feat(all): optimize hierarchy of files

infer/lib/audio.py

@@ -1,8 +1,7 @@
-import platform, os
+import platform
 import ffmpeg
 import numpy as np
 import av
-from io import BytesIO
 
 
 def wav2(i, o, format):

infer/lib/rtrvc.py

@@ -0,0 +1,437 @@
+from io import BytesIO
+import os
+import sys
+import traceback
+from infer.lib import jit
+from infer.lib.jit.get_synthesizer import get_synthesizer
+from time import time as ttime
+import fairseq
+import faiss
+import numpy as np
+import parselmouth
+import pyworld
+import scipy.signal as signal
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchcrepe
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from multiprocessing import Manager as M
+
+from configs.config import Config
+
+# config = Config()
+
+mm = M()
+
+
+def printt(strr, *args):
+    if len(args) == 0:
+        print(strr)
+    else:
+        print(strr % args)
+
+
+# config.device=torch.device("cpu")########强制cpu测试
+# config.is_half=False########强制cpu测试
+class RVC:
+    def __init__(
+        self,
+        key,
+        pth_path,
+        index_path,
+        index_rate,
+        n_cpu,
+        inp_q,
+        opt_q,
+        config: Config,
+        last_rvc=None,
+    ) -> None:
+        """
+        初始化
+        """
+        try:
+            if config.dml == True:
+
+                def forward_dml(ctx, x, scale):
+                    ctx.scale = scale
+                    res = x.clone().detach()
+                    return res
+
+                fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
+            # global config
+            self.config = config
+            self.inp_q = inp_q
+            self.opt_q = opt_q
+            # device="cpu"########强制cpu测试
+            self.device = config.device
+            self.f0_up_key = key
+            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.n_cpu = n_cpu
+            self.use_jit = self.config.use_jit
+            self.is_half = config.is_half
+
+            if index_rate != 0:
+                self.index = faiss.read_index(index_path)
+                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
+                printt("Index search enabled")
+            self.pth_path: str = pth_path
+            self.index_path = index_path
+            self.index_rate = index_rate
+            self.cache_pitch: torch.Tensor = torch.zeros(
+                1024, device=self.device, dtype=torch.long
+            )
+            self.cache_pitchf = torch.zeros(
+                1024, device=self.device, dtype=torch.float32
+            )
+
+            if last_rvc is None:
+                models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
+                    ["assets/hubert/hubert_base.pt"],
+                    suffix="",
+                )
+                hubert_model = models[0]
+                hubert_model = hubert_model.to(self.device)
+                if self.is_half:
+                    hubert_model = hubert_model.half()
+                else:
+                    hubert_model = hubert_model.float()
+                hubert_model.eval()
+                self.model = hubert_model
+            else:
+                self.model = last_rvc.model
+
+            self.net_g: nn.Module = None
+
+            def set_default_model():
+                self.net_g, cpt = get_synthesizer(self.pth_path, self.device)
+                self.tgt_sr = cpt["config"][-1]
+                cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
+                self.if_f0 = cpt.get("f0", 1)
+                self.version = cpt.get("version", "v1")
+                if self.is_half:
+                    self.net_g = self.net_g.half()
+                else:
+                    self.net_g = self.net_g.float()
+
+            def set_jit_model():
+                jit_pth_path = self.pth_path.rstrip(".pth")
+                jit_pth_path += ".half.jit" if self.is_half else ".jit"
+                reload = False
+                if str(self.device) == "cuda":
+                    self.device = torch.device("cuda:0")
+                if os.path.exists(jit_pth_path):
+                    cpt = jit.load(jit_pth_path)
+                    model_device = cpt["device"]
+                    if model_device != str(self.device):
+                        reload = True
+                else:
+                    reload = True
+
+                if reload:
+                    cpt = jit.synthesizer_jit_export(
+                        self.pth_path,
+                        "script",
+                        None,
+                        device=self.device,
+                        is_half=self.is_half,
+                    )
+
+                self.tgt_sr = cpt["config"][-1]
+                self.if_f0 = cpt.get("f0", 1)
+                self.version = cpt.get("version", "v1")
+                self.net_g = torch.jit.load(
+                    BytesIO(cpt["model"]), map_location=self.device
+                )
+                self.net_g.infer = self.net_g.forward
+                self.net_g.eval().to(self.device)
+
+            def set_synthesizer():
+                if self.use_jit and not config.dml:
+                    if self.is_half and "cpu" in str(self.device):
+                        printt(
+                            "Use default Synthesizer model. \
+                                    Jit is not supported on the CPU for half floating point"
+                        )
+                        set_default_model()
+                    else:
+                        set_jit_model()
+                else:
+                    set_default_model()
+
+            if last_rvc is None or last_rvc.pth_path != self.pth_path:
+                set_synthesizer()
+            else:
+                self.tgt_sr = last_rvc.tgt_sr
+                self.if_f0 = last_rvc.if_f0
+                self.version = last_rvc.version
+                self.is_half = last_rvc.is_half
+                if last_rvc.use_jit != self.use_jit:
+                    set_synthesizer()
+                else:
+                    self.net_g = last_rvc.net_g
+
+            if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
+                self.model_rmvpe = last_rvc.model_rmvpe
+            if last_rvc is not None and hasattr(last_rvc, "model_fcpe"):
+                self.device_fcpe = last_rvc.device_fcpe
+                self.model_fcpe = last_rvc.model_fcpe
+        except:
+            printt(traceback.format_exc())
+
+    def change_key(self, new_key):
+        self.f0_up_key = new_key
+
+    def change_index_rate(self, new_index_rate):
+        if new_index_rate != 0 and self.index_rate == 0:
+            self.index = faiss.read_index(self.index_path)
+            self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
+            printt("Index search enabled")
+        self.index_rate = new_index_rate
+
+    def get_f0_post(self, f0):
+        if not torch.is_tensor(f0):
+            f0 = torch.from_numpy(f0)
+        f0 = f0.float().to(self.device).squeeze()
+        f0_mel = 1127 * torch.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 = torch.round(f0_mel).long()
+        return f0_coarse, f0
+
+    def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
+        n_cpu = int(n_cpu)
+        if method == "crepe":
+            return self.get_f0_crepe(x, f0_up_key)
+        if method == "rmvpe":
+            return self.get_f0_rmvpe(x, f0_up_key)
+        if method == "fcpe":
+            return self.get_f0_fcpe(x, f0_up_key)
+        x = x.cpu().numpy()
+        if method == "pm":
+            p_len = x.shape[0] // 160 + 1
+            f0_min = 65
+            l_pad = int(np.ceil(1.5 / f0_min * 16000))
+            r_pad = l_pad + 1
+            s = parselmouth.Sound(np.pad(x, (l_pad, r_pad)), 16000).to_pitch_ac(
+                time_step=0.01,
+                voicing_threshold=0.6,
+                pitch_floor=f0_min,
+                pitch_ceiling=1100,
+            )
+            assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
+            f0 = s.selected_array["frequency"]
+            if len(f0) < p_len:
+                f0 = np.pad(f0, (0, p_len - len(f0)))
+            f0 = f0[:p_len]
+            f0 *= pow(2, f0_up_key / 12)
+            return self.get_f0_post(f0)
+        if n_cpu == 1:
+            f0, t = pyworld.harvest(
+                x.astype(np.double),
+                fs=16000,
+                f0_ceil=1100,
+                f0_floor=50,
+                frame_period=10,
+            )
+            f0 = signal.medfilt(f0, 3)
+            f0 *= pow(2, f0_up_key / 12)
+            return self.get_f0_post(f0)
+        f0bak = np.zeros(x.shape[0] // 160 + 1, dtype=np.float64)
+        length = len(x)
+        part_length = 160 * ((length // 160 - 1) // n_cpu + 1)
+        n_cpu = (length // 160 - 1) // (part_length // 160) + 1
+        ts = ttime()
+        res_f0 = mm.dict()
+        for idx in range(n_cpu):
+            tail = part_length * (idx + 1) + 320
+            if idx == 0:
+                self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
+            else:
+                self.inp_q.put(
+                    (idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
+                )
+        while 1:
+            res_ts = self.opt_q.get()
+            if res_ts == ts:
+                break
+        f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
+        for idx, f0 in enumerate(f0s):
+            if idx == 0:
+                f0 = f0[:-3]
+            elif idx != n_cpu - 1:
+                f0 = f0[2:-3]
+            else:
+                f0 = f0[2:]
+            f0bak[part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]] = (
+                f0
+            )
+        f0bak = signal.medfilt(f0bak, 3)
+        f0bak *= pow(2, f0_up_key / 12)
+        return self.get_f0_post(f0bak)
+
+    def get_f0_crepe(self, x, f0_up_key):
+        if "privateuseone" in str(
+            self.device
+        ):  ###不支持dml，cpu又太慢用不成，拿fcpe顶替
+            return self.get_f0(x, f0_up_key, 1, "fcpe")
+        # printt("using crepe,device:%s"%self.device)
+        f0, pd = torchcrepe.predict(
+            x.unsqueeze(0).float(),
+            16000,
+            160,
+            self.f0_min,
+            self.f0_max,
+            "full",
+            batch_size=512,
+            # device=self.device if self.device.type!="privateuseone" else "cpu",###crepe不用半精度全部是全精度所以不愁###cpu延迟高到没法用
+            device=self.device,
+            return_periodicity=True,
+        )
+        pd = torchcrepe.filter.median(pd, 3)
+        f0 = torchcrepe.filter.mean(f0, 3)
+        f0[pd < 0.1] = 0
+        f0 *= pow(2, f0_up_key / 12)
+        return self.get_f0_post(f0)
+
+    def get_f0_rmvpe(self, x, f0_up_key):
+        if hasattr(self, "model_rmvpe") == False:
+            from infer.lib.rmvpe import RMVPE
+
+            printt("Loading rmvpe model")
+            self.model_rmvpe = RMVPE(
+                "assets/rmvpe/rmvpe.pt",
+                is_half=self.is_half,
+                device=self.device,
+                use_jit=self.config.use_jit,
+            )
+        f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+        f0 *= pow(2, f0_up_key / 12)
+        return self.get_f0_post(f0)
+
+    def get_f0_fcpe(self, x, f0_up_key):
+        if hasattr(self, "model_fcpe") == False:
+            from torchfcpe import spawn_bundled_infer_model
+
+            printt("Loading fcpe model")
+            if "privateuseone" in str(self.device):
+                self.device_fcpe = "cpu"
+            else:
+                self.device_fcpe = self.device
+            self.model_fcpe = spawn_bundled_infer_model(self.device_fcpe)
+        f0 = self.model_fcpe.infer(
+            x.to(self.device_fcpe).unsqueeze(0).float(),
+            sr=16000,
+            decoder_mode="local_argmax",
+            threshold=0.006,
+        )
+        f0 *= pow(2, f0_up_key / 12)
+        return self.get_f0_post(f0)
+
+    def infer(
+        self,
+        input_wav: torch.Tensor,
+        block_frame_16k,
+        skip_head,
+        return_length,
+        f0method,
+    ) -> np.ndarray:
+        t1 = ttime()
+        with torch.no_grad():
+            if self.config.is_half:
+                feats = input_wav.half().view(1, -1)
+            else:
+                feats = input_wav.float().view(1, -1)
+            padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
+            inputs = {
+                "source": feats,
+                "padding_mask": padding_mask,
+                "output_layer": 9 if self.version == "v1" else 12,
+            }
+            logits = self.model.extract_features(**inputs)
+            feats = (
+                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
+            )
+            feats = torch.cat((feats, feats[:, -1:, :]), 1)
+        t2 = ttime()
+        try:
+            if hasattr(self, "index") and self.index_rate != 0:
+                npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
+                score, ix = self.index.search(npy, k=8)
+                if (ix >= 0).all():
+                    weight = np.square(1 / score)
+                    weight /= weight.sum(axis=1, keepdims=True)
+                    npy = np.sum(
+                        self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1
+                    )
+                    if self.config.is_half:
+                        npy = npy.astype("float16")
+                    feats[0][skip_head // 2 :] = (
+                        torch.from_numpy(npy).unsqueeze(0).to(self.device)
+                        * self.index_rate
+                        + (1 - self.index_rate) * feats[0][skip_head // 2 :]
+                    )
+                else:
+                    printt(
+                        "Invalid index. You MUST use added_xxxx.index but not trained_xxxx.index!"
+                    )
+            else:
+                printt("Index search FAILED or disabled")
+        except:
+            traceback.print_exc()
+            printt("Index search FAILED")
+        t3 = ttime()
+        p_len = input_wav.shape[0] // 160
+        if self.if_f0 == 1:
+            f0_extractor_frame = block_frame_16k + 800
+            if f0method == "rmvpe":
+                f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
+            pitch, pitchf = self.get_f0(
+                input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
+            )
+            shift = block_frame_16k // 160
+            self.cache_pitch[:-shift] = self.cache_pitch[shift:].clone()
+            self.cache_pitchf[:-shift] = self.cache_pitchf[shift:].clone()
+            self.cache_pitch[4 - pitch.shape[0] :] = pitch[3:-1]
+            self.cache_pitchf[4 - pitch.shape[0] :] = pitchf[3:-1]
+            cache_pitch = self.cache_pitch[None, -p_len:]
+            cache_pitchf = self.cache_pitchf[None, -p_len:]
+        t4 = ttime()
+        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+        feats = feats[:, :p_len, :]
+        p_len = torch.LongTensor([p_len]).to(self.device)
+        sid = torch.LongTensor([0]).to(self.device)
+        skip_head = torch.LongTensor([skip_head])
+        return_length = torch.LongTensor([return_length])
+        with torch.no_grad():
+            if self.if_f0 == 1:
+                infered_audio, _, _ = self.net_g.infer(
+                    feats,
+                    p_len,
+                    cache_pitch,
+                    cache_pitchf,
+                    sid,
+                    skip_head,
+                    return_length,
+                )
+            else:
+                infered_audio, _, _ = self.net_g.infer(
+                    feats, p_len, sid, skip_head, return_length
+                )
+        t5 = ttime()
+        printt(
+            "Spent time: fea = %.3fs, index = %.3fs, f0 = %.3fs, model = %.3fs",
+            t2 - t1,
+            t3 - t2,
+            t4 - t3,
+            t5 - t4,
+        )
+        return infered_audio.squeeze().float()

infer/modules/gui/__init__.py

@@ -0,0 +1,13 @@
+"""
+TorchGating is a PyTorch-based implementation of Spectral Gating
+================================================
+Author: Asaf Zorea
+
+Contents
+--------
+torchgate imports all the functions from PyTorch, and in addition provides:
+ TorchGating       --- A PyTorch module that applies a spectral gate to an input signal
+
+"""
+
+from .torchgate import TorchGate

infer/modules/gui/torchgate.py

@@ -0,0 +1,280 @@
+import torch
+from infer.lib.rmvpe import STFT
+from torch.nn.functional import conv1d, conv2d
+from typing import Union, Optional
+from .utils import linspace, temperature_sigmoid, amp_to_db
+
+
+class TorchGate(torch.nn.Module):
+    """
+    A PyTorch module that applies a spectral gate to an input signal.
+
+    Arguments:
+        sr {int} -- Sample rate of the input signal.
+        nonstationary {bool} -- Whether to use non-stationary or stationary masking (default: {False}).
+        n_std_thresh_stationary {float} -- Number of standard deviations above mean to threshold noise for
+                                           stationary masking (default: {1.5}).
+        n_thresh_nonstationary {float} -- Number of multiplies above smoothed magnitude spectrogram. for
+                                        non-stationary masking (default: {1.3}).
+        temp_coeff_nonstationary {float} -- Temperature coefficient for non-stationary masking (default: {0.1}).
+        n_movemean_nonstationary {int} -- Number of samples for moving average smoothing in non-stationary masking
+                                          (default: {20}).
+        prop_decrease {float} -- Proportion to decrease signal by where the mask is zero (default: {1.0}).
+        n_fft {int} -- Size of FFT for STFT (default: {1024}).
+        win_length {[int]} -- Window length for STFT. If None, defaults to `n_fft` (default: {None}).
+        hop_length {[int]} -- Hop length for STFT. If None, defaults to `win_length` // 4 (default: {None}).
+        freq_mask_smooth_hz {float} -- Frequency smoothing width for mask (in Hz). If None, no smoothing is applied
+                                     (default: {500}).
+        time_mask_smooth_ms {float} -- Time smoothing width for mask (in ms). If None, no smoothing is applied
+                                     (default: {50}).
+    """
+
+    @torch.no_grad()
+    def __init__(
+        self,
+        sr: int,
+        nonstationary: bool = False,
+        n_std_thresh_stationary: float = 1.5,
+        n_thresh_nonstationary: float = 1.3,
+        temp_coeff_nonstationary: float = 0.1,
+        n_movemean_nonstationary: int = 20,
+        prop_decrease: float = 1.0,
+        n_fft: int = 1024,
+        win_length: bool = None,
+        hop_length: int = None,
+        freq_mask_smooth_hz: float = 500,
+        time_mask_smooth_ms: float = 50,
+    ):
+        super().__init__()
+
+        # General Params
+        self.sr = sr
+        self.nonstationary = nonstationary
+        assert 0.0 <= prop_decrease <= 1.0
+        self.prop_decrease = prop_decrease
+
+        # STFT Params
+        self.n_fft = n_fft
+        self.win_length = self.n_fft if win_length is None else win_length
+        self.hop_length = self.win_length // 4 if hop_length is None else hop_length
+
+        # Stationary Params
+        self.n_std_thresh_stationary = n_std_thresh_stationary
+
+        # Non-Stationary Params
+        self.temp_coeff_nonstationary = temp_coeff_nonstationary
+        self.n_movemean_nonstationary = n_movemean_nonstationary
+        self.n_thresh_nonstationary = n_thresh_nonstationary
+
+        # Smooth Mask Params
+        self.freq_mask_smooth_hz = freq_mask_smooth_hz
+        self.time_mask_smooth_ms = time_mask_smooth_ms
+        self.register_buffer("smoothing_filter", self._generate_mask_smoothing_filter())
+
+    @torch.no_grad()
+    def _generate_mask_smoothing_filter(self) -> Union[torch.Tensor, None]:
+        """
+        A PyTorch module that applies a spectral gate to an input signal using the STFT.
+
+        Returns:
+            smoothing_filter (torch.Tensor): a 2D tensor representing the smoothing filter,
+            with shape (n_grad_freq, n_grad_time), where n_grad_freq is the number of frequency
+            bins to smooth and n_grad_time is the number of time frames to smooth.
+            If both self.freq_mask_smooth_hz and self.time_mask_smooth_ms are None, returns None.
+        """
+        if self.freq_mask_smooth_hz is None and self.time_mask_smooth_ms is None:
+            return None
+
+        n_grad_freq = (
+            1
+            if self.freq_mask_smooth_hz is None
+            else int(self.freq_mask_smooth_hz / (self.sr / (self.n_fft / 2)))
+        )
+        if n_grad_freq < 1:
+            raise ValueError(
+                f"freq_mask_smooth_hz needs to be at least {int((self.sr / (self._n_fft / 2)))} Hz"
+            )
+
+        n_grad_time = (
+            1
+            if self.time_mask_smooth_ms is None
+            else int(self.time_mask_smooth_ms / ((self.hop_length / self.sr) * 1000))
+        )
+        if n_grad_time < 1:
+            raise ValueError(
+                f"time_mask_smooth_ms needs to be at least {int((self.hop_length / self.sr) * 1000)} ms"
+            )
+
+        if n_grad_time == 1 and n_grad_freq == 1:
+            return None
+
+        v_f = torch.cat(
+            [
+                linspace(0, 1, n_grad_freq + 1, endpoint=False),
+                linspace(1, 0, n_grad_freq + 2),
+            ]
+        )[1:-1]
+        v_t = torch.cat(
+            [
+                linspace(0, 1, n_grad_time + 1, endpoint=False),
+                linspace(1, 0, n_grad_time + 2),
+            ]
+        )[1:-1]
+        smoothing_filter = torch.outer(v_f, v_t).unsqueeze(0).unsqueeze(0)
+
+        return smoothing_filter / smoothing_filter.sum()
+
+    @torch.no_grad()
+    def _stationary_mask(
+        self, X_db: torch.Tensor, xn: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """
+        Computes a stationary binary mask to filter out noise in a log-magnitude spectrogram.
+
+        Arguments:
+            X_db (torch.Tensor): 2D tensor of shape (frames, freq_bins) containing the log-magnitude spectrogram.
+            xn (torch.Tensor): 1D tensor containing the audio signal corresponding to X_db.
+
+        Returns:
+            sig_mask (torch.Tensor): Binary mask of the same shape as X_db, where values greater than the threshold
+            are set to 1, and the rest are set to 0.
+        """
+        if xn is not None:
+            if "privateuseone" in str(xn.device):
+                if not hasattr(self, "stft"):
+                    self.stft = STFT(
+                        filter_length=self.n_fft,
+                        hop_length=self.hop_length,
+                        win_length=self.win_length,
+                        window="hann",
+                    ).to(xn.device)
+                XN = self.stft.transform(xn)
+            else:
+                XN = torch.stft(
+                    xn,
+                    n_fft=self.n_fft,
+                    hop_length=self.hop_length,
+                    win_length=self.win_length,
+                    return_complex=True,
+                    pad_mode="constant",
+                    center=True,
+                    window=torch.hann_window(self.win_length).to(xn.device),
+                )
+            XN_db = amp_to_db(XN).to(dtype=X_db.dtype)
+        else:
+            XN_db = X_db
+
+        # calculate mean and standard deviation along the frequency axis
+        std_freq_noise, mean_freq_noise = torch.std_mean(XN_db, dim=-1)
+
+        # compute noise threshold
+        noise_thresh = mean_freq_noise + std_freq_noise * self.n_std_thresh_stationary
+
+        # create binary mask by thresholding the spectrogram
+        sig_mask = X_db > noise_thresh.unsqueeze(2)
+        return sig_mask
+
+    @torch.no_grad()
+    def _nonstationary_mask(self, X_abs: torch.Tensor) -> torch.Tensor:
+        """
+        Computes a non-stationary binary mask to filter out noise in a log-magnitude spectrogram.
+
+        Arguments:
+            X_abs (torch.Tensor): 2D tensor of shape (frames, freq_bins) containing the magnitude spectrogram.
+
+        Returns:
+            sig_mask (torch.Tensor): Binary mask of the same shape as X_abs, where values greater than the threshold
+            are set to 1, and the rest are set to 0.
+        """
+        X_smoothed = (
+            conv1d(
+                X_abs.reshape(-1, 1, X_abs.shape[-1]),
+                torch.ones(
+                    self.n_movemean_nonstationary,
+                    dtype=X_abs.dtype,
+                    device=X_abs.device,
+                ).view(1, 1, -1),
+                padding="same",
+            ).view(X_abs.shape)
+            / self.n_movemean_nonstationary
+        )
+
+        # Compute slowness ratio and apply temperature sigmoid
+        slowness_ratio = (X_abs - X_smoothed) / (X_smoothed + 1e-6)
+        sig_mask = temperature_sigmoid(
+            slowness_ratio, self.n_thresh_nonstationary, self.temp_coeff_nonstationary
+        )
+
+        return sig_mask
+
+    def forward(
+        self, x: torch.Tensor, xn: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """
+        Apply the proposed algorithm to the input signal.
+
+        Arguments:
+            x (torch.Tensor): The input audio signal, with shape (batch_size, signal_length).
+            xn (Optional[torch.Tensor]): The noise signal used for stationary noise reduction. If `None`, the input
+                                         signal is used as the noise signal. Default: `None`.
+
+        Returns:
+            torch.Tensor: The denoised audio signal, with the same shape as the input signal.
+        """
+
+        # Compute short-time Fourier transform (STFT)
+        if "privateuseone" in str(x.device):
+            if not hasattr(self, "stft"):
+                self.stft = STFT(
+                    filter_length=self.n_fft,
+                    hop_length=self.hop_length,
+                    win_length=self.win_length,
+                    window="hann",
+                ).to(x.device)
+            X, phase = self.stft.transform(x, return_phase=True)
+        else:
+            X = torch.stft(
+                x,
+                n_fft=self.n_fft,
+                hop_length=self.hop_length,
+                win_length=self.win_length,
+                return_complex=True,
+                pad_mode="constant",
+                center=True,
+                window=torch.hann_window(self.win_length).to(x.device),
+            )
+
+        # Compute signal mask based on stationary or nonstationary assumptions
+        if self.nonstationary:
+            sig_mask = self._nonstationary_mask(X.abs())
+        else:
+            sig_mask = self._stationary_mask(amp_to_db(X), xn)
+
+        # Propagate decrease in signal power
+        sig_mask = self.prop_decrease * (sig_mask.float() - 1.0) + 1.0
+
+        # Smooth signal mask with 2D convolution
+        if self.smoothing_filter is not None:
+            sig_mask = conv2d(
+                sig_mask.unsqueeze(1),
+                self.smoothing_filter.to(sig_mask.dtype),
+                padding="same",
+            )
+
+        # Apply signal mask to STFT magnitude and phase components
+        Y = X * sig_mask.squeeze(1)
+
+        # Inverse STFT to obtain time-domain signal
+        if "privateuseone" in str(Y.device):
+            y = self.stft.inverse(Y, phase)
+        else:
+            y = torch.istft(
+                Y,
+                n_fft=self.n_fft,
+                hop_length=self.hop_length,
+                win_length=self.win_length,
+                center=True,
+                window=torch.hann_window(self.win_length).to(Y.device),
+            )
+
+        return y.to(dtype=x.dtype)

infer/modules/gui/utils.py

@@ -0,0 +1,70 @@
+import torch
+from torch.types import Number
+
+
+@torch.no_grad()
+def amp_to_db(
+    x: torch.Tensor, eps=torch.finfo(torch.float64).eps, top_db=40
+) -> torch.Tensor:
+    """
+    Convert the input tensor from amplitude to decibel scale.
+
+    Arguments:
+        x {[torch.Tensor]} -- [Input tensor.]
+
+    Keyword Arguments:
+        eps {[float]} -- [Small value to avoid numerical instability.]
+                          (default: {torch.finfo(torch.float64).eps})
+        top_db {[float]} -- [threshold the output at ``top_db`` below the peak]
+            `             (default: {40})
+
+    Returns:
+        [torch.Tensor] -- [Output tensor in decibel scale.]
+    """
+    x_db = 20 * torch.log10(x.abs() + eps)
+    return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1))
+
+
+@torch.no_grad()
+def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor:
+    """
+    Apply a sigmoid function with temperature scaling.
+
+    Arguments:
+        x {[torch.Tensor]} -- [Input tensor.]
+        x0 {[float]} -- [Parameter that controls the threshold of the sigmoid.]
+        temp_coeff {[float]} -- [Parameter that controls the slope of the sigmoid.]
+
+    Returns:
+        [torch.Tensor] -- [Output tensor after applying the sigmoid with temperature scaling.]
+    """
+    return torch.sigmoid((x - x0) / temp_coeff)
+
+
+@torch.no_grad()
+def linspace(
+    start: Number, stop: Number, num: int = 50, endpoint: bool = True, **kwargs
+) -> torch.Tensor:
+    """
+    Generate a linearly spaced 1-D tensor.
+
+    Arguments:
+        start {[Number]} -- [The starting value of the sequence.]
+        stop {[Number]} -- [The end value of the sequence, unless `endpoint` is set to False.
+                            In that case, the sequence consists of all but the last of ``num + 1``
+                            evenly spaced samples, so that `stop` is excluded. Note that the step
+                            size changes when `endpoint` is False.]
+
+    Keyword Arguments:
+        num {[int]} -- [Number of samples to generate. Default is 50. Must be non-negative.]
+        endpoint {[bool]} -- [If True, `stop` is the last sample. Otherwise, it is not included.
+                              Default is True.]
+        **kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.]
+
+    Returns:
+        [torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.]
+    """
+    if endpoint:
+        return torch.linspace(start, stop, num, **kwargs)
+    else:
+        return torch.linspace(start, stop, num + 1, **kwargs)[:-1]