add 3 augmentor and unittest

README.md

@@ -51,13 +51,13 @@ python compute_mean_std.py --help
 For GPU Training:
 
 ```
-CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --trainer_count 4
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py
 ```
 
 For CPU Training:
 
 ```
-python train.py --trainer_count 8 --use_gpu False
+python train.py --use_gpu False
 ```
 
 More help for arguments:

data_utils/audio.py

@@ -66,6 +66,54 @@ class AudioSegment(object):
         samples, sample_rate = soundfile.read(file, dtype='float32')
         return cls(samples, sample_rate)
 
+    @classmethod
+    def slice_from_file(cls, file, start=None, end=None):
+        """Loads a small section of an audio without having to load
+        the entire file into the memory which can be incredibly wasteful.
+
+        :param file: Input audio filepath or file object.
+        :type file: basestring|file
+        :param start: Start time in seconds. If start is negative, it wraps
+                      around from the end. If not provided, this function 
+                      reads from the very beginning.
+        :type start: float
+        :param end: End time in seconds. If end is negative, it wraps around
+                    from the end. If not provided, the default behvaior is
+                    to read to the end of the file.
+        :type end: float
+        :return: AudioSegment instance of the specified slice of the input
+                 audio file.
+        :rtype: AudioSegment
+        :raise ValueError: If start or end is incorrectly set, e.g. out of
+                           bounds in time.
+        """
+        sndfile = soundfile.SoundFile(file)
+        sample_rate = sndfile.samplerate
+        duration = float(len(sndfile)) / sample_rate
+        start = 0. if start is None else start
+        end = 0. if end is None else end
+        if start < 0.0:
+            start += duration
+        if end < 0.0:
+            end += duration
+        if start < 0.0:
+            raise ValueError("The slice start position (%f s) is out of "
+                             "bounds." % start)
+        if end < 0.0:
+            raise ValueError("The slice end position (%f s) is out of bounds." %
+                             end)
+        if start > end:
+            raise ValueError("The slice start position (%f s) is later than "
+                             "the slice end position (%f s)." % (start, end))
+        if end > duration:
+            raise ValueError("The slice end position (%f s) is out of bounds "
+                             "(> %f s)" % (end, duration))
+        start_frame = int(start * sample_rate)
+        end_frame = int(end * sample_rate)
+        sndfile.seek(start_frame)
+        data = sndfile.read(frames=end_frame - start_frame, dtype='float32')
+        return cls(data, sample_rate)
+
     @classmethod
     def from_bytes(cls, bytes):
         """Create audio segment from a byte string containing audio samples.
@@ -105,6 +153,20 @@ class AudioSegment(object):
         samples = np.concatenate([seg.samples for seg in segments])
         return cls(samples, sample_rate)
 
+    @classmethod
+    def make_silence(cls, duration, sample_rate):
+        """Creates a silent audio segment of the given duration and sample rate.
+
+        :param duration: Length of silence in seconds.
+        :type duration: float
+        :param sample_rate: Sample rate.
+        :type sample_rate: float
+        :return: Silent AudioSegment instance of the given duration.
+        :rtype: AudioSegment
+        """
+        samples = np.zeros(int(duration * sample_rate))
+        return cls(samples, sample_rate)
+
     def to_wav_file(self, filepath, dtype='float32'):
         """Save audio segment to disk as wav file.
         
@@ -130,68 +192,6 @@ class AudioSegment(object):
             format='WAV',
             subtype=subtype_map[dtype])
 
-    @classmethod
-    def slice_from_file(cls, file, start=None, end=None):
-        """Loads a small section of an audio without having to load
-        the entire file into the memory which can be incredibly wasteful.
-
-        :param file: Input audio filepath or file object.
-        :type file: basestring|file
-        :param start: Start time in seconds. If start is negative, it wraps
-                      around from the end. If not provided, this function 
-                      reads from the very beginning.
-        :type start: float
-        :param end: End time in seconds. If end is negative, it wraps around
-                    from the end. If not provided, the default behvaior is
-                    to read to the end of the file.
-        :type end: float
-        :return: AudioSegment instance of the specified slice of the input
-                 audio file.
-        :rtype: AudioSegment
-        :raise ValueError: If start or end is incorrectly set, e.g. out of
-                           bounds in time.
-        """
-        sndfile = soundfile.SoundFile(file)
-        sample_rate = sndfile.samplerate
-        duration = float(len(sndfile)) / sample_rate
-        start = 0. if start is None else start
-        end = 0. if end is None else end
-        if start < 0.0:
-            start += duration
-        if end < 0.0:
-            end += duration
-        if start < 0.0:
-            raise ValueError("The slice start position (%f s) is out of "
-                             "bounds." % start)
-        if end < 0.0:
-            raise ValueError("The slice end position (%f s) is out of bounds." %
-                             end)
-        if start > end:
-            raise ValueError("The slice start position (%f s) is later than "
-                             "the slice end position (%f s)." % (start, end))
-        if end > duration:
-            raise ValueError("The slice end position (%f s) is out of bounds "
-                             "(> %f s)" % (end, duration))
-        start_frame = int(start * sample_rate)
-        end_frame = int(end * sample_rate)
-        sndfile.seek(start_frame)
-        data = sndfile.read(frames=end_frame - start_frame, dtype='float32')
-        return cls(data, sample_rate)
-
-    @classmethod
-    def make_silence(cls, duration, sample_rate):
-        """Creates a silent audio segment of the given duration and sample rate.
-
-        :param duration: Length of silence in seconds.
-        :type duration: float
-        :param sample_rate: Sample rate.
-        :type sample_rate: float
-        :return: Silent AudioSegment instance of the given duration.
-        :rtype: AudioSegment
-        """
-        samples = np.zeros(int(duration * sample_rate))
-        return cls(samples, sample_rate)
-
     def superimpose(self, other):
         """Add samples from another segment to those of this segment
         (sample-wise addition, not segment concatenation).
@@ -225,7 +225,7 @@ class AudioSegment(object):
         samples = self._convert_samples_from_float32(self._samples, dtype)
         return samples.tostring()
 
-    def apply_gain(self, gain):
+    def gain_db(self, gain):
         """Apply gain in decibels to samples.
 
         Note that this is an in-place transformation.
@@ -278,7 +278,7 @@ class AudioSegment(object):
                 "Unable to normalize segment to %f dB because the "
                 "the probable gain have exceeds max_gain_db (%f dB)" %
                 (target_db, max_gain_db))
-        self.apply_gain(min(max_gain_db, target_db - self.rms_db))
+        self.gain_db(min(max_gain_db, target_db - self.rms_db))
 
     def normalize_online_bayesian(self,
                                   target_db,
@@ -319,7 +319,7 @@ class AudioSegment(object):
         rms_estimate_db = 10 * np.log10(mean_squared_estimate)
         # Compute required time-varying gain.
         gain_db = target_db - rms_estimate_db
-        self.apply_gain(gain_db)
+        self.gain_db(gain_db)
 
     def resample(self, target_sample_rate, filter='kaiser_best'):
         """Resample the audio to a target sample rate.
@@ -329,9 +329,10 @@ class AudioSegment(object):
         :param target_sample_rate: Target sample rate.
         :type target_sample_rate: int
         :param filter: The resampling filter to use one of {'kaiser_best',
-                       'kaiser_fast'}.               
+                       'kaiser_fast'}.
         :type filter: str
         """
+        resample_ratio = target_sample_rate / self._sample_rate
         self._samples = resampy.resample(
             self.samples, self.sample_rate, target_sample_rate, filter=filter)
         self._sample_rate = target_sample_rate
@@ -364,6 +365,31 @@ class AudioSegment(object):
             raise ValueError("Unknown value for the sides %s" % sides)
         self._samples = padded._samples
 
+    def shift(self, shift_ms):
+        """Shift the audio in time. If `shift_ms` is positive, shift with time
+        advance; if negative, shift with time delay. Silence are padded to
+        keep the duration unchanged.
+
+        Note that this is an in-place transformation.
+
+        :param shift_ms: Shift time in millseconds. If positive, shift with
+                         time advance; if negative; shift with time delay.
+        :type shift_ms: float
+        :raises ValueError: If shift_ms is longer than audio duration.
+        """
+        if abs(shift_ms) / 1000.0 > self.duration:
+            raise ValueError("Absolute value of shift_ms should be smaller "
+                             "than audio duration.")
+        shift_samples = int(shift_ms * self._sample_rate / 1000)
+        if shift_samples > 0:
+            # time advance
+            self._samples[:-shift_samples] = self._samples[shift_samples:]
+            self._samples[-shift_samples:] = 0
+        elif shift_samples < 0:
+            # time delay
+            self._samples[-shift_samples:] = self._samples[:shift_samples]
+            self._samples[:-shift_samples] = 0
+
     def subsegment(self, start_sec=None, end_sec=None):
         """Cut the AudioSegment between given boundaries.
 
@@ -503,7 +529,7 @@ class AudioSegment(object):
         noise_gain_db = min(self.rms_db - noise.rms_db - snr_dB, max_gain_db)
         noise_new = copy.deepcopy(noise)
         noise_new.random_subsegment(self.duration, rng=rng)
-        noise_new.apply_gain(noise_gain_db)
+        noise_new.gain_db(noise_gain_db)
         self.superimpose(noise_new)
 
     @property

data_utils/augmentor/augmentation.py

@@ -6,6 +6,7 @@ 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
@@ -79,11 +80,13 @@ class AugmentationPipeline(object):
         """Return an augmentation model by the type name, and pass in params."""
         if augmentor_type == "volume":
             return VolumePerturbAugmentor(self._rng, **params)
-        if augmentor_type == "speed":
+        elif augmentor_type == "shift":
+            return ShiftPerturbAugmentor(self._rng, **params)
+        elif augmentor_type == "speed":
             return SpeedPerturbAugmentor(self._rng, **params)
-        if augmentor_type == "resample":
+        elif augmentor_type == "resample":
             return ResampleAugmentor(self._rng, **params)
-        if augmentor_type == "bayesian_normal":
+        elif augmentor_type == "bayesian_normal":
             return OnlineBayesianNormalizationAugmentor(self._rng, **params)
         else:
             raise ValueError("Unknown augmentor type [%s]." % augmentor_type)

data_utils/augmentor/resample.py

@@ -30,4 +30,4 @@ class ResampleAugmentor(AugmentorBase):
         :param audio: Audio segment to add effects to.
         :type audio: AudioSegment|SpeechSegment
         """
-        audio_segment.resample(self._new_sample_rate)
\ No newline at end of file
+        audio_segment.resample(self._new_sample_rate)

data_utils/augmentor/shift_perturb.py

+"""Contains the volume perturb augmentation model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from data_utils.augmentor.base import AugmentorBase
+
+
+class ShiftPerturbAugmentor(AugmentorBase):
+    """Augmentation model for adding random shift perturbation.
+    
+    :param rng: Random generator object.
+    :type rng: random.Random
+    :param min_shift_ms: Minimal shift in milliseconds.
+    :type min_shift_ms: float
+    :param max_shift_ms: Maximal shift in milliseconds.
+    :type max_shift_ms: float
+    """
+
+    def __init__(self, rng, min_shift_ms, max_shift_ms):
+        self._min_shift_ms = min_shift_ms
+        self._max_shift_ms = max_shift_ms
+        self._rng = rng
+
+    def transform_audio(self, audio_segment):
+        """Shift audio.
+
+        Note that this is an in-place transformation.
+
+        :param audio_segment: Audio segment to add effects to.
+        :type audio_segment: AudioSegmenet|SpeechSegment
+        """
+        shift_ms = self._rng.uniform(self._min_shift_ms, self._max_shift_ms)
+        audio_segment.shift(shift_ms)

data_utils/augmentor/speed_perturb.py

@@ -14,20 +14,21 @@ class SpeedPerturbAugmentor(AugmentorBase):
 
     :param rng: Random generator object.
     :type rng: random.Random
-    :param min_speed_rate: Lower bound of new speed rate to sample.
+    :param min_speed_rate: Lower bound of new speed rate to sample and should
+                           not below 0.9.
     :type min_speed_rate: float
-    :param max_speed_rate: Upper bound of new speed rate to sample.
+    :param max_speed_rate: Upper bound of new speed rate to sample and should
+                           not above 1.1.
     :type max_speed_rate: float
     """
 
     def __init__(self, rng, min_speed_rate, max_speed_rate):
-
-        if (min_speed_rate < 0.5):
-            raise ValueError("Sampling speed below 0.9 can cause unnatural "\
-                             "effects")
-        if (max_speed_rate > 1.5):
-            raise ValueError("Sampling speed above 1.1 can cause unnatural "\
-                             "effects")
+        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

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)

data_utils/data.py

@@ -7,6 +7,7 @@ from __future__ import print_function
 
 import random
 import numpy as np
+import multiprocessing
 import paddle.v2 as paddle
 from data_utils import utils
 from data_utils.augmentor.augmentation import AugmentationPipeline
@@ -44,6 +45,11 @@ class DataGenerator(object):
     :types max_freq: None|float
     :param specgram_type: Specgram feature type. Options: 'linear'.
     :type specgram_type: str
+    :param use_dB_normalization: Whether to normalize the audio to -20 dB
+                                 before extracting the features.
+    :type use_dB_normalization: bool
+    :param num_threads: Number of CPU threads for processing data.
+    :type num_threads: int
     :param random_seed: Random seed.
     :type random_seed: int
     """
@@ -58,6 +64,8 @@ class DataGenerator(object):
                  window_ms=20.0,
                  max_freq=None,
                  specgram_type='linear',
+                 use_dB_normalization=True,
+                 num_threads=multiprocessing.cpu_count(),
                  random_seed=0):
         self._max_duration = max_duration
         self._min_duration = min_duration
@@ -69,7 +77,9 @@ class DataGenerator(object):
             specgram_type=specgram_type,
             stride_ms=stride_ms,
             window_ms=window_ms,
-            max_freq=max_freq)
+            max_freq=max_freq,
+            use_dB_normalization=use_dB_normalization)
+        self._num_threads = num_threads
         self._rng = random.Random(random_seed)
         self._epoch = 0
 
@@ -207,10 +217,14 @@ class DataGenerator(object):
 
         def reader():
             for instance in manifest:
-                yield self._process_utterance(instance["audio_filepath"],
-                                              instance["text"])
+                yield instance
 
-        return reader
+        def mapper(instance):
+            return self._process_utterance(instance["audio_filepath"],
+                                           instance["text"])
+
+        return paddle.reader.xmap_readers(
+            mapper, reader, self._num_threads, 1024, order=True)
 
     def _padding_batch(self, batch, padding_to=-1, flatten=False):
         """

data_utils/featurizer/audio_featurizer.py

@@ -24,26 +24,64 @@ class AudioFeaturizer(object):
                      corresponding to frequencies between [0, max_freq] are
                      returned.
     :types max_freq: None|float
+    :param target_sample_rate: Audio are resampled (if upsampling or
+                               downsampling is allowed) to this before
+                               extracting spectrogram features.
+    :type target_sample_rate: float
+    :param use_dB_normalization: Whether to normalize the audio to a certain
+                                 decibels before extracting the features.
+    :type use_dB_normalization: bool
+    :param target_dB: Target audio decibels for normalization.
+    :type target_dB: float
     """
 
     def __init__(self,
                  specgram_type='linear',
                  stride_ms=10.0,
                  window_ms=20.0,
-                 max_freq=None):
+                 max_freq=None,
+                 target_sample_rate=16000,
+                 use_dB_normalization=True,
+                 target_dB=-20):
         self._specgram_type = specgram_type
         self._stride_ms = stride_ms
         self._window_ms = window_ms
         self._max_freq = max_freq
+        self._target_sample_rate = target_sample_rate
+        self._use_dB_normalization = use_dB_normalization
+        self._target_dB = target_dB
 
-    def featurize(self, audio_segment):
+    def featurize(self,
+                  audio_segment,
+                  allow_downsampling=True,
+                  allow_upsamplling=True):
         """Extract audio features from AudioSegment or SpeechSegment.
 
         :param audio_segment: Audio/speech segment to extract features from.
         :type audio_segment: AudioSegment|SpeechSegment
+        :param allow_downsampling: Whether to allow audio downsampling before
+                                   featurizing.
+        :type allow_downsampling: bool
+        :param allow_upsampling: Whether to allow audio upsampling before
+                                 featurizing.
+        :type allow_upsampling: bool
         :return: Spectrogram audio feature in 2darray.
         :rtype: ndarray
+        :raises ValueError: If audio sample rate is not supported.
         """
+        # upsampling or downsampling
+        if ((audio_segment.sample_rate > self._target_sample_rate and
+             allow_downsampling) or
+            (audio_segment.sample_rate < self._target_sample_rate and
+             allow_upsampling)):
+            audio_segment.resample(self._target_sample_rate)
+        if audio_segment.sample_rate != self._target_sample_rate:
+            raise ValueError("Audio sample rate is not supported. "
+                             "Turn allow_downsampling or allow up_sampling on.")
+        # decibel normalization
+        if self._use_dB_normalization:
+            audio_segment.normalize(target_db=self._target_dB)
+        # extract spectrogram
         return self._compute_specgram(audio_segment.samples,
                                       audio_segment.sample_rate)
 

data_utils/featurizer/speech_featurizer.py

@@ -29,6 +29,15 @@ class SpeechFeaturizer(object):
                      corresponding to frequencies between [0, max_freq] are
                      returned.
     :types max_freq: None|float
+    :param target_sample_rate: Speech are resampled (if upsampling or
+                               downsampling is allowed) to this before
+                               extracting spectrogram features.
+    :type target_sample_rate: float
+    :param use_dB_normalization: Whether to normalize the audio to a certain
+                                 decibels before extracting the features.
+    :type use_dB_normalization: bool
+    :param target_dB: Target audio decibels for normalization.
+    :type target_dB: float
     """
 
     def __init__(self,
@@ -36,9 +45,18 @@ class SpeechFeaturizer(object):
                  specgram_type='linear',
                  stride_ms=10.0,
                  window_ms=20.0,
-                 max_freq=None):
-        self._audio_featurizer = AudioFeaturizer(specgram_type, stride_ms,
-                                                 window_ms, max_freq)
+                 max_freq=None,
+                 target_sample_rate=16000,
+                 use_dB_normalization=True,
+                 target_dB=-20):
+        self._audio_featurizer = AudioFeaturizer(
+            specgram_type=specgram_type,
+            stride_ms=stride_ms,
+            window_ms=window_ms,
+            max_freq=max_freq,
+            target_sample_rate=target_sample_rate,
+            use_dB_normalization=use_dB_normalization,
+            target_dB=target_dB)
         self._text_featurizer = TextFeaturizer(vocab_filepath)
 
     def featurize(self, speech_segment):

data_utils/speech.py

@@ -94,7 +94,7 @@ class SpeechSegment(AudioSegment):
         return cls(samples, sample_rate, transcripts)
 
     @classmethod
-    def slice_from_file(cls, filepath, start=None, end=None, transcript):
+    def slice_from_file(cls, filepath, transcript, start=None, end=None):
         """Loads a small section of an speech without having to load
         the entire file into the memory which can be incredibly wasteful.
 

infer.py

@@ -6,6 +6,7 @@ from __future__ import print_function
 import argparse
 import gzip
 import distutils.util
+import multiprocessing
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
 from model import deep_speech2
@@ -38,6 +39,11 @@ parser.add_argument(
     default=True,
     type=distutils.util.strtobool,
     help="Use gpu or not. (default: %(default)s)")
+parser.add_argument(
+    "--num_threads_data",
+    default=multiprocessing.cpu_count(),
+    type=int,
+    help="Number of cpu threads for preprocessing data. (default: %(default)s)")
 parser.add_argument(
     "--mean_std_filepath",
     default='mean_std.npz',
@@ -50,7 +56,7 @@ parser.add_argument(
     help="Manifest path for decoding. (default: %(default)s)")
 parser.add_argument(
     "--model_filepath",
-    default='./params.tar.gz',
+    default='checkpoints/params.latest.tar.gz',
     type=str,
     help="Model filepath. (default: %(default)s)")
 parser.add_argument(
@@ -67,7 +73,8 @@ def infer():
     data_generator = DataGenerator(
         vocab_filepath=args.vocab_filepath,
         mean_std_filepath=args.mean_std_filepath,
-        augmentation_config='{}')
+        augmentation_config='{}',
+        num_threads=args.num_threads_data)
 
     # create network config
     # paddle.data_type.dense_array is used for variable batch input.

tests/test_augmentor.py

+"""Test augmentor class."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import unittest
+from data_utils import audio
+from data_utils.augmentor.augmentation import AugmentationPipeline
+import random
+import numpy as np
+
+random_seed = 0
+#audio instance
+audio_data = [3.0517571e-05, -8.54492188e-04, -1.09863281e-03, -9.4604492e-04,\
+            -1.31225586e-03, -1.09863281e-03, -1.73950195e-03, -2.1057189e-03,\
+            -2.04467773e-03, -1.46484375e-03, -1.43432617e-03, -9.4604492e-04,\
+            -1.95312500e-03, -1.86157227e-03, -2.10571289e-03, -2.3193354e-03,\
+            -2.01416016e-03, -2.62451172e-03, -2.07519531e-03, -2.3803719e-03]
+audio_data = np.array(audio_data)
+samplerate = 10
+
+
+class TestAugmentor(unittest.TestCase):
+    def test_volume(self):
+        config_json = '[{"type": "volume","params": {"min_gain_dBFS": -15, '\
+        '"max_gain_dBFS": 15},"prob": 1.0}]'
+        aug_pipeline = AugmentationPipeline(
+            augmentation_config=config_json, random_seed=random_seed)
+        audio_seg = audio.AudioSegment(audio_data, samplerate)
+        aug_pipeline.transform_audio(audio_seg)
+        orig_audio = audio.AudioSegment(audio_data, samplerate)
+        self.assertFalse(np.any(audio_seg.samples == orig_audio.samples))
+
+    def test_speed(self):
+        config_json = '[{"type":"speed","params": {"min_speed_rate": 0.9,' \
+        '"max_speed_rate": 1.1},"prob": 1.0}]'
+        aug_pipeline = AugmentationPipeline(
+            augmentation_config=config_json, random_seed=random_seed)
+        audio_seg = audio.AudioSegment(audio_data, samplerate)
+        aug_pipeline.transform_audio(audio_seg)
+        orig_audio = audio.AudioSegment(audio_data, samplerate)
+        self.assertFalse(np.any(audio_seg.samples == orig_audio.samples))
+
+    def test_resample(self):
+        config_json = '[{"type":"resample","params": {"new_sample_rate":5},'\
+        '"prob": 1.0}]'
+        aug_pipeline = AugmentationPipeline(
+            augmentation_config=config_json, random_seed=random_seed)
+        audio_seg = audio.AudioSegment(audio_data, samplerate)
+        aug_pipeline.transform_audio(audio_seg)
+        self.assertTrue(audio_seg.sample_rate == 5)
+
+    def test_bayesial(self):
+        config_json = '[{"type":"bayesian_normal","params":{"target_db":-20,' \
+        '"prior_db":-4, "prior_samples": -8, "startup_delay": 0.0},"prob":1.0}]'
+        aug_pipeline = AugmentationPipeline(
+            augmentation_config=config_json, random_seed=random_seed)
+        audio_seg = audio.AudioSegment(audio_data, samplerate)
+        aug_pipeline.transform_audio(audio_seg)
+        orig_audio = audio.AudioSegment(audio_data, samplerate)
+        self.assertFalse(np.any(audio_seg.samples == orig_audio.samples))
+
+
+if __name__ == '__main__':
+    unittest.main()

train.py

@@ -9,6 +9,7 @@ import argparse
 import gzip
 import time
 import distutils.util
+import multiprocessing
 import paddle.v2 as paddle
 from model import deep_speech2
 from data_utils.data import DataGenerator
@@ -16,10 +17,10 @@ import utils
 
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
-    "--batch_size", default=32, type=int, help="Minibatch size.")
+    "--batch_size", default=256, type=int, help="Minibatch size.")
 parser.add_argument(
     "--num_passes",
-    default=20,
+    default=200,
     type=int,
     help="Training pass number. (default: %(default)s)")
 parser.add_argument(
@@ -52,17 +53,34 @@ parser.add_argument(
     default=True,
     type=distutils.util.strtobool,
     help="Use sortagrad or not. (default: %(default)s)")
+parser.add_argument(
+    "--max_duration",
+    default=27.0,
+    type=float,
+    help="Audios with duration larger than this will be discarded. "
+    "(default: %(default)s)")
+parser.add_argument(
+    "--min_duration",
+    default=0.0,
+    type=float,
+    help="Audios with duration smaller than this will be discarded. "
+    "(default: %(default)s)")
 parser.add_argument(
     "--shuffle_method",
-    default='instance_shuffle',
+    default='batch_shuffle_clipped',
     type=str,
     help="Shuffle method: 'instance_shuffle', 'batch_shuffle', "
     "'batch_shuffle_batch'. (default: %(default)s)")
 parser.add_argument(
     "--trainer_count",
-    default=4,
+    default=8,
     type=int,
     help="Trainer number. (default: %(default)s)")
+parser.add_argument(
+    "--num_threads_data",
+    default=multiprocessing.cpu_count(),
+    type=int,
+    help="Number of cpu threads for preprocessing data. (default: %(default)s)")
 parser.add_argument(
     "--mean_std_filepath",
     default='mean_std.npz',
@@ -92,7 +110,9 @@ parser.add_argument(
     "the existing model of this path. (default: %(default)s)")
 parser.add_argument(
     "--augmentation_config",
-    default='{}',
+    default='[{"type": "shift", '
+    '"params": {"min_shift_ms": -5, "max_shift_ms": 5},'
+    '"prob": 1.0}]',
     type=str,
     help="Augmentation configuration in json-format. "
     "(default: %(default)s)")
@@ -107,7 +127,10 @@ def train():
         return DataGenerator(
             vocab_filepath=args.vocab_filepath,
             mean_std_filepath=args.mean_std_filepath,
-            augmentation_config=args.augmentation_config)
+            augmentation_config=args.augmentation_config,
+            max_duration=args.max_duration,
+            min_duration=args.min_duration,
+            num_threads=args.num_threads_data)
 
     train_generator = data_generator()
     test_generator = data_generator()
@@ -168,7 +191,7 @@ def train():
                 print("\nPass: %d, Batch: %d, TrainCost: %f" % (
                     event.pass_id, event.batch_id + 1, cost_sum / cost_counter))
                 cost_sum, cost_counter = 0.0, 0
-                with gzip.open("params.tar.gz", 'w') as f:
+                with gzip.open("checkpoints/params.latest.tar.gz", 'w') as f:
                     parameters.to_tar(f)
             else:
                 sys.stdout.write('.')
@@ -181,6 +204,9 @@ def train():
                 reader=test_batch_reader, feeding=test_generator.feeding)
             print("\n------- Time: %d sec,  Pass: %d, ValidationCost: %s" %
                   (time.time() - start_time, event.pass_id, result.cost))
+            with gzip.open("checkpoints/params.pass-%d.tar.gz" % event.pass_id,
+                           'w') as f:
+                parameters.to_tar(f)
 
     # run train
     trainer.train(