asteroid.metrics module

asteroid.metrics.get_metrics(mix, clean, estimate, sample_rate=16000, metrics_list='all', average=True, compute_permutation=False, ignore_metrics_errors=False, filename=None)

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

Parameters:

• mix (np.array) – mixture array.
• clean (np.array) – reference array.
• estimate (np.array) – estimate array.
• sample_rate (int) – sampling rate of the audio clips.
• metrics_list (Union[List[str], str) – 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)
• ignore_metrics_errors (bool) – Whether to ignore errors that occur in computing the metrics. A warning will be printed instead.
• filename (str, optional) – If computing a metric fails, print this filename along with the exception/warning message for debugging purposes.

Shape:

• mix: \((D, N)\) or (N, ).
• clean: \((K\_source, N)\) or (N, ).
• estimate: \((K\_target, N)\) or (N, ).

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}

class asteroid.metrics.MetricTracker(sample_rate, metrics_list=('si_sdr', 'sdr', 'sir', 'sar', 'stoi', 'pesq'), average=True, compute_permutation=False, ignore_metrics_errors=False)

Bases: object

Metric tracker, subject to change.

Parameters:

• sample_rate (int) – sampling rate of the audio clips.
• metrics_list (Union[List[str], str) – 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)
• ignore_metrics_errors (bool) – Whether to ignore errors that occur in computing the metrics. A warning will be printed instead.

__call__(*, mix: numpy.ndarray, clean: numpy.ndarray, estimate: numpy.ndarray, filename=None, **kwargs)

Compute metrics for mix/clean/estimate and log it to the class.

Parameters:

• mix (np.array) – mixture array.
• clean (np.array) – reference array.
• estimate (np.array) – estimate array.
• sample_rate (int) – sampling rate of the audio clips.
• filename (str, optional) – If computing a metric fails, print this filename along with the exception/warning message for debugging purposes.
• **kwargs – Any key, value pair to log in the utterance metric (filename, speaker ID, etc…)

as_df()

Return dataframe containing the results (cached).

final_report(dump_path: str = None)

Return dict of average metrics. Dump to JSON if dump_path is not None.

class asteroid.metrics.MockWERTracker(*args, **kwargs)

Bases: object

final_report_as_markdown()

class asteroid.metrics.WERTracker(model_name, trans_df)

Bases: object

Word Error Rate Tracker. Subject to change.

Parameters:

• model_name (str) – Name of the petrained model to use.
• trans_df (dataframe) – Containing field utt_id and text. See librimix/ConvTasNet recipe.

__call__(*, mix: numpy.ndarray, clean: numpy.ndarray, estimate: numpy.ndarray, sample_rate: int, wav_id: List[str])

Compute and store best hypothesis for the mixture and the estimates

static wer_from_hsdi(hits=0, substitutions=0, deletions=0, insertions=0)

static hsdi(truth, hypothesis, transformation)

predict_hypothesis(wav)

static resample(*wavs, fs_from=None, fs_to=None)

final_df()

Generate a MarkDown table, as done by ESPNet.

final_report_as_markdown()