Create pipeline.py

pipeline.py

@@ -0,0 +1,770 @@
+import os
+import gc
+import re
+import sys
+import torch
+import torch.nn.functional as F
+import parselmouth
+import torchcrepe
+import pyworld
+import faiss
+import librosa
+import numpy as np
+from scipy import signal
+from functools import lru_cache
+from torch import Tensor
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from rvc.lib.predictors.RMVPE import RMVPE0Predictor
+from rvc.lib.predictors.FCPE import FCPEF0Predictor
+
+
+# Constants for high-pass filter
+FILTER_ORDER = 5
+CUTOFF_FREQUENCY = 48  # Hz
+SAMPLE_RATE = 16000  # Hz
+bh, ah = signal.butter(
+    N=FILTER_ORDER, Wn=CUTOFF_FREQUENCY, btype="high", fs=SAMPLE_RATE
+)
+
+input_audio_path2wav = {}
+
+
+class AudioProcessor:
+    """
+    A class for processing audio signals, specifically for adjusting RMS levels.
+    """
+
+    def change_rms(
+        source_audio: np.ndarray,
+        source_rate: int,
+        target_audio: np.ndarray,
+        target_rate: int,
+        rate: float,
+    ) -> np.ndarray:
+        """
+        Adjust the RMS level of target_audio to match the RMS of source_audio, with a given blending rate.
+
+        Args:
+            source_audio: The source audio signal as a NumPy array.
+            source_rate: The sampling rate of the source audio.
+            target_audio: The target audio signal to adjust.
+            target_rate: The sampling rate of the target audio.
+            rate: The blending rate between the source and target RMS levels.
+
+        Returns:
+            The adjusted target audio signal with RMS level modified to match the source audio.
+        """
+        # Calculate RMS of both audio data
+        rms1 = librosa.feature.rms(
+            y=source_audio,
+            frame_length=source_rate // 2 * 2,
+            hop_length=source_rate // 2,
+        )
+        rms2 = librosa.feature.rms(
+            y=target_audio,
+            frame_length=target_rate // 2 * 2,
+            hop_length=target_rate // 2,
+        )
+
+        # Interpolate RMS to match target audio length
+        rms1 = F.interpolate(
+            torch.from_numpy(rms1).float().unsqueeze(0),
+            size=target_audio.shape[0],
+            mode="linear",
+        ).squeeze()
+        rms2 = F.interpolate(
+            torch.from_numpy(rms2).float().unsqueeze(0),
+            size=target_audio.shape[0],
+            mode="linear",
+        ).squeeze()
+        rms2 = torch.maximum(rms2, torch.zeros_like(rms2) + 1e-6)
+
+        # Adjust target audio RMS based on the source audio RMS
+        adjusted_audio = (
+            target_audio
+            * (torch.pow(rms1, 1 - rate) * torch.pow(rms2, rate - 1)).numpy()
+        )
+        return adjusted_audio
+
+
+class Autotune:
+    """
+    A class for applying autotune to a given fundamental frequency (F0) contour.
+    """
+
+    def __init__(self, ref_freqs):
+        """
+        Initializes the Autotune class with a set of reference frequencies.
+
+        Args:
+            ref_freqs: A list of reference frequencies representing musical notes.
+        """
+        self.ref_freqs = ref_freqs
+        self.note_dict = self.generate_interpolated_frequencies()
+
+    def generate_interpolated_frequencies(self):
+        """
+        Generates a dictionary of interpolated frequencies between reference frequencies.
+
+        Returns:
+            A list of interpolated frequencies, including the original reference frequencies.
+        """
+        note_dict = []
+        for i in range(len(self.ref_freqs) - 1):
+            freq_low = self.ref_freqs[i]
+            freq_high = self.ref_freqs[i + 1]
+            interpolated_freqs = np.linspace(
+                freq_low, freq_high, num=10, endpoint=False
+            )
+            note_dict.extend(interpolated_freqs)
+        note_dict.append(self.ref_freqs[-1])
+        return note_dict
+
+    def autotune_f0(self, f0):
+        """
+        Autotunes a given F0 contour by snapping each frequency to the closest reference frequency.
+
+        Args:
+            f0: The input F0 contour as a NumPy array.
+
+        Returns:
+            The autotuned F0 contour.
+        """
+        autotuned_f0 = np.zeros_like(f0)
+        for i, freq in enumerate(f0):
+            closest_note = min(self.note_dict, key=lambda x: abs(x - freq))
+            autotuned_f0[i] = closest_note
+        return autotuned_f0
+
+
+class Pipeline:
+    """
+    The main pipeline class for performing voice conversion, including preprocessing, F0 estimation,
+    voice conversion using a model, and post-processing.
+    """
+
+    def __init__(self, tgt_sr, config):
+        """
+        Initializes the Pipeline class with target sampling rate and configuration parameters.
+
+        Args:
+            tgt_sr: The target sampling rate for the output audio.
+            config: A configuration object containing various parameters for the pipeline.
+        """
+        self.x_pad = config.x_pad
+        self.x_query = config.x_query
+        self.x_center = config.x_center
+        self.x_max = config.x_max
+        self.is_half = config.is_half
+        self.sample_rate = 16000
+        self.window = 160
+        self.t_pad = self.sample_rate * self.x_pad
+        self.t_pad_tgt = tgt_sr * self.x_pad
+        self.t_pad2 = self.t_pad * 2
+        self.t_query = self.sample_rate * self.x_query
+        self.t_center = self.sample_rate * self.x_center
+        self.t_max = self.sample_rate * self.x_max
+        self.time_step = self.window / self.sample_rate * 1000
+        self.f0_min = 50
+        self.f0_max = 1100
+        self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
+        self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
+        self.device = config.device
+        self.ref_freqs = [
+            65.41,
+            82.41,
+            110.00,
+            146.83,
+            196.00,
+            246.94,
+            329.63,
+            440.00,
+            587.33,
+            783.99,
+            1046.50,
+        ]
+        self.autotune = Autotune(self.ref_freqs)
+        self.note_dict = self.autotune.note_dict
+
+    @staticmethod
+    @lru_cache
+    def get_f0_harvest(input_audio_path, fs, f0max, f0min, frame_period):
+        """
+        Estimates the fundamental frequency (F0) of a given audio file using the Harvest algorithm.
+
+        Args:
+            input_audio_path: Path to the input audio file.
+            fs: Sampling rate of the audio file.
+            f0max: Maximum F0 value to consider.
+            f0min: Minimum F0 value to consider.
+            frame_period: Frame period in milliseconds for F0 analysis.
+
+        Returns:
+            The estimated F0 contour as a NumPy array.
+        """
+        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 get_f0_crepe(
+        self,
+        x,
+        f0_min,
+        f0_max,
+        p_len,
+        hop_length,
+        model="full",
+    ):
+        """
+        Estimates the fundamental frequency (F0) of a given audio signal using the Crepe model.
+
+        Args:
+            x: The input audio signal as a NumPy array.
+            f0_min: Minimum F0 value to consider.
+            f0_max: Maximum F0 value to consider.
+            p_len: Desired length of the F0 output.
+            hop_length: Hop length for the Crepe model.
+            model: Crepe model size to use ("full" or "tiny").
+
+        Returns:
+            The estimated F0 contour as a NumPy array.
+        """
+        x = x.astype(np.float32)
+        x /= np.quantile(np.abs(x), 0.999)
+        audio = torch.from_numpy(x).to(self.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()
+        pitch: Tensor = torchcrepe.predict(
+            audio,
+            self.sample_rate,
+            hop_length,
+            f0_min,
+            f0_max,
+            model,
+            batch_size=hop_length * 2,
+            device=self.device,
+            pad=True,
+        )
+        p_len = p_len or x.shape[0] // hop_length
+        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
+
+    def get_f0_hybrid(
+        self,
+        methods_str,
+        x,
+        f0_min,
+        f0_max,
+        p_len,
+        hop_length,
+    ):
+        """
+        Estimates the fundamental frequency (F0) using a hybrid approach combining multiple methods.
+
+        Args:
+            methods_str: A string specifying the methods to combine (e.g., "hybrid[crepe+rmvpe]").
+            x: The input audio signal as a NumPy array.
+            f0_min: Minimum F0 value to consider.
+            f0_max: Maximum F0 value to consider.
+            p_len: Desired length of the F0 output.
+            hop_length: Hop length for F0 estimation methods.
+
+        Returns:
+            The estimated F0 contour as a NumPy array, obtained by combining the specified methods.
+        """
+        methods_str = re.search("hybrid\[(.+)\]", methods_str)
+        if methods_str:
+            methods = [method.strip() for method in methods_str.group(1).split("+")]
+        f0_computation_stack = []
+        print(f"Calculating f0 pitch estimations for methods {str(methods)}")
+        x = x.astype(np.float32)
+        x /= np.quantile(np.abs(x), 0.999)
+        for method in methods:
+            f0 = None
+            if method == "crepe":
+                f0 = self.get_f0_crepe_computation(
+                    x, f0_min, f0_max, p_len, int(hop_length)
+                )
+            elif method == "rmvpe":
+                self.model_rmvpe = RMVPE0Predictor(
+                    os.path.join("rvc", "models", "predictors", "rmvpe.pt"),
+                    is_half=self.is_half,
+                    device=self.device,
+                )
+                f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+                f0 = f0[1:]
+            elif method == "fcpe":
+                self.model_fcpe = FCPEF0Predictor(
+                    os.path.join("rvc", "models", "predictors", "fcpe.pt"),
+                    f0_min=int(f0_min),
+                    f0_max=int(f0_max),
+                    dtype=torch.float32,
+                    device=self.device,
+                    sampling_rate=self.sample_rate,
+                    threshold=0.03,
+                )
+                f0 = self.model_fcpe.compute_f0(x, p_len=p_len)
+                del self.model_fcpe
+                gc.collect()
+            f0_computation_stack.append(f0)
+
+        f0_computation_stack = [fc for fc in f0_computation_stack if fc is not None]
+        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,
+        hop_length,
+        f0_autotune,
+        inp_f0=None,
+    ):
+        """
+        Estimates the fundamental frequency (F0) of a given audio signal using various methods.
+
+        Args:
+            input_audio_path: Path to the input audio file.
+            x: The input audio signal as a NumPy array.
+            p_len: Desired length of the F0 output.
+            f0_up_key: Key to adjust the pitch of the F0 contour.
+            f0_method: Method to use for F0 estimation (e.g., "pm", "harvest", "crepe").
+            filter_radius: Radius for median filtering the F0 contour.
+            hop_length: Hop length for F0 estimation methods.
+            f0_autotune: Whether to apply autotune to the F0 contour.
+            inp_f0: Optional input F0 contour to use instead of estimating.
+
+        Returns:
+            A tuple containing the quantized F0 contour and the original F0 contour.
+        """
+        global input_audio_path2wav
+        if f0_method == "pm":
+            f0 = (
+                parselmouth.Sound(x, self.sample_rate)
+                .to_pitch_ac(
+                    time_step=self.time_step / 1000,
+                    voicing_threshold=0.6,
+                    pitch_floor=self.f0_min,
+                    pitch_ceiling=self.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 = self.get_f0_harvest(
+                input_audio_path, self.sample_rate, self.f0_max, self.f0_min, 10
+            )
+            if int(filter_radius) > 2:
+                f0 = signal.medfilt(f0, 3)
+        elif f0_method == "dio":
+            f0, t = pyworld.dio(
+                x.astype(np.double),
+                fs=self.sample_rate,
+                f0_ceil=self.f0_max,
+                f0_floor=self.f0_min,
+                frame_period=10,
+            )
+            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sample_rate)
+            f0 = signal.medfilt(f0, 3)
+        elif f0_method == "crepe":
+            f0 = self.get_f0_crepe(x, self.f0_min, self.f0_max, p_len, int(hop_length))
+        elif f0_method == "crepe-tiny":
+            f0 = self.get_f0_crepe(
+                x, self.f0_min, self.f0_max, p_len, int(hop_length), "tiny"
+            )
+        elif f0_method == "rmvpe":
+            self.model_rmvpe = RMVPE0Predictor(
+                os.path.join("rvc", "models", "predictors", "rmvpe.pt"),
+                is_half=self.is_half,
+                device=self.device,
+            )
+            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+        elif f0_method == "fcpe":
+            self.model_fcpe = FCPEF0Predictor(
+                os.path.join("rvc", "models", "predictors", "fcpe.pt"),
+                f0_min=int(self.f0_min),
+                f0_max=int(self.f0_max),
+                dtype=torch.float32,
+                device=self.device,
+                sampling_rate=self.sample_rate,
+                threshold=0.03,
+            )
+            f0 = self.model_fcpe.compute_f0(x, p_len=p_len)
+            del self.model_fcpe
+            gc.collect()
+        elif "hybrid" in f0_method:
+            input_audio_path2wav[input_audio_path] = x.astype(np.double)
+            f0 = self.get_f0_hybrid(
+                f0_method,
+                x,
+                self.f0_min,
+                self.f0_max,
+                p_len,
+                hop_length,
+            )
+
+        if f0_autotune == "True":
+            f0 = Autotune.autotune_f0(self, f0)
+
+        f0 *= pow(2, f0_up_key / 12)
+        tf0 = self.sample_rate // self.window
+        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] - self.f0_mel_min) * 254 / (
+            self.f0_mel_max - self.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
+
+    def voice_conversion(
+        self,
+        model,
+        net_g,
+        sid,
+        audio0,
+        pitch,
+        pitchf,
+        index,
+        big_npy,
+        index_rate,
+        version,
+        protect,
+    ):
+        """
+        Performs voice conversion on a given audio segment.
+
+        Args:
+            model: The feature extractor model.
+            net_g: The generative model for synthesizing speech.
+            sid: Speaker ID for the target voice.
+            audio0: The input audio segment.
+            pitch: Quantized F0 contour for pitch guidance.
+            pitchf: Original F0 contour for pitch guidance.
+            index: FAISS index for speaker embedding retrieval.
+            big_npy: Speaker embeddings stored in a NumPy array.
+            index_rate: Blending rate for speaker embedding retrieval.
+            version: Model version ("v1" or "v2").
+            protect: Protection level for preserving the original pitch.
+
+        Returns:
+            The voice-converted audio segment.
+        """
+        feats = torch.from_numpy(audio0)
+        if self.is_half:
+            feats = feats.half()
+        else:
+            feats = feats.float()
+        if feats.dim() == 2:
+            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,
+        }
+        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
+            )
+        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()
+        return audio1
+
+    def pipeline(
+        self,
+        model,
+        net_g,
+        sid,
+        audio,
+        input_audio_path,
+        f0_up_key,
+        f0_method,
+        file_index,
+        index_rate,
+        pitch_guidance,
+        filter_radius,
+        tgt_sr,
+        resample_sr,
+        rms_mix_rate,
+        version,
+        protect,
+        hop_length,
+        f0_autotune,
+        f0_file,
+    ):
+        """
+        The main pipeline function for performing voice conversion.
+
+        Args:
+            model: The feature extractor model.
+            net_g: The generative model for synthesizing speech.
+            sid: Speaker ID for the target voice.
+            audio: The input audio signal.
+            input_audio_path: Path to the input audio file.
+            f0_up_key: Key to adjust the pitch of the F0 contour.
+            f0_method: Method to use for F0 estimation.
+            file_index: Path to the FAISS index file for speaker embedding retrieval.
+            index_rate: Blending rate for speaker embedding retrieval.
+            pitch_guidance: Whether to use pitch guidance during voice conversion.
+            filter_radius: Radius for median filtering the F0 contour.
+            tgt_sr: Target sampling rate for the output audio.
+            resample_sr: Resampling rate for the output audio.
+            rms_mix_rate: Blending rate for adjusting the RMS level of the output audio.
+            version: Model version.
+            protect: Protection level for preserving the original pitch.
+            hop_length: Hop length for F0 estimation methods.
+            f0_autotune: Whether to apply autotune to the F0 contour.
+            f0_file: Path to a file containing an F0 contour to use.
+
+        Returns:
+            The voice-converted audio signal.
+        """
+        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 Exception as error:
+                print(error)
+                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
+        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 Exception as error:
+                print(error)
+        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
+        pitch, pitchf = None, None
+        if pitch_guidance == 1:
+            pitch, pitchf = self.get_f0(
+                input_audio_path,
+                audio_pad,
+                p_len,
+                f0_up_key,
+                f0_method,
+                filter_radius,
+                hop_length,
+                f0_autotune,
+                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()
+        for t in opt_ts:
+            t = t // self.window * self.window
+            if pitch_guidance == 1:
+                audio_opt.append(
+                    self.voice_conversion(
+                        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],
+                        index,
+                        big_npy,
+                        index_rate,
+                        version,
+                        protect,
+                    )[self.t_pad_tgt : -self.t_pad_tgt]
+                )
+            else:
+                audio_opt.append(
+                    self.voice_conversion(
+                        model,
+                        net_g,
+                        sid,
+                        audio_pad[s : t + self.t_pad2 + self.window],
+                        None,
+                        None,
+                        index,
+                        big_npy,
+                        index_rate,
+                        version,
+                        protect,
+                    )[self.t_pad_tgt : -self.t_pad_tgt]
+                )
+            s = t
+        if pitch_guidance == 1:
+            audio_opt.append(
+                self.voice_conversion(
+                    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,
+                    index,
+                    big_npy,
+                    index_rate,
+                    version,
+                    protect,
+                )[self.t_pad_tgt : -self.t_pad_tgt]
+            )
+        else:
+            audio_opt.append(
+                self.voice_conversion(
+                    model,
+                    net_g,
+                    sid,
+                    audio_pad[t:],
+                    None,
+                    None,
+                    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 = AudioProcessor.change_rms(
+                audio, self.sample_rate, audio_opt, tgt_sr, rms_mix_rate
+            )
+        if resample_sr >= self.sample_rate 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