def METHOD_NAME(self, y, keyshift=0, speed=1, center=False):
    sampling_rate = self.target_sr
    n_mels = self.n_mels
    n_fft = self.n_fft
    win_size = self.win_size
    hop_length = self.hop_length
    fmin = self.fmin
    fmax = self.fmax
    clip_val = self.clip_val
    factor = 2 ** (keyshift / 12)
    n_fft_new = int(np.round(n_fft * factor))
    win_size_new = int(np.round(win_size * factor))
    hop_length_new = int(np.round(hop_length * speed))
    if torch.min(y) < -1.0:
        print("min value is ", torch.min(y))
    if torch.max(y) > 1.0:
        print("max value is ", torch.max(y))
    mel_basis_key = str(fmax) + "_" + str(y.device)
    if mel_basis_key not in self.mel_basis:
        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
        self.mel_basis[mel_basis_key] = torch.from_numpy(mel).float().to(y.device)
    keyshift_key = str(keyshift) + "_" + str(y.device)
    if keyshift_key not in self.hann_window:
        self.hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device)
    pad_left = (win_size_new - hop_length_new) // 2
    pad_right = max((win_size_new - hop_length_new + 1) // 2, win_size_new - y.size(-1) - pad_left)
    if pad_right < y.size(-1):
        mode = "reflect"
    else:
        mode = "constant"
    y = torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode=mode)
    y = y.squeeze(1)
    spec = torch.stft(y, n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=self.hann_window[keyshift_key], center=center, pad_mode="reflect", normalized=False, onesided=True, return_complex=False)
    # print(111,spec)
    spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
    if keyshift != 0:
        size = n_fft // 2 + 1
        resize = spec.size(1)
        if resize < size:
            spec = F.pad(spec, (0, 0, 0, size - resize))
        spec = spec[:, :size, :] * win_size / win_size_new
    # print(222,spec)
    spec = torch.matmul(self.mel_basis[mel_basis_key], spec)
    # print(333,spec)
    spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
    # print(444,spec)
    return spec