`athena.transform.feats.framepow`¶

“This model extracts framepow features per frame.

Module Contents¶

Classes¶

Framepow

Compute power of every frame in speech.

class athena.transform.feats.framepow.Framepow(config: dict)¶

Bases: athena.transform.feats.base_frontend.BaseFrontend

Compute power of every frame in speech.

Parameters: config – contains four optional parameters.

Shape:

output: \((T, 1)\).

Examples::

>>> config = {'window_length': 0.25, 'remove_dc_offset': True}
>>> framepow_op = Framepow.params(config).instantiate()
>>> framepow_out = framepow_op('test.wav', 16000)

classmethod params(config=None)¶

Set params.

Parameters

config – contains the following four optional parameters:
'window_length' – Window length in seconds. (float, default = 0.025)
'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)
'remove_dc_offset' – Subtract mean from waveform on each frame. (bool, default = true)

Note

Return an object of class HParams, which is a set of hyperparameters as name-value pairs.

call(audio_data, sample_rate)¶

Caculate power of every frame in speech.