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test on stimmen data is added.

master
yemaozi88 3 years ago
parent
commit
b1b1942fa0
  1. BIN
      .vs/acoustic_model/v15/.suo
  2. 2
      acoustic_model/acoustic_model.pyproj
  3. 27
      acoustic_model/fame_functions.py
  4. 143
      acoustic_model/fame_hmm.py
  5. 16
      acoustic_model/htk_vs_kaldi.py
  6. 6
      acoustic_model/phoneset/fame_asr.py
  7. 15
      acoustic_model/stimmen_test.py

BIN
.vs/acoustic_model/v15/.suo

2
acoustic_model/acoustic_model.pyproj

@ -4,7 +4,7 @@
<SchemaVersion>2.0</SchemaVersion>
<ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
<ProjectHome>.</ProjectHome>
<StartupFile>htk_vs_kaldi.py</StartupFile>
<StartupFile>fame_hmm.py</StartupFile>
<SearchPath>
</SearchPath>
<WorkingDirectory>.</WorkingDirectory>

27
acoustic_model/fame_functions.py

@ -12,6 +12,10 @@ import defaultfiles as default
import convert_phoneset
from phoneset import fame_ipa, fame_asr
sys.path.append(default.toolbox_dir)
from htk import pyhtk
#def read_fileFA(fileFA):
# """
# read the result file of HTK forced alignment.
@ -371,4 +375,25 @@ def ipa2htk(ipa):
asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
asr_splitted = fame_asr.phone_reduction(asr_splitted)
htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
return ''.join(htk_splitted)
return ''.join(htk_splitted)
def performance_on_stimmen(stimmen_dir, hmmdefs):
#hmmdefs = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\model_\hmm1\iter20\hmmdefs'
#stimmen_dir = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\stimmen'
lattice_file = os.path.join(stimmen_dir, 'word_lattice.ltc')
hvite_scp = os.path.join(stimmen_dir, 'hvite.scp')
#fh.make_filelist(os.path.join(stimmen_dir, 'mfc'), hvite_scp, file_type='mfc')
hresult_scp = os.path.join(stimmen_dir, 'hresult.scp')
#fh.make_filelist(os.path.join(stimmen_dir, 'mfc'), hresult_scp, file_type='rec')
lexicon_file = os.path.join(stimmen_dir, 'lexicon_recognition.dic')
chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_file)
result = chtk.recognition(
lattice_file,
hmmdefs,
hvite_scp
)
per_sentence, per_word = chtk.calc_recognition_performance(hresult_scp)
return per_sentence['accuracy']

143
acoustic_model/fame_hmm.py

@ -22,30 +22,27 @@ from htk import pyhtk
# procedure
make_lexicon = 0
make_label = 0 # it takes roughly 4800 sec on Surface pro 2.
make_htk_files = 0
make_mlf = 0
extract_features = 0
flat_start = 0
train_model_without_sp = 0
add_sp = 0
train_model_with_sp = 0
train_model_with_sp_align_mlf = 1
train_model_with_sp_align_mlf = 0
train_triphone = 0
# pre-defined values.
dataset_list = ['devel', 'test', 'train']
hmmdefs_name = 'hmmdefs'
proto_name = 'proto39'
proto_name = 'proto'
lexicon_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
lexicon_oov = os.path.join(default.fame_dir, 'lexicon', 'lex.oov')
config_dir = os.path.join(default.htk_dir, 'config')
config_hcopy = os.path.join(config_dir, 'config.HCopy')
config_train = os.path.join(config_dir, 'config.train')
global_ded = os.path.join(config_dir, 'global.ded')
mkphones_led = os.path.join(config_dir, 'mkphones.led')
sil_hed = os.path.join(config_dir, 'sil.hed')
prototype = os.path.join(config_dir, proto_name)
@ -53,25 +50,20 @@ model_dir = os.path.join(default.htk_dir, 'model')
# directories / files to be made.
lexicon_dir = os.path.join(default.htk_dir, 'lexicon')
lexicon_htk_asr = os.path.join(lexicon_dir, 'lex.htk_asr')
lexicon_htk_oov = os.path.join(lexicon_dir, 'lex.htk_oov')
lexicon_htk = os.path.join(lexicon_dir, 'lex.htk')
phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
model0_dir = os.path.join(model_dir, 'hmm0')
model1_dir = os.path.join(model_dir, 'hmm1')
#model1_dir = os.path.join(model_dir, 'hmm1')
feature_dir = os.path.join(default.htk_dir, 'mfc')
if not os.path.exists(feature_dir):
os.makedirs(feature_dir)
fh.make_new_directory(feature_dir, existing_dir='leave')
tmp_dir = os.path.join(default.htk_dir, 'tmp')
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
fh.make_new_directory(tmp_dir, existing_dir='leave')
label_dir = os.path.join(default.htk_dir, 'label')
if not os.path.exists(label_dir):
os.makedirs(label_dir)
fh.make_new_directory(label_dir, existing_dir='leave')
## training
hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
@ -98,20 +90,21 @@ if make_lexicon:
# therefore there is no overlap between lex_asr and lex_oov.
fame_functions.combine_lexicon(lexicon_htk_asr, lexicon_htk_oov, lexicon_htk)
## =======================
## manually make changes to the pronunciation dictionary and save it as lex.htk
## =======================
## fixing the lexicon for HTK.
# (1) Replace all tabs with single space;
# (2) Put a '\' before any dictionary entry beginning with single quote
#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
# http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
print('>>> fixing the lexicon...')
fame_functions.fix_lexicon(lexicon_htk)
print("elapsed time: {}".format(time.time() - timer_start))
## intialize the instance for HTK.
chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_htk)
## ======================= make label files =======================
if make_label:
# train_2002_gongfansaken_10347.lab is empty. should be removed.
for dataset in dataset_list:
timer_start = time.time()
print("==== making label files on dataset {}".format(dataset))
@ -120,7 +113,7 @@ if make_label:
wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
label_dir_ = os.path.join(label_dir, dataset)
dictionary_file = os.path.join(label_dir_, 'temp.dic')
fh.make_new_directory(label_dir_)
fh.make_new_directory(label_dir_, existing_dir='leave')
# list of scripts
with open(script_list, "rt", encoding="utf-8") as fin:
@ -135,56 +128,48 @@ if make_label:
sentence_htk = fame_functions.word2htk(sentence)
wav_file = os.path.join(wav_dir_, filename + '.wav')
if os.path.exists(wav_file) and pyhtk.can_be_ascii(sentence_htk) == 0:
if pyhtk.create_dictionary_without_log(
sentence_htk, global_ded, dictionary_file, lexicon_htk) == 0:
if os.path.exists(wav_file) and chtk.can_be_ascii(sentence_htk) == 0:
if chtk.get_number_of_missing_words(
sentence_htk, dictionary_file) == 0:
# when the file name is too long, HDMan command does not work.
# therefore first temporary dictionary_file is made, then renamed.
shutil.move(dictionary_file, os.path.join(label_dir_, filename + '.dic'))
label_file = os.path.join(label_dir_, filename + '.lab')
pyhtk.create_label_file(sentence_htk, label_file)
chtk.create_label_file(sentence_htk, label_file)
else:
os.remove(dictionary_file)
print("elapsed time: {}".format(time.time() - timer_start))
## ======================= make other required files =======================
if make_htk_files:
## ======================= make master label files =======================
if make_mlf:
timer_start = time.time()
print("==== making files required for HTK ====")
print("==== making master label files ====")
print(">>> making a phonelist...")
pyhtk.create_phonelist_file(fame_asr.phoneset_htk, phonelist_txt)
# train_2002_gongfansaken_10347.lab is empty. should be removed.
empty_lab_file = os.path.join(label_dir, 'train', 'train_2002_gongfansaken_10347.lab')
empty_dic_file = empty_lab_file.replace('.lab', '.dic')
if os.path.exists(empty_lab_file):
os.remove(empty_lab_file)
if os.path.exists(empty_dic_file):
os.remove(empty_dic_file)
for dataset in dataset_list:
wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
#wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
feature_dir_ = os.path.join(feature_dir, dataset)
label_dir_ = os.path.join(label_dir, dataset)
mlf_word = os.path.join(label_dir, dataset + '_word.mlf')
mlf_phone = os.path.join(label_dir, dataset + '_phone.mlf')
#print(">>> making a script file for {}...".format(dataset))
#listdir = glob.glob(os.path.join(wav_dir_, '*.dic'))
#mfc_list = [filename.replace(wav_dir_, feature_dir_).replace('.dic', '.mfc') for filename in listdir]
#hcompv_scp = os.path.join(tmp_dir, dataset + '.scp')
#with open(hcompv_scp, 'wb') as f:
# f.write(bytes('\n'.join(mfc_list) + '\n', 'ascii'))
print(">>> making a mlf file for {}...".format(dataset))
lab_list = glob.glob(os.path.join(label_dir_, '*.lab'))
with open(mlf_word, 'wb') as fmlf:
fmlf.write(bytes('#!MLF!#\n', 'ascii'))
for label_file in lab_list:
filename = os.path.basename(label_file)
fmlf.write(bytes('\"*/{}\"\n'.format(filename), 'ascii'))
with open(label_file) as flab:
lines = flab.read()
fmlf.write(bytes(lines + '.\n', 'ascii'))
print(">>> generating phone level transcription for {}...".format(dataset))
pyhtk.mlf_word2phone(lexicon_htk, mlf_phone, mlf_word, mkphones_led)
print("elapsed time: {}".format(time.time() - timer_start))
print(">>> generating a word level mlf file for {}...".format(dataset))
chtk.label2mlf(label_dir_, mlf_word)
print(">>> generating a phone level mlf file for {}...".format(dataset))
chtk.mlf_word2phone(mlf_phone, mlf_word)
print("elapsed time: {}".format(time.time() - timer_start))
## ======================= extract features =======================
@ -196,7 +181,7 @@ if extract_features:
wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
label_dir_ = os.path.join(label_dir, dataset)
feature_dir_ = os.path.join(feature_dir, dataset)
fh.make_new_directory(feature_dir_)
fh.make_new_directory(feature_dir_, existing_dir='delete')
# a script file for HCopy
print(">>> making a script file for HCopy...")
@ -212,12 +197,15 @@ if extract_features:
os.path.join(wav_dir_, os.path.basename(lab_file).replace('.lab', '.wav')) + '\t'
+ os.path.join(feature_dir_, os.path.basename(lab_file).replace('.lab', '.mfc'))
for lab_file in lab_list]
if os.path.exists(empty_mfc_file):
os.remove(empty_mfc_file)
with open(hcopy_scp.name, 'wb') as f:
f.write(bytes('\n'.join(feature_list), 'ascii'))
# extract features.
print(">>> extracting features on {}...".format(dataset))
pyhtk.wav2mfc(config_hcopy, hcopy_scp.name)
chtk.wav2mfc(hcopy_scp.name)
os.remove(hcopy_scp.name)
# make hcompv.scp.
@ -235,21 +223,18 @@ if extract_features:
if flat_start:
timer_start = time.time()
print('==== flat start ====')
pyhtk.flat_start(config_train, hcompv_scp_train, model0_dir, prototype)
feature_size = 39
model0_dir = os.path.join(model_dir, 'hmm0')
fh.make_new_directory(model0_dir, existing_dir='leave')
chtk.flat_start(hcompv_scp_train, model0_dir, feature_size)
# allocate mean & variance to all phones in the phone list
print('>>> allocating mean & variance to all phones in the phone list...')
pyhtk.create_hmmdefs(
chtk.create_hmmdefs(
os.path.join(model0_dir, proto_name),
os.path.join(model0_dir, 'hmmdefs'),
phonelist_txt)
# make macros
print('>>> making macros...')
with open(os.path.join(model0_dir, 'vFloors')) as f:
lines = f.read()
with open(os.path.join(model0_dir, 'macros'), 'wb') as f:
f.write(bytes('~o <MFCC_0_D_A> <VecSize> 39\n' + lines, 'ascii'))
os.path.join(model0_dir, 'hmmdefs')
)
print("elapsed time: {}".format(time.time() - timer_start))
@ -362,4 +347,24 @@ if train_model_with_sp_align_mlf:
hcompv_scp_train, phonelist_txt,
mlf_file=mlf_file_train_aligned,
macros=os.path.join(modeln_dir_pre, 'macros'))
print("elapsed time: {}".format(time.time() - timer_start))
print("elapsed time: {}".format(time.time() - timer_start))
# train triphone.
if train_triphone:
triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
macros = os.path.join(model_dir, 'hmm1_tri', 'iter0', 'macros')
hmmdefs = os.path.join(model_dir, 'hmm1_tri', 'iter0', 'hmmdefs')
model_out_dir = os.path.join(model_dir, 'hmm1_tri', 'iter1')
run_command([
'HERest', '-B',
'-C', config_train,
'-I', triphone_mlf,
'-t', '250.0', '150.0', '1000.0',
'-s', 'stats'
'-S', hcompv_scp_train,
'-H', macros,
'-H', hmmdefs,
'-M', model_out_dir,
os.path.join(config_dir, 'triphonelist.txt')
])

16
acoustic_model/htk_vs_kaldi.py

@ -53,7 +53,7 @@ from htk import pyhtk
# procedure
make_dic_file = 0
make_HTK_files = 1
make_HTK_files = 0
extract_features = 0
#make_htk_dict_files = 0
#do_forced_alignment_htk = 0
@ -171,7 +171,7 @@ if make_HTK_files:
filename = row['filename'].replace('.wav', '.lab')
label_file = os.path.join(feature_dir, filename)
with open(label_file, 'wb') as f:
label_string = 'START\n' + row['word'].upper() + '\nEND\n'
label_string = 'SILENCE\n' + row['word'].upper() + '\nSILENCE\n'
f.write(bytes(label_string, 'ascii'))
@ -249,7 +249,7 @@ with open(hresult_scp, 'wb') as f:
# calculate result
performance = np.zeros((1, 2))
for niter in range(1, 50):
for niter in range(50, 60):
output = pyhtk.recognition(
os.path.join(config_dir, 'config.rec'),
lattice_file,
@ -265,6 +265,16 @@ for niter in range(1, 50):
#output = run_command_with_output([
# 'HVite', '-T', '1',
# '-C', config_rec,
# '-w', lattice_file,
# '-H', hmm,
# dictionary_file, phonelist_txt,
# '-S', HVite_scp
#])
## ======================= forced alignment using HTK =======================
if do_forced_alignment_htk:

6
acoustic_model/phoneset/fame_asr.py

@ -128,7 +128,11 @@ translation_key_word2htk = {
'ä': 'ao', 'ë': 'ee', 'ï': 'ie', 'ö': 'oe', 'ü': 'ue',
}
#[translation_key_word2htk.get(i, i) for i in not_in_ascii]
#Stop: p, b, t, d, k, g
#Nasal: m, n, ng(ŋ)
#Fricative: s, z, f, v, h, x
#Liquid: l, r
#Vowel: a, a:, e:, i, i:, i_(i̯), o, o:, u, u:, u_(ṷ), oe(ö), oe:(ö:), ue(ü), ue:(ü:), O(ɔ), O:(ɔ:), Oe(ɔ̈), A(ə), E(ɛ), E:(ɛ:), I(ɪ), I:(ɪ:)
## the list of multi character phones.

15
acoustic_model/stimmen_test.py

@ -77,4 +77,17 @@ for word in word_list:
for key, value in zip(c.keys(), c.values()):
if value > 3:
pronunciations[key] = value
print(pronunciations)
print(pronunciations)
monophone_mlf = os.path.join(default.htk_dir, 'label', 'train_phone_aligned.mlf')
triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
def filenames_in_mlf(file_mlf):
with open(file_mlf) as f:
lines_ = f.read().split('\n')
lines = [line for line in lines_ if len(line.split(' ')) == 1 and line != '.']
filenames = [line.replace('"', '').replace('*/', '') for line in lines[1:-1]]
return filenames
filenames_mono = filenames_in_mlf(monophone_mlf)
filenames_tri = filenames_in_mlf(triphone_mlf)
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