Refine submitting scripts for deepspeech2 on paddle cloud.

deep_speech_2/cloud/README.md

+#DeepSpeech2 on paddle cloud
+
+## Run DS2 by public data
+
+**Step1: ** Make sure current dir is `models/deep_speech_2/cloud/`
+
+**Step2:**  Submit job by cmd: `sh pcloud_submit.sh`
+
+```
+$ sh pcloud_submit.sh
+$ uploading: deepspeech.tar.gz...
+$ uploading: pcloud_prepare_data.py...
+$ uploading: pcloud_split_data.py...
+$ uploading: pcloud_submit.sh...
+$ uploading: pcloud_train.sh...
+$ deepspeech20170727130129 submited.
+```
+The we can get job name 'deepspeech20170727130129' at last line
+
+**Step3:** Get logs from paddle cloud by cmd: `paddlecloud logs -n 10000 deepspeech20170727130129`.
+
+```
+$ paddlecloud logs -n 10000 deepspeech20170727130129
+$ ==========================deepspeech20170727130129-trainer-6vk3m==========================
+label selector: paddle-job-pserver=deepspeech20170727130129, desired: 1
+running pod list:  [('Running', '10.1.3.6')]
+label selector: paddle-job=deepspeech20170727130129, desired: 1
+running pod list:  [('Running', '10.1.83.14')]
+Starting training job:  /pfs/dlnel/home/yanxu05@baidu.com/jobs/deepspeech20170727130129, num_gradient_servers: 1, trainer_id:  0, version:  v2
+I0727 05:01:42.969719    25 Util.cpp:166] commandline:  --num_gradient_servers=1 --ports_num_for_sparse=1 --use_gpu=1 --trainer_id=0 --pservers=10.1.3.6 --trainer_count=4 --num_passes=1 --ports_num=1 --port=7164
+[INFO 2017-07-27 05:01:50,279 layers.py:2430] output for __conv_0__: c = 32, h = 81, w = 54, size = 139968
+[WARNING 2017-07-27 05:01:50,280 layers.py:2789] brelu is not recommend for batch normalization's activation, maybe the relu is better
+[INFO 2017-07-27 05:01:50,283 layers.py:2430] output for __conv_1__: c = 32, h = 41, w = 54, size = 70848
+[WARNING 2017-07-27 05:01:50,283 layers.py:2789] brelu is not recommend for batch normalization's activation, maybe the relu is better
+[WARNING 2017-07-27 05:01:50,287 layers.py:2789]  is not recommend for batch normalization's activation, maybe the relu is better
+[WARNING 2017-07-27 05:01:50,291 layers.py:2789]  is not recommend for batch normalization's activation, maybe the relu is better
+[WARNING 2017-07-27 05:01:50,295 layers.py:2789]  is not recommend for batch normalization's activation, maybe the relu is better
+I0727 05:01:50.316176    25 MultiGradientMachine.cpp:99] numLogicalDevices=1 numThreads=4 numDevices=4
+I0727 05:01:50.454787    25 GradientMachine.cpp:85] Initing parameters..
+I0727 05:01:50.690007    25 GradientMachine.cpp:92] Init parameters done.
+```
+[More  optins and cmd aoubt paddle cloud](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md)
+
+## Run DS2 by customize data
+TODO

deep_speech_2/datasets/librispeech/pcloud_data.py → deep_speech_2/cloud/pcloud_prepare_data.py

+"""
+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.
+2. Tar all local sound files into one tar file.
+3. Modify original manifest to remove the local path information.
+
+Finally, we will get a tar file and a manifest with sound file name, duration
+and text.
+"""
 import json
 import os
 import tarfile
 import sys
 import argparse
+sys.path.append('../')
+from data_utils.utils import read_manifest
 
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     "--manifest_path",
-    default="/manifest.train",
+    default="../datasets/manifest.train",
     type=str,
     help="Manifest of target data. (default: %(default)s)")
 parser.add_argument(
     "--out_tar_path",
-    default="/dev.tar",
+    default="./data/dev.tar",
     type=str,
     help="Output tar file path. (default: %(default)s)")
 parser.add_argument(
     "--out_manifest_path",
-    default="/dev.mani",
+    default="./data/dev.mani",
     type=str,
     help="Manifest of output data. (default: %(default)s)")
 args = parser.parse_args()
@@ -29,19 +42,16 @@ def gen_pcloud_data(manifest_path, out_tar_path, out_manifest_path):
     2. Generate a new manifest for output tar file
     '''
     out_tar = tarfile.open(out_tar_path, 'w')
-    manifest = []
-    for json_line in open(manifest_path):
-        try:
-            json_data = json.loads(json_line)
-        except Exception as e:
-            raise IOError("Error reading manifest: %s" % str(e))
+    manifest = read_manifest(manifest_path)
+    results = []
+    for json_data in manifest:
         sound_file = json_data['audio_filepath']
         filename = os.path.basename(sound_file)
         out_tar.add(sound_file, arcname=filename)
         json_data['audio_filepath'] = filename
-        manifest.append("%s\n" % json.dumps(json_data))
+        results.append("%s\n" % json.dumps(json_data))
     with open(out_manifest_path, 'w') as out_manifest:
-        out_manifest.writelines(manifest)
+        out_manifest.writelines(results)
     out_manifest.close()
     out_tar.close()
 

deep_speech_2/cloud/pcloud_split_data.py

+"""
+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.
+"""
+import os
+import json
+import argparse
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument(
+    "--in_manifest_path",
+    default='./cloud/data/dev.mani',
+    type=str,
+    help="Input manifest path. (default: %(default)s)")
+parser.add_argument(
+    "--data_tar_path",
+    default='./cloud/data/dev.tar',
+    type=str,
+    help="Data tar file path. (default: %(default)s)")
+parser.add_argument(
+    "--out_manifest_path",
+    default='./cloud/data/dev.mani.split',
+    type=str,
+    help="Out manifest file path. (default: %(default)s)")
+args = parser.parse_args()
+
+
+def split_data(in_manifest, tar_path, out_manifest):
+    with open("/trainer_id", "r") as f:
+        trainer_id = int(f.readline()[:-1])
+    with open("/trainer_count", "r") as f:
+        trainer_count = int(f.readline()[:-1])
+
+    tar_path = os.path.abspath(tar_path)
+    result = []
+    for index, json_line in enumerate(open(in_manifest)):
+        if (index % trainer_count) == trainer_id:
+            json_data = json.loads(json_line)
+            json_data['audio_filepath'] = "tar:%s#%s" % (
+                tar_path, json_data['audio_filepath'])
+            result.append("%s\n" % json.dumps(json_data))
+    with open(out_manifest, 'w') as manifest:
+        manifest.writelines(result)
+
+
+if __name__ == '__main__':
+    split_data(args.in_manifest_path, args.data_tar_path,
+               args.out_manifest_path)

deep_speech_2/pcloud_submit.sh → deep_speech_2/cloud/pcloud_submit.sh

+DS2_PATH=../
+tar -czf deepspeech.tar.gz ${DS2_PATH}
+JOB_NAME=deepspeech`date +%Y%m%d%H%M%S`
+cp pcloud_train.sh ${DS2_PATH}
 paddlecloud submit \
--image wanghaoshuang/pcloud_ds2 \
--jobname ds23 \
--cpu 1 \
--gpu 0 \
+-image wanghaoshuang/pcloud_ds2:latest-gpu-cudnn \
+-jobname ${JOB_NAME} \
+-cpu 4 \
+-gpu 4 \
 -memory 10Gi \
 -parallelism 1 \
 -pscpu 1 \
@@ -10,4 +14,4 @@ paddlecloud submit \
 -psmemory 10Gi \
 -passes 1 \
 -entry "sh pcloud_train.sh" \
-./deep_speech_2
+.

deep_speech_2/cloud/pcloud_train.sh

+DATA_PATH=/pfs/dlnel/public/dataset/speech/libri
+#setted by user
+TRAIN_MANI=${DATA_PATH}/manifest_pcloud.train
+#setted by user
+DEV_MANI=${DATA_PATH}/manifest_pcloud.dev
+#setted by user
+TRAIN_TAR=${DATA_PATH}/data.train.tar
+#setted by user
+DEV_TAR=${DATA_PATH}/data.dev.tar
+#setted by user
+VOCAB_PATH=${DATA_PATH}/eng_vocab.txt
+#setted by user
+MEAN_STD_FILE=${DATA_PATH}/mean_std.npz
+
+tar -xzf deepspeech.tar.gz
+rm -rf ./cloud/data/*
+
+# split train data for each pcloud node
+python ./cloud/pcloud_split_data.py \
+--in_manifest_path=$TRAIN_MANI \
+--data_tar_path=$TRAIN_TAR \
+--out_manifest_path='./cloud/data/train.mani'
+
+# split dev data for each pcloud node
+python pcloud_split_data.py \
+--in_manifest_path=$DEV_MANI \
+--data_tar_path=$DEV_TAR \
+--out_manifest_path='./cloud/data/dev.mani'
+
+python train.py \
+--use_gpu=1 \
+--trainer_count=4 \
+--batch_size=256 \
+--mean_std_filepath=$MEAN_STD_FILE \
+--train_manifest_path='./cloud/data/train.mani' \
+--dev_manifest_path='./cloud/data/dev.mani' \
+--vocab_filepath=$VOCAB_PATH \

deep_speech_2/data_utils/data.py

@@ -6,11 +6,11 @@ from __future__ import division
 from __future__ import print_function
 
 import random
-import numpy as np
+import tarfile
 import multiprocessing
-from threading import local
+import numpy as np
 import paddle.v2 as paddle
-import tarfile
+from threading import local
 from data_utils import utils
 from data_utils.augmentor.augmentation import AugmentationPipeline
 from data_utils.featurizer.speech_featurizer import SpeechFeaturizer
@@ -52,6 +52,9 @@ class DataGenerator(object):
     :types max_freq: None|float
     :param specgram_type: Specgram feature type. Options: 'linear'.
     :type specgram_type: str
+    :param use_dB_normalization: Whether to normalize the audio to -20 dB
+                                before extracting the features.
+    :type use_dB_normalization: bool
     :param num_threads: Number of CPU threads for processing data.
     :type num_threads: int
     :param random_seed: Random seed.
@@ -68,6 +71,7 @@ class DataGenerator(object):
                  window_ms=20.0,
                  max_freq=None,
                  specgram_type='linear',
+                 use_dB_normalization=True,
                  num_threads=multiprocessing.cpu_count(),
                  random_seed=0):
         self._max_duration = max_duration
@@ -80,7 +84,8 @@ class DataGenerator(object):
             specgram_type=specgram_type,
             stride_ms=stride_ms,
             window_ms=window_ms,
-            max_freq=max_freq)
+            max_freq=max_freq,
+            use_dB_normalization=use_dB_normalization)
         self._num_threads = num_threads
         self._rng = random.Random(random_seed)
         self._epoch = 0

deep_speech_2/pcloud_split_data.py

-import os
-import json
-import argparse
-
-
-def split_data(inManifest, tar_path, outManifest):
-    trainer_id = 1
-    trainer_count = 2
-    #with open("/trainer_id", "r") as f:
-    #    trainer_id = int(f.readline()[:-1])
-    #with open("/trainer_count", "r") as f:
-    #    trainer_count = int(f.readline()[:-1])
-
-    tarPath = os.path.abspath(tar_path)
-    result = []
-    for index, json_line in enumerate(open(inManifest)):
-        if (index % trainer_count) == trainer_id:
-            json_data = json.loads(json_line)
-            json_data['audio_filepath'] = "tar:%s#%s" % (
-                tarPath, json_data['audio_filepath'])
-            result.append("%s\n" % json.dumps(json_data))
-    with open(outManifest, 'w') as manifest:
-        manifest.writelines(result)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description=__doc__)
-
-    parser.add_argument(
-        "--in_manifest_path",
-        default='datasets/dev.mani',
-        type=str,
-        help="Input manifest path. (default: %(default)s)")
-    parser.add_argument(
-        "--data_tar_path",
-        default='datasets/dev.tar',
-        type=str,
-        help="Data tar file path. (default: %(default)s)")
-    parser.add_argument(
-        "--out_manifest_path",
-        default='datasets/dev.mani.split',
-        type=str,
-        help="Out manifest file path. (default: %(default)s)")
-    args = parser.parse_args()
-
-    split_data(args.in_manifest_path, args.data_tar_path,
-               args.out_manifest_path)

deep_speech_2/pcloud_train.sh

+DATA_PATH=/pfs/dlnel/public/dataset/speech/libri
 #setted by user
-TRAIN_MANI='/pfs/dlnel/home/yanxu05@baidu.com/wanghaoshuang/data/ds2_data/demo.mani'
+TRAIN_MANI=${DATA_PATH}/manifest_pcloud.train
 #setted by user
-DEV_MANI='/pfs/dlnel/home/yanxu05@baidu.com/wanghaoshuang/data/ds2_data/demo.mani'
+DEV_MANI=${DATA_PATH}/manifest_pcloud.dev
 #setted by user
-TRAIN_TAR='/pfs/dlnel/home/yanxu05@baidu.com/wanghaoshuang/data/ds2_data/demo.tar'
+TRAIN_TAR=${DATA_PATH}/data.train.tar
 #setted by user
-DEV_TAR='/pfs/dlnel/home/yanxu05@baidu.com/wanghaoshuang/data/ds2_data/demo.tar'
+DEV_TAR=${DATA_PATH}/data.dev.tar
 #setted by user
-VOCAB_PATH='/pfs/dlnel/home/yanxu05@baidu.com/wanghaoshuang/data/ds2_data/eng_vocab.txt'
+VOCAB_PATH=${DATA_PATH}/eng_vocab.txt
 #setted by user
-MEAN_STD_FILE='/pfs/dlnel/home/yanxu05@baidu.com/wanghaoshuang/data/ds2_data/mean_std.npz'
+MEAN_STD_FILE=${DATA_PATH}/mean_std.npz
+
+tar -xzvf deepspeech.tar.gz
+rm -rf ./cloud/data/*
 
 # split train data for each pcloud node
-python pcloud_split_data.py \
+python ./cloud/pcloud_split_data.py \
 --in_manifest_path=$TRAIN_MANI \
 --data_tar_path=$TRAIN_TAR \
---out_manifest_path='./train.mani'
+--out_manifest_path='./cloud/data/train.mani'
+
 # split dev data for each pcloud node
 python pcloud_split_data.py \
 --in_manifest_path=$DEV_MANI \
 --data_tar_path=$DEV_TAR \
---out_manifest_path='./dev.mani'
+--out_manifest_path='./cloud/data/dev.mani'
 
 python train.py \
---use_gpu=0 \
+--use_gpu=1 \
 --trainer_count=4 \
---batch_size=2 \
+--batch_size=256 \
 --mean_std_filepath=$MEAN_STD_FILE \
---train_manifest_path='./train.mani' \
---dev_manifest_path='./dev.mani' \
+--train_manifest_path='./cloud/data/train.mani' \
+--dev_manifest_path='./cloud/data/dev.mani' \
 --vocab_filepath=$VOCAB_PATH \