Upload pipeline.py

lib/pipeline.py

@@ -0,0 +1,773 @@
+import os
+import sys
+import gc
+import traceback
+import logging
+
+logger = logging.getLogger(__name__)
+
+from functools import lru_cache
+from time import time as ttime
+from torch import Tensor
+import faiss
+import librosa
+import numpy as np
+import parselmouth
+import pyworld
+import torch.nn.functional as F
+from scipy import signal
+from tqdm import tqdm
+
+import random
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+import re
+from functools import partial
+bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
+
+input_audio_path2wav = {}
+import torchcrepe  # Fork Feature. Crepe algo for training and preprocess
+from torchfcpe import spawn_bundled_infer_model
+import torch
+from lib.infer_libs.rmvpe import RMVPE
+from lib.infer_libs.fcpe import FCPE
+
+@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
+        self.model_rmvpe = RMVPE(os.environ["rmvpe_model_path"], is_half=self.is_half, device=self.device)
+
+        self.note_dict = [
+            65.41, 69.30, 73.42, 77.78, 82.41, 87.31,
+            92.50, 98.00, 103.83, 110.00, 116.54, 123.47,
+            130.81, 138.59, 146.83, 155.56, 164.81, 174.61,
+            185.00, 196.00, 207.65, 220.00, 233.08, 246.94,
+            261.63, 277.18, 293.66, 311.13, 329.63, 349.23,
+            369.99, 392.00, 415.30, 440.00, 466.16, 493.88,
+            523.25, 554.37, 587.33, 622.25, 659.25, 698.46,
+            739.99, 783.99, 830.61, 880.00, 932.33, 987.77,
+            1046.50, 1108.73, 1174.66, 1244.51, 1318.51, 1396.91,
+            1479.98, 1567.98, 1661.22, 1760.00, 1864.66, 1975.53,
+            2093.00, 2217.46, 2349.32, 2489.02, 2637.02, 2793.83,
+            2959.96, 3135.96, 3322.44, 3520.00, 3729.31, 3951.07
+        ]
+
+    # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
+    def get_optimal_torch_device(self, index: int = 0) -> torch.device:
+        if torch.cuda.is_available():
+            return torch.device(
+                f"cuda:{index % torch.cuda.device_count()}"
+            )  # Very fast
+        elif torch.backends.mps.is_available():
+            return torch.device("mps")
+        return torch.device("cpu")
+
+    # Fork Feature: Compute f0 with the crepe method
+    def get_f0_crepe_computation(
+        self,
+        x,
+        f0_min,
+        f0_max,
+        p_len,
+        *args,  # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
+        **kwargs,  # Either use crepe-tiny "tiny" or crepe "full". Default is full
+    ):
+        x = x.astype(
+            np.float32
+        )  # fixes the F.conv2D exception. We needed to convert double to float.
+        x /= np.quantile(np.abs(x), 0.999)
+        torch_device = self.get_optimal_torch_device()
+        audio = torch.from_numpy(x).to(torch_device, copy=True)
+        audio = torch.unsqueeze(audio, dim=0)
+        if audio.ndim == 2 and audio.shape[0] > 1:
+            audio = torch.mean(audio, dim=0, keepdim=True).detach()
+        audio = audio.detach()
+        hop_length = kwargs.get('crepe_hop_length', 160)
+        model = kwargs.get('model', 'full') 
+        print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
+        pitch: Tensor = torchcrepe.predict(
+            audio,
+            self.sr,
+            hop_length,
+            f0_min,
+            f0_max,
+            model,
+            batch_size=hop_length * 2,
+            device=torch_device,
+            pad=True,
+        )
+        p_len = p_len or x.shape[0] // hop_length
+        # Resize the pitch for final f0
+        source = np.array(pitch.squeeze(0).cpu().float().numpy())
+        source[source < 0.001] = np.nan
+        target = np.interp(
+            np.arange(0, len(source) * p_len, len(source)) / p_len,
+            np.arange(0, len(source)),
+            source,
+        )
+        f0 = np.nan_to_num(target)
+        return f0  # Resized f0
+    
+    def get_f0_official_crepe_computation(
+        self,
+        x,
+        f0_min,
+        f0_max,
+        *args,
+        **kwargs
+    ):
+        # 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()
+        model = kwargs.get('model', 'full')
+        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()
+        return f0
+
+    # Fork Feature: Compute pYIN f0 method
+    def get_f0_pyin_computation(self, x, f0_min, f0_max):
+        y, sr = librosa.load(x, sr=self.sr, mono=True)
+        f0, _, _ = librosa.pyin(y, fmin=f0_min, fmax=f0_max, sr=self.sr)
+        f0 = f0[1:]  # Get rid of extra first frame
+        return f0
+
+    def get_rmvpe(self, x, *args, **kwargs):
+        if not hasattr(self, "model_rmvpe"):
+            from lib.infer.infer_libs.rmvpe import RMVPE
+            
+            logger.info(
+                f"Loading rmvpe model, {os.environ['rmvpe_model_path']}"
+            )
+            self.model_rmvpe = RMVPE(
+                os.environ["rmvpe_model_path"],
+                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")
+            
+        return f0
+    
+
+    def get_pitch_dependant_rmvpe(self, x, f0_min=1, f0_max=40000, *args, **kwargs):
+        if not hasattr(self, "model_rmvpe"):
+            from lib.infer.infer_libs.rmvpe import RMVPE
+            
+            logger.info(
+                f"Loading rmvpe model, {os.environ['rmvpe_model_path']}"
+            )
+            self.model_rmvpe = RMVPE(
+                os.environ["rmvpe_model_path"],
+                is_half=self.is_half,
+                device=self.device,
+            )
+        f0 = self.model_rmvpe.infer_from_audio_with_pitch(x, thred=0.03, f0_min=f0_min, f0_max=f0_max)   
+        if "privateuseone" in str(self.device):  # clean ortruntime memory
+                del self.model_rmvpe.model
+                del self.model_rmvpe
+                logger.info("Cleaning ortruntime memory")
+            
+        return f0
+        
+    def get_fcpe(self, x, f0_min, f0_max, p_len, *args, **kwargs):
+        self.model_fcpe = FCPE(os.environ["fcpe_model_path"], f0_min=f0_min, f0_max=f0_max, dtype=torch.float32, device=self.device, sampling_rate=self.sr, threshold=0.03)
+        f0 = self.model_fcpe.compute_f0(x, p_len=p_len)
+        del self.model_fcpe
+        gc.collect()
+        return f0
+
+    def get_torchfcpe(self, x, sr, f0_min, f0_max, p_len, *args, **kwargs):
+        self.model_torchfcpe = spawn_bundled_infer_model(device=self.device)
+        f0 = self.model_torchfcpe.infer(
+            torch.from_numpy(x).float().unsqueeze(0).unsqueeze(-1).to(self.device),
+            sr=sr,
+            decoder_mode="local_argmax",
+            threshold=0.03,
+            f0_min=f0_min,
+            f0_max=f0_max,
+            output_interp_target_length=p_len
+        )
+        return f0.squeeze().cpu().numpy()
+
+    def autotune_f0(self, f0):
+        autotuned_f0 = []
+        for freq in f0:
+            closest_notes = [x for x in self.note_dict if abs(x - freq) == min(abs(n - freq) for n in self.note_dict)]
+            autotuned_f0.append(random.choice(closest_notes))
+        return np.array(autotuned_f0, np.float64)
+
+
+    # Fork Feature: Acquire median hybrid f0 estimation calculation
+    def get_f0_hybrid_computation(
+        self,
+        methods_str,
+        input_audio_path,
+        x,
+        f0_min,
+        f0_max,
+        p_len,
+        filter_radius,
+        crepe_hop_length,
+        time_step,
+    ):
+        # Get various f0 methods from input to use in the computation stack
+        methods_str = re.search('hybrid\[(.+)\]', methods_str)
+        if methods_str:  # Ensure a match was found
+            methods = [method.strip() for method in methods_str.group(1).split('+')]
+        f0_computation_stack = []
+
+        print("Calculating f0 pitch estimations for methods: %s" % str(methods))
+        x = x.astype(np.float32)
+        x /= np.quantile(np.abs(x), 0.999)
+        # Get f0 calculations for all methods specified
+        for method in methods:
+            f0 = None
+            if 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 method == "crepe":
+                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, model="full")
+                f0 = f0[1:]
+            elif method == "crepe-tiny":
+                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, model="tiny")
+                f0 = f0[1:]  # Get rid of extra first frame
+            elif method == "mangio-crepe":
+                f0 = self.get_f0_crepe_computation(
+                    x, f0_min, f0_max, p_len, crepe_hop_length=crepe_hop_length
+                )
+            elif method == "mangio-crepe-tiny":
+                f0 = self.get_f0_crepe_computation(
+                    x, f0_min, f0_max, p_len, crepe_hop_length=crepe_hop_length, model="tiny"
+                )
+            elif 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 method == "dio":
+                f0, t = pyworld.dio(
+                    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)
+                f0 = signal.medfilt(f0, 3)
+                f0 = f0[1:]
+            elif method == "rmvpe":
+                f0 = self.get_rmvpe(x)
+                f0 = f0[1:]
+            elif method == "fcpe_legacy":
+                f0 = self.get_fcpe(x, f0_min=f0_min, f0_max=f0_max, p_len=p_len)
+            elif method == "fcpe":
+                f0 = self.get_torchfcpe(x, self.sr, f0_min, f0_max, p_len)
+            elif method == "pyin":
+                f0 = self.get_f0_pyin_computation(input_audio_path, f0_min, f0_max)
+            # Push method to the stack
+            f0_computation_stack.append(f0)
+
+        for fc in f0_computation_stack:
+            print(len(fc))
+
+        print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
+        f0_median_hybrid = None
+        if len(f0_computation_stack) == 1:
+            f0_median_hybrid = f0_computation_stack[0]
+        else:
+            f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
+        return f0_median_hybrid
+    
+    def get_f0(
+        self,
+        input_audio_path,
+        x,
+        p_len,
+        f0_up_key,
+        f0_method,
+        filter_radius,
+        crepe_hop_length,
+        f0_autotune,
+        inp_f0=None,
+        f0_min=50,
+        f0_max=1100,
+    ):
+        global input_audio_path2wav
+        time_step = self.window / self.sr * 1000
+        f0_min = f0_min
+        f0_max = f0_max
+        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+
+        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 == "dio":  # Potentially Buggy?
+            f0, t = pyworld.dio(
+                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)
+            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 == "crepe-tiny":
+            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, model="tiny")
+        elif f0_method == "mangio-crepe":
+            f0 = self.get_f0_crepe_computation(
+                x, f0_min, f0_max, p_len, crepe_hop_length=crepe_hop_length
+            )
+        elif f0_method == "mangio-crepe-tiny":
+            f0 = self.get_f0_crepe_computation(
+                x, f0_min, f0_max, p_len, crepe_hop_length=crepe_hop_length, model="tiny"
+            )
+        elif f0_method == "rmvpe":
+            if not hasattr(self, "model_rmvpe"):
+                from lib.infer.infer_libs.rmvpe import RMVPE
+
+                logger.info(
+                    f"Loading rmvpe model, {os.environ['rmvpe_model_path']}"
+                )
+                self.model_rmvpe = RMVPE(
+                    os.environ["rmvpe_model_path"],
+                    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")
+        elif f0_method == "rmvpe+":
+            params = {'x': x, 'p_len': p_len, 'f0_up_key': f0_up_key, 'f0_min': f0_min, 
+                      'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius, 
+                      'crepe_hop_length': crepe_hop_length, 'model': "full"
+                      }
+            f0 = self.get_pitch_dependant_rmvpe(**params)
+        elif f0_method == "pyin":
+            f0 = self.get_f0_pyin_computation(input_audio_path, f0_min, f0_max)
+        elif f0_method == "fcpe_legacy":
+            f0 = self.get_fcpe(x, f0_min=f0_min, f0_max=f0_max, p_len=p_len)
+        elif f0_method == "fcpe":
+            f0 = self.get_torchfcpe(x, self.sr, f0_min, f0_max, p_len)
+        elif "hybrid" in f0_method:
+            # Perform hybrid median pitch estimation
+            input_audio_path2wav[input_audio_path] = x.astype(np.double)
+            f0 = self.get_f0_hybrid_computation(
+                f0_method,
+                input_audio_path,
+                x,
+                f0_min,
+                f0_max,
+                p_len,
+                filter_radius,
+                crepe_hop_length,
+                time_step,
+            )
+        #print("Autotune:", f0_autotune)
+        if f0_autotune == True:
+            print("Autotune:", f0_autotune)
+            f0 = self.autotune_f0(f0)
+
+        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[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 <= 1] = 1
+        f0_mel[f0_mel > 255] = 255
+        f0_coarse = np.rint(f0_mel).astype(np.int32)
+        return f0_coarse, f0bak  # 1-0
+
+    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:
+            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).to(self.device).fill_(False)
+
+        inputs = {
+            "source": feats.to(self.device),
+            "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]) 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]
+            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():
+            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
+        return audio1
+    def process_t(self, t, s, window, audio_pad, pitch, pitchf, times, index, big_npy, index_rate, version, protect, t_pad_tgt, if_f0, sid, model, net_g):
+        t = t // window * window
+        if if_f0 == 1:
+            return self.vc(
+                model,
+                net_g,
+                sid,
+                audio_pad[s : t + t_pad_tgt + window],
+                pitch[:, s // window : (t + t_pad_tgt) // window],
+                pitchf[:, s // window : (t + t_pad_tgt) // window],
+                times,
+                index,
+                big_npy,
+                index_rate,
+                version,
+                protect,
+            )[t_pad_tgt : -t_pad_tgt]
+        else:
+            return self.vc(
+                model,
+                net_g,
+                sid,
+                audio_pad[s : t + t_pad_tgt + window],
+                None,
+                None,
+                times,
+                index,
+                big_npy,
+                index_rate,
+                version,
+                protect,
+            )[t_pad_tgt : -t_pad_tgt]
+
+
+    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,
+        crepe_hop_length, 
+        f0_autotune, 
+        f0_min=50, 
+        f0_max=1100
+    ):
+        if (
+            file_index != ""
+            and isinstance(file_index, str)
+            # and file_big_npy != ""
+            # and os.path.exists(file_big_npy) == True
+            and os.path.exists(file_index)
+            and index_rate != 0
+        ):
+            try:
+                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()
+                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:
+            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
+        
+        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
+        pitch, pitchf = None, None
+        if if_f0:
+            pitch, pitchf = self.get_f0(
+                input_audio_path,
+                audio_pad,
+                p_len,
+                f0_up_key,
+                f0_method,
+                filter_radius, 
+                crepe_hop_length, 
+                f0_autotune,
+                inp_f0, 
+                f0_min, 
+                f0_max
+            )
+            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
+
+        with tqdm(total=len(opt_ts), desc="Processing", unit="window") as pbar:
+            for i, t in enumerate(opt_ts):
+                t = t // self.window * self.window
+                start = s
+                end = t + self.t_pad2 + self.window
+                audio_slice = audio_pad[start:end]
+                pitch_slice = pitch[:, start // self.window:end // self.window] if if_f0 else None
+                pitchf_slice = pitchf[:, start // self.window:end // self.window] if if_f0 else None
+                audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt])
+                s = t
+                pbar.update(1)
+                pbar.refresh()
+
+        audio_slice = audio_pad[t:]
+        pitch_slice = pitch[:, t // self.window:] if if_f0 and t is not None else pitch
+        pitchf_slice = pitchf[:, t // self.window:] if if_f0 and t is not None else pitchf
+        audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, 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()
+
+        print("Returning completed audio...")
+        return audio_opt