Fix some syntax errors.

deep_speech_2/cloud/README.md

 # Run DS2 on PaddleCloud
 
->Note: Make sure current directory is `models/deep_speech_2/cloud/`
+>Note:
+>Make sure [PaddleCloud client](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#%E4%B8%8B%E8%BD%BD%E5%B9%B6%E9%85%8D%E7%BD%AEpaddlecloud) has be installed and current directory is `models/deep_speech_2/cloud/`
 
-## Step1  Configure data set
+## Step-1  Configure data set
 
-You can configure your input data and output path in pcloud_submit.sh:
+Configure your input data and output path in pcloud_submit.sh:
 
 -  `TRAIN_MANIFEST`： Absolute path of train data manifest file in local file system.This file has format as bellow:
 
@@ -15,19 +16,18 @@ You can configure your input data and output path in pcloud_submit.sh:
 ": "nor is mister ..."}
 ```
 
-- `TEST_MANIFEST`: Absolute path of train data manifest file in local filesystem.This file has format like  TRAIN_MANIFEST.
-
+- `TEST_MANIFEST`: Absolute path of train data manifest file in local filesystem. This file has format like `TRAIN_MANIFEST`.
 - `VOCAB_FILE`:  Absolute path of vocabulary file in local filesytem.
-- `MEAN_STD_FILE`: Absolute path of vocabulary file in local filesytem.
+- `MEAN_STD_FILE`: Absolute path of normalizer's statistic file in local filesytem.
 - `CLOUD_DATA_DIR:`  Absolute path in PaddleCloud filesystem. We will upload local train data to this directory.
 - `CLOUD_MODEL_DIR`: Absolute path in PaddleCloud filesystem. PaddleCloud trainer will save model to this directory.
 
+>Note: Upload will be skipped if target file has existed in `CLOUD_DATA_DIR`.
 
->Note: Upload will be skipped if target file has existed in  ${CLOUD_DATA_DIR}.
+## Step-2  Configure computation resource
 
-## Step2  Configure computation resource
+Configure computation resource in pcloud_submit.sh:
 
-You can configure computation resource in pcloud_submit.sh:
 ```
 # Configure computation resource and submit job to PaddleCloud
  paddlecloud submit \
@@ -44,10 +44,10 @@ You can configure computation resource in pcloud_submit.sh:
  -entry "sh pcloud_train.sh ${CLOUD_DATA_DIR} ${CLOUD_MODEL_DIR}" \
  ${DS2_PATH}
 ```
-For more information, please refer to[PaddleCloud](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#提交任务)
+For more information, please refer to [PaddleCloud](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#提交任务)
 
-## Step3  Configure algorithm options
-You can configure algorithm options in pcloud_train.sh:
+## Step-3  Configure algorithm options
+Configure algorithm options in pcloud_train.sh:
 ```
 python train.py \
 --use_gpu=1 \
@@ -65,13 +65,13 @@ cd ..
 python train.py --help
 ```
 
-## Step4  Submit job
+## Step-4  Submit job
 ```
 $ sh pcloud_submit.sh
 ```
 
 
-## Step5 Get logs
+## Step-5 Get logs
 ```
 $ paddlecloud logs -n 10000 deepspeech20170727130129
 ```

deep_speech_2/cloud/pcloud_submit.sh

@@ -15,6 +15,11 @@ python upload_data.py \
 --vocab_file=${VOCAB_FILE} \
 --mean_std_file=${MEAN_STD_FILE} \
 --cloud_data_path=${CLOUD_DATA_DIR}
+if [ $? -ne 0 ]
+then
+    echo "upload data failed!"
+    exit 1
+fi
 
 JOB_NAME=deepspeech`date +%Y%m%d%H%M%S`
 DS2_PATH=${PWD%/*}
@@ -27,7 +32,7 @@ paddlecloud submit \
 -cpu 4 \
 -gpu 4 \
 -memory 10Gi \
--parallelism 1 \
+-parallelism 2 \
 -pscpu 1 \
 -pservers 1 \
 -psmemory 10Gi \

deep_speech_2/cloud/pcloud_train.sh

@@ -22,8 +22,6 @@ python ./cloud/split_data.py \
 python train.py \
 --use_gpu=1 \
 --trainer_count=4 \
---batch_size=32 \
---num_threads_data=4 \
 --mean_std_filepath=$MEAN_STD_FILE \
 --train_manifest_path='./local.train.manifest' \
 --dev_manifest_path='./local.test.manifest' \

deep_speech_2/cloud/split_data.py

-"""
-This tool is used for splitting data into each node of
+"""This tool is used for splitting data into each node of
 paddle cloud by total trainer count and current trainer id.
 The meaning of trainer is a instance of k8s cluster.
 This script should be called in paddle cloud.
 """
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import json
 import argparse

deep_speech_2/cloud/upload_data.py

-"""
-This tool is used for preparing data for DeepSpeech2 trainning on paddle cloud.
+"""This tool is used for preparing data for DeepSpeech2 trainning on paddle cloud.
 
 Steps:
 1. Read original manifest and get the local path of sound files.
@@ -9,6 +8,9 @@ Steps:
 Finally, we will get a tar file and a manifest with sound file name, duration
 and text.
 """
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import json
 import os
 import tarfile
@@ -50,7 +52,6 @@ parser.add_argument(
 parser.add_argument(
     "--cloud_data_path",
     required=True,
-    default="",
     type=str,
     help="Destination path on  paddlecloud. (default: %(default)s)")
 args = parser.parse_args()
@@ -64,8 +65,7 @@ args = parser.parse_args()
 
 
 def pack_data(manifest_path, out_tar_path, out_manifest_path):
-    '''
-    1. According manifest, tar sound files into out_tar_path
+    '''1. According to the manifest, tar sound files into out_tar_path
     2. Generate a new manifest for output tar file
     '''
     out_tar = tarfile.open(out_tar_path, 'w')
@@ -83,65 +83,65 @@ def pack_data(manifest_path, out_tar_path, out_manifest_path):
     out_tar.close()
 
 
+def pcloud_cp(src, dst):
+    """Copy src from local filesytem to dst in PaddleCloud filesystem.
+    """
+    ret = call(['paddlecloud', 'cp', src, dst])
+    return ret
+
+
+def pcloud_exist(path):
+    """Check if file or directory exists in PaddleCloud filesystem.
+    """
+    ret = call(['paddlecloud', 'ls', path])
+    return ret
+
+
 if __name__ == '__main__':
-    cloud_train_manifest = "%s/%s" % (args.cloud_data_path, TRAIN_MANIFEST)
-    cloud_train_tar = "%s/%s" % (args.cloud_data_path, TRAIN_TAR)
-    cloud_test_manifest = "%s/%s" % (args.cloud_data_path, TEST_MANIFEST)
-    cloud_test_tar = "%s/%s" % (args.cloud_data_path, TEST_TAR)
-    cloud_vocab_file = "%s/%s" % (args.cloud_data_path, VOCAB_FILE)
-    cloud_mean_file = "%s/%s" % (args.cloud_data_path, MEAN_STD_FILE)
-
-    local_train_manifest = "%s/%s" % (args.local_tmp_path, TRAIN_MANIFEST)
-    local_train_tar = "%s/%s" % (args.local_tmp_path, TRAIN_TAR)
-    local_test_manifest = "%s/%s" % (args.local_tmp_path, TEST_MANIFEST)
-    local_test_tar = "%s/%s" % (args.local_tmp_path, TEST_TAR)
+    cloud_train_manifest = os.path.join(args.cloud_data_path, TRAIN_MANIFEST)
+    cloud_train_tar = os.path.join(args.cloud_data_path, TRAIN_TAR)
+    cloud_test_manifest = os.path.join(args.cloud_data_path, TEST_MANIFEST)
+    cloud_test_tar = os.path.join(args.cloud_data_path, TEST_TAR)
+    cloud_vocab_file = os.path.join(args.cloud_data_path, VOCAB_FILE)
+    cloud_mean_file = os.path.join(args.cloud_data_path, MEAN_STD_FILE)
+
+    local_train_manifest = os.path.join(args.local_tmp_path, TRAIN_MANIFEST)
+    local_train_tar = os.path.join(args.local_tmp_path, TRAIN_TAR)
+    local_test_manifest = os.path.join(args.local_tmp_path, TEST_MANIFEST)
+    local_test_tar = os.path.join(args.local_tmp_path, TEST_TAR)
 
     if os.path.exists(args.local_tmp_path):
         shutil.rmtree(args.local_tmp_path)
     os.makedirs(args.local_tmp_path)
 
-    ret = 1
     # train data
     if args.train_manifest_path != "":
-        ret = call(['paddlecloud', 'ls', cloud_train_manifest])
+        ret = pcloud_exist(cloud_train_manifest)
         if ret != 0:
-            print "%s does't exist" % cloud_train_manifest
             pack_data(args.train_manifest_path, local_train_tar,
                       local_train_manifest)
-            call([
-                'paddlecloud', 'cp', local_train_manifest, cloud_train_manifest
-            ])
-            call(['paddlecloud', 'cp', local_train_tar, cloud_train_tar])
+            pcloud_cp(local_train_manifest, cloud_train_manifest)
+            pcloud_cp(local_train_tar, cloud_train_tar)
 
     # test data
     if args.test_manifest_path != "":
-        try:
-            ret = call(['paddlecloud', 'ls', cloud_test_manifest])
-        except Exception:
-            ret = 1
+        ret = pcloud_exist(cloud_test_manifest)
         if ret != 0:
             pack_data(args.test_manifest_path, local_test_tar,
                       local_test_manifest)
-            call(
-                ['paddlecloud', 'cp', local_test_manifest, cloud_test_manifest])
-            call(['paddlecloud', 'cp', local_test_tar, cloud_test_tar])
+            pcloud_cp(local_test_manifest, cloud_test_manifest)
+            pcloud_cp(local_test_tar, cloud_test_tar)
 
     # vocab file
     if args.vocab_file != "":
-        try:
-            ret = call(['paddlecloud', 'ls', cloud_vocab_file])
-        except Exception:
-            ret = 1
+        ret = pcloud_exist(cloud_vocab_file)
         if ret != 0:
-            call(['paddlecloud', 'cp', args.vocab_file, cloud_vocab_file])
+            pcloud_cp(args.vocab_file, cloud_vocab_file)
 
     # mean_std file
     if args.mean_std_file != "":
-        try:
-            ret = call(['paddlecloud', 'ls', cloud_mean_file])
-        except Exception:
-            ret = 1
+        ret = pcloud_exist(cloud_mean_file)
         if ret != 0:
-            call(['paddlecloud', 'cp', args.mean_std_file, cloud_mean_file])
+            pcloud_cp(args.mean_std_file, cloud_mean_file)
 
-    os.removedirs(args.local_tmp_path)
+    shutil.rmtree(args.local_tmp_path)

deep_speech_2/data_utils/data.py

@@ -89,9 +89,6 @@ class DataGenerator(object):
         self._num_threads = num_threads
         self._rng = random.Random(random_seed)
         self._epoch = 0
-        # for caching tar files info
-        self.tar2info = {}
-        self.tar2object = {}
 
     def process_utterance(self, filename, transcript):
         """Load, augment, featurize and normalize for speech data.
@@ -237,6 +234,7 @@ class DataGenerator(object):
 
     def _get_file_object(self, file):
         """Get file object by file path.
+
         If file startwith tar, it will return a tar file object
         and cached tar file info for next reading request.
         It will return file directly, if the type of file is not str.
@@ -254,7 +252,7 @@ class DataGenerator(object):
             return local_data.tar2object[tarpath].extractfile(
                 local_data.tar2info[tarpath][filename])
         else:
-            return open(file)
+            return open(file, 'r')
 
     def _instance_reader_creator(self, manifest):
         """