Source code for asteroid.metrics

from .utils import average_arrays_in_dic
from pb_bss_eval import InputMetrics, OutputMetrics

ALL_METRICS = ["si_sdr", "sdr", "sir", "sar", "stoi", "pesq"]

[docs]def 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. Args: 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} """ if metrics_list == "all": metrics_list = ALL_METRICS if isinstance(metrics_list, str): metrics_list = [metrics_list] # For each utterance, we get a dictionary with the input and output metrics input_metrics = InputMetrics( observation=mix, speech_source=clean, enable_si_sdr=True, sample_rate=sample_rate ) utt_metrics = {"input_" + n: input_metrics[n] for n in metrics_list} output_metrics = OutputMetrics( speech_prediction=estimate, speech_source=clean, enable_si_sdr=True, sample_rate=sample_rate, compute_permutation=compute_permutation, ) utt_metrics.update(output_metrics[metrics_list]) if average is True: return average_arrays_in_dic(utt_metrics) else: return utt_metrics
Read the Docs v: v0.3.3
On Read the Docs
Project Home

Free document hosting provided by Read the Docs.