Python torch.blackman_window() Examples

def _feature_window_function(window_type: str,
                             window_size: int,
                             blackman_coeff: float,
                             device: torch.device,
                             dtype: int,
                             ) -> Tensor:
    r"""Returns a window function with the given type and size
    """
    if window_type == HANNING:
        return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype)
    elif window_type == HAMMING:
        return torch.hamming_window(window_size, periodic=False, alpha=0.54, beta=0.46, device=device, dtype=dtype)
    elif window_type == POVEY:
        # like hanning but goes to zero at edges
        return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype).pow(0.85)
    elif window_type == RECTANGULAR:
        return torch.ones(window_size, device=device, dtype=dtype)
    elif window_type == BLACKMAN:
        a = 2 * math.pi / (window_size - 1)
        window_function = torch.arange(window_size, device=device, dtype=dtype)
        # can't use torch.blackman_window as they use different coefficients
        return (blackman_coeff - 0.5 * torch.cos(a * window_function) +
                (0.5 - blackman_coeff) * torch.cos(2 * a * window_function)).to(device=device, dtype=dtype)
    else:
        raise Exception('Invalid window type ' + window_type) 

def get_window(name, window_length, squared=False):
    """
    Returns a windowing function.
    
    Arguments:
    ----------
        window (str)                : name of the window, currently only 'hann' is available
        window_length (int)         : length of the window
        squared (bool)              : if true, square the window
        
    Returns:
    ----------
        torch.FloatTensor           : window of size `window_length`
    """
    if name == "hann":
        window = torch.hann_window(window_length)
    elif name == "hamming":
        window = torch.hamming_window(window_length)
    elif name == "blackman":
        window = torch.blackman_window(window_length)
    else:
        raise ValueError("Invalid window name {}".format(name))
    if squared:
        window *= window
    return window 

def __init__(self, win_length, hop_length):
        super().__init__()

        self.win_length = win_length
        self.hop_length = hop_length

        self.disable_casts = self._opt_level == Optimization.mxprO1

        self.torch_windows = {
            'hann': torch.hann_window,
            'hamming': torch.hamming_window,
            'blackman': torch.blackman_window,
            'bartlett': torch.bartlett_window,
            'ones': torch.ones,
            None: torch.ones,
        } 

def __init__(self, sample_rate=8000, window_size=0.02, window_stride=0.01,
                       n_fft=None,
                       window="hamming", normalize="per_feature", log=True, center=True,
                       dither=constant, pad_to=8, max_duration=16.7,
                       frame_splicing=1):
        super(SpectrogramFeatures, self).__init__()
        torch_windows = {
            'hann': torch.hann_window,
            'hamming': torch.hamming_window,
            'blackman': torch.blackman_window,
            'bartlett': torch.bartlett_window,
            'none': None,
        }
        self.win_length = int(sample_rate * window_size)
        self.hop_length = int(sample_rate * window_stride)
        self.n_fft = n_fft or 2 ** math.ceil(math.log2(self.win_length))

        window_fn = torch_windows.get(window, None)
        window_tensor = window_fn(self.win_length,
                                  periodic=False) if window_fn else None
        self.window = window_tensor

        self.normalize = normalize
        self.log = log
        self.center = center
        self.dither = dither
        self.pad_to = pad_to
        self.frame_splicing = frame_splicing

        max_length = 1 + math.ceil(
                (max_duration * sample_rate - self.win_length) / self.hop_length
        )
        max_pad = 16 - (max_length % 16)
        self.max_length = max_length + max_pad 

def __init__(self, sample_rate=8000, window_size=0.02, window_stride=0.01,
                 window="hamming", normalize="per_feature", n_fft=None,
                 preemph=0.97,
                 nfilt=64, lowfreq=0, highfreq=None, log=True, dither=constant,
                 pad_to=8,
                 max_duration=16.7,
                 frame_splicing=1):
        super(FilterbankFeatures, self).__init__()
#        print("PADDING: {}".format(pad_to))

        torch_windows = {
            'hann': torch.hann_window,
            'hamming': torch.hamming_window,
            'blackman': torch.blackman_window,
            'bartlett': torch.bartlett_window,
            'none': None,
        }

        self.win_length = int(sample_rate * window_size)  # frame size
        self.hop_length = int(sample_rate * window_stride)
        self.n_fft = n_fft or 2 ** math.ceil(math.log2(self.win_length))

        self.normalize = normalize
        self.log = log
        self.dither = dither
        self.frame_splicing = frame_splicing
        self.nfilt = nfilt
        self.preemph = preemph
        self.pad_to = pad_to
        # For now, always enable this.
        # See https://docs.google.com/presentation/d/1IVC3J-pHB-ipJpKsJox_SqmDHYdkIaoCXTbKmJmV2-I/edit?usp=sharing for elaboration
        self.use_deterministic_dithering = True
        highfreq = highfreq or sample_rate / 2
        window_fn = torch_windows.get(window, None)
        window_tensor = window_fn(self.win_length,
                                  periodic=False) if window_fn else None
        filterbanks = torch.tensor(
            librosa.filters.mel(sample_rate, self.n_fft, n_mels=nfilt, fmin=lowfreq,
                                fmax=highfreq), dtype=torch.float).unsqueeze(0)
        # self.fb = filterbanks
        # self.window = window_tensor
        self.register_buffer("fb", filterbanks)
        self.register_buffer("window", window_tensor)
        # Calculate maximum sequence length (# frames)
        max_length = 1 + math.ceil(
            (max_duration * sample_rate - self.win_length) / self.hop_length
        )
        max_pad = 16 - (max_length % 16)
        self.max_length = max_length + max_pad 

def __init__(self, sample_rate=8000, window_size=0.02, window_stride=0.01,
                       window="hamming", normalize="per_feature", n_fft=None,
                       preemph=0.97,
                       nfilt=64, lowfreq=0, highfreq=None, log=True, dither=constant,
                       pad_to=8,
                       max_duration=16.7,
                       frame_splicing=1):
        super(FilterbankFeatures, self).__init__()
#        print("PADDING: {}".format(pad_to))

        torch_windows = {
            'hann': torch.hann_window,
            'hamming': torch.hamming_window,
            'blackman': torch.blackman_window,
            'bartlett': torch.bartlett_window,
            'none': None,
        }

        self.win_length = int(sample_rate * window_size) # frame size
        self.hop_length = int(sample_rate * window_stride)
        self.n_fft = n_fft or 2 ** math.ceil(math.log2(self.win_length))

        self.normalize = normalize
        self.log = log
        self.dither = dither
        self.frame_splicing = frame_splicing
        self.nfilt = nfilt
        self.preemph = preemph
        self.pad_to = pad_to
        highfreq = highfreq or sample_rate / 2
        window_fn = torch_windows.get(window, None)
        window_tensor = window_fn(self.win_length,
                                  periodic=False) if window_fn else None
        filterbanks = torch.tensor(
            librosa.filters.mel(sample_rate, self.n_fft, n_mels=nfilt, fmin=lowfreq,
                                                    fmax=highfreq), dtype=torch.float).unsqueeze(0)
        # self.fb = filterbanks
        # self.window = window_tensor
        self.register_buffer("fb", filterbanks)
        self.register_buffer("window", window_tensor)
        # Calculate maximum sequence length (# frames)
        max_length = 1 + math.ceil(
                (max_duration * sample_rate - self.win_length) / self.hop_length
        )
        max_pad = 16 - (max_length % 16)
        self.max_length = max_length + max_pad