Delete lib/pipeline.py

lib/pipeline.py

@@ -1,773 +0,0 @@
-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":
-                f0 = self.get_fcpe(x, f0_min=f0_min, f0_max=f0_max, p_len=p_len)
-            elif method == "torchfcpe":
-                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":
-            f0 = self.get_fcpe(x, f0_min=f0_min, f0_max=f0_max, p_len=p_len)
-        elif f0_method == "torchfcpe":
-            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