fix

deepspeech/frontend/augmentor/augmentation.py

@@ -103,7 +103,7 @@ class AugmentationPipeline():
         """
         for augmentor, rate in zip(self._augmentors, self._rates):
             augmentor.randomize_parameters()
-    
+
     def randomize_parameters_feature_transform(self, n_frames, n_bins):
         """Run the pre-processing pipeline for data augmentation.
 
@@ -142,7 +142,7 @@ class AugmentationPipeline():
     #     """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
     #     """
@@ -152,7 +152,7 @@ class AugmentationPipeline():
 
     # def transform_feature(self, spec_segment, single=True):
     #     """spectrogram augmentation.
-         
+
     #     Args:
     #         spec_segment (np.ndarray): audio feature, (D, T).
     #     """

deepspeech/frontend/augmentor/shift_perturb.py

@@ -32,7 +32,8 @@ class ShiftPerturbAugmentor(AugmentorBase):
         self._rng = rng
 
     def randomize_parameters(self):
-        self.shift_ms = self._rng.uniform(self._min_shift_ms, self._max_shift_ms)
+        self.shift_ms = self._rng.uniform(self._min_shift_ms,
+                                          self._max_shift_ms)
 
     def apply(self, audio_segment):
         audio_segment.shift(self.shift_ms)
@@ -49,7 +50,6 @@ class ShiftPerturbAugmentor(AugmentorBase):
     #         self.randomize_parameters()
     #     self.apply(audio_segment)
 
-
     # def transform_audio(self, audio_segment):
     #     """Shift audio.
 
@@ -60,5 +60,3 @@ class ShiftPerturbAugmentor(AugmentorBase):
     #     """
     #     shift_ms = self._rng.uniform(self._min_shift_ms, self._max_shift_ms)
     #     audio_segment.shift(shift_ms)
-
-

deepspeech/frontend/augmentor/spec_augment.py

@@ -123,18 +123,18 @@ class SpecAugmentor(AugmentorBase):
 
     def time_warp(xs, W=40):
         raise NotImplementedError
-    
+
     def randomize_parameters(self, n_frames, n_bins):
         # n_bins = xs.shape[0]
         # n_frames = xs.shape[1]
 
-        self.f=[]
-        self.f_0=[]
-        self.t=[]
-        self.t_0=[]
+        self.f = []
+        self.f_0 = []
+        self.t = []
+        self.t_0 = []
 
         for i in range(0, self.n_freq_masks):
-            f=int(self._rng.uniform(low=0, high=self.F))
+            f = int(self._rng.uniform(low=0, high=self.F))
             self.f.append(f)
             self.f_0.append(int(self._rng.uniform(low=0, high=n_bins - f)))
 
@@ -166,7 +166,7 @@ class SpecAugmentor(AugmentorBase):
             f_0 = self.f_0[i]
             xs[:, f_0:f_0 + f] = 0
             assert f_0 <= f_0 + f
-        
+
         for i in range(self.n_masks):
             t = self.t[i]
             t_0 = self.t_0[i]
@@ -174,7 +174,6 @@ class SpecAugmentor(AugmentorBase):
             assert t_0 <= t_0 + t
         return xs
 
-
     # def mask_freq(self, xs, replace_with_zero=False):
     #     n_bins = xs.shape[0]
     #     for i in range(0, self.n_freq_masks):
@@ -208,7 +207,6 @@ class SpecAugmentor(AugmentorBase):
     #         self._time_mask = (t_0, t_0 + t)
     #     return xs
 
-
     # def transform_feature(self, xs: np.ndarray, single=True):
     #     """
     #     Args:

deepspeech/frontend/augmentor/speed_perturb.py

@@ -79,7 +79,6 @@ class SpeedPerturbAugmentor(AugmentorBase):
             self._rates = np.linspace(
                 self._min_rate, self._max_rate, self._num_rates, endpoint=True)
 
-
     def randomize_parameters(self):
         if self._num_rates < 0:
             self.speed_rate = self._rng.uniform(self._min_rate, self._max_rate)
@@ -92,8 +91,8 @@ class SpeedPerturbAugmentor(AugmentorBase):
             return
 
         audio_segment.change_speed(speed_rate)
-    
-    def transform_audio(self, audio_segment,single=True):
+
+    def transform_audio(self, audio_segment, single=True):
         """Sample a new speed rate from the given range and
         changes the speed of the given audio clip.
 
@@ -102,7 +101,7 @@ class SpeedPerturbAugmentor(AugmentorBase):
         :param audio_segment: Audio segment to add effects to.
         :type audio_segment: AudioSegment|SpeechSegment
         """
-        if(single):
+        if (single):
             self.randomize_parameters()
         self.apply(audio_segment)
 

deepspeech/frontend/featurizer/audio_featurizer.py

@@ -195,7 +195,7 @@ class AudioFeaturizer(object):
         ind = np.where(freqs <= max_freq)[0][-1] + 1
         specgram = np.log(specgram[:ind, :] + eps)
 
-        specgram = np.transpose(specgram) #T,D
+        specgram = np.transpose(specgram)  #T,D
         return specgram
 
     def _specgram_real(self, samples, window_size, stride_size, sample_rate):
@@ -299,7 +299,7 @@ class AudioFeaturizer(object):
             ceplifter=22,
             useEnergy=True,
             winfunc='povey')
-        
+
         mfcc_feat = np.transpose(mfcc_feat)
         if delta_delta:
             mfcc_feat = self._concat_delta_delta(mfcc_feat)

deepspeech/io/collator.py

@@ -173,7 +173,6 @@ class SpeechCollator():
 
         self._stride_ms = stride_ms
         self._target_sample_rate = target_sample_rate
-        
 
         self._speech_featurizer = SpeechFeaturizer(
             unit_type=unit_type,
@@ -229,9 +228,10 @@ class SpeechCollator():
 
     def randomize_audio_parameters(self):
         self._augmentation_pipeline.randomize_parameters_audio_transform()
-    
+
     def randomize_feature_parameters(self, n_frames, n_bins):
-        self._augmentation_pipeline.randomize_parameters_feature_transform(n_frames, n_bins)
+        self._augmentation_pipeline.randomize_parameters_feature_transform(
+            n_frames, n_bins)
 
     def process_feature_and_transform(self, audio_file, transcript):
         """Load, augment, featurize and normalize for speech data.
@@ -252,7 +252,7 @@ class SpeechCollator():
         # Spectrum transform
         specgram, transcript_part = self._speech_featurizer.featurize(
             speech_segment, self._keep_transcription_text)
-        
+
         if self._normalizer:
             specgram = self._normalizer.apply(specgram)
 
@@ -261,7 +261,6 @@ class SpeechCollator():
 
         return specgram, transcript_part
 
-
     # def process_utterance(self, audio_file, transcript, single=True):
     #     """Load, augment, featurize and normalize for speech data.
 
@@ -282,11 +281,10 @@ class SpeechCollator():
     #     # audio augment
     #     self._augmentation_pipeline.transform_audio(speech_segment)
 
-
     #     # Spectrum transform
     #     specgram, transcript_part = self._speech_featurizer.featurize(
     #         speech_segment, self._keep_transcription_text)
-        
+
     #     if self._normalizer:
     #         specgram = self._normalizer.apply(specgram)
 
@@ -350,14 +348,16 @@ class SpeechCollator():
         padded_texts = pad_sequence(
             texts, padding_value=IGNORE_ID).astype(np.int64)
         text_lens = np.array(text_lens).astype(np.int64)
-        
+
         #spec augment
-        n_bins=padded_audios.shape[2]
+        n_bins = padded_audios.shape[2]
         self.randomize_feature_parameters(min(audio_lens), n_bins)
         for i in range(len(padded_audios)):
-            if not self._randomize_each_batch: 
+            if not self._randomize_each_batch:
                 self.randomize_feature_parameters(audio_lens[i], n_bins)
-            padded_audios[i] = self._augmentation_pipeline.apply_feature_transform(padded_audios[i])
+            padded_audios[
+                i] = self._augmentation_pipeline.apply_feature_transform(
+                    padded_audios[i])
 
         return utts, padded_audios, audio_lens, padded_texts, text_lens