update

Former-commit-id: 35641e8

README.md

@@ -2,7 +2,7 @@
 End-to-end automatic speech recognition system implemented in TensorFlow.
 
 ## Content
-Automatic Speech Recognition implemented in TensorFlow contains followings:
+This is a powerful library for automatic speech recognition, it is implemented in TensorFlow and support training with CPU/GPU. This library contains followings models you can choose to train your own model:
 * Data Pre-processing
 * Acoustic Modeling
   * RNN
@@ -11,15 +11,16 @@ Automatic Speech Recognition implemented in TensorFlow contains followings:
   * BLSTM
   * GRU
   * BGRU
-  * Deep CNN
+  * Dynamic RNN
+  * Deep Residual Network
+  * Seq2Seq with attention decoder
   * etc.
 * CTC Decoding
 * Evaluation  
 * Saving or Restoring Model
 * Mini-batch Training
 * Training with GPU or CPU with TensorFlow
 * Keeping logging of epoch time and error rate in disk
-* Support TIMIT and Libri
 
 ## Usage
 <pre>

src/trash/ed.py

@@ -0,0 +1,95 @@
+#!/usr/bin/python
+#-*- coding:utf-8 -*-
+
+import tensorflow as tf
+import numpy as np
+## 改写list_to_sparse_tensor函数使得其变成统一的编码 ##
+# 原始的音素如下面的phn变量所示，但是在测试的时候，为
+# 了提高性能表现，一些音素需要进行合并，具体的合并办法
+# 在截图中，请根据合并方法，使得targetList变成只包含合
+# 并之后的音素，可以进行重新编码，如原来是0...61，现在
+# 可以变成0...47，目的是计算两个字符串之间的编辑距离。
+# 并且需要改写一下get_edit_distance，使得其函数签名变成
+# 如下形式：
+#	get_edit_distance(hyp_arr,truth_arr,mode='train')
+# mode可以取值'train'或'test'，如果是'train'模式，那么
+# 就使用原始的list_to_sparse_tensor函数，如果是'test'模
+# 式，那么就使用你改写后的list_to_sparse_tensor函数，换
+# 句话说，你把list_to_sparse_tensor函数的签名也写成如下
+# 形式：
+#	list_to_sparse_tensor(targetList,mode='train')
+# 这样就可以在内部设置两种模式的转换。
+
+
+phn = ['aa', 'ae', 'ah', 'ao', 'aw', 'ax', 'ax-h',
+       'axr', 'ay', 'b', 'bcl', 'ch', 'd', 'dcl',
+       'dh', 'dx', 'eh', 'el', 'em', 'en', 'eng',
+       'epi', 'er', 'ey', 'f', 'g', 'gcl', 'h#',
+       'hh', 'hv', 'ih', 'ix', 'iy', 'jh', 'k',
+       'kcl', 'l', 'm', 'n', 'ng', 'nx', 'ow',
+       'oy', 'p', 'pau', 'pcl', 'q', 'r', 's',
+       'sh', 't', 'tcl', 'th', 'uh', 'uw', 'ux',
+       'v', 'w', 'y', 'z', 'zh']
+
+mapping = {'ux':'uw','axr':'er','em':'m','nx':'en','n':'en',
+              'eng':'ng','hv':'hh','cl':'sil','bcl':'sil','dcl':'sil',
+              'gcl':'sil','epi':'sil','h#':'sil','kcl':'sil','pau':'sil',
+              'pcl':'sil','tcl':'sil','vcl':'sil','l':'el','zh':'sh',
+              'aa':'ao','ix':'ih','ax':'ah'}
+
+def group_phoneme(orig_phn,mapping):
+    group_phn = []
+    for val in orig_phn:
+        group_phn.append(val)
+    group_phn.append('sil')
+    for key in mapping.keys():
+        if key in orig_phn:
+            group_phn.remove(key)
+    group_phn.sort()
+    return group_phn
+
+def list_to_sparse_tensor(targetList,mode='train'):
+    ''' turn 2-D List to SparseTensor
+    '''
+    # NOTE: 'sil' is a new phoneme, you should care this.
+
+    indices = [] #index
+    vals = [] #value
+    group_phn = group_phoneme(phn,mapping)
+    for tI, target in enumerate(targetList):
+        for seqI, val in enumerate(target):
+            if(mode == 'train'):
+                indices.append([tI, seqI])
+                vals.append(val)
+            elif(mode == 'test'):
+                if(phn[val] in mapping.keys()):
+                    val = group_phn.index(mapping[phn[val]])
+                indices.append([tI, seqI])
+                vals.append(val)
+            else:
+                raise ValueError("Invalid mode.",mode)
+    shape = [len(targetList), np.asarray(indices).max(0)[1]+1] #shape
+    return (np.array(indices), np.array(vals), np.array(shape))
+
+def get_edit_distance(hyp_arr,truth_arr,mode='train'):
+    ''' calculate edit distance
+    '''
+    graph = tf.Graph()
+    with graph.as_default():
+        truth = tf.sparse_placeholder(tf.int32)
+        hyp = tf.sparse_placeholder(tf.int32)
+        editDist = tf.edit_distance(hyp, truth, normalize=True)
+
+    with tf.Session(graph=graph) as session:
+        truthTest = list_to_sparse_tensor(truth_arr, mode)
+        hypTest = list_to_sparse_tensor(hyp_arr, mode)
+        feedDict = {truth: truthTest, hyp: hypTest}
+        dist = session.run(editDist, feed_dict=feedDict)
+    return dist
+
+if __name__ == '__main__':
+    a=[[0,5,49]]	
+    b=[[21,5,10]]
+    print(get_edit_distance(a,b,mode='test'))
+    print(len(phn))
+    print(len(mapping))