torchaudio.compliance.kaldi.spectrogram(waveform: Tensor, blackman_coeff: float = 0.42, channel: int = -1, dither: float = 0.0, energy_floor: float = 1.0, frame_length: float = 25.0, frame_shift: float = 10.0, min_duration: float = 0.0, preemphasis_coefficient: float = 0.97, raw_energy: bool = True, remove_dc_offset: bool = True, round_to_power_of_two: bool = True, sample_frequency: float = 16000.0, snip_edges: bool = True, subtract_mean: bool = False, window_type: str = 'povey') Tensor[source]

Create a spectrogram from a raw audio signal. This matches the input/output of Kaldi’s compute-spectrogram-feats.

  • waveform (Tensor) – Tensor of audio of size (c, n) where c is in the range [0,2)

  • blackman_coeff (float, optional) – Constant coefficient for generalized Blackman window. (Default: 0.42)

  • channel (int, optional) – Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (Default: -1)

  • dither (float, optional) – Dithering constant (0.0 means no dither). If you turn this off, you should set the energy_floor option, e.g. to 1.0 or 0.1 (Default: 0.0)

  • energy_floor (float, optional) – Floor on energy (absolute, not relative) in Spectrogram computation. Caution: this floor is applied to the zeroth component, representing the total signal energy. The floor on the individual spectrogram elements is fixed at std::numeric_limits<float>::epsilon(). (Default: 1.0)

  • frame_length (float, optional) – Frame length in milliseconds (Default: 25.0)

  • frame_shift (float, optional) – Frame shift in milliseconds (Default: 10.0)

  • min_duration (float, optional) – Minimum duration of segments to process (in seconds). (Default: 0.0)

  • preemphasis_coefficient (float, optional) – Coefficient for use in signal preemphasis (Default: 0.97)

  • raw_energy (bool, optional) – If True, compute energy before preemphasis and windowing (Default: True)

  • remove_dc_offset (bool, optional) – Subtract mean from waveform on each frame (Default: True)

  • round_to_power_of_two (bool, optional) – If True, round window size to power of two by zero-padding input to FFT. (Default: True)

  • sample_frequency (float, optional) – Waveform data sample frequency (must match the waveform file, if specified there) (Default: 16000.0)

  • snip_edges (bool, optional) – If True, end effects will be handled by outputting only frames that completely fit in the file, and the number of frames depends on the frame_length. If False, the number of frames depends only on the frame_shift, and we reflect the data at the ends. (Default: True)

  • subtract_mean (bool, optional) – Subtract mean of each feature file [CMS]; not recommended to do it this way. (Default: False)

  • window_type (str, optional) – Type of window (‘hamming’|’hanning’|’povey’|’rectangular’|’blackman’) (Default: 'povey')


A spectrogram identical to what Kaldi would output. The shape is (m, padded_window_size // 2 + 1) where m is calculated in _get_strided

Return type:



Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources