[scripts,egs] Changes for Python 2/3 compatibility (kaldi-asr#2925)

egs/aishell2/s5/local/word_segmentation.py

@@ -4,6 +4,7 @@
 #           2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)
 # Apache 2.0
 
+from __future__ import print_function
 import sys
 import jieba
 reload(sys)

egs/ami/s5/local/sort_bad_utts.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 
+from __future__ import print_function
 import sys
 import argparse
 import logging
@@ -38,10 +39,10 @@ def GetSortedWers(utt_info_file):
     utt_wer_sorted = sorted(utt_wer, key = lambda k : k[1])
     try:
         import numpy as np
-        bins = range(0,105,5)
+        bins = list(range(0,105,5))
         bins.append(sys.float_info.max)
 
-        hist, bin_edges = np.histogram(map(lambda x: x[1], utt_wer_sorted),
+        hist, bin_edges = np.histogram([x[1] for x in utt_wer_sorted],
                                        bins = bins)
         num_utts = len(utt_wer)
         string = ''

egs/an4/s5/local/data_prep.py

@@ -15,6 +15,7 @@
 # See the Apache 2 License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import print_function
 import os
 import re
 import sys

egs/an4/s5/local/lexicon_prep.py

@@ -15,6 +15,7 @@
 # See the Apache 2 License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import print_function
 import os
 import re
 import sys

egs/aspire/s5/local/multi_condition/create_uniform_segments.py

@@ -4,13 +4,14 @@
 # creates a segments file in the provided data directory
 # into uniform segments with specified window and overlap
 
+from __future__ import division
 import imp, sys, argparse, os, math, subprocess
 
 min_segment_length = 10 # in seconds
 def segment(total_length, window_length, overlap = 0):
   increment = window_length - overlap
   num_windows = int(math.ceil(float(total_length)/increment))
-  segments = map(lambda x: (x * increment, min( total_length, (x * increment) + window_length)), range(0, num_windows))
+  segments = [(x * increment, min( total_length, (x * increment) + window_length)) for x in range(0, num_windows)]
   if segments[-1][1] - segments[-1][0] < min_segment_length:
     segments[-2] = (segments[-2][0], segments[-1][1])
     segments.pop()
@@ -53,7 +54,7 @@ def prepare_segments_file(kaldi_data_dir, window_length, overlap):
   parser = argparse.ArgumentParser()
   parser.add_argument('--window-length', type = float, default = 30.0, help = 'length of the window used to cut the segment')
   parser.add_argument('--overlap', type = float, default = 5.0, help = 'overlap of neighboring windows')
-  parser.add_argument('data_dir', type=str, help='directory such as data/train')
+  parser.add_argument('data_dir', help='directory such as data/train')
 
   params = parser.parse_args()
 

egs/aspire/s5/local/multi_condition/fill_missing_recordings.py

@@ -38,14 +38,14 @@ def fill_ctm(input_ctm_file, output_ctm_file, recording_names):
 
   sys.stderr.write(str(" ".join(sys.argv)))
   parser = argparse.ArgumentParser(usage)
-  parser.add_argument('input_ctm_file', type=str, help='ctm file for the recordings')
-  parser.add_argument('output_ctm_file', type=str, help='ctm file for the recordings')
-  parser.add_argument('recording_name_file', type=str, help='file with names of the recordings')
+  parser.add_argument('input_ctm_file', help='ctm file for the recordings')
+  parser.add_argument('output_ctm_file', help='ctm file for the recordings')
+  parser.add_argument('recording_name_file', help='file with names of the recordings')
 
   params = parser.parse_args()
 
   try:
-    file_names = map(lambda x: x.strip(), open("{0}".format(params.recording_name_file)).readlines())
+    file_names = [x.strip() for x in open("{0}".format(params.recording_name_file)).readlines()]
   except IOError:
     raise Exception("Expected to find {0}".format(params.recording_name_file))
 

egs/aspire/s5/local/multi_condition/get_air_file_patterns.py

@@ -3,6 +3,7 @@
 
 # script to generate the file_patterns of the AIR database
 # see load_air.m file in AIR db to understand the naming convention
+from __future__ import print_function
 import sys, glob, re, os.path
 
 air_dir = sys.argv[1]
@@ -45,4 +46,4 @@
                 file_patterns.append(file_pattern+" "+output_file_name)
 file_patterns = list(set(file_patterns))
 file_patterns.sort()
-print "\n".join(file_patterns)
+print("\n".join(file_patterns))

egs/aspire/s5/local/multi_condition/normalize_wavs.py

@@ -3,6 +3,8 @@
 
 # normalizes the wave files provided in input file list with a common scaling factor
 # the common scaling factor is computed to 1/\sqrt(1/(total_samples) * \sum_i{\sum_j x_i(j)^2}) where total_samples is sum of all samples of all wavefiles. If the data is multi-channel then each channel is treated as a seperate wave files
+from __future__ import division
+from __future__ import print_function
 import argparse, scipy.io.wavfile, warnings, numpy as np, math
 
 def get_normalization_coefficient(file_list, is_rir, additional_scaling):
@@ -29,7 +31,7 @@ def get_normalization_coefficient(file_list, is_rir, additional_scaling):
         assert(rate == sampling_rate)
       else:
         sampling_rate = rate
-      data = data / dtype_max_value
+      data = data/dtype_max_value
       if is_rir:
         # just count the energy of the direct impulse response
         # this is treated as energy of signal from 0.001 seconds before impulse
@@ -55,8 +57,8 @@ def get_normalization_coefficient(file_list, is_rir, additional_scaling):
     except IOError:
       warnings.warn("Did not find the file {0}.".format(file))
   assert(total_samples > 0)
-  scaling_coefficient = np.sqrt(total_samples / total_energy)
-  print "Scaling coefficient is {0}.".format(scaling_coefficient)
+  scaling_coefficient = np.sqrt(total_samples/total_energy)
+  print("Scaling coefficient is {0}.".format(scaling_coefficient))
   if math.isnan(scaling_coefficient):
     raise Exception(" Nan encountered while computing scaling coefficient. This is mostly due to numerical overflow")
   return scaling_coefficient

egs/aspire/s5/local/multi_condition/read_rir.py

@@ -29,9 +29,9 @@ def usage():
   #sys.stderr.write(" ".join(sys.argv)+"\n")
   parser = argparse.ArgumentParser(usage())
   parser.add_argument('--output-sampling-rate', type = int, default = 8000,  help = 'sampling rate of the output')
-  parser.add_argument('type', type = str, default = None,  help = 'database type', choices = ['air'])
-  parser.add_argument('input', type = str, default = None,  help = 'directory containing the multi-channel data for a particular recording, or file name or file-regex-pattern')
-  parser.add_argument('output_filename', type = str, default = None,  help = 'output filename (if "-" then output is written to output pipe)')
+  parser.add_argument('type', default = None,  help = 'database type', choices = ['air'])
+  parser.add_argument('input', default = None,  help = 'directory containing the multi-channel data for a particular recording, or file name or file-regex-pattern')
+  parser.add_argument('output_filename', default = None,  help = 'output filename (if "-" then output is written to output pipe)')
   params = parser.parse_args()
 
   if params.output_filename == "-":

egs/aspire/s5/local/multi_condition/reverberate_wavs.py

@@ -4,18 +4,20 @@
 # script to generate multicondition training data / dev data / test data
 import argparse, glob, math, os, random, scipy.io.wavfile, sys
 
-class list_cyclic_iterator:
+class list_cyclic_iterator(object):
   def __init__(self, list, random_seed = 0):
     self.list_index = 0
     self.list = list
     random.seed(random_seed)
     random.shuffle(self.list)
 
-  def next(self):
+  def __next__(self):
     item = self.list[self.list_index]
     self.list_index = (self.list_index + 1) % len(self.list)
     return item
 
+  next = __next__  # for Python 2
+
 def return_nonempty_lines(lines):
   new_lines = []
   for line in lines:
@@ -71,15 +73,15 @@ def return_nonempty_lines(lines):
   for i in range(len(wav_files)):
     wav_file = " ".join(wav_files[i].split()[1:])
     output_wav_file = wav_out_files[i]
-    impulse_file = impulses.next()
+    impulse_file = next(impulses)
     noise_file = ''
     snr = ''
     found_impulse = False
     if add_noise:
-      for i in xrange(len(impulse_noise_index)):
+      for i in range(len(impulse_noise_index)):
         if impulse_file in impulse_noise_index[i][0]:
-          noise_file = impulse_noise_index[i][1].next()
-          snr = snrs.next()
+          noise_file = next(impulse_noise_index[i][1])
+          snr = next(snrs)
           assert(len(wav_file.strip()) > 0)
           assert(len(impulse_file.strip()) > 0)
           assert(len(noise_file.strip()) > 0)

egs/babel/s5b/local/lonestar.py

@@ -1,4 +1,5 @@
 #!/usr/bin/env python
+from __future__ import print_function
 from pylauncher import *
 import pylauncher
 import sys
@@ -39,7 +40,7 @@ def KaldiLauncher(lo, **kwargs):
 
 	logfiles = list()
 	commands = list()
-	for q in xrange(lo.jobstart, lo.jobend+1):
+	for q in range(lo.jobstart, lo.jobend+1):
 		s = "bash " + lo.queue_scriptfile + " " + str(q) 
 		commands.append(s)
 
@@ -74,7 +75,7 @@ def KaldiLauncher(lo, **kwargs):
 			time.sleep(delay);
 
 			lines=tail(10, logfile)
-			with_status=filter(lambda x:re.search(r'with status (\d+)', x), lines)
+			with_status=[x for x in lines if re.search(r'with status (\d+)', x)]
 
 			if len(with_status) == 0:
 				sys.stderr.write("The last line(s) of the log-file " + logfile + " does not seem"
@@ -98,7 +99,7 @@ def KaldiLauncher(lo, **kwargs):
 		sys.exit(-1);
 
 	#Remove service files. Be careful not to remove something that might be needed in problem diagnostics	
-	for i in xrange(len(commands)):
+	for i in range(len(commands)):
 		out_file=os.path.join(qdir, ce.outstring+str(i))
 
 		#First, let's wait on files missing (it might be that those are missing
@@ -149,7 +150,7 @@ def KaldiLauncher(lo, **kwargs):
 
 	#print job.final_report()
 
-class LauncherOpts:
+class LauncherOpts(object):
 	def __init__(self):
 		self.sync=0
 		self.nof_threads = 1
@@ -199,7 +200,7 @@ def CmdLineParser(argv):
 		jobend=int(m.group(2))
 		argv.pop(0)
 	elif re.match("^.+=.*:.*$", argv[0]):
-		print >> sys.stderr, "warning: suspicious JOB argument " + argv[0];
+		print("warning: suspicious JOB argument " + argv[0], file=sys.stderr);
 
 	if jobstart > jobend:
 		sys.stderr.write("lonestar.py: JOBSTART("+ str(jobstart) + ") must be lower than JOBEND(" + str(jobend) + ")\n")
@@ -238,8 +239,8 @@ def setup_paths_and_vars(opts):
 	cwd = os.getcwd()
 
 	if opts.varname and (opts.varname not in opts.logfile ) and (opts.jobstart != opts.jobend):
-		print >>sys.stderr, "lonestar.py: you are trying to run a parallel job" \
-			"but you are putting the output into just one log file (" + opts.logfile + ")";
+		print("lonestar.py: you are trying to run a parallel job" \
+			"but you are putting the output into just one log file (" + opts.logfile + ")", file=sys.stderr);
 		sys.exit(1)
 
 	if not os.path.isabs(opts.logfile):
@@ -261,8 +262,8 @@ def setup_paths_and_vars(opts):
 	taskname=os.path.basename(queue_logfile)
 	taskname = taskname.replace(".log", "");
 	if taskname == "":
-		print >> sys.stderr, "lonestar.py: you specified the log file name in such form " \
-			"that leads to an empty task name ("+logfile + ")";
+		print("lonestar.py: you specified the log file name in such form " \
+			"that leads to an empty task name ("+logfile + ")", file=sys.stderr);
 		sys.exit(1)
 
 	if not os.path.isabs(queue_logfile):

egs/babel/s5b/local/resegment/segmentation.py

@@ -3,6 +3,7 @@
 # Copyright 2014  Vimal Manohar
 # Apache 2.0
 
+from __future__ import division
 import os, glob, argparse, sys, re, time
 from argparse import ArgumentParser
 
@@ -19,12 +20,12 @@
 
 def mean(l):
   if len(l) > 0:
-    return float(sum(l)) / len(l)
+    return (float(sum(l))/len(l))
   return 0
 
 # Analysis class
 # Stores statistics like the confusion matrix, length of the segments etc.
-class Analysis:
+class Analysis(object):
   def __init__(self, file_id, frame_shift, prefix):
     self.confusion_matrix = [0] * 9
     self.type_counts = [ [[] for j in range(0,9)] for i in range(0,3) ]
@@ -274,8 +275,8 @@ def read_rttm_file(rttm_file, temp_dir, frame_shift):
     i = len(this_file)
     category = splits[6]
     word = splits[5]
-    start_time = int(float(splits[3])/frame_shift + 0.5)
-    duration = int(float(splits[4])/frame_shift + 0.5)
+    start_time = int((float(splits[3])/frame_shift) + 0.5)
+    duration = int((float(splits[4])/frame_shift) + 0.5)
     if i < start_time:
       this_file.extend(["0"]*(start_time - i))
     if type1 == "NON-LEX":
@@ -295,7 +296,7 @@ def read_rttm_file(rttm_file, temp_dir, frame_shift):
 # Stats class to store some basic stats about the number of
 # times the post-processor goes through particular loops or blocks
 # of code in the algorithm. This is just for debugging.
-class Stats:
+class Stats(object):
   def __init__(self):
     self.inter_utt_nonspeech = 0
     self.merge_nonspeech_segment = 0
@@ -321,7 +322,7 @@ def reset(self):
     self.noise_only = 0
 
 # Timer class to time functions
-class Timer:
+class Timer(object):
   def __enter__(self):
     self.start = time.clock()
     return self
@@ -332,7 +333,7 @@ def __exit__(self, *args):
 # The main class for post-processing a file.
 # This does the segmentation either looking at the file isolated
 # or by looking at both classes simultaneously
-class JointResegmenter:
+class JointResegmenter(object):
   def __init__(self, P, A, f, options, phone_map, stats = None, reference = None):
 
     # Pointers to prediction arrays and Initialization
@@ -1290,22 +1291,22 @@ def main():
       dest='hard_max_segment_length', default=15.0, \
       help="Hard maximum on the segment length above which the segment " \
       + "will be broken even if in the middle of speech (default: %(default)s)")
-  parser.add_argument('--first-separator', type=str, \
+  parser.add_argument('--first-separator', \
       dest='first_separator', default="-", \
       help="Separator between recording-id and start-time (default: %(default)s)")
-  parser.add_argument('--second-separator', type=str, \
+  parser.add_argument('--second-separator', \
       dest='second_separator', default="-", \
       help="Separator between start-time and end-time (default: %(default)s)")
-  parser.add_argument('--remove-noise-only-segments', type=str, \
+  parser.add_argument('--remove-noise-only-segments', \
       dest='remove_noise_only_segments', default="true", choices=("true", "false"), \
       help="Remove segments that have only noise. (default: %(default)s)")
   parser.add_argument('--min-inter-utt-silence-length', type=float, \
       dest='min_inter_utt_silence_length', default=1.0, \
       help="Minimum silence that must exist between two separate utterances (default: %(default)s)");
-  parser.add_argument('--channel1-file', type=str, \
+  parser.add_argument('--channel1-file', \
       dest='channel1_file', default="inLine", \
       help="String that matches with the channel 1 file (default: %(default)s)")
-  parser.add_argument('--channel2-file', type=str, \
+  parser.add_argument('--channel2-file', \
       dest='channel2_file', default="outLine", \
       help="String that matches with the channel 2 file (default: %(default)s)")
   parser.add_argument('--isolated-resegmentation', \
@@ -1388,7 +1389,7 @@ def main():
 
   speech_cap = None
   if options.speech_cap_length != None:
-    speech_cap = int( options.speech_cap_length / options.frame_shift )
+    speech_cap = int(options.speech_cap_length/options.frame_shift)
   # End if
 
   for f in pred_files:
@@ -1454,7 +1455,7 @@ def main():
         f2 = f3
       # End if
 
-      if (len(A1) - len(A2)) > options.max_length_diff / options.frame_shift:
+      if (len(A1) - len(A2)) > int(options.max_length_diff/options.frame_shift):
         sys.stderr.write( \
             "%s: Warning: Lengths of %s and %s differ by more than %f. " \
             % (sys.argv[0], f1,f2, options.max_length_diff) \