You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
94 lines
3.8 KiB
94 lines
3.8 KiB
"""Contains the data augmentation pipeline."""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import json
|
|
import random
|
|
from data_utils.augmentor.volume_perturb import VolumePerturbAugmentor
|
|
from data_utils.augmentor.shift_perturb import ShiftPerturbAugmentor
|
|
from data_utils.augmentor.speed_perturb import SpeedPerturbAugmentor
|
|
from data_utils.augmentor.resample import ResampleAugmentor
|
|
from data_utils.augmentor.online_bayesian_normalization import \
|
|
OnlineBayesianNormalizationAugmentor
|
|
|
|
|
|
class AugmentationPipeline(object):
|
|
"""Build a pre-processing pipeline with various augmentation models.Such a
|
|
data augmentation pipeline is oftern leveraged to augment the training
|
|
samples to make the model invariant to certain types of perturbations in the
|
|
real world, improving model's generalization ability.
|
|
|
|
The pipeline is built according the the augmentation configuration in json
|
|
string, e.g.
|
|
|
|
.. code-block::
|
|
|
|
'[{"type": "volume",
|
|
"params": {"min_gain_dBFS": -15,
|
|
"max_gain_dBFS": 15},
|
|
"prob": 0.5},
|
|
{"type": "speed",
|
|
"params": {"min_speed_rate": 0.8,
|
|
"max_speed_rate": 1.2},
|
|
"prob": 0.5}
|
|
]'
|
|
|
|
This augmentation configuration inserts two augmentation models
|
|
into the pipeline, with one is VolumePerturbAugmentor and the other
|
|
SpeedPerturbAugmentor. "prob" indicates the probability of the current
|
|
augmentor to take effect.
|
|
|
|
:param augmentation_config: Augmentation configuration in json string.
|
|
:type augmentation_config: str
|
|
:param random_seed: Random seed.
|
|
:type random_seed: int
|
|
:raises ValueError: If the augmentation json config is in incorrect format".
|
|
"""
|
|
|
|
def __init__(self, augmentation_config, random_seed=0):
|
|
self._rng = random.Random(random_seed)
|
|
self._augmentors, self._rates = self._parse_pipeline_from(
|
|
augmentation_config)
|
|
|
|
def transform_audio(self, audio_segment):
|
|
"""Run the pre-processing pipeline for data augmentation.
|
|
|
|
Note that this is an in-place transformation.
|
|
|
|
:param audio_segment: Audio segment to process.
|
|
:type audio_segment: AudioSegmenet|SpeechSegment
|
|
"""
|
|
for augmentor, rate in zip(self._augmentors, self._rates):
|
|
if self._rng.uniform(0., 1.) <= rate:
|
|
augmentor.transform_audio(audio_segment)
|
|
|
|
def _parse_pipeline_from(self, config_json):
|
|
"""Parse the config json to build a augmentation pipelien."""
|
|
try:
|
|
configs = json.loads(config_json)
|
|
augmentors = [
|
|
self._get_augmentor(config["type"], config["params"])
|
|
for config in configs
|
|
]
|
|
rates = [config["prob"] for config in configs]
|
|
except Exception as e:
|
|
raise ValueError("Failed to parse the augmentation config json: "
|
|
"%s" % str(e))
|
|
return augmentors, rates
|
|
|
|
def _get_augmentor(self, augmentor_type, params):
|
|
"""Return an augmentation model by the type name, and pass in params."""
|
|
if augmentor_type == "volume":
|
|
return VolumePerturbAugmentor(self._rng, **params)
|
|
elif augmentor_type == "shift":
|
|
return ShiftPerturbAugmentor(self._rng, **params)
|
|
elif augmentor_type == "speed":
|
|
return SpeedPerturbAugmentor(self._rng, **params)
|
|
elif augmentor_type == "resample":
|
|
return ResampleAugmentor(self._rng, **params)
|
|
elif augmentor_type == "bayesian_normal":
|
|
return OnlineBayesianNormalizationAugmentor(self._rng, **params)
|
|
else:
|
|
raise ValueError("Unknown augmentor type [%s]." % augmentor_type)
|