diff --git a/util/importers/fisher.py b/util/importers/fisher.py
index b143d022..120e5e65 100644
--- a/util/importers/fisher.py
+++ b/util/importers/fisher.py
@@ -13,7 +13,7 @@ from Queue import PriorityQueue
 from threading import Thread
 from util.audio import audiofile_to_input_vector
 from util.gpu import get_available_gpus
-from util.text import text_to_char_array, validate_label
+from util.text import text_to_char_array, validate_label, ctc_label_dense_to_sparse
 
 class DataSets(object):
     def __init__(self, train, dev, test):
@@ -77,7 +77,7 @@ class DataSet(object):
             files_list.append((txt_file, wav_file))
         return cycle(files_list)
     
-    def _populate_batch_queue(self):
+    def _populate_batch_queue(self, session):
         for txt_file, wav_file in self._files_circular_list:
             source = audiofile_to_input_vector(wav_file, self._numcep, self._numcontext)
             source_len = len(source)
@@ -85,22 +85,26 @@ class DataSet(object):
                 target = unicodedata.normalize("NFKD", open_txt_file.read()).encode("ascii", "ignore")
                 target = text_to_char_array(target)
             target_len = len(target)
-            session.run(self._enqueue_op, feed_dict={
-                self._x: source,
-                self._x_length: source_len,
-                self._y: target,
-                self._y_length: target_len})
+            try:
+                session.run(self._enqueue_op, feed_dict={
+                    self._x: source,
+                    self._x_length: source_len,
+                    self._y: target,
+                    self._y_length: target_len})
+            except (RuntimeError, tf.errors.CancelledError):
+                return
     
     def next_batch(self):
         source, source_lengths, target, target_lengths = self._example_queue.dequeue_many(self._batch_size)
-        return source, source_lengths, target, target_lengths
+        sparse_labels = ctc_label_dense_to_sparse(target, target_lengths, self._batch_size)
+        return source, source_lengths, sparse_labels
     
     @property
     def total_batches(self):
         # Note: If len(_txt_files) % _batch_size != 0, this re-uses initial _txt_files
         return int(ceil(float(len(self._txt_files)) /float(self._batch_size)))
 
-def read_data_sets(data_dir, batch_size, numcep, numcontext, thread_count=8):
+def read_data_sets(data_dir, train_batch_size, dev_batch_size, test_batch_size, numcep, numcontext, thread_count=8, limit_dev=0, limit_test=0, limit_train=0):
     # Assume data_dir contains extracted LDC2004S13, LDC2004T19, LDC2005S13, LDC2005T19
 
     # Conditionally convert Fisher sph data to wav
@@ -120,13 +124,13 @@ def read_data_sets(data_dir, batch_size, numcep, numcontext, thread_count=8):
     _maybe_split_sets(data_dir, "fisher-2005-split-wav", "fisher-2005-split-wav-sets")
     
     # Create train DataSet
-    train = _read_data_set(data_dir, "fisher-200?-split-wav-sets/train", thread_count, batch_size, numcep, numcontext)
+    train = _read_data_set(data_dir, "fisher-200?-split-wav-sets/train", thread_count, train_batch_size, numcep, numcontext, limit=limit_train)
 
     # Create dev DataSet
-    dev = _read_data_set(data_dir, "fisher-200?-split-wav-sets/dev", thread_count, batch_size, numcep, numcontext)
+    dev = _read_data_set(data_dir, "fisher-200?-split-wav-sets/dev", thread_count, dev_batch_size, numcep, numcontext, limit=limit_dev)
 
     # Create test DataSet
-    test = _read_data_set(data_dir, "fisher-200?-split-wav-sets/test", thread_count, batch_size, numcep, numcontext)
+    test = _read_data_set(data_dir, "fisher-200?-split-wav-sets/test", thread_count, test_batch_size, numcep, numcontext, limit=limit_test)
 
     # Return DataSets
     return DataSets(train, dev, test)
@@ -299,12 +303,14 @@ def _maybe_split_dataset(filelist, target_dir):
             new_wav_file = os.path.join(target_dir, os.path.basename(wav_file))
             os.rename(wav_file, new_wav_file)
 
-def _read_data_set(work_dir, data_set, thread_count, batch_size, numcep, numcontext):
+def _read_data_set(work_dir, data_set, thread_count, batch_size, numcep, numcontext, limit=0):
     # Create data set dir
     dataset_dir = os.path.join(work_dir, data_set)
     
     # Obtain list of txt files
     txt_files = glob(os.path.join(dataset_dir, "*.txt"))
+    if limit > 0:
+        txt_files = txt_files[:limit]
     
     # Return DataSet
     return DataSet(txt_files, thread_count, batch_size, numcep, numcontext)