move batch_size, work_nums, shuffle_method, sortagrad to collator

deepspeech/exps/deepspeech2/config.py

@@ -28,20 +28,6 @@ _C.data = CN(
         augmentation_config="",
         max_duration=float('inf'),
         min_duration=0.0,
-        stride_ms=10.0,  # ms
-        window_ms=20.0,  # ms
-        n_fft=None,  # fft points
-        max_freq=None,  # None for samplerate/2
-        specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
-        feat_dim=0,  # 'mfcc', 'fbank'
-        delat_delta=False,  # 'mfcc', 'fbank'
-        target_sample_rate=16000,  # target sample rate
-        use_dB_normalization=True,
-        target_dB=-20,
-        batch_size=32,  # batch size
-        num_workers=0,  # data loader workers
-        sortagrad=False,  # sorted in first epoch when True
-        shuffle_method="batch_shuffle",  # 'batch_shuffle', 'instance_shuffle'
     ))
 
 _C.model = CN(
@@ -72,7 +58,11 @@ _C.collator =CN(
         use_dB_normalization=True,
         target_dB=-20,
         dither=1.0,  # feature dither
-        keep_transcription_text=False
+        keep_transcription_text=False,
+        batch_size=32,  # batch size
+        num_workers=0,  # data loader workers
+        sortagrad=False,  # sorted in first epoch when True
+        shuffle_method="batch_shuffle",  # 'batch_shuffle', 'instance_shuffle'
     ))
 
 DeepSpeech2Model.params(_C.model)

deepspeech/exps/deepspeech2/model.py

@@ -55,7 +55,7 @@ class DeepSpeech2Trainer(Trainer):
             'train_loss': float(loss),
         }
         msg += "train time: {:>.3f}s, ".format(iteration_time)
-        msg += "batch size: {}, ".format(self.config.data.batch_size)
+        msg += "batch size: {}, ".format(self.config.collator.batch_size)
         msg += ', '.join('{}: {:>.6f}'.format(k, v)
                          for k, v in losses_np.items())
         logger.info(msg)
@@ -149,31 +149,31 @@ class DeepSpeech2Trainer(Trainer):
         if self.parallel:
             batch_sampler = SortagradDistributedBatchSampler(
                 train_dataset,
-                batch_size=config.data.batch_size,
+                batch_size=config.collator.batch_size,
                 num_replicas=None,
                 rank=None,
                 shuffle=True,
                 drop_last=True,
-                sortagrad=config.data.sortagrad,
-                shuffle_method=config.data.shuffle_method)
+                sortagrad=config.collator.sortagrad,
+                shuffle_method=config.collator.shuffle_method)
         else:
             batch_sampler = SortagradBatchSampler(
                 train_dataset,
                 shuffle=True,
-                batch_size=config.data.batch_size,
+                batch_size=config.collator.batch_size,
                 drop_last=True,
-                sortagrad=config.data.sortagrad,
-                shuffle_method=config.data.shuffle_method)
+                sortagrad=config.collator.sortagrad,
+                shuffle_method=config.collator.shuffle_method)
 
         collate_fn = SpeechCollator.from_config(config)
         self.train_loader = DataLoader(
             train_dataset,
             batch_sampler=batch_sampler,
             collate_fn=collate_fn,
-            num_workers=config.data.num_workers)
+            num_workers=config.collator.num_workers)
         self.valid_loader = DataLoader(
             dev_dataset,
-            batch_size=config.data.batch_size,
+            batch_size=config.collator.batch_size,
             shuffle=False,
             drop_last=False,
             collate_fn=collate_fn)

deepspeech/exps/u2/config.py

@@ -26,7 +26,11 @@ _C.collator =CfgNode(
     dict(
         augmentation_config="",
         unit_type="char",
-        keep_transcription_text=False
+        keep_transcription_text=False,
+        batch_size=32,  # batch size
+        num_workers=0,  # data loader workers
+        sortagrad=False,  # sorted in first epoch when True
+        shuffle_method="batch_shuffle"  # 'batch_shuffle', 'instance_shuffle'
     ))
 
 _C.model = U2Model.params()

deepspeech/frontend/featurizer/speech_featurizer.py

@@ -151,13 +151,3 @@ class SpeechFeaturizer(object):
             TextFeaturizer: object.
         """
         return self._text_featurizer
-        
-
-    # @property
-    # def text_feature(self):
-    #     """Return the text feature object.
-
-    #     Returns:
-    #         TextFeaturizer: object.
-    #     """
-    #     return self._text_featurizer

deepspeech/io/collator.py

@@ -203,34 +203,22 @@ class SpeechCollator():
                  where transcription part could be token ids or text.
         :rtype: tuple of (2darray, list)
         """
-        start_time = time.time()
         if isinstance(audio_file, str) and audio_file.startswith('tar:'):
             speech_segment = SpeechSegment.from_file(
                 self._subfile_from_tar(audio_file), transcript)
         else:
             speech_segment = SpeechSegment.from_file(audio_file, transcript)
-        load_wav_time = time.time() - start_time
-        #logger.debug(f"load wav time: {load_wav_time}")
 
         # audio augment
-        start_time = time.time()
         self._augmentation_pipeline.transform_audio(speech_segment)
-        audio_aug_time = time.time() - start_time
-        #logger.debug(f"audio augmentation time: {audio_aug_time}")
 
-        start_time = time.time()
         specgram, transcript_part = self._speech_featurizer.featurize(
             speech_segment, self._keep_transcription_text)
         if self._normalizer:
             specgram = self._normalizer.apply(specgram)
-        feature_time = time.time() - start_time
-        #logger.debug(f"audio & test feature time: {feature_time}")
 
         # specgram augment
-        start_time = time.time()
         specgram = self._augmentation_pipeline.transform_feature(specgram)
-        feature_aug_time = time.time() - start_time
-        #logger.debug(f"audio feature augmentation time: {feature_aug_time}")
         return specgram, transcript_part
 
     def __call__(self, batch):
@@ -283,16 +271,6 @@ class SpeechCollator():
         return utts, padded_audios, audio_lens, padded_texts, text_lens
 
 
-    # @property
-    # def text_feature(self):
-    #     return self._speech_featurizer.text_feature
-
-
-    # @property
-    # def stride_ms(self):
-    #     return self._speech_featurizer.stride_ms
-
-###########
     
     @property
     def manifest(self):

examples/aishell/s0/conf/deepspeech2.yaml

@@ -5,16 +5,13 @@ data:
   test_manifest: data/manifest.test
   mean_std_filepath: data/mean_std.json
   vocab_filepath: data/vocab.txt 
-  batch_size: 64 # one gpu
   min_input_len: 0.0
   max_input_len: 27.0 # second
   min_output_len: 0.0
   max_output_len: .inf
   min_output_input_ratio: 0.00
   max_output_input_ratio: .inf
-  sortagrad: True
-  shuffle_method: batch_shuffle
-  num_workers: 0
+
 
 collator:
   augmentation_config: conf/augmentation.json
@@ -32,6 +29,10 @@ collator:
   target_dB: -20
   dither: 1.0
   keep_transcription_text: False
+  sortagrad: True
+  shuffle_method: batch_shuffle
+  num_workers: 0
+  batch_size: 64 # one gpu
 
 model:
   num_conv_layers: 2

examples/tiny/s0/conf/deepspeech2.yaml

@@ -6,16 +6,13 @@ data:
   mean_std_filepath: data/mean_std.json
   unit_type: char
   vocab_filepath: data/vocab.txt 
-  batch_size: 4
   min_input_len: 0.0
   max_input_len: 27.0
   min_output_len: 0.0
   max_output_len: 400.0
   min_output_input_ratio: 0.05
   max_output_input_ratio: 10.0
-  sortagrad: True 
-  shuffle_method: batch_shuffle
-  num_workers: 0
+
 
 collator:
   augmentation_config: conf/augmentation.json
@@ -33,6 +30,10 @@ collator:
   target_dB: -20
   dither: 1.0
   keep_transcription_text: False
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 0
+  batch_size: 4
   
 model:
   num_conv_layers: 2