-f0nsf" % model_log_dir
         names = (
             set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
-            & set([name.split(".")[0] for name in os.listdir(feature256_dir)])
+            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
             & set([name.split(".")[0] for name in os.listdir(f0_dir)])
             & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
         )
     else:
         names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
-            [name.split(".")[0] for name in os.listdir(feature256_dir)]
+            [name.split(".")[0] for name in os.listdir(feature_dir)]
         )
     opt = []
     for name in names:
@@ -847,7 +865,7 @@ def train1key(
                 % (
                     gt_wavs_dir.replace("\\", "\\\\"),
                     name,
-                    feature256_dir.replace("\\", "\\\\"),
+                    feature_dir.replace("\\", "\\\\"),
                     name,
                     f0_dir.replace("\\", "\\\\"),
                     name,
@@ -862,22 +880,23 @@ def train1key(
                 % (
                     gt_wavs_dir.replace("\\", "\\\\"),
                     name,
-                    feature256_dir.replace("\\", "\\\\"),
+                    feature_dir.replace("\\", "\\\\"),
                     name,
                     spk_id5,
                 )
             )
+    fea_dim=256 if version19=="v1"else 768
     if if_f0_3:
         for _ in range(2):
             opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
-                % (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+                % (now_dir, sr2, now_dir,fea_dim, now_dir, now_dir, spk_id5)
             )
     else:
         for _ in range(2):
             opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
-                % (now_dir, sr2, now_dir, spk_id5)
+                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
+                % (now_dir, sr2, now_dir,fea_dim, spk_id5)
             )
     shuffle(opt)
     with open("%s/filelist.txt" % model_log_dir, "w") as f:
@@ -886,7 +905,7 @@ def train1key(
     if gpus16:
         cmd = (
             config.python_cmd
-            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
             % (
                 exp_dir1,
                 sr2,
@@ -899,12 +918,14 @@ def train1key(
                 pretrained_D15,
                 1 if if_save_latest13 == i18n("是") else 0,
                 1 if if_cache_gpu17 == i18n("是") else 0,
+                1 if if_save_every_weights18 == i18n("是") else 0,
+                version19,
             )
         )
     else:
         cmd = (
             config.python_cmd
-            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
+            + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
             % (
                 exp_dir1,
                 sr2,
@@ -916,6 +937,8 @@ def train1key(
                 pretrained_D15,
                 1 if if_save_latest13 == i18n("是") else 0,
                 1 if if_cache_gpu17 == i18n("是") else 0,
+                1 if if_save_every_weights18 == i18n("是") else 0,
+                version19,
             )
         )
     yield get_info_str(cmd)
@@ -924,9 +947,9 @@ def train1key(
     yield get_info_str(i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"))
     #######step3b:训练索引
     npys = []
-    listdir_res = list(os.listdir(feature256_dir))
+    listdir_res = list(os.listdir(feature_dir))
     for name in sorted(listdir_res):
-        phone = np.load("%s/%s" % (feature256_dir, name))
+        phone = np.load("%s/%s" % (feature_dir, name))
         npys.append(phone)
     big_npy = np.concatenate(npys, 0)
 
@@ -938,15 +961,15 @@ def train1key(
     # n_ivf =  big_npy.shape[0] // 39
     n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
     yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
-    index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
+    index = faiss.index_factory(256 if version19=="v1"else 768, "IVF%s,Flat" % n_ivf)
     yield get_info_str("training index")
     index_ivf = faiss.extract_index_ivf(index)  #
     index_ivf.nprobe = 1
     index.train(big_npy)
     faiss.write_index(
         index,
-        "%s/trained_IVF%s_Flat_nprobe_%s.index"
-        % (model_log_dir, n_ivf, index_ivf.nprobe),
+        "%s/trained_IVF%s_Flat_nprobe_%s_%s.index"
+        % (model_log_dir, n_ivf, index_ivf.nprobe,version19),
     )
     yield get_info_str("adding index")
     batch_size_add = 8192
@@ -954,11 +977,11 @@ def train1key(
         index.add(big_npy[i : i + batch_size_add])
     faiss.write_index(
         index,
-        "%s/added_IVF%s_Flat_nprobe_%s.index"
-        % (model_log_dir, n_ivf, index_ivf.nprobe),
+        "%s/added_IVF%s_Flat_nprobe_%s_%s.index"
+        % (model_log_dir, n_ivf, index_ivf.nprobe,version19),
     )
     yield get_info_str(
-        "成功构建索引, added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe)
+        "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index" % (n_ivf, index_ivf.nprobe,version19)
     )
     yield get_info_str(i18n("全流程结束！"))
 
@@ -969,17 +992,18 @@ def change_info_(ckpt_path):
         os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log"))
         == False
     ):
-        return {"__type__": "update"}, {"__type__": "update"}
+        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
     try:
         with open(
             ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
         ) as f:
             info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
             sr, f0 = info["sample_rate"], info["if_f0"]
-            return sr, str(f0)
+            version="v2"if("version"in info and info["version"]=="v2")else"v1"
+            return sr, str(f0),version
     except:
         traceback.print_exc()
-        return {"__type__": "update"}, {"__type__": "update"}
+        return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
 
 
 from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
@@ -1112,7 +1136,7 @@ with gr.Blocks() as app:
                             value="pm",
                             interactive=True,
                         )
-                        filter_radius0 = gr.Slider(
+                        filter_radius0=gr.Slider(
                             minimum=0,
                             maximum=7,
                             label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
@@ -1131,9 +1155,7 @@ with gr.Blocks() as app:
                             choices=sorted(index_paths),
                             interactive=True,
                         )
-                        refresh_button.click(
-                            fn=change_choices, inputs=[], outputs=[sid0, file_index2]
-                        )
+                        refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0, file_index2])
                         # file_big_npy1 = gr.Textbox(
                         #     label=i18n("特征文件路径"),
                         #     value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
@@ -1146,7 +1168,7 @@ with gr.Blocks() as app:
                             value=0.76,
                             interactive=True,
                         )
-                        resample_sr0 = gr.Slider(
+                        resample_sr0=gr.Slider(
                             minimum=0,
                             maximum=48000,
                             label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
@@ -1154,6 +1176,13 @@ with gr.Blocks() as app:
                             step=1,
                             interactive=True,
                         )
+                        rms_mix_rate0 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                            value=1,
+                            interactive=True,
+                        )
                     f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
                     but0 = gr.Button(i18n("转换"), variant="primary")
                     with gr.Column():
@@ -1173,6 +1202,7 @@ with gr.Blocks() as app:
                             index_rate1,
                             filter_radius0,
                             resample_sr0,
+                            rms_mix_rate0
                         ],
                         [vc_output1, vc_output2],
                     )
@@ -1192,7 +1222,7 @@ with gr.Blocks() as app:
                             value="pm",
                             interactive=True,
                         )
-                        filter_radius1 = gr.Slider(
+                        filter_radius1=gr.Slider(
                             minimum=0,
                             maximum=7,
                             label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
@@ -1223,7 +1253,7 @@ with gr.Blocks() as app:
                             value=1,
                             interactive=True,
                         )
-                        resample_sr1 = gr.Slider(
+                        resample_sr1=gr.Slider(
                             minimum=0,
                             maximum=48000,
                             label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
@@ -1231,6 +1261,13 @@ with gr.Blocks() as app:
                             step=1,
                             interactive=True,
                         )
+                        rms_mix_rate1 = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                            value=1,
+                            interactive=True,
+                        )
                     with gr.Column():
                         dir_input = gr.Textbox(
                             label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
@@ -1256,6 +1293,7 @@ with gr.Blocks() as app:
                             index_rate2,
                             filter_radius1,
                             resample_sr1,
+                            rms_mix_rate1
                         ],
                         [vc_output3],
                     )
@@ -1324,6 +1362,13 @@ with gr.Blocks() as app:
                     value=True,
                     interactive=True,
                 )
+                version19 = gr.Radio(
+                    label=i18n("版本(目前仅40k支持了v2)"),
+                    choices=["v1", "v2"],
+                    value="v1",
+                    interactive=True,
+                    visible=True,
+                )
                 np7 = gr.Slider(
                     minimum=0,
                     maximum=ncpu,
@@ -1353,7 +1398,7 @@ with gr.Blocks() as app:
                     but1 = gr.Button(i18n("处理数据"), variant="primary")
                     info1 = gr.Textbox(label=i18n("输出信息"), value="")
                     but1.click(
-                        preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
+                        preprocess_dataset, [trainset_dir4, exp_dir1, sr2,np7], [info1]
                     )
             with gr.Group():
                 gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)"))
@@ -1378,7 +1423,7 @@ with gr.Blocks() as app:
                     info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
                     but2.click(
                         extract_f0_feature,
-                        [gpus6, np7, f0method8, if_f0_3, exp_dir1],
+                        [gpus6, np7, f0method8, if_f0_3, exp_dir1,version19],
                         [info2],
                     )
             with gr.Group():
@@ -1422,6 +1467,14 @@ with gr.Blocks() as app:
                         value=i18n("否"),
                         interactive=True,
                     )
+                    if_save_every_weights18 = gr.Radio(
+                        label=i18n(
+                            "是否在每次保存时间点将最终小模型保存至weights文件夹"
+                        ),
+                        choices=[i18n("是"), i18n("否")],
+                        value=i18n("否"),
+                        interactive=True,
+                    )
                 with gr.Row():
                     pretrained_G14 = gr.Textbox(
                         label=i18n("加载预训练底模G路径"),
@@ -1434,12 +1487,15 @@ with gr.Blocks() as app:
                         interactive=True,
                     )
                     sr2.change(
-                        change_sr2, [sr2, if_f0_3], [pretrained_G14, pretrained_D15]
+                        change_sr2, [sr2, if_f0_3,version19], [pretrained_G14, pretrained_D15,version19]
+                    )
+                    version19.change(
+                        change_version19, [sr2, if_f0_3,version19], [pretrained_G14, pretrained_D15]
                     )
                     if_f0_3.change(
                         change_f0,
-                        [if_f0_3, sr2],
-                        [np7, f0method8, pretrained_G14, pretrained_D15],
+                        [if_f0_3, sr2,version19],
+                        [f0method8, pretrained_G14, pretrained_D15],
                     )
                     gpus16 = gr.Textbox(
                         label=i18n("以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"),
@@ -1465,10 +1521,12 @@ with gr.Blocks() as app:
                             pretrained_D15,
                             gpus16,
                             if_cache_gpu17,
+                            if_save_every_weights18,
+                            version19,
                         ],
                         info3,
                     )
-                    but4.click(train_index, [exp_dir1], info3)
+                    but4.click(train_index, [exp_dir1,version19], info3)
                     but5.click(
                         train1key,
                         [
@@ -1487,6 +1545,8 @@ with gr.Blocks() as app:
                             pretrained_D15,
                             gpus16,
                             if_cache_gpu17,
+                            if_save_every_weights18,
+                            version19,
                         ],
                         info3,
                     )
@@ -1526,12 +1586,18 @@ with gr.Blocks() as app:
                         max_lines=1,
                         interactive=True,
                     )
+                    version_2=gr.Radio(
+                        label=i18n("模型版本型号"),
+                        choices=["v1", "v2"],
+                        value="v1",
+                        interactive=True,
+                    )
                 with gr.Row():
                     but6 = gr.Button(i18n("融合"), variant="primary")
                     info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
                 but6.click(
                     merge,
-                    [ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0],
+                    [ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0,version_2],
                     info4,
                 )  # def merge(path1,path2,alpha1,sr,f0,info):
             with gr.Group():
@@ -1589,15 +1655,21 @@ with gr.Blocks() as app:
                         value="1",
                         interactive=True,
                     )
+                    version_1=gr.Radio(
+                        label=i18n("模型版本型号"),
+                        choices=["v1", "v2"],
+                        value="v1",
+                        interactive=True,
+                    )
                     info___ = gr.Textbox(
                         label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
                     )
                     but9 = gr.Button(i18n("提取"), variant="primary")
                     info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                    ckpt_path2.change(change_info_, [ckpt_path2], [sr__, if_f0__])
+                    ckpt_path2.change(change_info_, [ckpt_path2], [sr__, if_f0__,version_1])
                 but9.click(
                     extract_small_model,
-                    [ckpt_path2, save_name, sr__, if_f0__, info___],
+                    [ckpt_path2, save_name, sr__, if_f0__, info___,version_1],
                     info7,
                 )
 
@@ -1615,16 +1687,16 @@ with gr.Blocks() as app:
                 butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
             butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx)
 
-        tab_faq = i18n("常见问题解答")
+        tab_faq=i18n("常见问题解答")
         with gr.TabItem(tab_faq):
             try:
-                if tab_faq == "常见问题解答":
-                    with open("docs/faq.md", "r", encoding="utf8") as f:
-                        info = f.read()
+                if(tab_faq=="常见问题解答"):
+                    with open("docs/faq.md","r",encoding="utf8")as f:info=f.read()
                 else:
-                    with open("docs/faq_en.md", "r") as f:
-                        info = f.read()
-                gr.Markdown(value=info)
+                    with open("docs/faq_en.md", "r")as f:info = f.read()
+                gr.Markdown(
+                    value=info
+                )
             except:
                 gr.Markdown(traceback.format_exc())
 
diff --git a/train_nsf_sim_cache_sid_load_pretrain.py b/train_nsf_sim_cache_sid_load_pretrain.py
index 6078490..494c561 100644
--- a/train_nsf_sim_cache_sid_load_pretrain.py
+++ b/train_nsf_sim_cache_sid_load_pretrain.py
@@ -31,14 +31,21 @@ from data_utils import (
     TextAudioCollate,
     DistributedBucketSampler,
 )
-from infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    MultiPeriodDiscriminator,
-)
+if(hps.version=="v1"):
+    from infer_pack.models import (
+        SynthesizerTrnMs256NSFsid as RVC_Model_f0,
+        SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
+        MultiPeriodDiscriminator,
+    )
+else:
+    from infer_pack.models import (
+        SynthesizerTrnMs768NSFsid as RVC_Model_f0,
+        SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
+        MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
+    )
 from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
 from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-
+from process_ckpt import savee
 
 global_step = 0
 
@@ -63,7 +70,7 @@ def run(rank, n_gpus, hps):
     if rank == 0:
         logger = utils.get_logger(hps.model_dir)
         logger.info(hps)
-        utils.check_git_hash(hps.model_dir)
+        # utils.check_git_hash(hps.model_dir)
         writer = SummaryWriter(log_dir=hps.model_dir)
         writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
 
@@ -104,7 +111,7 @@ def run(rank, n_gpus, hps):
         prefetch_factor=8,
     )
     if hps.if_f0 == 1:
-        net_g = SynthesizerTrnMs256NSFsid(
+        net_g = RVC_Model_f0(
             hps.data.filter_length // 2 + 1,
             hps.train.segment_size // hps.data.hop_length,
             **hps.model,
@@ -112,7 +119,7 @@ def run(rank, n_gpus, hps):
             sr=hps.sample_rate,
         )
     else:
-        net_g = SynthesizerTrnMs256NSFsid_nono(
+        net_g = RVC_Model_nof0(
             hps.data.filter_length // 2 + 1,
             hps.train.segment_size // hps.data.hop_length,
             **hps.model,
@@ -343,7 +350,7 @@ def train_and_evaluate(
             spec = spec.cuda(rank, non_blocking=True)
             spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
             wave = wave.cuda(rank, non_blocking=True)
-            wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
+            # wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
 
         # Calculate
         with autocast(enabled=hps.train.fp16_run):
@@ -428,10 +435,10 @@ def train_and_evaluate(
                     )
                 )
                 # Amor For Tensorboard display
-                if loss_mel > 50:
-                    loss_mel = 50
-                if loss_kl > 5:
-                    loss_kl = 5
+                if loss_mel > 75:
+                    loss_mel = 75
+                if loss_kl > 9:
+                    loss_kl = 9
 
                 logger.info([global_step, lr])
                 logger.info(
@@ -512,12 +519,20 @@ def train_and_evaluate(
                 epoch,
                 os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
             )
+        if(rank==0 and hps.save_every_weights=="1"):
+            if hasattr(net_g, "module"):
+                ckpt = net_g.module.state_dict()
+            else:
+                ckpt = net_g.state_dict()
+            logger.info(
+                "saving ckpt %s_e%s:%s"
+                % (hps.name,epoch,savee(ckpt, hps.sample_rate, hps.if_f0, hps.name+"_e%s"%epoch, epoch,hps.version))
+            )
 
     if rank == 0:
         logger.info("====> Epoch: {}".format(epoch))
     if epoch >= hps.total_epoch and rank == 0:
         logger.info("Training is done. The program is closed.")
-        from process_ckpt import savee  # def savee(ckpt,sr,if_f0,name,epoch):
 
         if hasattr(net_g, "module"):
             ckpt = net_g.module.state_dict()
@@ -525,7 +540,7 @@ def train_and_evaluate(
             ckpt = net_g.state_dict()
         logger.info(
             "saving final ckpt:%s"
-            % (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch))
+            % (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch,hps.version))
         )
         sleep(1)
         os._exit(2333333)
diff --git a/trainset_preprocess_pipeline_print.py b/trainset_preprocess_pipeline_print.py
index 1f4039c..5c87061 100644
--- a/trainset_preprocess_pipeline_print.py
+++ b/trainset_preprocess_pipeline_print.py
@@ -32,19 +32,19 @@ class PreProcess:
     def __init__(self, sr, exp_dir):
         self.slicer = Slicer(
             sr=sr,
-            threshold=-40,
-            min_length=800,
+            threshold=-42,
+            min_length=1500,
             min_interval=400,
             hop_size=15,
-            max_sil_kept=150,
+            max_sil_kept=500,
         )
         self.sr = sr
         self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
         self.per = 3.7
         self.overlap = 0.3
         self.tail = self.per + self.overlap
-        self.max = 0.95
-        self.alpha = 0.8
+        self.max = 0.9
+        self.alpha = 0.75
         self.exp_dir = exp_dir
         self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
         self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
diff --git a/vc_infer_pipeline.py b/vc_infer_pipeline.py
index 9564493..7470a91 100644
--- a/vc_infer_pipeline.py
+++ b/vc_infer_pipeline.py
@@ -2,18 +2,16 @@ import numpy as np, parselmouth, torch, pdb
 from time import time as ttime
 import torch.nn.functional as F
 import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa
+import pyworld, os, traceback, faiss,librosa
 from scipy import signal
 from functools import lru_cache
 
 bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
 
-input_audio_path2wav = {}
-
-
+input_audio_path2wav={}
 @lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
+def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period):
+    audio=input_audio_path2wav[input_audio_path]
     f0, t = pyworld.harvest(
         audio,
         fs=fs,
@@ -24,6 +22,17 @@ def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
     f0 = pyworld.stonemask(audio, f0, t, fs)
     return f0
 
+def change_rms(data1,sr1,data2,sr2,rate):#1是输入音频，2是输出音频,rate是2的占比
+    # print(data1.max(),data2.max())
+    rms1 = librosa.feature.rms(y=data1, frame_length=sr1//2*2, hop_length=sr1//2)#每半秒一个点
+    rms2 = librosa.feature.rms(y=data2, frame_length=sr2//2*2, hop_length=sr2//2)
+    rms1=torch.from_numpy(rms1)
+    rms1=F.interpolate(rms1.unsqueeze(0), size=data2.shape[0],mode='linear').squeeze()
+    rms2=torch.from_numpy(rms2)
+    rms2=F.interpolate(rms2.unsqueeze(0), size=data2.shape[0],mode='linear').squeeze()
+    rms2=torch.max(rms2,torch.zeros_like(rms2)+1e-6)
+    data2*=(torch.pow(rms1,torch.tensor(1-rate))*torch.pow(rms2,torch.tensor(rate-1))).numpy()
+    return data2
 
 class VC(object):
     def __init__(self, tgt_sr, config):
@@ -44,16 +53,7 @@ class VC(object):
         self.t_max = self.sr * self.x_max  # 免查询时长阈值
         self.device = config.device
 
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        inp_f0=None,
-    ):
+    def get_f0(self, input_audio_path,x, p_len, f0_up_key, f0_method,filter_radius, inp_f0=None):
         global input_audio_path2wav
         time_step = self.window / self.sr * 1000
         f0_min = 50
@@ -77,9 +77,9 @@ class VC(object):
                     f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
                 )
         elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-            if filter_radius > 2:
+            input_audio_path2wav[input_audio_path]=x.astype(np.double)
+            f0=cache_harvest_f0(input_audio_path,self.sr,f0_max,f0_min,10)
+            if(filter_radius>2):
                 f0 = signal.medfilt(f0, 3)
         f0 *= pow(2, f0_up_key / 12)
         # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
@@ -118,6 +118,7 @@ class VC(object):
         index,
         big_npy,
         index_rate,
+        version,
     ):  # ,file_index,file_big_npy
         feats = torch.from_numpy(audio0)
         if self.is_half:
@@ -133,12 +134,12 @@ class VC(object):
         inputs = {
             "source": feats.to(self.device),
             "padding_mask": padding_mask,
-            "output_layer": 9,  # layer 9
+            "output_layer": 9if version=="v1"else 12,
         }
         t0 = ttime()
         with torch.no_grad():
             logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
+            feats = model.final_proj(logits[0])if version=="v1"else logits[0]
 
         if (
             isinstance(index, type(None)) == False
@@ -176,14 +177,14 @@ class VC(object):
         with torch.no_grad():
             if pitch != None and pitchf != None:
                 audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
+                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
                     .data.cpu()
                     .float()
                     .numpy()
                 )
             else:
                 audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
+                    (net_g.infer(feats, p_len, sid)[0][0, 0])
                     .data.cpu()
                     .float()
                     .numpy()
@@ -213,6 +214,8 @@ class VC(object):
         filter_radius,
         tgt_sr,
         resample_sr,
+        rms_mix_rate,
+        version,
         f0_file=None,
     ):
         if (
@@ -267,15 +270,7 @@ class VC(object):
         sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
         pitch, pitchf = None, None
         if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius,
-                inp_f0,
-            )
+            pitch, pitchf = self.get_f0(input_audio_path,audio_pad, p_len, f0_up_key, f0_method,filter_radius, inp_f0)
             pitch = pitch[:p_len]
             pitchf = pitchf[:p_len]
             if self.device == "mps":
@@ -299,6 +294,7 @@ class VC(object):
                         index,
                         big_npy,
                         index_rate,
+                        version,
                     )[self.t_pad_tgt : -self.t_pad_tgt]
                 )
             else:
@@ -314,6 +310,7 @@ class VC(object):
                         index,
                         big_npy,
                         index_rate,
+                        version,
                     )[self.t_pad_tgt : -self.t_pad_tgt]
                 )
             s = t
@@ -330,6 +327,7 @@ class VC(object):
                     index,
                     big_npy,
                     index_rate,
+                    version,
                 )[self.t_pad_tgt : -self.t_pad_tgt]
             )
         else:
@@ -345,14 +343,20 @@ class VC(object):
                     index,
                     big_npy,
                     index_rate,
+                    version,
                 )[self.t_pad_tgt : -self.t_pad_tgt]
             )
         audio_opt = np.concatenate(audio_opt)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
+        if(rms_mix_rate!=1):
+            audio_opt=change_rms(audio,16000,audio_opt,tgt_sr,rms_mix_rate)
+        if(resample_sr>=16000 and tgt_sr!=resample_sr):
             audio_opt = librosa.resample(
                 audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
             )
-        audio_opt = audio_opt.astype(np.int16)
+        audio_max=np.abs(audio_opt).max()/0.99
+        max_int16=32768
+        if(audio_max>1):max_int16/=audio_max
+        audio_opt=(audio_opt * max_int16).astype(np.int16)
         del pitch, pitchf, sid
         if torch.cuda.is_available():
             torch.cuda.empty_cache()