Add ImpulseResponseAugmentor and augmentation.config file.

augmentation.config

@@ -0,0 +1,34 @@
+[
+    {
+        "type": "noise",
+        "params": {"min_snr_dB": 50,
+                   "max_snr_dB": 50,
+                   "noise_manifest": "datasets/manifest.noise"},
+        "prob": 0.0
+    },
+    {
+        "type": "speed",
+        "params": {"min_speed_rate": 0.9,
+                   "max_speed_rate": 1.1},
+        "prob": 0.0
+    },
+    {
+        "type": "shift",
+        "params": {"min_shift_ms": -5,
+                   "max_shift_ms": 5},
+        "prob": 1.0
+    },
+    {
+        "type": "volume",
+        "params": {"min_gain_dBFS": -10,
+                   "max_gain_dBFS": 10},
+        "prob": 0.0
+    },
+    {
+        "type": "bayesian_normal",
+        "params": {"target_db": -20,
+                   "prior_db": -20,
+                   "prior_samples": 100},
+        "prob": 0.0
+    }
+]

data_utils/audio.py

@@ -204,7 +204,7 @@ class AudioSegment(object):
         :raise ValueError: If the sample rates of the two segments are not
                            equal, or if the lengths of segments don't match.
         """
-        if type(self) != type(other):
+        if isinstance(other, type(self)):
             raise TypeError("Cannot add segments of different types: %s "
                             "and %s." % (type(self), type(other)))
         if self._sample_rate != other._sample_rate:
@@ -231,7 +231,7 @@ class AudioSegment(object):
         Note that this is an in-place transformation.
         
         :param gain: Gain in decibels to apply to samples. 
-        :type gain: float
+        :type gain: float|1darray
         """
         self._samples *= 10.**(gain / 20.)
 
@@ -457,9 +457,9 @@ class AudioSegment(object):
                             audio segments when resample is not allowed.
         """
         if allow_resample and self.sample_rate != impulse_segment.sample_rate:
-            impulse_segment = impulse_segment.resample(self.sample_rate)
+            impulse_segment.resample(self.sample_rate)
         if self.sample_rate != impulse_segment.sample_rate:
-            raise ValueError("Impulse segment's sample rate (%d Hz) is not"
+            raise ValueError("Impulse segment's sample rate (%d Hz) is not "
                              "equal to base signal sample rate (%d Hz)." %
                              (impulse_segment.sample_rate, self.sample_rate))
         samples = signal.fftconvolve(self.samples, impulse_segment.samples,

data_utils/augmentor/augmentation.py

@@ -9,6 +9,7 @@ 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.noise_perturb import NoisePerturbAugmentor
+from data_utils.augmentor.impulse_response import ImpulseResponseAugmentor
 from data_utils.augmentor.resample import ResampleAugmentor
 from data_utils.augmentor.online_bayesian_normalization import \
      OnlineBayesianNormalizationAugmentor
@@ -24,21 +25,46 @@ class AugmentationPipeline(object):
     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}
-         ]' 
 
+        [ {
+                "type": "noise",
+                "params": {"min_snr_dB": 10,
+                           "max_snr_dB": 20,
+                           "noise_manifest": "datasets/manifest.noise"},
+                "prob": 0.0
+            },
+            {
+                "type": "speed",
+                "params": {"min_speed_rate": 0.9,
+                           "max_speed_rate": 1.1},
+                "prob": 1.0
+            },
+            {
+                "type": "shift",
+                "params": {"min_shift_ms": -5,
+                           "max_shift_ms": 5},
+                "prob": 1.0
+            },
+            {
+                "type": "volume",
+                "params": {"min_gain_dBFS": -10,
+                           "max_gain_dBFS": 10},
+                "prob": 0.0
+            },
+            {
+                "type": "bayesian_normal",
+                "params": {"target_db": -20,
+                           "prior_db": -20,
+                           "prior_samples": 100},
+                "prob": 0.0
+            }
+        ]
+        
     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.
+    augmentor to take effect. If "prob" is zero, the augmentor does not take
+    effect.
 
     :param augmentation_config: Augmentation configuration in json string.
     :type augmentation_config: str
@@ -61,7 +87,7 @@ class AugmentationPipeline(object):
         :type audio_segment: AudioSegmenet|SpeechSegment
         """
         for augmentor, rate in zip(self._augmentors, self._rates):
-            if self._rng.uniform(0., 1.) <= rate:
+            if self._rng.uniform(0., 1.) < rate:
                 augmentor.transform_audio(audio_segment)
 
     def _parse_pipeline_from(self, config_json):
@@ -92,5 +118,7 @@ class AugmentationPipeline(object):
             return OnlineBayesianNormalizationAugmentor(self._rng, **params)
         elif augmentor_type == "noise":
             return NoisePerturbAugmentor(self._rng, **params)
+        elif augmentor_type == "impulse":
+            return ImpulseResponseAugmentor(self._rng, **params)
         else:
             raise ValueError("Unknown augmentor type [%s]." % augmentor_type)

data_utils/augmentor/impulse_response.py

@@ -0,0 +1,34 @@
+"""Contains the impulse response augmentation model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from data_utils.augmentor.base import AugmentorBase
+from data_utils import utils
+from data_utils.audio import AudioSegment
+
+
+class ImpulseResponseAugmentor(AugmentorBase):
+    """Augmentation model for adding impulse response effect.
+    
+    :param rng: Random generator object.
+    :type rng: random.Random
+    :param impulse_manifest: Manifest path for impulse audio data.
+    :type impulse_manifest: basestring 
+    """
+
+    def __init__(self, rng, impulse_manifest):
+        self._rng = rng
+        self._manifest = utils.read_manifest(manifest_path=impulse_manifest)
+
+    def transform_audio(self, audio_segment):
+        """Add impulse response effect.
+
+        Note that this is an in-place transformation.
+
+        :param audio_segment: Audio segment to add effects to.
+        :type audio_segment: AudioSegmenet|SpeechSegment
+        """
+        noise_json = self._rng.sample(self._manifest, 1)[0]
+        noise_segment = AudioSegment.from_file(noise_json['audio_filepath'])
+        audio_segment.convolve(noise_segment, allow_resample=True)

data_utils/augmentor/noise_perturb.py

@@ -5,7 +5,7 @@ from __future__ import print_function
 
 from data_utils.augmentor.base import AugmentorBase
 from data_utils import utils
-from data_utils.speech import SpeechSegment
+from data_utils.audio import AudioSegment
 
 
 class NoisePerturbAugmentor(AugmentorBase):
@@ -17,6 +17,8 @@ class NoisePerturbAugmentor(AugmentorBase):
     :type min_snr_dB: float
     :param max_snr_dB: Maximal signal noise ratio, in decibels.
     :type max_snr_dB: float
+    :param noise_manifest: Manifest path for noise audio data.
+    :type noise_manifest: basestring 
     """
 
     def __init__(self, rng, min_snr_dB, max_snr_dB, noise_manifest):
@@ -40,8 +42,8 @@ class NoisePerturbAugmentor(AugmentorBase):
         diff_duration = noise_json['duration'] - audio_segment.duration
         start = self._rng.uniform(0, diff_duration)
         end = start + audio_segment.duration
-        noise_segment = SpeechSegment.slice_from_file(
-            noise_json['audio_filepath'], transcript="", start=start, end=end)
+        noise_segment = AudioSegment.slice_from_file(
+            noise_json['audio_filepath'], start=start, end=end)
         snr_dB = self._rng.uniform(self._min_snr_dB, self._max_snr_dB)
         audio_segment.add_noise(
             noise_segment, snr_dB, allow_downsampling=True, rng=self._rng)

data_utils/data.py

@@ -169,7 +169,7 @@ class DataGenerator(object):
                         manifest, batch_size, clipped=True)
                 elif shuffle_method == "instance_shuffle":
                     self._rng.shuffle(manifest)
-                elif not shuffle_method:
+                elif shuffle_method == None:
                     pass
                 else:
                     raise ValueError("Unknown shuffle method %s." %

data_utils/speech.py

@@ -115,7 +115,7 @@ class SpeechSegment(AudioSegment):
                  speech file.
         :rtype: SpeechSegment
         """
-        audio = Audiosegment.slice_from_file(filepath, start, end)
+        audio = AudioSegment.slice_from_file(filepath, start, end)
         return cls(audio.samples, audio.sample_rate, transcript)
 
     @classmethod

train.py

@@ -123,9 +123,7 @@ parser.add_argument(
     help="Directory for saving models. (default: %(default)s)")
 parser.add_argument(
     "--augmentation_config",
-    default='[{"type": "shift", '
-    '"params": {"min_shift_ms": -5, "max_shift_ms": 5},'
-    '"prob": 1.0}]',
+    default=open('augmentation.config', 'r').read(),
     type=str,
     help="Augmentation configuration in json-format. "
     "(default: %(default)s)")