Merge pull request #108 from PaddlePaddle/new_augmentation

add 3 augmentor class

deep_speech_2/data_utils/audio.py

@@ -6,7 +6,7 @@ from __future__ import print_function
 import numpy as np
 import io
 import soundfile
-import scikits.samplerate
+import resampy
 from scipy import signal
 import random
 import copy
@@ -308,7 +308,7 @@ class AudioSegment(object):
         prior_mean_squared = 10.**(prior_db / 10.)
         prior_sum_of_squares = prior_mean_squared * prior_samples
         cumsum_of_squares = np.cumsum(self.samples**2)
-        sample_count = np.arange(len(self.num_samples)) + 1
+        sample_count = np.arange(self.num_samples) + 1
         if startup_sample_idx > 0:
             cumsum_of_squares[:startup_sample_idx] = \
                 cumsum_of_squares[startup_sample_idx]
@@ -321,21 +321,19 @@ class AudioSegment(object):
         gain_db = target_db - rms_estimate_db
         self.gain_db(gain_db)
 
-    def resample(self, target_sample_rate, quality='sinc_medium'):
+    def resample(self, target_sample_rate, filter='kaiser_best'):
         """Resample the audio to a target sample rate.
 
         Note that this is an in-place transformation.
 
         :param target_sample_rate: Target sample rate.
         :type target_sample_rate: int
-        :param quality: One of {'sinc_fastest', 'sinc_medium', 'sinc_best'}.
-                        Sets resampling speed/quality tradeoff.
-                        See http://www.mega-nerd.com/SRC/api_misc.html#Converters
-        :type quality: str
+        :param filter: The resampling filter to use one of {'kaiser_best',
+                       'kaiser_fast'}.
+        :type filter: str
         """
-        resample_ratio = target_sample_rate / self._sample_rate
-        self._samples = scikits.samplerate.resample(
-            self._samples, r=resample_ratio, type=quality)
+        self._samples = resampy.resample(
+            self.samples, self.sample_rate, target_sample_rate, filter=filter)
         self._sample_rate = target_sample_rate
 
     def pad_silence(self, duration, sides='both'):

deep_speech_2/data_utils/augmentor/augmentation.py

@@ -7,6 +7,10 @@ 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):
@@ -79,5 +83,11 @@ class AugmentationPipeline(object):
             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)

deep_speech_2/data_utils/augmentor/online_bayesian_normalization.py

+"""Contain the online bayesian normalization augmentation model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from data_utils.augmentor.base import AugmentorBase
+
+
+class OnlineBayesianNormalizationAugmentor(AugmentorBase):
+    """Augmentation model for adding online bayesian normalization.
+
+    :param rng: Random generator object.
+    :type rng: random.Random
+    :param target_db: Target RMS value in decibels.
+    :type target_db: float
+    :param prior_db: Prior RMS estimate in decibels.
+    :type prior_db: float
+    :param prior_samples: Prior strength in number of samples.
+    :type prior_samples: int
+    :param startup_delay: Default 0.0s. If provided, this function will
+                          accrue statistics for the first startup_delay 
+                          seconds before applying online normalization.
+    :type starup_delay: float.
+    """
+
+    def __init__(self,
+                 rng,
+                 target_db,
+                 prior_db,
+                 prior_samples,
+                 startup_delay=0.0):
+        self._target_db = target_db
+        self._prior_db = prior_db
+        self._prior_samples = prior_samples
+        self._rng = rng
+        self._startup_delay = startup_delay
+
+    def transform_audio(self, audio_segment):
+        """Normalizes the input audio using the online Bayesian approach.
+
+        Note that this is an in-place transformation.
+
+        :param audio_segment: Audio segment to add effects to.
+        :type audio_segment: AudioSegment|SpeechSegment
+        """
+        audio_segment.normalize_online_bayesian(self._target_db, self._prior_db,
+                                                self._prior_samples,
+                                                self._startup_delay)

deep_speech_2/data_utils/augmentor/resample.py

+"""Contain the resample augmentation model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from data_utils.augmentor.base import AugmentorBase
+
+
+class ResampleAugmentor(AugmentorBase):
+    """Augmentation model for resampling.
+
+    See more info here:
+    https://ccrma.stanford.edu/~jos/resample/index.html
+    
+    :param rng: Random generator object.
+    :type rng: random.Random
+    :param new_sample_rate: New sample rate in Hz.
+    :type new_sample_rate: int
+    """
+
+    def __init__(self, rng, new_sample_rate):
+        self._new_sample_rate = new_sample_rate
+        self._rng = rng
+
+    def transform_audio(self, audio_segment):
+        """Resamples the input audio to a target sample rate.
+
+        Note that this is an in-place transformation.
+
+        :param audio: Audio segment to add effects to.
+        :type audio: AudioSegment|SpeechSegment
+        """
+        audio_segment.resample(self._new_sample_rate)

deep_speech_2/data_utils/augmentor/speed_perturb.py

+"""Contain the speech perturbation augmentation model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from data_utils.augmentor.base import AugmentorBase
+
+
+class SpeedPerturbAugmentor(AugmentorBase):
+    """Augmentation model for adding speed perturbation.
+
+    See reference paper here:
+    http://www.danielpovey.com/files/2015_interspeech_augmentation.pdf
+
+    :param rng: Random generator object.
+    :type rng: random.Random
+    :param min_speed_rate: Lower bound of new speed rate to sample and should
+                           not be smaller than 0.9.
+    :type min_speed_rate: float
+    :param max_speed_rate: Upper bound of new speed rate to sample and should
+                           not be larger than 1.1.
+    :type max_speed_rate: float
+    """
+
+    def __init__(self, rng, min_speed_rate, max_speed_rate):
+        if min_speed_rate < 0.9:
+            raise ValueError(
+                "Sampling speed below 0.9 can cause unnatural effects")
+        if max_speed_rate > 1.1:
+            raise ValueError(
+                "Sampling speed above 1.1 can cause unnatural effects")
+        self._min_speed_rate = min_speed_rate
+        self._max_speed_rate = max_speed_rate
+        self._rng = rng
+
+    def transform_audio(self, audio_segment):
+        """Sample a new speed rate from the given range and
+        changes the speed of the given audio clip.
+
+        Note that this is an in-place transformation.
+
+        :param audio_segment: Audio segment to add effects to.
+        :type audio_segment: AudioSegment|SpeechSegment
+        """
+        sampled_speed = self._rng.uniform(self._min_speed_rate,
+                                          self._max_speed_rate)
+        audio_segment.change_speed(sampled_speed)

deep_speech_2/data_utils/augmentor/volume_perturb.py

@@ -37,4 +37,4 @@ class VolumePerturbAugmentor(AugmentorBase):
         :type audio_segment: AudioSegmenet|SpeechSegment
         """
         gain = self._rng.uniform(self._min_gain_dBFS, self._max_gain_dBFS)
-        audio_segment.apply_gain(gain)
+        audio_segment.gain_db(gain)

deep_speech_2/requirements.txt

 wget==3.2
 scipy==0.13.1
+resampy==0.1.5
\ No newline at end of file