add aishell test script

speechx/examples/aishell/local/compute-wer.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+
+import re, sys, unicodedata
+import codecs
+
+remove_tag = True
+spacelist= [' ', '\t', '\r', '\n']
+puncts = ['!', ',', '?',
+          '、', '。', '！', '，', '；', '？',
+          '：', '「', '」', '︰',  '『', '』', '《', '》']
+
+def characterize(string) :
+  res = []
+  i = 0
+  while i < len(string):
+    char = string[i]
+    if char in puncts:
+      i += 1
+      continue
+    cat1 = unicodedata.category(char)
+    #https://unicodebook.readthedocs.io/unicode.html#unicode-categories
+    if cat1 == 'Zs' or cat1 == 'Cn' or char in spacelist: # space or not assigned
+       i += 1
+       continue
+    if cat1 == 'Lo': # letter-other
+       res.append(char)
+       i += 1
+    else:
+       # some input looks like: <unk><noise>, we want to separate it to two words.
+       sep = ' '
+       if char == '<': sep = '>'
+       j = i+1
+       while j < len(string):
+         c = string[j]
+         if ord(c) >= 128 or (c in spacelist) or (c==sep):
+           break
+         j += 1
+       if j < len(string) and string[j] == '>':
+         j += 1
+       res.append(string[i:j])
+       i = j
+  return res
+
+def stripoff_tags(x):
+  if not x: return ''
+  chars = []
+  i = 0; T=len(x)
+  while i < T:
+    if x[i] == '<':
+      while i < T and x[i] != '>':
+         i += 1
+      i += 1
+    else:
+      chars.append(x[i])
+      i += 1
+  return ''.join(chars)
+
+
+def normalize(sentence, ignore_words, cs, split=None):
+    """ sentence, ignore_words are both in unicode
+    """
+    new_sentence = []
+    for token in sentence:
+        x = token
+        if not cs:
+           x = x.upper()
+        if x in ignore_words:
+           continue
+        if remove_tag:
+          x = stripoff_tags(x)
+        if not x:
+          continue
+        if split and x in split:
+          new_sentence += split[x]
+        else:
+          new_sentence.append(x)
+    return new_sentence
+
+class Calculator :
+  def __init__(self) :
+    self.data = {}
+    self.space = []
+    self.cost = {}
+    self.cost['cor'] = 0
+    self.cost['sub'] = 1
+    self.cost['del'] = 1
+    self.cost['ins'] = 1
+  def calculate(self, lab, rec) :
+    # Initialization
+    lab.insert(0, '')
+    rec.insert(0, '')
+    while len(self.space) < len(lab) :
+      self.space.append([])
+    for row in self.space :
+      for element in row :
+        element['dist'] = 0
+        element['error'] = 'non'
+      while len(row) < len(rec) :
+        row.append({'dist' : 0, 'error' : 'non'})
+    for i in range(len(lab)) :
+      self.space[i][0]['dist'] = i
+      self.space[i][0]['error'] = 'del'
+    for j in range(len(rec)) :
+      self.space[0][j]['dist'] = j
+      self.space[0][j]['error'] = 'ins'
+    self.space[0][0]['error'] = 'non'
+    for token in lab :
+      if token not in self.data and len(token) > 0 :
+        self.data[token] = {'all' : 0, 'cor' : 0, 'sub' : 0, 'ins' : 0, 'del' : 0}
+    for token in rec :
+      if token not in self.data and len(token) > 0 :
+        self.data[token] = {'all' : 0, 'cor' : 0, 'sub' : 0, 'ins' : 0, 'del' : 0}
+    # Computing edit distance
+    for i, lab_token in enumerate(lab) :
+      for j, rec_token in enumerate(rec) :
+        if i == 0 or j == 0 :
+          continue
+        min_dist = sys.maxsize
+        min_error = 'none'
+        dist = self.space[i-1][j]['dist'] + self.cost['del']
+        error = 'del'
+        if dist < min_dist :
+          min_dist = dist
+          min_error = error
+        dist = self.space[i][j-1]['dist'] + self.cost['ins']
+        error = 'ins'
+        if dist < min_dist :
+          min_dist = dist
+          min_error = error
+        if lab_token == rec_token :
+          dist = self.space[i-1][j-1]['dist'] + self.cost['cor']
+          error = 'cor'
+        else :
+          dist = self.space[i-1][j-1]['dist'] + self.cost['sub']
+          error = 'sub'
+        if dist < min_dist :
+          min_dist = dist
+          min_error = error
+        self.space[i][j]['dist'] = min_dist
+        self.space[i][j]['error'] = min_error
+    # Tracing back
+    result = {'lab':[], 'rec':[], 'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
+    i = len(lab) - 1
+    j = len(rec) - 1
+    while True :
+      if self.space[i][j]['error'] == 'cor' : # correct
+        if len(lab[i]) > 0 :
+          self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
+          self.data[lab[i]]['cor'] = self.data[lab[i]]['cor'] + 1
+          result['all'] = result['all'] + 1
+          result['cor'] = result['cor'] + 1
+        result['lab'].insert(0, lab[i])
+        result['rec'].insert(0, rec[j])
+        i = i - 1
+        j = j - 1
+      elif self.space[i][j]['error'] == 'sub' : # substitution
+        if len(lab[i]) > 0 :
+          self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
+          self.data[lab[i]]['sub'] = self.data[lab[i]]['sub'] + 1
+          result['all'] = result['all'] + 1
+          result['sub'] = result['sub'] + 1
+        result['lab'].insert(0, lab[i])
+        result['rec'].insert(0, rec[j])
+        i = i - 1
+        j = j - 1
+      elif self.space[i][j]['error'] == 'del' : # deletion
+        if len(lab[i]) > 0 :
+          self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
+          self.data[lab[i]]['del'] = self.data[lab[i]]['del'] + 1
+          result['all'] = result['all'] + 1
+          result['del'] = result['del'] + 1
+        result['lab'].insert(0, lab[i])
+        result['rec'].insert(0, "")
+        i = i - 1
+      elif self.space[i][j]['error'] == 'ins' : # insertion
+        if len(rec[j]) > 0 :
+          self.data[rec[j]]['ins'] = self.data[rec[j]]['ins'] + 1
+          result['ins'] = result['ins'] + 1
+        result['lab'].insert(0, "")
+        result['rec'].insert(0, rec[j])
+        j = j - 1
+      elif self.space[i][j]['error'] == 'non' : # starting point
+        break
+      else : # shouldn't reach here
+        print('this should not happen , i = {i} , j = {j} , error = {error}'.format(i = i, j = j, error = self.space[i][j]['error']))
+    return result
+  def overall(self) :
+    result = {'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
+    for token in self.data :
+      result['all'] = result['all'] + self.data[token]['all']
+      result['cor'] = result['cor'] + self.data[token]['cor']
+      result['sub'] = result['sub'] + self.data[token]['sub']
+      result['ins'] = result['ins'] + self.data[token]['ins']
+      result['del'] = result['del'] + self.data[token]['del']
+    return result
+  def cluster(self, data) :
+    result = {'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
+    for token in data :
+      if token in self.data :
+        result['all'] = result['all'] + self.data[token]['all']
+        result['cor'] = result['cor'] + self.data[token]['cor']
+        result['sub'] = result['sub'] + self.data[token]['sub']
+        result['ins'] = result['ins'] + self.data[token]['ins']
+        result['del'] = result['del'] + self.data[token]['del']
+    return result
+  def keys(self) :
+      return list(self.data.keys())
+
+def width(string):
+  return sum(1 + (unicodedata.east_asian_width(c) in "AFW") for c in string)
+
+def default_cluster(word) :
+  unicode_names = [ unicodedata.name(char) for char in word ]
+  for i in reversed(range(len(unicode_names))) :
+    if unicode_names[i].startswith('DIGIT') :  # 1
+      unicode_names[i] = 'Number'  # 'DIGIT'
+    elif (unicode_names[i].startswith('CJK UNIFIED IDEOGRAPH') or
+          unicode_names[i].startswith('CJK COMPATIBILITY IDEOGRAPH')) :
+      # 明 / 郎
+      unicode_names[i] = 'Mandarin'  # 'CJK IDEOGRAPH'
+    elif (unicode_names[i].startswith('LATIN CAPITAL LETTER') or
+          unicode_names[i].startswith('LATIN SMALL LETTER')) :
+      # A / a
+      unicode_names[i] = 'English'  # 'LATIN LETTER'
+    elif unicode_names[i].startswith('HIRAGANA LETTER') :  # は こ め
+      unicode_names[i] = 'Japanese'  # 'GANA LETTER'
+    elif (unicode_names[i].startswith('AMPERSAND') or
+          unicode_names[i].startswith('APOSTROPHE') or
+          unicode_names[i].startswith('COMMERCIAL AT') or
+          unicode_names[i].startswith('DEGREE CELSIUS') or
+          unicode_names[i].startswith('EQUALS SIGN') or
+          unicode_names[i].startswith('FULL STOP') or
+          unicode_names[i].startswith('HYPHEN-MINUS') or
+          unicode_names[i].startswith('LOW LINE') or
+          unicode_names[i].startswith('NUMBER SIGN') or
+          unicode_names[i].startswith('PLUS SIGN') or
+          unicode_names[i].startswith('SEMICOLON')) :
+      # & / ' / @ / ℃ / = / . / - / _ / # / + / ;
+      del unicode_names[i]
+    else :
+      return 'Other'
+  if len(unicode_names) == 0 :
+      return 'Other'
+  if len(unicode_names) == 1 :
+      return unicode_names[0]
+  for i in range(len(unicode_names)-1) :
+    if unicode_names[i] != unicode_names[i+1] :
+      return 'Other'
+  return unicode_names[0]
+
+def usage() :
+  print("compute-wer.py : compute word error rate (WER) and align recognition results and references.")
+  print("         usage : python compute-wer.py [--cs={0,1}] [--cluster=foo] [--ig=ignore_file] [--char={0,1}] [--v={0,1}] [--padding-symbol={space,underline}] test.ref test.hyp > test.wer")
+
+if __name__ == '__main__':
+  if len(sys.argv) == 1 :
+    usage()
+    sys.exit(0)
+  calculator = Calculator()
+  cluster_file = ''
+  ignore_words = set()
+  tochar = False
+  verbose= 1
+  padding_symbol= ' '
+  case_sensitive = False
+  max_words_per_line = sys.maxsize
+  split = None
+  while len(sys.argv) > 3:
+     a = '--maxw='
+     if sys.argv[1].startswith(a):
+        b = sys.argv[1][len(a):]
+        del sys.argv[1]
+        max_words_per_line = int(b)
+        continue
+     a = '--rt='
+     if sys.argv[1].startswith(a):
+        b = sys.argv[1][len(a):].lower()
+        del sys.argv[1]
+        remove_tag = (b == 'true') or (b != '0')
+        continue
+     a = '--cs='
+     if sys.argv[1].startswith(a):
+        b = sys.argv[1][len(a):].lower()
+        del sys.argv[1]
+        case_sensitive = (b == 'true') or (b != '0')
+        continue
+     a = '--cluster='
+     if sys.argv[1].startswith(a):
+       cluster_file = sys.argv[1][len(a):]
+       del sys.argv[1]
+       continue
+     a = '--splitfile='
+     if sys.argv[1].startswith(a):
+       split_file = sys.argv[1][len(a):]
+       del sys.argv[1]
+       split = dict()
+       with codecs.open(split_file, 'r', 'utf-8') as fh:
+         for line in fh:  # line in unicode
+           words = line.strip().split()
+           if len(words) >= 2:
+             split[words[0]] = words[1:]
+       continue
+     a = '--ig='
+     if sys.argv[1].startswith(a):
+       ignore_file = sys.argv[1][len(a):]
+       del sys.argv[1]
+       with codecs.open(ignore_file, 'r', 'utf-8') as fh:
+         for line in fh:  # line in unicode
+           line = line.strip()
+           if len(line) > 0:
+             ignore_words.add(line)
+       continue
+     a = '--char='
+     if sys.argv[1].startswith(a):
+        b = sys.argv[1][len(a):].lower()
+        del sys.argv[1]
+        tochar = (b == 'true') or (b != '0')
+        continue
+     a = '--v='
+     if sys.argv[1].startswith(a):
+        b = sys.argv[1][len(a):].lower()
+        del sys.argv[1]
+        verbose=0
+        try:
+          verbose=int(b)
+        except:
+           if b == 'true' or b != '0':
+              verbose = 1
+        continue
+     a = '--padding-symbol='
+     if sys.argv[1].startswith(a):
+        b = sys.argv[1][len(a):].lower()
+        del sys.argv[1]
+        if b == 'space':
+          padding_symbol= ' '
+        elif b == 'underline':
+          padding_symbol= '_'
+        continue
+     if True or sys.argv[1].startswith('-'):
+        #ignore invalid switch
+        del sys.argv[1]
+        continue
+
+  if not case_sensitive:
+     ig=set([w.upper() for w in ignore_words])
+     ignore_words = ig
+
+  default_clusters = {}
+  default_words = {}
+
+  ref_file = sys.argv[1]
+  hyp_file = sys.argv[2]
+  rec_set = {}
+  if split and not case_sensitive:
+     newsplit = dict()
+     for w in split:
+        words = split[w]
+        for i in range(len(words)):
+           words[i] = words[i].upper()
+        newsplit[w.upper()] = words
+     split = newsplit
+
+  with codecs.open(hyp_file, 'r', 'utf-8') as fh:
+     for line in fh:
+        if tochar:
+            array = characterize(line)
+        else:
+            array = line.strip().split()
+        if len(array)==0: continue
+        fid = array[0]
+        rec_set[fid] = normalize(array[1:], ignore_words, case_sensitive, split)
+
+  # compute error rate on the interaction of reference file and hyp file
+  for line in open(ref_file, 'r', encoding='utf-8') :
+    if tochar:
+          array = characterize(line)
+    else:
+          array = line.rstrip('\n').split()
+    if len(array)==0: continue
+    fid = array[0]
+    if fid not in rec_set:
+       continue
+    lab = normalize(array[1:], ignore_words, case_sensitive, split)
+    rec = rec_set[fid]
+    if verbose:
+      print('\nutt: %s' % fid)
+
+    for word in rec + lab :
+      if word not in default_words :
+         default_cluster_name = default_cluster(word)
+         if default_cluster_name not in default_clusters :
+           default_clusters[default_cluster_name] = {}
+         if word not in default_clusters[default_cluster_name] :
+           default_clusters[default_cluster_name][word] = 1
+         default_words[word] = default_cluster_name
+
+    result = calculator.calculate(lab, rec)
+    if verbose:
+      if result['all'] != 0 :
+        wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
+      else :
+        wer = 0.0
+      print('WER: %4.2f %%' % wer, end = ' ')
+      print('N=%d C=%d S=%d D=%d I=%d' %
+          (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
+      space = {}
+      space['lab'] = []
+      space['rec'] = []
+      for idx in range(len(result['lab'])) :
+        len_lab = width(result['lab'][idx])
+        len_rec = width(result['rec'][idx])
+        length = max(len_lab, len_rec)
+        space['lab'].append(length-len_lab)
+        space['rec'].append(length-len_rec)
+      upper_lab = len(result['lab'])
+      upper_rec = len(result['rec'])
+      lab1, rec1 = 0, 0
+      while lab1 < upper_lab or rec1 < upper_rec:
+         if verbose > 1:
+             print('lab(%s):' % fid.encode('utf-8'), end = ' ')
+         else:
+             print('lab:', end = ' ')
+         lab2 = min(upper_lab, lab1 + max_words_per_line)
+         for idx in range(lab1, lab2):
+           token = result['lab'][idx]
+           print('{token}'.format(token = token), end = '')
+           for n in range(space['lab'][idx]) :
+             print(padding_symbol, end = '')
+           print(' ',end='')
+         print()
+         if verbose > 1:
+            print('rec(%s):' % fid.encode('utf-8'), end = ' ')
+         else:
+            print('rec:', end = ' ')
+         rec2 = min(upper_rec, rec1 + max_words_per_line)
+         for idx in range(rec1, rec2):
+           token = result['rec'][idx]
+           print('{token}'.format(token = token), end = '')
+           for n in range(space['rec'][idx]) :
+             print(padding_symbol, end = '')
+           print(' ',end='')
+         print('\n', end='\n')
+         lab1 = lab2
+         rec1 = rec2
+
+  if verbose:
+    print('===========================================================================')
+    print()
+
+  result = calculator.overall()
+  if result['all'] != 0 :
+    wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
+  else :
+    wer = 0.0
+  print('Overall -> %4.2f %%' % wer, end = ' ')
+  print('N=%d C=%d S=%d D=%d I=%d' %
+        (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
+  if not verbose:
+     print()
+
+  if verbose:
+   for cluster_id in default_clusters :
+     result = calculator.cluster([ k for k in default_clusters[cluster_id] ])
+     if result['all'] != 0 :
+        wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
+     else :
+        wer = 0.0
+     print('%s -> %4.2f %%' % (cluster_id, wer), end = ' ')
+     print('N=%d C=%d S=%d D=%d I=%d' %
+          (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
+   if len(cluster_file) > 0 : # compute separated WERs for word clusters
+     cluster_id = ''
+     cluster = []
+     for line in open(cluster_file, 'r', encoding='utf-8') :
+       for token in line.decode('utf-8').rstrip('\n').split() :
+        # end of cluster reached, like </Keyword>
+        if token[0:2] == '</' and token[len(token)-1] == '>' and \
+           token.lstrip('</').rstrip('>') == cluster_id :
+          result = calculator.cluster(cluster)
+          if result['all'] != 0 :
+            wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
+          else :
+            wer = 0.0
+          print('%s -> %4.2f %%' % (cluster_id, wer), end = ' ')
+          print('N=%d C=%d S=%d D=%d I=%d' %
+                (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
+          cluster_id = ''
+          cluster = []
+        # begin of cluster reached, like <Keyword>
+        elif token[0] == '<' and token[len(token)-1] == '>' and \
+             cluster_id == '' :
+          cluster_id = token.lstrip('<').rstrip('>')
+          cluster = []
+        # general terms, like WEATHER / CAR / ...
+        else :
+          cluster.append(token)
+   print()
+   print('===========================================================================')

speechx/examples/aishell/local/split_data.sh

+#!/usr/bin/env bash
+
+data=$1
+feat_scp=$2
+numsplit=$3
+
+if ! [ "$numsplit" -gt 0 ]; then
+  echo "Invalid num-split argument";
+  exit 1;
+fi
+
+directories=$(for n in `seq $numsplit`; do echo $data/split${numsplit}/$n; done)
+feat_split_scp=$(for n in `seq $numsplit`; do echo $data/split${numsplit}/$n/feats.scp; done)
+echo $feat_split_scp
+# if this mkdir fails due to argument-list being too long, iterate.
+if ! mkdir -p $directories >&/dev/null; then
+  for n in `seq $numsplit`; do
+    mkdir -p $data/split${numsplit}/$n
+  done
+fi
+
+utils/split_scp.pl $feat_scp $feat_split_scp

speechx/examples/aishell/path.sh

+# This contains the locations of binarys build required for running the examples.
+
+SPEECHX_ROOT=$PWD/../..
+SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
+
+SPEECHX_TOOLS=$SPEECHX_ROOT/tools
+TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
+
+[ -d $SPEECHX_EXAMPLES ] || { echo "Error: 'build/examples' directory not found. please ensure that the project build successfully"; }
+
+export LC_AL=C
+
+SPEECHX_BIN=$SPEECHX_EXAMPLES/decoder:$SPEECHX_EXAMPLES/feat
+export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN

speechx/examples/aishell/run.sh

+#!/bin/bash
+set +x
+set -e
+
+. path.sh
+
+# 1. compile
+if [ ! -d ${SPEECHX_EXAMPLES} ]; then
+    pushd ${SPEECHX_ROOT} 
+    bash build.sh
+    popd
+fi
+
+
+# 2. download model
+if [ ! -d ../paddle_asr_model ]; then
+    wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/paddle_asr_model.tar.gz
+    tar xzfv paddle_asr_model.tar.gz
+    mv ./paddle_asr_model ../
+    # produce wav scp
+    echo "utt1 " $PWD/../paddle_asr_model/BAC009S0764W0290.wav > ../paddle_asr_model/wav.scp
+fi
+
+mkdir -p data
+if [ ! -d ./test ]; then
+    wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
+    unzip aishell_test.zip
+    realpath ./test/*/*.wav > wavlist
+    awk -F '/' '{ print $(NF) }' wavlist | awk -F '.' '{ print $1 }' > utt_id
+    paste utt_id wavlist > aishell_test.scp
+fi
+
+if [ ! -d aishell_ds2_online_model ]; then
+    mkdir -p aishell_ds2_online_model 
+    wget -P ./aishell_ds2_online_model -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz
+    tar xzfv ./aishell_ds2_online_model/aishell_ds2_online_cer8.00_release.tar.gz -C ./aishell_ds2_online_model
+fi
+
+# 3. make feature
+aishell_wav_scp=./aishell_test.scp
+aishell_online_model=./aishell_ds2_online_model/exp/deepspeech2_online/checkpoints
+model_dir=../paddle_asr_model
+feat_ark=./feats.ark
+feat_scp=./aishell_feat.scp
+cmvn=./cmvn.ark
+label_file=./aishell_result
+wer=./aishell_wer
+
+export GLOG_logtostderr=1
+
+# 3. gen linear feat
+linear_spectrogram_main \
+    --wav_rspecifier=scp:$aishell_wav_scp \
+    --feature_wspecifier=ark,scp:$feat_ark,$feat_scp \
+    --cmvn_write_path=$cmvn \
+    --streaming_chunk=10
+
+nj=10
+data=./data
+text=./test/text
+# recognizer
+./local/split_data.sh data aishell_feat.scp $nj
+
+utils/run.pl JOB=1:$nj $data/split${nj}/JOB/log \
+  offline_decoder_sliding_chunk_main \
+    --feature_rspecifier=scp:$data/split${nj}/JOB/feats.scp \
+    --model_path=$aishell_online_model/avg_1.jit.pdmodel \
+    --param_path=$aishell_online_model/avg_1.jit.pdiparams \
+    --dict_file=$model_dir/vocab.txt \
+    --lm_path=$model_dir/avg_1.jit.klm \
+    --result_wspecifier=ark,t:$data/split${nj}/JOB/result
+
+cat $data/split${nj}/*/result > $label_file
+
+local/compute-wer.py --char=1 --v=1 $label_file $text > $wer

speechx/examples/aishell/utils

+../../../utils
\ No newline at end of file

speechx/examples/decoder/offline_decoder_sliding_chunk_main.cc

@@ -22,7 +22,8 @@
 #include "nnet/decodable.h"
 #include "nnet/paddle_nnet.h"
 
-DEFINE_string(feature_respecifier, "", "test feature rspecifier");
+DEFINE_string(feature_rspecifier, "", "test feature rspecifier");
+DEFINE_string(result_wspecifier, "", "test result wspecifier");
 DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
 DEFINE_string(param_path, "avg_1.jit.pdiparams", "paddle nnet model param");
 DEFINE_string(dict_file, "vocab.txt", "vocabulary of lm");
@@ -45,7 +46,8 @@ int main(int argc, char* argv[]) {
     google::InitGoogleLogging(argv[0]);
 
     kaldi::SequentialBaseFloatMatrixReader feature_reader(
-        FLAGS_feature_respecifier);
+        FLAGS_feature_rspecifier);
+    kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
     std::string model_graph = FLAGS_model_path;
     std::string model_params = FLAGS_param_path;
     std::string dict_file = FLAGS_dict_file;
@@ -130,6 +132,7 @@ int main(int argc, char* argv[]) {
         std::string result;
         result = decoder.GetFinalBestPath();
         KALDI_LOG << " the result of " << utt << " is " << result;
+        result_writer.Write(utt, result);
         decodable->Reset();
         decoder.Reset();
         ++num_done;

speechx/examples/feat/linear_spectrogram_main.cc

@@ -30,6 +30,7 @@
 DEFINE_string(wav_rspecifier, "", "test wav scp path");
 DEFINE_string(feature_wspecifier, "", "output feats wspecifier");
 DEFINE_string(cmvn_write_path, "./cmvn.ark", "write cmvn");
+DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
 
 
 std::vector<float> mean_{
@@ -198,7 +199,7 @@ int main(int argc, char* argv[]) {
     LOG(INFO) << "feat dim: " << feature_cache.Dim();
 
     int sample_rate = 16000;
-    float streaming_chunk = 0.36;
+    float streaming_chunk = FLAGS_streaming_chunk;
     int chunk_sample_size = streaming_chunk * sample_rate;
     LOG(INFO) << "sr: " << sample_rate;
     LOG(INFO) << "chunk size (s): " << streaming_chunk;