athena.transform.feats.fbank

This model extracts Fbank features per frame.

Module Contents

Classes

Fbank

Computing filter banks is applying triangular filters on a Mel-scale to the power

class athena.transform.feats.fbank.Fbank(config: dict)

Bases: athena.transform.feats.base_frontend.BaseFrontend

Computing filter banks is applying triangular filters on a Mel-scale to the power spectrum to extract frequency bands. Return a float tensor with shape (num_channels, num_frames, num_frequencies).

classmethod params(cls, config=None)

Set params. :param config: contains thirteen optional parameters: :param window_length: Window length in seconds. (float, default = 0.025) :param frame_length: Hop length in seconds. (float, default = 0.010)

snip_edges: If 1, the last frame (shorter than window_length) will be

cutoff. If 2, 1 // 2 frame_length data will be padded to data. (int, default = 1)

raw_energy: If 1, compute frame energy before preemphasis and

windowing. If 2, compute frame energy after preemphasis and windowing. (int, default = 1)

preEph_coeff: Coefficient for use in frame-signal preemphasis.

(float, default = 0.97)

window_type: Type of window (“hamm”|”hann”|”povey”|”rect”|”blac”|”tria”).

(string, default = “povey”)

remove_dc_offset: Subtract mean from waveform on each frame.

(bool, default = true)

is_fbank: If true, compute power spetrum without frame energy.

If false, using the frame energy instead of the square of the constant component of the signal. (bool, default = true)

is_log10: If true, using log10 to fbank. If false, using loge.

(bool, default = false)

output_type: If 1, return power spectrum. If 2, return log-power

spectrum. (int, default = 1)

upper_frequency_limit: High cutoff frequency for mel bins (if <= 0, offset

from Nyquist) (float, default = 0)

lower_frequency_limit: Low cutoff frequency for mel bins (float, default = 20) filterbank_channel_count: Number of triangular mel-frequency bins.

(float, default = 23)

dither: Dithering constant (0.0 means no dither).

(float, default = 1) [add robust to training]

Returns:An object of class HParams, which is a set of hyperparameters as name-value pairs.

call(self, audio_data, sample_rate)

Caculate fbank features of audio data. :param audio_data: the audio signal from which to compute spectrum.

Should be an (1, N) tensor.

Parameters:sample_rate – the samplerate of the signal we working with, default is 16kHz. Returns:A float tensor of size (num_channels, num_frames, num_frequencies) containing fbank features of every frame in speech.

dim(self)

Return dimension of fbank.

num_channels(self)

Return number of channels of fbank.