Update vc_infer_pipeline.py

vc_infer_pipeline.py

@@ -1,225 +1,457 @@
-import numpy as np,parselmouth,torch,pdb
+import os
+import sys
+import traceback
+import logging
+
+logger = logging.getLogger(__name__)
+
+from functools import lru_cache
 from time import time as ttime
+
+import faiss
+import librosa
+import numpy as np
+import parselmouth
+import pyworld
+import torch
 import torch.nn.functional as F
-from config import x_pad,x_query,x_center,x_max
-from sklearn.cluster import KMeans
-
-def resize2d(x, target_len,is1):
-    minn=1 if is1==True else 0
-    ss = np.array(x).astype("float32")
-    ss[ss <=minn] = np.nan
-    target = np.interp(np.arange(0, len(ss) * target_len, len(ss)) / target_len, np.arange(0, len(ss)), ss)
-    res = np.nan_to_num(target)
-    return res
-
-class VC(object):
-    def __init__(self,tgt_sr,device,is_half):
-        self.sr=16000#hubert输入采样率
-        self.window=160#每帧点数
-        self.t_pad=self.sr*x_pad#每条前后pad时间
-        self.t_pad_tgt=tgt_sr*x_pad
-        self.t_pad2=self.t_pad*2
-        self.t_query=self.sr*x_query#查询切点前后查询时间
-        self.t_center=self.sr*x_center#查询切点位置
-        self.t_max=self.sr*x_max#免查询时长阈值
-        self.device=device
-        self.is_half=is_half
-
-    def get_f0(self,x, p_len,f0_up_key=0,inp_f0=None):
+import torchcrepe
+from scipy import signal
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+
+bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
+
+input_audio_path2wav = {}
+
+
+@lru_cache
+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,
+        f0_ceil=f0max,
+        f0_floor=f0min,
+        frame_period=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 Pipeline(object):
+    def __init__(self, tgt_sr, config):
+        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
+            config.x_pad,
+            config.x_query,
+            config.x_center,
+            config.x_max,
+            config.is_half,
+        )
+        self.sr = 16000  # hubert输入采样率
+        self.window = 160  # 每帧点数
+        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
+        self.t_pad_tgt = tgt_sr * self.x_pad
+        self.t_pad2 = self.t_pad * 2
+        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
+        self.t_center = self.sr * self.x_center  # 查询切点位置
+        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,
+    ):
+        global input_audio_path2wav
         time_step = self.window / self.sr * 1000
         f0_min = 50
         f0_max = 1100
         f0_mel_min = 1127 * np.log(1 + f0_min / 700)
         f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0 = parselmouth.Sound(x, self.sr).to_pitch_ac(
-            time_step=time_step / 1000, voicing_threshold=0.6,
-            pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-        pad_size=(p_len - len(f0) + 1) // 2
-        if(pad_size>0 or p_len - len(f0) - pad_size>0):
-            f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
+        if f0_method == "pm":
+            f0 = (
+                parselmouth.Sound(x, self.sr)
+                .to_pitch_ac(
+                    time_step=time_step / 1000,
+                    voicing_threshold=0.6,
+                    pitch_floor=f0_min,
+                    pitch_ceiling=f0_max,
+                )
+                .selected_array["frequency"]
+            )
+            pad_size = (p_len - len(f0) + 1) // 2
+            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
+                f0 = np.pad(
+                    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:
+                f0 = signal.medfilt(f0, 3)
+        elif f0_method == "crepe":
+            model = "full"
+            # Pick a batch size that doesn't cause memory errors on your gpu
+            batch_size = 512
+            # Compute pitch using first gpu
+            audio = torch.tensor(np.copy(x))[None].float()
+            f0, pd = torchcrepe.predict(
+                audio,
+                self.sr,
+                self.window,
+                f0_min,
+                f0_max,
+                model,
+                batch_size=batch_size,
+                device=self.device,
+                return_periodicity=True,
+            )
+            pd = torchcrepe.filter.median(pd, 3)
+            f0 = torchcrepe.filter.mean(f0, 3)
+            f0[pd < 0.1] = 0
+            f0 = f0[0].cpu().numpy()
+        elif f0_method == "rmvpe":
+            if not hasattr(self, "model_rmvpe"):
+                from infer.lib.rmvpe import RMVPE
+
+                logger.info(
+                    "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"]
+                )
+                self.model_rmvpe = RMVPE(
+                    "%s/rmvpe.pt" % os.environ["rmvpe_root"],
+                    is_half=self.is_half,
+                    device=self.device,
+                )
+            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+
+            if "privateuseone" in str(self.device):  # clean ortruntime memory
+                del self.model_rmvpe.model
+                del self.model_rmvpe
+                logger.info("Cleaning ortruntime memory")
+
         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()]))
-        tf0=self.sr//self.window#每秒f0点数
-        if (inp_f0 is not None):
-            delta_t=np.round((inp_f0[:,0].max()-inp_f0[:,0].min())*tf0+1).astype("int16")
-            replace_f0=np.interp(list(range(delta_t)), inp_f0[:, 0]*100, inp_f0[:, 1])
-            shape=f0[x_pad*tf0:x_pad*tf0+len(replace_f0)].shape[0]
-            f0[x_pad*tf0:x_pad*tf0+len(replace_f0)]=replace_f0[:shape]
+        tf0 = self.sr // self.window  # 每秒f0点数
+        if inp_f0 is not None:
+            delta_t = np.round(
+                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
+            ).astype("int16")
+            replace_f0 = np.interp(
+                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
+            )
+            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
+            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
+                :shape
+            ]
         # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
         f0bak = f0.copy()
         f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
+        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
+            f0_mel_max - f0_mel_min
+        ) + 1
         f0_mel[f0_mel <= 1] = 1
         f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak#1-0
+        f0_coarse = np.rint(f0_mel).astype(np.int32)
+        return f0_coarse, f0bak  # 1-0
 
-    def vc(self,model,net_g,dv,audio0,pitch,pitchf,times):
+    def vc(
+        self,
+        model,
+        net_g,
+        sid,
+        audio0,
+        pitch,
+        pitchf,
+        times,
+        index,
+        big_npy,
+        index_rate,
+        version,
+        protect,
+    ):  # ,file_index,file_big_npy
         feats = torch.from_numpy(audio0)
-        if(self.is_half==True):feats=feats.half()
-        else:feats=feats.float()
+        if self.is_half:
+            feats = feats.half()
+        else:
+            feats = feats.float()
         if feats.dim() == 2:  # double channels
             feats = feats.mean(-1)
         assert feats.dim() == 1, feats.dim()
         feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
+        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
 
         inputs = {
             "source": feats.to(self.device),
-            "padding_mask": padding_mask.to(self.device),
-            "output_layer": 9,  # layer 9
+            "padding_mask": padding_mask,
+            "output_layer": 9 if 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 protect < 0.5 and pitch is not None and pitchf is not None:
+            feats0 = feats.clone()
+        if (
+            not isinstance(index, type(None))
+            and not isinstance(big_npy, type(None))
+            and index_rate != 0
+        ):
+            npy = feats[0].cpu().numpy()
+            if self.is_half:
+                npy = npy.astype("float32")
+
+            # _, I = index.search(npy, 1)
+            # npy = big_npy[I.squeeze()]
+
+            score, ix = index.search(npy, k=8)
+            weight = np.square(1 / score)
+            weight /= weight.sum(axis=1, keepdims=True)
+            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
+
+            if self.is_half:
+                npy = npy.astype("float16")
+            feats = (
+                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
+                + (1 - index_rate) * feats
+            )
+
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+        if protect < 0.5 and pitch is not None and pitchf is not None:
+            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
+                0, 2, 1
+            )
         t1 = ttime()
-        p_len = audio0.shape[0]//self.window
-        if(feats.shape[1]<p_len):
-            p_len=feats.shape[1]
-            pitch=pitch[:,:p_len]
-            pitchf=pitchf[:,:p_len]
-        p_len=torch.LongTensor([p_len]).to(self.device)
-        with torch.no_grad():
-            audio1 = (net_g.infer(feats, p_len, pitch, pitchf, dv)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
-        del feats,p_len,padding_mask
-        torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += (t1 - t0)
-        times[2] += (t2 - t1)
-        return audio1
-    def vc_km(self,model,net_g,dv,audio0,pitch,pitchf,times):
-        kmeans = KMeans(500)
-        def get_cluster_result(x):
-            """x: np.array [t, 256]"""
-            return kmeans.predict(x)
-        checkpoint = torch.load("lulu_contentvec_kmeans_500.pt")
-        kmeans.__dict__["n_features_in_"] = checkpoint["n_features_in_"]
-        kmeans.__dict__["_n_threads"] = checkpoint["_n_threads"]
-        kmeans.__dict__["cluster_centers_"] = checkpoint["cluster_centers_"]
-        feats = torch.from_numpy(audio0).float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        inputs = {
-            "source": feats.half().to(self.device),
-            "padding_mask": padding_mask.to(self.device),
-            "output_layer": 9,  # layer 9
-        }
-        torch.cuda.synchronize()
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
-        feats = get_cluster_result(feats.cpu().numpy()[0].astype("float32"))
-        feats = torch.from_numpy(feats).to(self.device)
-        feats = F.interpolate(feats.half().unsqueeze(0).unsqueeze(0), scale_factor=2).long().squeeze(0)
-        t1 = ttime()
-        p_len = audio0.shape[0]//self.window
-        if(feats.shape[1]<p_len):
-            p_len=feats.shape[1]
-            pitch=pitch[:,:p_len]
-            pitchf=pitchf[:,:p_len]
-        p_len=torch.LongTensor([p_len]).to(self.device)
+        p_len = audio0.shape[0] // self.window
+        if feats.shape[1] < p_len:
+            p_len = feats.shape[1]
+            if pitch is not None and pitchf is not None:
+                pitch = pitch[:, :p_len]
+                pitchf = pitchf[:, :p_len]
+
+        if protect < 0.5 and pitch is not None and pitchf is not None:
+            pitchff = pitchf.clone()
+            pitchff[pitchf > 0] = 1
+            pitchff[pitchf < 1] = protect
+            pitchff = pitchff.unsqueeze(-1)
+            feats = feats * pitchff + feats0 * (1 - pitchff)
+            feats = feats.to(feats0.dtype)
+        p_len = torch.tensor([p_len], device=self.device).long()
         with torch.no_grad():
-            audio1 = (net_g.infer(feats, p_len, pitch, pitchf, dv)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
-        del feats,p_len,padding_mask
-        torch.cuda.empty_cache()
+            hasp = pitch is not None and pitchf is not None
+            arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)
+            audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
+            del hasp, arg
+        del feats, p_len, padding_mask
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
         t2 = ttime()
-        times[0] += (t1 - t0)
-        times[2] += (t2 - t1)
+        times[0] += t1 - t0
+        times[2] += t2 - t1
         return audio1
 
-    def pipeline(self,model,net_g,dv,audio,times,f0_up_key,f0_file=None):
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode='reflect')
-        opt_ts = []
-        if(audio_pad.shape[0]>self.t_max):
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window): audio_sum += audio_pad[i:i - self.window]
-            for t in range(self.t_center, audio.shape[0],self.t_center):opt_ts.append(t - self.t_query + np.where(np.abs(audio_sum[t - self.t_query:t + self.t_query]) == np.abs(audio_sum[t - self.t_query:t + self.t_query]).min())[0][0])
-        s = 0
-        audio_opt=[]
-        t=None
-        t1=ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode='reflect')
-        p_len=audio_pad.shape[0]//self.window
-        inp_f0=None
-        if(hasattr(f0_file,'name') ==True):
+    def pipeline(
+        self,
+        model,
+        net_g,
+        sid,
+        audio,
+        input_audio_path,
+        times,
+        f0_up_key,
+        f0_method,
+        file_index,
+        index_rate,
+        if_f0,
+        filter_radius,
+        tgt_sr,
+        resample_sr,
+        rms_mix_rate,
+        version,
+        protect,
+        f0_file=None,
+    ):
+        if (
+            file_index != ""
+            # and file_big_npy != ""
+            # and os.path.exists(file_big_npy) == True
+            and os.path.exists(file_index)
+            and index_rate != 0
+        ):
             try:
-                with open(f0_file.name,"r")as f:
-                    lines=f.read().strip("\n").split("\n")
-                inp_f0=[]
-                for line in lines:inp_f0.append([float(i)for i in line.split(",")])
-                inp_f0=np.array(inp_f0,dtype="float32")
+                index = faiss.read_index(file_index)
+                # big_npy = np.load(file_big_npy)
+                big_npy = index.reconstruct_n(0, index.ntotal)
             except:
                 traceback.print_exc()
-        pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key,inp_f0)
-
-        pitch = pitch[:p_len]
-        pitchf = pitchf[:p_len]
-        # if(inp_f0 is None):
-        #     pitch = pitch[:p_len]
-        #     pitchf = pitchf[:p_len]
-        # else:
-        #     pitch=resize2d(pitch,p_len,is1=True)
-        #     pitchf=resize2d(pitchf,p_len,is1=False)
-        pitch = torch.LongTensor(pitch).unsqueeze(0).to(self.device)
-        pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(self.device)
-        t2=ttime()
-        times[1] += (t2 - t1)
-        for t in opt_ts:
-            t=t//self.window*self.window
-            audio_opt.append(self.vc(model,net_g,dv,audio_pad[s:t+self.t_pad2+self.window],pitch[:,s//self.window:(t+self.t_pad2)//self.window],pitchf[:,s//self.window:(t+self.t_pad2)//self.window],times)[self.t_pad_tgt:-self.t_pad_tgt])
-            s = t
-        audio_opt.append(self.vc(model,net_g,dv,audio_pad[t:],pitch[:,t//self.window:]if t is not None else pitch,pitchf[:,t//self.window:]if t is not None else pitchf,times)[self.t_pad_tgt:-self.t_pad_tgt])
-        audio_opt=np.concatenate(audio_opt)
-        del pitch,pitchf
-        return audio_opt
-    def pipeline_km(self,model,net_g,dv,audio,times,f0_up_key,f0_file=None):
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode='reflect')
+                index = big_npy = None
+        else:
+            index = big_npy = None
+        audio = signal.filtfilt(bh, ah, audio)
+        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
         opt_ts = []
-        if(audio_pad.shape[0]>self.t_max):
+        if audio_pad.shape[0] > self.t_max:
             audio_sum = np.zeros_like(audio)
-            for i in range(self.window): audio_sum += audio_pad[i:i - self.window]
-            for t in range(self.t_center, audio.shape[0],self.t_center):opt_ts.append(t - self.t_query + np.where(np.abs(audio_sum[t - self.t_query:t + self.t_query]) == np.abs(audio_sum[t - self.t_query:t + self.t_query]).min())[0][0])
+            for i in range(self.window):
+                audio_sum += np.abs(audio_pad[i : i - self.window])
+            for t in range(self.t_center, audio.shape[0], self.t_center):
+                opt_ts.append(
+                    t
+                    - self.t_query
+                    + np.where(
+                        audio_sum[t - self.t_query : t + self.t_query]
+                        == audio_sum[t - self.t_query : t + self.t_query].min()
+                    )[0][0]
+                )
         s = 0
-        audio_opt=[]
-        t=None
-        t1=ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode='reflect')
-        p_len=audio_pad.shape[0]//self.window
-        inp_f0=None
-        if(hasattr(f0_file,'name') ==True):
+        audio_opt = []
+        t = None
+        t1 = ttime()
+        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
+        p_len = audio_pad.shape[0] // self.window
+        inp_f0 = None
+        if hasattr(f0_file, "name"):
             try:
-                with open(f0_file.name,"r")as f:
-                    lines=f.read().strip("\n").split("\n")
-                inp_f0=[]
-                for line in lines:inp_f0.append([float(i)for i in line.split(",")])
-                inp_f0=np.array(inp_f0,dtype="float32")
+                with open(f0_file.name, "r") as f:
+                    lines = f.read().strip("\n").split("\n")
+                inp_f0 = []
+                for line in lines:
+                    inp_f0.append([float(i) for i in line.split(",")])
+                inp_f0 = np.array(inp_f0, dtype="float32")
             except:
                 traceback.print_exc()
-        pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key,inp_f0)
-
-        pitch = pitch[:p_len]
-        pitchf = pitchf[:p_len]
-        # if(inp_f0 is None):
-        #     pitch = pitch[:p_len]
-        #     pitchf = pitchf[:p_len]
-        # else:
-        #     pitch=resize2d(pitch,p_len,is1=True)
-        #     pitchf=resize2d(pitchf,p_len,is1=False)
-        pitch = torch.LongTensor(pitch).unsqueeze(0).to(self.device)
-        pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(self.device)
-        t2=ttime()
-        times[1] += (t2 - t1)
+        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 = pitch[:p_len]
+            pitchf = pitchf[:p_len]
+            if "mps" not in str(self.device) or "xpu" not in str(self.device):
+                pitchf = pitchf.astype(np.float32)
+            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
+            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
+        t2 = ttime()
+        times[1] += t2 - t1
         for t in opt_ts:
-            t=t//self.window*self.window
-            audio_opt.append(self.vc_km(model,net_g,dv,audio_pad[s:t+self.t_pad2+self.window],pitch[:,s//self.window:(t+self.t_pad2)//self.window],pitchf[:,s//self.window:(t+self.t_pad2)//self.window],times)[self.t_pad_tgt:-self.t_pad_tgt])
+            t = t // self.window * self.window
+            if if_f0 == 1:
+                audio_opt.append(
+                    self.vc(
+                        model,
+                        net_g,
+                        sid,
+                        audio_pad[s : t + self.t_pad2 + self.window],
+                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
+                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
+                        times,
+                        index,
+                        big_npy,
+                        index_rate,
+                        version,
+                        protect,
+                    )[self.t_pad_tgt : -self.t_pad_tgt]
+                )
+            else:
+                audio_opt.append(
+                    self.vc(
+                        model,
+                        net_g,
+                        sid,
+                        audio_pad[s : t + self.t_pad2 + self.window],
+                        None,
+                        None,
+                        times,
+                        index,
+                        big_npy,
+                        index_rate,
+                        version,
+                        protect,
+                    )[self.t_pad_tgt : -self.t_pad_tgt]
+                )
             s = t
-        audio_opt.append(self.vc_km(model,net_g,dv,audio_pad[t:],pitch[:,t//self.window:]if t is not None else pitch,pitchf[:,t//self.window:]if t is not None else pitchf,times)[self.t_pad_tgt:-self.t_pad_tgt])
-        audio_opt=np.concatenate(audio_opt)
-        del pitch,pitchf
-        return audio_opt
+        if if_f0 == 1:
+            audio_opt.append(
+                self.vc(
+                    model,
+                    net_g,
+                    sid,
+                    audio_pad[t:],
+                    pitch[:, t // self.window :] if t is not None else pitch,
+                    pitchf[:, t // self.window :] if t is not None else pitchf,
+                    times,
+                    index,
+                    big_npy,
+                    index_rate,
+                    version,
+                    protect,
+                )[self.t_pad_tgt : -self.t_pad_tgt]
+            )
+        else:
+            audio_opt.append(
+                self.vc(
+                    model,
+                    net_g,
+                    sid,
+                    audio_pad[t:],
+                    None,
+                    None,
+                    times,
+                    index,
+                    big_npy,
+                    index_rate,
+                    version,
+                    protect,
+                )[self.t_pad_tgt : -self.t_pad_tgt]
+            )
+        audio_opt = np.concatenate(audio_opt)
+        if rms_mix_rate != 1:
+            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
+        if tgt_sr != resample_sr >= 16000:
+            audio_opt = librosa.resample(
+                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
+            )
+        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()
+        return audio_opt