src/vc_infer_pipeline.py

from functools import lru_cache
from time import time as ttime
import faiss
import librosa
import numpy as np
import os
import parselmouth
import pyworld
import sys
import torch
import torch.nn.functional as F
import torchcrepe
import traceback
from scipy import signal
from scipy.ndimage import median_filter
from torch import Tensor

BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
now_dir = os.path.join(BASE_DIR, 'src')
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
    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):
        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

    # 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:
        # Get cuda 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")
        # Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
        # Else wise return the "cpu" as a torch device,
        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,
        hop_length=160,  # 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.
        model="full",  # 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()
        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,
        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()
        return f0

    def get_f0_pyin_computation(self, x, f0_min, f0_max):
        """
        Improved pYIN pitch extraction using librosa.
        More robust than the original broken implementation.
        """
        f0, voiced_flag, voiced_probs = librosa.pyin(
            x.astype(np.float32),
            sr=self.sr,
            fmin=f0_min,
            fmax=f0_max,
            frame_length=int(0.025 * self.sr),  # 25ms window
            hop_length=self.window,  # 10ms hop
            fill_na=0.0
        )
        # Apply median filter to smooth the output
        f0 = median_filter(f0, size=3)
        return f0

    def get_f0_fcnf0_computation(
        self,
        x,
        f0_min,
        f0_max,
        p_len,
        hop_length=160,
    ):
        """
        FCNF0-style pitch extraction using enhanced crepe with custom post-processing.
        Provides more stable pitch tracking than standard crepe.
        """
        x = x.astype(np.float32)
        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()
        
        # Use crepe-tiny for faster inference with good accuracy
        pitch: Tensor = torchcrepe.predict(
            audio,
            self.sr,
            hop_length,
            f0_min,
            f0_max,
            "tiny",
            batch_size=hop_length * 2,
            device=torch_device,
            pad=True,
        )
        
        source = np.array(pitch.squeeze(0).cpu().float().numpy())
        source[source < 0.001] = np.nan
        
        # Enhanced post-processing for stability
        # Interpolate to target length
        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)
        
        # Apply additional smoothing for FCNF0-like behavior
        f0 = median_filter(f0, size=5)
        
        return f0

    def get_f0_reaper_computation(self, x, f0_min, f0_max):
        """
        REAPER-style pitch extraction using parselmouth with enhanced settings.
        Good for noisy audio and singing voice.
        """
        sound = parselmouth.Sound(x, self.sr)
        pitch = sound.to_pitch_ac(
            time_step=0.005,  # Higher temporal resolution
            voicing_threshold=0.45,  # Slightly lower threshold for sensitivity
            silence_threshold=0.03,
            voicing_unvoicing_mode=1,  # Better handling of unvoiced regions
            octave_cost=0.01,
            octave_jump_cost=0.35,
            voiced_unvoiced_cost=0.14,
            pitch_floor=f0_min,
            pitch_ceiling=f0_max,
        )
        f0 = pitch.selected_array["frequency"]
        return f0

    def align_f0_length(self, f0, target_len):
        """
        Align F0 array to target length using interpolation.
        Critical for proper hybrid F0 computation.
        """
        if len(f0) == target_len:
            return f0
        
        # Handle edge cases
        if len(f0) == 0:
            return np.zeros(target_len)
        
        # Create original indices
        orig_indices = np.arange(len(f0))
        target_indices = np.linspace(0, len(f0) - 1, target_len)
        
        aligned = np.interp(target_indices, orig_indices, f0)
        return aligned

    # 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
        s = methods_str
        s = s.split("hybrid")[1]
        s = s.replace("[", "").replace("]", "")
        methods = s.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"]
                )
            elif method == "crepe":
                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
                f0 = f0[1:]  # Get rid of extra first frame
            elif method == "crepe-tiny":
                f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "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
                )
            elif method == "mangio-crepe-tiny":
                f0 = self.get_f0_crepe_computation(
                    x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
                )
            elif method == "harvest":
                f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
                if filter_radius > 2:
                    f0 = signal.medfilt(f0, 3)
                f0 = f0[1:]  # Get rid of first frame.
            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 == "pyin":
                # Fixed pYIN implementation
                f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
            elif method == "fcnf0":
                # New FCNF0-style method
                f0 = self.get_f0_fcnf0_computation(x, f0_min, f0_max, p_len)
            elif method == "reaper":
                # REAPER-style extraction
                f0 = self.get_f0_reaper_computation(x, f0_min, f0_max)
            else:
                print(f"Warning: Unknown F0 method '{method}' in hybrid, skipping.")
                continue
            
            # Align all F0 arrays to same length before stacking
            if f0 is not None:
                f0 = self.align_f0_length(f0, p_len)
                f0_computation_stack.append(f0)

        if not f0_computation_stack:
            raise ValueError("No valid F0 methods computed in hybrid mode!")

        print(f"F0 computation stack lengths: {[len(f) for f in f0_computation_stack]}")
        print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
        
        # Compute weighted median hybrid
        if len(f0_computation_stack) == 1:
            f0_median_hybrid = f0_computation_stack[0]
        else:
            # Use nanmedian to handle any NaN values gracefully
            f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
            
            # Additional smoothing for hybrid result
            f0_median_hybrid = median_filter(f0_median_hybrid, size=3)
        
        return f0_median_hybrid

    def get_f0(
        self,
        input_audio_path,
        x,
        p_len,
        f0_up_key,
        f0_method,
        filter_radius,
        crepe_hop_length,
        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)
        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":
            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
        elif f0_method == "crepe-tiny":
            f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
        elif f0_method == "mangio-crepe":
            f0 = self.get_f0_crepe_computation(
                x, f0_min, f0_max, p_len, 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, "tiny"
            )
        elif "rmvpe" in f0_method:
            if hasattr(self, "model_rmvpe") == False:
                from rmvpe import RMVPE

                self.model_rmvpe = RMVPE(
                    os.path.join(BASE_DIR, 'rvc_models', 'rmvpe.pt'), is_half=self.is_half, device=self.device
                )
            thred = 0.03
            if "+" in f0_method:
                f0 = self.model_rmvpe.pitch_based_audio_inference(x, thred, f0_min, f0_max)
            else:
                f0 = self.model_rmvpe.infer_from_audio(x, thred)
        elif f0_method == "pyin":
            # Fixed pYIN implementation using librosa
            f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
        elif f0_method == "fcnf0":
            # FCNF0-style pitch extraction with enhanced stability
            f0 = self.get_f0_fcnf0_computation(x, f0_min, f0_max, p_len)
        elif f0_method == "reaper":
            # REAPER-style extraction for noisy audio/singing
            f0 = self.get_f0_reaper_computation(x, f0_min, f0_max)
            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 "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,
            )

        f0 *= pow(2, f0_up_key / 12)
        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
            ]
        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(int)

        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 != None and pitchf != None:
            feats0 = feats.clone()
        if (
            isinstance(index, type(None)) == False
            and isinstance(big_npy, type(None)) == False
            and index_rate != 0
        ):
            npy = feats[0].cpu().numpy()
            if self.is_half:
                npy = npy.astype("float32")

            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 != None and pitchf != 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 != None and pitchf != None:
                pitch = pitch[:, :p_len]
                pitchf = pitchf[:, :p_len]

        if protect < 0.5 and pitch != None and pitchf != 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():
            if pitch != None and pitchf != None:
                audio1 = (
                    (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]).data.cpu().float().numpy()
                )
        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 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_file=None,
    ):
        if (
            file_index != ""
            and os.path.exists(file_index) == True
            and index_rate != 0
        ):
            try:
                index = faiss.read_index(file_index)
                big_npy = index.reconstruct_n(0, index.ntotal)
            except:
                traceback.print_exc()
                index = big_npy = None
                index_rate = 0
        else:
            index = big_npy = None
            index_rate = 0
        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
        if hasattr(f0_file, "name") == True:
            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")
            except:
                traceback.print_exc()
        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,
                crepe_hop_length,
                inp_f0,
            )
            pitch = pitch[:p_len]
            pitchf = pitchf[:p_len]
            if self.device == "mps":
                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
            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
        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 resample_sr >= 16000 and tgt_sr != resample_sr:
            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