def calc_recognition_performance(self, HResults_scp, lexicon_file=None):
if lexicon_file == None:
lexicon_file = self.lexicon_file
self.command, self.output, self.error = run_command(
['HResults', '-T', '1', lexicon_file, '-S', HResults_scp])
return self._load_recognition_performance()
def recognition(self, lattice_file, hmmdefs, HVite_scp, lexicon_file=None):
if lexicon_file == None:
lexicon_file = self.lexicon_file
self.command, self.output, self.error = run_command([
'HVite', '-T', '1', '-C', self.config_rec, '-w', lattice_file,
'-H', hmmdefs, lexicon_file, self.phonelist_txt, '-S', HVite_scp
])
return self._load_recognition_result()
def create_hmmdefs(self, proto, hmmdefs):
""" allocate mean & variance to all phases in the phaselist """
_, output, _ = run_command(
['perl', HTK.mkhmmdefs_pl, proto, self.phonelist_txt])
if os.name == 'nt':
output = output.replace('\r', '')
output = output.replace('\n', '\r\n')
with open(hmmdefs, 'wb') as f:
f.write(bytes(output, 'ascii'))
return
def _network2lattice(self, network_file, lattice_file):
"""creats word level lattice files from a text file syntax description containing a set of rewrite rules based on extended Backus-Naur Form (EBNF).
Args:
network_file: word network.
lattice_file: word level lattice file.
Reference:
http://www1.icsi.berkeley.edu/Speech/docs/HTKBook/node247.html
"""
self.command, self.output, self.error = run_command(
['HParse', network_file, lattice_file])
return
def flat_start(self, HCompV_scp, model_dir):
"""
Args:
config_train:
HCompV_scp: a script file.
model_dir: the directory.
"""
proto = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\config\proto'
self._create_proto(proto)
self.command, self.output, self.error = run_command([
'HCompV', '-T', '1', '-C', self.config_train, '-f', '0.01', '-m',
'-S', HCompV_scp, '-M', model_dir, proto
])
return
def re_estimation(self,
hmmdefs,
output_dir,
HCompV_scp,
mlf_file=None,
macros=None):
command_list = []
command_list.extend([
'HERest', '-T', '1', '-C', self.config_train, '-v', '0.01', '-t',
'250.0', '150.0', '1000.0', '-H', hmmdefs, '-M', output_dir
])
if not mlf_file == None:
command_list.extend(['-I', mlf_file])
if not macros == None:
command_list.extend(['-H', macros])
command_list.extend(['-S', HCompV_scp, self.phonelist_txt])
self.command, self.output, self.error = run_command(command_list)
return
def create_dictionary(self, sentence, log_txt, dictionary_file):
""" when the length of the filename exceeds 32 characters, error.
"""
label_file = NamedTemporaryFile(mode='w',
delete=False,
encoding='utf-8')
label_file.close()
self.create_label_file(sentence, label_file.name)
phonelist_txt = NamedTemporaryFile(mode='w',
delete=False,
encoding='utf-8')
phonelist_txt.close()
self.command, self.output, self.error = run_command([
'HDMan', '-w', label_file.name, '-g', self.global_ded, '-n',
phonelist_txt.name, '-l', log_txt, dictionary_file,
self.lexicon_file
])
os.remove(label_file.name)
os.remove(phonelist_txt.name)
return
def increase_mixture(hmmdefs, nmix, output_dir, phonelist_txt):
fh.make_new_directory(output_dir)
header_file = os.path.join(output_dir, 'mix' + str(nmix) + '.hed')
with open(header_file, 'wb') as f:
f.write(bytes('MU ' + str(nmix) + ' {*.state[2-4].mix}', 'ascii'))
run_command([
'HHEd', '-T', '1', '-H', hmmdefs, '-M', output_dir, header_file,
phonelist_txt
])
#if __name__ == '__main__':
#def txt2label(file_txt, label_file):
# """
# Convert an orthographycal transcription to the HTK label.
# :param path file_txt: path to the text file in which the orthographycal transcription of the utterance is written in one line.
# :param path label_file: path to the text file in which the contents of file_txt is written as a word per line.
# """
# # read the first line where the sentence is written.
# with open(file_txt, 'r') as f:
# line1 = f.readline()
# # remove space at the end and comma
# line1 = line1.rstrip()
# line1 = line1.replace(',', '')
# # write each word in a capital letter
# line1list = line1.split(' ')
# with open(label_file, 'w') as f:
# for word in line1list:
# f.write("%s\n" % word.upper())
#def lab2HTKdic(label_file, fileHTKdic, lex, connect):
# """
# Make a HTK dictionary file from a HTK label file.
# Each word in the label file is first searched in database.
# If not found, the Grapheme-to-Phone(G2P) program is used.
# :param path label_file: path to the text file in which the contents of file_txt is written as a word per line.
# :param path fileHTKdic: path to the output dictionary file in which pronunciation variants will be written.
# :param instance lex: an instance of class cLexicon.
# :param pypyodbc connect: an object of pypyodbc.
# """
# cursor = connect.cursor()
# tableList = ['2010_2510_lexicon_pronvars_HTK', 'lexicon_pronvars_g2p', 'lexicon_pronvars_ipa']
# with open(label_file, 'r') as f:
# lines = f.read()
# words = lines.split()
# with open(fileHTKdic, 'w') as f:
# for WORD in words:
# word = WORD.lower()
# rows = np.array([])
# for tableNum in range(0, len(tableList)):
# SQL_string = """SELECT word, pronunciation FROM %s WHERE word =?""" %(tableList[tableNum])
# cursor.execute(SQL_string, (word,))
# rows_ = cursor.fetchall()
# rows = np.append(rows, rows_)
# # if the pronunciations are found in the database, retreive them.
# # otherwise obtain the pronunciation using g2p.
# if len(rows) > 2:
# # reshape into d x 2 array
# htkdic_ = rows.reshape((-1, 2))
# # remove duplicates
# htkdic = np.unique(htkdic_, axis=0)
# else:
# # get pronunciation using g2p
# fileWordList = tempfile.NamedTemporaryFile(mode='w', delete=False)
# fileWordList.write("%s\n" % (word))
# fileWordList.close()
# htkdic = lex.g2p2db(fileWordList.name, connect)
# fileWordList.close()
# os.remove(fileWordList.name)
# # output htkdic
# for line in htkdic:
# word = line[0].upper()
# pron = line[1]
# f.write("{0}\t{1}\n".format(word, pron))
#def loadHTKdic(fileHTKdic):
# """
# load dic file which is used for HTK.
# :param path fileHTKdic: path to the output dictionary file in which pronunciation variants will be written.
# """
# HTKdic = []
# with open(fileHTKdic, 'r') as f:
# lines = f.read()
# lines = lines.split('\n')
# for line in lines:
# line = line.split('\t')
# if len(line) > 1:
# HTKdic.append(line)
# return np.array(HTKdic)
#def HTKdic2list(fileHTKdic):
# """
# load an HTK dictionary file as a list.
# :param path fileHTKdic: the path to an HTK dictionary file in which [word] /t [pronunciation] is written each line.
# """
# with open(fileHTKdic, 'r') as fin:
# lines_ = fin.read()
# # split all text into lines
# lines = lines_.split('\n')
# htkdic = []
# for line in lines:
# lineSplit = line.split('\t')
# if len(lineSplit) == 2:
# word = lineSplit[0].lower()
# pronunciation = lineSplit[1]
# htkdic.append([word, pronunciation])
# return np.array(htkdic)
#def doHVite(fileWav, label_file, fileHTKdic, fileFA, configHVite, filePhoneList, AcousticModel):
# """
# Forced alignment using HVite of HTK.
# :param path fileWav: path to the wav file in which the utterance was recorded.
# :param path label_file: path to the text file in which the contents of file_txt is written as a word per line.
# :param path fileHTKdic: path to the HTK dictionary file in which pronunciation variants are written.
# :param path fileFA: path to the output file in which forced alignment (100ns unit) will be written.
# :param path configHVite: path to the config file of HVite.
# :param path filePhoneList: path to the list of phone used in the acoustic model and in the HTK dictionary file.
# :param path AcousticModel: path to the acoustic model.
# """
# with open(label_file, 'r') as f:
# lines = f.read()
# # Master Label File (= list of label files.)
# fileMlf = tempfile.NamedTemporaryFile(mode='w', delete=False)
# fileMlf.write("#!MLF!#\n")
# fileMlf.write('"' + label_file + '"\n')
# fileMlf.write(lines)
# fileMlf.close()
# # script
# fileScp = tempfile.NamedTemporaryFile(mode='w', delete=False)
# fileScp.write("%s" % (fileWav))
# fileScp.close()
# # HVite
# subprocessStr = 'HVite -T 1 -a -C ' + configHVite + ' -H ' + AcousticModel + ' -m -I ' + fileMlf.name + ' -i ' + fileFA + ' -S ' + fileScp.name + ' ' + fileHTKdic + ' ' + filePhoneList
# subprocess.call(subprocessStr, shell=True)
# # termination process
# os.remove(fileMlf.name)
# os.remove(fileScp.name)
#def conv100ns2ms(fileFA_in_100ns, fileFA_in_ms):
# """
# Convert the unit of forced alignment from 100ns to ms.
# :param path fileFA_in_100ns: path to the forced alignment file written in the unit of 100ns.
# :param path fileFA_in_ms: path to the output forced alignment file written in the unit of ms.
# """
# with open(fileFA_in_100ns, 'r') as fin:
# with open(fileFA_in_ms, 'w') as fout:
# line = fin.readline()
# fout.write(line)
# line = fin.readline()
# fout.write(line)
# while line:
# line = fin.readline()
# dur = line.split()
# # Each line of forced alignment file is:
# # [durStart] [durEnd] [phoneme] [likelihood] [word]
# if len(dur) == 4 or len(dur) == 5:
# # convert 100ns -> ms
# dur[0] = float(dur[0])/10000
# dur[1] = float(dur[1])/10000
# if len(dur) == 4:
# fout.write('{0} {1} {2} {3}\n'.format(dur[0], dur[1], dur[2], dur[3]))
# elif len(dur) == 5:
# fout.write('{0} {1} {2} {3} {4}\n'.format(dur[0], dur[1], dur[2], dur[3], dur[4]))
# else:
# fout.write(line)
#def ForcedAlignment(fileWav, file_txt, fileOut, configFile, saveIntermediateFiles):
# """
# Forced Alignment
# :param path fileWav: path to the wav file in which the utterance was recorded.
# :param path file_txt: path to the text file in which the orthographycal transcription of the utterance is written in one line.
# :param path fileOut: path to the output file in which forced alignment will be written in the unit of ms.
# :param path configFile: path to the general config file (not the config file for HVite).
# :param int saveIntermediateFile: if intermediate files (label file and dictionary file) should be saved. 0:no, 1:yes
# """
# # label file: the list of words that appears in the wave file.
# # should be in the same folder where wav file is stored.
# label_file = fileWav.replace('.wav', '.lab')
# # dic file: the pronunciation dictionary in which pronunciation(s) of each word in the label file are described.
# fileHTKdic = fileWav.replace('.wav', '.dic')
# # forced alignment output
# fileFA = tempfile.NamedTemporaryFile(delete=False)
# fileFA.close()
# # load the config file
# config = configparser.ConfigParser()
# config.sections()
# config.read(configFile)
# dbLexicon = config['cLexicon']['dbLexicon']
# scriptBarbara = config['cLexicon']['scriptBarbara']
# exeG2P = config['cLexicon']['exeG2P']
# configHVite = config['pyHTK']['configHVite']
# filePhoneList = config['pyHTK']['filePhoneList']
# AcousticModel = config['pyHTK']['AcousticModel']
# # make database connection
# param = r"Driver={Microsoft Access Driver (*.mdb, *.accdb)};dbq=" + dbLexicon + ";"
# connect = pypyodbc.connect(param)
# # instance of class lexicon
# if sys.platform == 'win32':
# scriptBarbara = scriptBarbara.replace('\\\\', '\\')
# lex = cLexicon.lexicon(scriptBarbara, exeG2P)
# # load the orthographical transcription
# # and output that word by word (e.g. one word per line) in capital letters...
# txt2label(file_txt, label_file)
# # for each words in the label file pronunciation(s) are searched in lexicon database...
# lab2HTKdic(label_file, fileHTKdic, lex, connect)
# # forced alignment using HVite
# doHVite(fileWav, label_file, fileHTKdic, fileFA.name, configHVite, filePhoneList, AcousticModel)
# conv100ns2ms(fileFA.name, fileOut)
# # termination process
# if saveIntermediateFiles == 0:
# os.remove(label_file)
# os.remove(fileHTKdic)
# os.remove(fileFA.name)
# connect.close()
def _tie_sp_to_sil(self, macros, hmmdefs, output_dir):
self.command, self.output, self.error = run_command([
'HHEd', '-H', macros, '-H', hmmdefs, '-M', output_dir,
self.sil_hed, self.phonelist_txt
])
return
def wav2mfc(self, hcopy_scp):
self.command, self.output, self.error = run_command(
['HCopy', '-C', self.config_hcopy, '-S', hcopy_scp])
return
def mlf_word2phone(self, mlf_phone, mlf_word):
self.command, self.output, self.error = run_command([
'HLEd', '-l', '*', '-d', self.lexicon_file, '-i', mlf_phone,
self.mkphones_led, mlf_word
])
