Merge pull request #642 from zhxfl/fix-627

Fix 627

fluid/DeepASR/data_utils/augmentor/tests/__init__.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
+import data_utils.augmentor.trans_add_delta as trans_add_delta
+import data_utils.augmentor.trans_splice as trans_splice

fluid/DeepASR/data_utils/data_reader.py

@@ -62,10 +62,22 @@ class SampleInfoBucket(object):
         label_bin_paths (list|tuple): Files containing the binary label data.
         label_desc_paths (list|tuple): Files containing the description of
                                        samples' label data.
+        split_perturb(int): Maximum perturbation value for length of 
+                            sub-sentence when splitting long sentence.
+        split_sentence_threshold(int): Sentence whose length larger than 
+                                the value will trigger split operation.
+        split_sub_sentence_len(int): sub-sentence length is equal to 
+                                    (split_sub_sentence_len + rand() % split_perturb).
     """
 
-    def __init__(self, feature_bin_paths, feature_desc_paths, label_bin_paths,
-                 label_desc_paths):
+    def __init__(self,
+                 feature_bin_paths,
+                 feature_desc_paths,
+                 label_bin_paths,
+                 label_desc_paths,
+                 split_perturb=50,
+                 split_sentence_threshold=512,
+                 split_sub_sentence_len=256):
         block_num = len(label_bin_paths)
         assert len(label_desc_paths) == block_num
         assert len(feature_bin_paths) == block_num
@@ -76,6 +88,10 @@ class SampleInfoBucket(object):
         self._feature_desc_paths = feature_desc_paths
         self._label_bin_paths = label_bin_paths
         self._label_desc_paths = label_desc_paths
+        self._split_perturb = split_perturb
+        self._split_sentence_threshold = split_sentence_threshold
+        self._split_sub_sentence_len = split_sub_sentence_len
+        self._rng = random.Random(0)
 
     def generate_sample_info_list(self):
         sample_info_list = []
@@ -102,12 +118,45 @@ class SampleInfoBucket(object):
                 label_start = int(label_desc_split[2])
                 label_size = int(label_desc_split[3])
                 label_frame_num = int(label_desc_split[4])
+                assert feature_frame_num == label_frame_num
+
+                if self._split_sentence_threshold == -1 or \
+                        self._split_perturb == -1 or \
+                        self._split_sub_sentence_len == -1 \
+                        or self._split_sentence_threshold >= feature_frame_num:
+                    sample_info_list.append(
+                        SampleInfo(feature_bin_path, feature_start,
+                                   feature_size, feature_frame_num, feature_dim,
+                                   label_bin_path, label_start, label_size,
+                                   label_frame_num))
+                #split sentence 
+                else:
+                    cur_frame_pos = 0
+                    cur_frame_len = 0
+                    remain_frame_num = feature_frame_num
+                    while True:
+                        if remain_frame_num > self._split_sentence_threshold:
+                            cur_frame_len = self._split_sub_sentence_len + \
+                                    self._rng.randint(0, self._split_perturb)
+                            if cur_frame_len > remain_frame_num:
+                                cur_frame_len = remain_frame_num
+                        else:
+                            cur_frame_len = remain_frame_num
 
                         sample_info_list.append(
-                    SampleInfo(feature_bin_path, feature_start, feature_size,
-                               feature_frame_num, feature_dim, label_bin_path,
-                               label_start, label_size, label_frame_num))
+                            SampleInfo(
+                                feature_bin_path, feature_start + cur_frame_pos
+                                * feature_dim * 4, cur_frame_len * feature_dim *
+                                4, cur_frame_len, feature_dim, label_bin_path,
+                                label_start + cur_frame_pos * 4, cur_frame_len *
+                                4, cur_frame_len))
+
+                        remain_frame_num -= cur_frame_len
+                        cur_frame_pos += cur_frame_len
+                        if remain_frame_num <= 0:
+                            break
 
+        print("generate_sample_info_list size ", len(sample_info_list))
         return sample_info_list
 
 
@@ -125,7 +174,7 @@ class DataReader(object):
         label_file_list (str): File containing paths of label data file and 
                                corresponding description file.
         drop_frame_len (int): Samples whose label length above the value will be
-                              dropped.
+                              dropped.(Using '-1' to disable the policy)
         process_num (int): Number of processes for processing data.
         sample_buffer_size (int): Buffer size to indicate the maximum samples 
                                   cached.
@@ -149,7 +198,7 @@ class DataReader(object):
                  sample_buffer_size=1024,
                  sample_info_buffer_size=1024,
                  batch_buffer_size=1024,
-                 shuffle_block_num=1,
+                 shuffle_block_num=10,
                  random_seed=0,
                  verbose=0):
         self._feature_file_list = feature_file_list
@@ -253,11 +302,21 @@ class DataReader(object):
                                            sample_info.feature_start,
                                            sample_info.feature_size)
 
+                assert sample_info.feature_frame_num * sample_info.feature_dim * 4 \
+                        == len(feature_bytes), \
+                        (sample_info.feature_bin_path,
+                         sample_info.feature_frame_num,
+                         sample_info.feature_dim,
+                         len(feature_bytes))
+
                 label_bytes = read_bytes(sample_info.label_bin_path,
                                          sample_info.label_start,
                                          sample_info.label_size)
 
-                assert sample_info.label_frame_num * 4 == len(label_bytes)
+                assert sample_info.label_frame_num * 4 == len(label_bytes), (
+                    sample_info.label_bin_path, sample_info.label_array,
+                    len(label_bytes))
+
                 label_array = struct.unpack('I' * sample_info.label_frame_num,
                                             label_bytes)
                 label_data = np.array(
@@ -282,7 +341,8 @@ class DataReader(object):
                     time.sleep(0.001)
 
                 # drop long sentence
-                if self._drop_frame_len >= sample_data[0].shape[0]:
+                if self._drop_frame_len == -1 or \
+                        self._drop_frame_len >= sample_data[0].shape[0]:
                     sample_queue.put(sample_data)
 
                 out_order[0] += 1

fluid/DeepASR/train.py

@@ -176,7 +176,7 @@ def train(args):
 
     # train data reader
     train_data_reader = reader.DataReader(args.train_feature_lst,
-                                          args.train_label_lst)
+                                          args.train_label_lst, -1)
     train_data_reader.set_transformers(ltrans)
     # train
     for pass_id in xrange(args.pass_num):