Merge branch 'develop' of https://github.com/PaddlePaddle/DeepSpeech into ds2_online_export

deepspeech/decoders/swig/setup.py

@@ -83,11 +83,13 @@ FILES = glob.glob('kenlm/util/*.cc') \
 
 FILES += glob.glob('openfst-1.6.3/src/lib/*.cc')
 
+# yapf: disable
 FILES = [
     fn for fn in FILES
     if not (fn.endswith('main.cc') or fn.endswith('test.cc') or fn.endswith(
         'unittest.cc'))
 ]
+# yapf: enable
 
 LIBS = ['stdc++']
 if platform.system() != 'Darwin':

deepspeech/exps/u2_kaldi/model.py

@@ -171,10 +171,7 @@ class U2Trainer(Trainer):
         if from_scratch:
             # save init model, i.e. 0 epoch
             self.save(tag='init')
-
         self.lr_scheduler.step(self.iteration)
-        if self.parallel:
-            self.train_loader.batch_sampler.set_epoch(self.epoch)
 
         logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.training.n_epoch:

deepspeech/frontend/augmentor/spec_augment.py

@@ -153,7 +153,7 @@ class SpecAugmentor(AugmentorBase):
         window = max_time_warp = self.W
         if window == 0:
             return x
-        
+
         if mode == "PIL":
             t = x.shape[0]
             if t - window <= window:

deepspeech/io/converter.py

@@ -43,7 +43,7 @@ class CustomConverter():
             batch (list): The batch to transform.
 
         Returns:
-            tuple(paddle.Tensor, paddle.Tensor, paddle.Tensor)
+            tuple(np.ndarray, nn.ndarray, nn.ndarray)
 
         """
         # batch should be located in list

deepspeech/io/dataloader.py

@@ -43,6 +43,18 @@ def feat_dim_and_vocab_size(data_json: List[Dict[Text, Any]],
     return feat_dim, vocab_size
 
 
+def batch_collate(x):
+    """de-tuple.
+
+    Args:
+        x (List[Tuple]): [(utts, xs, ilens, ys, olens)]
+
+    Returns:
+        Tuple: (utts, xs, ilens, ys, olens)
+    """
+    return x[0]
+
+
 class BatchDataLoader():
     def __init__(self,
                  json_file: str,
@@ -120,15 +132,15 @@ class BatchDataLoader():
         # actual bathsize is included in a list
         # default collate function converts numpy array to pytorch tensor
         # we used an empty collate function instead which returns list
-        self.dataset = TransformDataset(
-            self.minibaches,
-            lambda data: self.converter([self.reader(data, return_uttid=True)]))
+        self.dataset = TransformDataset(self.minibaches, self.converter,
+                                        self.reader)
+
         self.dataloader = DataLoader(
             dataset=self.dataset,
             batch_size=1,
-            shuffle=not self.use_sortagrad if train_mode else False,
-            collate_fn=lambda x: x[0],
-            num_workers=n_iter_processes, )
+            shuffle=not self.use_sortagrad if self.train_mode else False,
+            collate_fn=batch_collate,
+            num_workers=self.n_iter_processes, )
 
     def __repr__(self):
         echo = f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}> "

deepspeech/io/dataset.py

@@ -129,15 +129,16 @@ class TransformDataset(Dataset):
 
     Args:
         data: list object from make_batchset
-        transfrom: transform function
-
+        converter: batch function
+        reader: read data
     """
 
-    def __init__(self, data, transform):
+    def __init__(self, data, converter, reader):
         """Init function."""
         super().__init__()
         self.data = data
-        self.transform = transform
+        self.converter = converter
+        self.reader = reader
 
     def __len__(self):
         """Len function."""
@@ -145,4 +146,4 @@ class TransformDataset(Dataset):
 
     def __getitem__(self, idx):
         """[] operator."""
-        return self.transform(self.data[idx])
+        return self.converter([self.reader(self.data[idx], return_uttid=True)])

examples/aishell/s0/conf/augmentation.json

@@ -29,7 +29,7 @@
       "adaptive_number_ratio": 0,
       "adaptive_size_ratio": 0,
       "max_n_time_masks": 20,
-      "replace_with_zero": true 
+      "replace_with_zero": true
     },
     "prob": 1.0
   }

examples/librispeech/s2/conf/augmentation.json

@@ -2,17 +2,17 @@
   {
     "type": "specaug",
     "params": {
-      "F": 10,
-      "T": 50,
+      "W": 5,
+      "warp_mode": "PIL",
+      "F": 30,
       "n_freq_masks": 2,
+      "T": 40,
       "n_time_masks": 2,
       "p": 1.0,
-      "W": 80,
       "adaptive_number_ratio": 0,
       "adaptive_size_ratio": 0,
       "max_n_time_masks": 20,
-      "replace_with_zero": true,
-      "warp_mode": "PIL"
+      "replace_with_zero": false
     },
     "prob": 1.0
   }

examples/librispeech/s2/conf/transformer.yaml

@@ -8,26 +8,23 @@ collator:
   vocab_filepath: data/train_960_unigram5000_units.txt
   unit_type: 'spm'
   spm_model_prefix: 'data/train_960_unigram5000'
-  mean_std_filepath: ""
-  augmentation_config: conf/augmentation.json
-  batch_size: 64
-  raw_wav: True  # use raw_wav or kaldi feature
-  specgram_type: fbank #linear, mfcc, fbank
   feat_dim: 83
-  delta_delta: False
-  dither: 1.0
-  target_sample_rate: 16000
-  max_freq: None
-  n_fft: None
   stride_ms: 10.0
   window_ms: 25.0
-  use_dB_normalization: True
-  target_dB: -20
-  random_seed: 0
-  keep_transcription_text: False
-  sortagrad: True 
-  shuffle_method: batch_shuffle
+  sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs 
+  batch_size: 32 
+  maxlen_in: 512  # if input length  > maxlen-in, batchsize is automatically reduced
+  maxlen_out: 150  # if output length > maxlen-out, batchsize is automatically reduced
+  minibatches: 0 # for debug
+  batch_count: auto
+  batch_bins: 0 
+  batch_frames_in: 0
+  batch_frames_out: 0
+  batch_frames_inout: 0
+  augmentation_config: conf/augmentation.json
   num_workers: 2
+  subsampling_factor: 1
+  num_encs: 1
 
 
 # network architecture