我需要一些关于 python 脚本的帮助。它是 Penn 语音音频校准器。我安装了它，一切正常，只是有点太好了 :) 我正在尝试将成绩单与语音中的时间相匹配，这确实如此，但它也走得太远了，这也是提供每个“电话”的所有时间（如音节）。为了说明这里是一个小部分：
IntervalTier IntervalTier
phone word
0.012471655 0.012471655
1.758503401 1.758503401
sp sp
1.758503401 1.758503401
2.197505669 2.197505669
AY1 I
2.197505669 2.197505669
2.287301587 2.52675737
T TALKING
2.287301587 2.52675737
2.397052154 2.756235828
AO1
2.397052154
2.466893424
K
2.466893424
2.496825397
IH0
2.496825397
2.52675737
NG
2.52675737
2.556689342
你可以在发音指南中看到你，它使用 AY1 来表示I，然后将 TALKING 分解成一堆音节。一方面，我“可以”按原样使用数据，但是处理一个 20 分钟的文件需要 15 分钟，我认为如果它限制自己只查找单词，它可以做得更快，这将是很好，因为我有数百小时的时间来处理。有一种昂贵（11,000 美元）的商业产品可以在大约 30 秒内完成相同的工作，所以我认为如果我们能加快一点速度，那就太棒了。
我尝试注释掉它写出“电话”的部分，然后更改 HVite 中的选项以进行单词匹配 -o SWT（来自此页面：http ://www.ee.columbia.edu/ln/LabROSA/ doc/HTKBook21/node143.html），但我得到了这个错误：
Traceback (most recent call last):
File "./align.py", line 316, in <module>
writeTextGrid(outfile, readAlignedMLF(output_mlf, SR, float(wave_start)))
File "./align.py", line 146, in readAlignedMLF
ph = lines[j].split()[2]
IndexError: list index out of range
使用修改后的 HVite 命令，该脚本的运行速度明显更快，但当然以错误结束。这是python脚本：
#!/usr/bin/env python
""" Command-line usage:
python align.py [options] wave_file transcript_file output_file
where options may include:
-r sampling_rate -- override which sample rate model to use, one of 8000, 11025, and 16000
-s start_time -- start of portion of wavfile to align (in seconds, default 0)
-e end_time -- end of portion of wavfile to align (in seconds, defaul to end)
You can also import this file as a module and use the functions directly.
"""
import os
import sys
import getopt
import wave
import re
def prep_wav(orig_wav, out_wav, sr_override, wave_start, wave_end):
global sr_models
if os.path.exists(out_wav) and False :
f = wave.open(out_wav, 'r')
SR = f.getframerate()
f.close()
print "Already re-sampled the wav file to " + str(SR)
return SR
f = wave.open(orig_wav, 'r')
SR = f.getframerate()
f.close()
soxopts = ""
if float(wave_start) != 0.0 or wave_end != None :
soxopts += " trim " + wave_start
if wave_end != None :
soxopts += " " + str(float(wave_end)-float(wave_start))
if (sr_models != None and SR not in sr_models) or (sr_override != None and SR != sr_override) or soxopts != "":
new_sr = 11025
if sr_override != None :
new_sr = sr_override
print "Resampling wav file from " + str(SR) + " to " + str(new_sr) + soxopts + "..."
SR = new_sr
os.system("sox " + orig_wav + " -r " + str(SR) + " " + out_wav + " polyphase" + soxopts)
else:
#print "Using wav file, already at sampling rate " + str(SR) + "."
os.system("cp -f " + orig_wav + " " + out_wav)
return SR
def prep_mlf(trsfile, mlffile, word_dictionary, surround, between):
# Read in the dictionary to ensure all of the words
# we put in the MLF file are in the dictionary. Words
# that are not are skipped with a warning.
f = open(word_dictionary, 'r')
dict = { } # build hash table
for line in f.readlines():
if line != "\n" and line != "" :
dict[line.split()[0]] = True
f.close()
f = open(trsfile, 'r')
lines = f.readlines()
f.close()
words = []
if surround != None:
words += surround.split(',')
i = 0
# this pattern matches hyphenated words, such as TWENTY-TWO; however, it doesn't work with longer things like SOMETHING-OR-OTHER
hyphenPat = re.compile(r'([A-Z]+)-([A-Z]+)')
while (i < len(lines)):
txt = lines[i].replace('\n', '')
txt = txt.replace('{breath}', '{BR}').replace('<noise>', '{NS}')
txt = txt.replace('{laugh}', '{LG}').replace('{laughter}', '{LG}')
txt = txt.replace('{cough}', '{CG}').replace('{lipsmack}', '{LS}')
for pun in [',', '.', ':', ';', '!', '?', '"', '%', '(', ')', '--', '---']:
txt = txt.replace(pun, '')
txt = txt.upper()
# break up any hyphenated words into two separate words
txt = re.sub(hyphenPat, r'\1 \2', txt)
txt = txt.split()
for wrd in txt:
if (wrd in dict):
words.append(wrd)
if between != None:
words.append(between)
else:
print "SKIPPING WORD", wrd
i += 1
# remove the last 'between' token from the end
if between != None:
words.pop()
if surround != None:
words += surround.split(',')
writeInputMLF(mlffile, words)
def writeInputMLF(mlffile, words) :
fw = open(mlffile, 'w')
fw.write('#!MLF!#\n')
fw.write('"*/tmp.lab"\n')
for wrd in words:
fw.write(wrd + '\n')
fw.write('.\n')
fw.close()
def readAlignedMLF(mlffile, SR, wave_start):
# This reads a MLFalignment output file with phone and word
# alignments and returns a list of words, each word is a list containing
# the word label followed by the phones, each phone is a tuple
# (phone, start_time, end_time) with times in seconds.
f = open(mlffile, 'r')
lines = [l.rstrip() for l in f.readlines()]
f.close()
if len(lines) < 3 :
raise ValueError("Alignment did not complete succesfully.")
j = 2
ret = []
while (lines[j] <> '.'):
if (len(lines[j].split()) == 5): # Is this the start of a word; do we have a word label?
# Make a new word list in ret and put the word label at the beginning
wrd = lines[j].split()[4]
ret.append([wrd])
# Append this phone to the latest word (sub-)list
ph = lines[j].split()[2]
if (SR == 11025):
st = (float(lines[j].split()[0])/10000000.0 + 0.0125)*(11000.0/11025.0)
en = (float(lines[j].split()[1])/10000000.0 + 0.0125)*(11000.0/11025.0)
else:
st = float(lines[j].split()[0])/10000000.0 + 0.0125
en = float(lines[j].split()[1])/10000000.0 + 0.0125
if st < en:
ret[-1].append([ph, st+wave_start, en+wave_start])
j += 1
return ret
def writeTextGrid(outfile, word_alignments) :
# make the list of just phone alignments
phons = []
for wrd in word_alignments :
phons.extend(wrd[1:]) # skip the word label
# make the list of just word alignments
# we're getting elements of the form:
# ["word label", ["phone1", start, end], ["phone2", start, end], ...]
wrds = []
for wrd in word_alignments :
# If no phones make up this word, then it was an optional word
# like a pause that wasn't actually realized.
if len(wrd) == 1 :
continue
wrds.append([wrd[0], wrd[1][1], wrd[-1][2]]) # word label, first phone start time, last phone end time
#write the phone interval tier
fw = open(outfile, 'w')
fw.write('File type = "ooTextFile short"\n')
fw.write('"TextGrid"\n')
fw.write('\n')
fw.write(str(phons[0][1]) + '\n')
fw.write(str(phons[-1][2]) + '\n')
fw.write('<exists>\n')
fw.write('2\n')
fw.write('"IntervalTier"\n')
fw.write('"phone"\n')
fw.write(str(phons[0][1]) + '\n')
fw.write(str(phons[-1][-1]) + '\n')
fw.write(str(len(phons)) + '\n')
for k in range(len(phons)):
fw.write(str(phons[k][1]) + '\n')
fw.write(str(phons[k][2]) + '\n')
fw.write('"' + phons[k][0] + '"' + '\n')
#write the word interval tier
fw.write('"IntervalTier"\n')
fw.write('"word"\n')
fw.write(str(phons[0][1]) + '\n')
fw.write(str(phons[-1][-1]) + '\n')
fw.write(str(len(wrds)) + '\n')
for k in range(len(wrds) - 1):
fw.write(str(wrds[k][1]) + '\n')
fw.write(str(wrds[k+1][1]) + '\n')
fw.write('"' + wrds[k][0] + '"' + '\n')
fw.write(str(wrds[-1][1]) + '\n')
fw.write(str(phons[-1][2]) + '\n')
fw.write('"' + wrds[-1][0] + '"' + '\n')
fw.close()
def prep_working_directory() :
os.system("rm -r -f ./tmp")
os.system("mkdir ./tmp")
def prep_scp(wavfile) :
fw = open('./tmp/codetr.scp', 'w')
fw.write(wavfile + ' ./tmp/tmp.plp\n')
fw.close()
fw = open('./tmp/test.scp', 'w')
fw.write('./tmp/tmp.plp\n')
fw.close()
def create_plp(hcopy_config) :
os.system('HCopy -T 1 -C ' + hcopy_config + ' -S ./tmp/codetr.scp')
def viterbi(input_mlf, word_dictionary, output_mlf, phoneset, hmmdir) :
os.system('HVite -T 1 -a -m -I ' + input_mlf + ' -H ' + hmmdir + '/macros -H ' + hmmdir + '/hmmdefs -S ./tmp/test.scp -i ' + output_mlf + ' -p 0.0 -s 5.0 ' + word_dictionary + ' ' + phoneset + ' > ./tmp/aligned.results')
def getopt2(name, opts, default = None) :
value = [v for n,v in opts if n==name]
if len(value) == 0 :
return default
return value[0]
if __name__ == '__main__':
try:
opts, args = getopt.getopt(sys.argv[1:], "r:s:e:", ["model="])
# get the three mandatory arguments
if len(args) != 3 :
raise ValueError("Specify wavefile, a transcript file, and an output file!")
wavfile, trsfile, outfile = args
sr_override = getopt2("-r", opts, None)
wave_start = getopt2("-s", opts, "0.0")
wave_end = getopt2("-e", opts, None)
surround_token = "sp" #getopt2("-p", opts, 'sp')
between_token = "sp" #getopt2("-b", opts, 'sp')
if surround_token.strip() == "":
surround_token = None
if between_token.strip() == "":
between_token = None
mypath = getopt2("--model", opts, None)
except :
print __doc__
(type, value, traceback) = sys.exc_info()
print value
sys.exit(0)
# If no model directory was said explicitly, get directory containing this script.
hmmsubdir = ""
sr_models = None
if mypath == None :
mypath = os.path.dirname(os.path.abspath(sys.argv[0])) + "/model"
hmmsubdir = "FROM-SR"
# sample rates for which there are acoustic models set up, otherwise
# the signal must be resampled to one of these rates.
sr_models = [8000, 11025, 16000]
if sr_override != None and sr_models != None and not sr_override in sr_models :
raise ValueError, "invalid sample rate: not an acoustic model available"
word_dictionary = "./tmp/dict"
input_mlf = './tmp/tmp.mlf'
output_mlf = './tmp/aligned.mlf'
# create working directory
prep_working_directory()
# create ./tmp/dict by concatening our dict with a local one
if os.path.exists("dict.local"):
os.system("cat " + mypath + "/dict dict.local > " + word_dictionary)
else:
os.system("cat " + mypath + "/dict > " + word_dictionary)
#prepare wavefile: do a resampling if necessary
tmpwav = "./tmp/sound.wav"
SR = prep_wav(wavfile, tmpwav, sr_override, wave_start, wave_end)
if hmmsubdir == "FROM-SR" :
hmmsubdir = "/" + str(SR)
#prepare mlfile
prep_mlf(trsfile, input_mlf, word_dictionary, surround_token, between_token)
#prepare scp files
prep_scp(tmpwav)
# generate the plp file using a given configuration file for HCopy
create_plp(mypath + hmmsubdir + '/config')
# run Verterbi decoding
#print "Running HVite..."
mpfile = mypath + '/monophones'
if not os.path.exists(mpfile) :
mpfile = mypath + '/hmmnames'
viterbi(input_mlf, word_dictionary, output_mlf, mpfile, mypath + hmmsubdir)
# output the alignment as a Praat TextGrid
writeTextGrid(outfile, readAlignedMLF(output_mlf, SR, float(wave_start)))