asteroid.metrics module

asteroid.metrics.get_metrics(mix, clean, estimate, sample_rate=16000, metrics_list='all', average=True, compute_permutation=False)

Get speech separation/enhancement metrics from mix/clean/estimate.

Parameters:

• mix (np.array) – ‘Shape(D, N)’ or ‘Shape(N, )’.
• clean (np.array) – ‘Shape(K_source, N)’ or ‘Shape(N, )’.
• estimate (np.array) – ‘Shape(K_target, N)’ or ‘Shape(N, )’.
• sample_rate (int) – sampling rate of the audio clips.
• metrics_list (Union [str, list]) – List of metrics to compute. Defaults to ‘all’ ([‘si_sdr’, ‘sdr’, ‘sir’, ‘sar’, ‘stoi’, ‘pesq’]).
• average (bool) – Return dict([float]) if True, else dict([array]).
• compute_permutation (bool) – Whether to compute the permutation on estimate sources for the output metrics (default False)

Returns:

dict –

Dictionary with all requested metrics, with ‘input_’ prefix

for metrics at the input (mixture against clean), no prefix at the output (estimate against clean). Output format depends on average.

Examples

>>> import numpy as np
>>> import pprint
>>> from asteroid.metrics import get_metrics
>>> mix = np.random.randn(1, 16000)
>>> clean = np.random.randn(2, 16000)
>>> est = np.random.randn(2, 16000)
>>> metrics_dict = get_metrics(mix, clean, est, sample_rate=8000,
>>>                            metrics_list='all')
>>> pprint.pprint(metrics_dict)
{'input_pesq': 1.924380898475647,
 'input_sar': -11.67667585294225,
 'input_sdr': -14.88667106190552,
 'input_si_sdr': -52.43849784881705,
 'input_sir': -0.10419427290163795,
 'input_stoi': 0.015112115177091223,
 'pesq': 1.7713886499404907,
 'sar': -11.610963379923195,
 'sdr': -14.527246041125844,
 'si_sdr': -46.26557128489802,
 'sir': 0.4799929272243427,
 'stoi': 0.022023073540350643}