def get_metrics(
mix,
clean,
estimate,
sample_rate=16000,
metrics_list="all",
average=True,
compute_permutation=False,
ignore_metrics_errors=False,
filename=None,
):
r"""Get speech separation/enhancement metrics from mix/clean/estimate.
Args:
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: :math:`(D, N)` or `(N, )`.
- clean: :math:`(K\_source, N)` or `(N, )`.
- estimate: :math:`(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}
"""
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)
output_metrics = OutputMetrics(
speech_prediction=estimate,
speech_source=clean,
enable_si_sdr=True,
sample_rate=sample_rate,
compute_permutation=compute_permutation,
)
utt_metrics = {}
for src, prefix in [(input_metrics, "input_"), (output_metrics, "")]:
for metric in metrics_list:
# key: eg. "input_pesq" or "pesq"
key = prefix + metric
try:
utt_metrics[key] = src[metric]
except Exception as err:
if ignore_metrics_errors:
warnings.warn(
f"Error computing {key} for {filename or '<unknown file>'},"
f" ignoring. Error was: {err}",
RuntimeWarning,
)
traceback.print_stack()
utt_metrics[key] = None
else:
raise RuntimeError(
f"Error computing {key} for {filename or '<unknown file>'}"
) from err
if average:
return average_arrays_in_dic(utt_metrics)
else:
return utt_metrics
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