make sure all the phones in stimmen transcription can be treated correctly.
This commit is contained in:
yemaozi88
parent
ab3887c6ca
commit
da0242b0e1
Binary file not shown.
Binary file not shown.
@ -51,6 +51,7 @@
|
||||
<Compile Include="fame_hmm.py" />
|
||||
<Compile Include="phoneset\fame_asr.py" />
|
||||
<Compile Include="phoneset\fame_ipa.py" />
|
||||
<Compile Include="stimmen_functions.py" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<Content Include="config.ini" />
|
||||
|
||||
@ -20,12 +20,12 @@ from forced_alignment import convert_phone_set
|
||||
#import acoustic_model_functions as am_func
|
||||
import convert_xsampa2ipa
|
||||
import novoapi_functions
|
||||
import stimmen_functions
|
||||
sys.path.append(default.accent_classification_dir)
|
||||
import output_confusion_matrix
|
||||
|
||||
## procedure
|
||||
forced_alignment_novo70 = True
|
||||
balance_sample_numbers = False
|
||||
|
||||
|
||||
## ===== load novo phoneset =====
|
||||
@ -98,36 +98,7 @@ def search_phone_ipa(x, phone_list):
|
||||
|
||||
|
||||
## ===== load all transcriptions (df) =====
|
||||
df = pd.read_excel(stimmen_transcription_, 'original')
|
||||
|
||||
# mapping from ipa to xsampa
|
||||
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
|
||||
#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
|
||||
# ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
|
||||
# if not ipa_converted == ipa:
|
||||
# print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
|
||||
|
||||
ipas = []
|
||||
famehtks = []
|
||||
for xsampa in df['Self Xsampa']:
|
||||
if not isinstance(xsampa, float): # 'NaN'
|
||||
# typo?
|
||||
xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
|
||||
xsampa = xsampa.replace(';', ':')
|
||||
|
||||
ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
|
||||
ipa = ipa.replace('ː', ':')
|
||||
ipa = ipa.replace(' ', '')
|
||||
ipas.append(ipa)
|
||||
else:
|
||||
ipas.append('')
|
||||
|
||||
# extract interesting cols.
|
||||
df = pd.DataFrame({'filename': df['Filename'],
|
||||
'word': df['Word'],
|
||||
'xsampa': df['Self Xsampa'],
|
||||
'ipa': pd.Series(ipas)})
|
||||
|
||||
df = stimmen_functions.load_transcriptions()
|
||||
word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
|
||||
word_list = sorted(word_list)
|
||||
|
||||
@ -184,21 +155,6 @@ if forced_alignment_novo70:
|
||||
# samples in which all pronunciations are written in novo70.
|
||||
samples = df_.query("ipa in @pronunciation_ipa")
|
||||
|
||||
|
||||
## ===== balance sample numbers =====
|
||||
if balance_sample_numbers:
|
||||
c = Counter(samples['ipa'])
|
||||
sample_num_list = [c[key] for key in c.keys()]
|
||||
sample_num = np.min(sample_num_list)
|
||||
|
||||
samples_balanced = pd.DataFrame(index=[], columns=list(samples.keys()))
|
||||
for key in c.keys():
|
||||
samples_ = samples[samples['ipa'] == key]
|
||||
samples_balanced = samples_balanced.append(samples_.sample(sample_num), ignore_index = True)
|
||||
|
||||
samples = samples_balanced
|
||||
|
||||
|
||||
results = pd.DataFrame(index=[],
|
||||
columns=['filename', 'word', 'xsampa', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
|
||||
|
||||
|
||||
@ -2,63 +2,40 @@ import os
|
||||
# add path of the parent directory
|
||||
#os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
#cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
|
||||
|
||||
#htk_dir = r'C:\Aki\htk_fame'
|
||||
htk_dir = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk'
|
||||
|
||||
|
||||
#config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
|
||||
#mkhmmdefs_pl = os.path.join(cygwin_dir, 'src', 'acoustic_model', 'mkhmmdefs.pl')
|
||||
|
||||
#dbLexicon = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\lexicon.accdb
|
||||
#scriptBarbara = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\pronvars_barbara.perl
|
||||
#exeG2P = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\string2phon.exe
|
||||
|
||||
#[pyHTK]
|
||||
#configHVite = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\config.HVite
|
||||
#filePhoneList = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\phonelist_barbara.txt
|
||||
#AcousticModel = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\hmmdefs_16-2_barbara.compo
|
||||
|
||||
#dbLexicon = config['cLexicon']['dbLexicon']
|
||||
#scriptBarbara = config['cLexicon']['scriptBarbara']
|
||||
#exeG2P = config['cLexicon']['exeG2P']
|
||||
|
||||
#configHVite = config['pyHTK']['configHVite']
|
||||
#filePhoneList = config['pyHTK']['filePhoneList']
|
||||
#AcousticModel = config['pyHTK']['AcousticModel']
|
||||
|
||||
# repos
|
||||
repo_dir = r'C:\Users\Aki\source\repos'
|
||||
ipa_xsampa_converter_dir = os.path.join(repo_dir, 'ipa-xsama-converter')
|
||||
forced_alignment_module_dir = os.path.join(repo_dir, 'forced_alignment')
|
||||
accent_classification_dir = os.path.join(repo_dir, 'accent_classification', 'accent_classification')
|
||||
toolbox_dir = os.path.join(repo_dir, 'toolbox')
|
||||
|
||||
#htk_config_dir = r'c:\Users\A.Kunikoshi\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
|
||||
#config_hvite = os.path.join(htk_config_dir, 'config.HVite')
|
||||
#acoustic_model = os.path.join(htk_config_dir, 'hmmdefs.compo')
|
||||
#acoustic_model = r'c:\cygwin64\home\A.Kunikoshi\acoustic_model\model\barbara\hmm128-2\hmmdefs.compo'
|
||||
phonelist_txt = os.path.join(htk_dir, 'config', 'phonelist.txt')
|
||||
|
||||
WSL_dir = r'C:\OneDrive\WSL'
|
||||
#fame_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
|
||||
fame_dir = r'c:\OneDrive\Research\rug\_data\FAME'
|
||||
novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
|
||||
#novo_api_dir = r'c:\Python36-32\Lib\site-packages\novoapi'
|
||||
|
||||
fame_s5_dir = os.path.join(fame_dir, 's5')
|
||||
fame_corpus_dir = os.path.join(fame_dir, 'corpus')
|
||||
|
||||
experiments_dir = r'c:\OneDrive\Research\rug\experiments'
|
||||
# working directories
|
||||
rug_dir = r'c:\OneDrive\Research\rug'
|
||||
experiments_dir = os.path.join(rug_dir, 'experiments')
|
||||
htk_dir = os.path.join(experiments_dir, 'acoustic_model', 'fame', 'htk')
|
||||
stimmen_dir = os.path.join(experiments_dir, 'stimmen')
|
||||
stimmen_data_dir = os.path.join(stimmen_dir, 'data')
|
||||
|
||||
# data
|
||||
fame_dir = os.path.join(rug_dir, '_data', 'FAME')
|
||||
#fame_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
|
||||
# 44.1 kHz
|
||||
#stimmen_wav_dir = os.path.join(stimmen_dir, 'wav')
|
||||
# 16 kHz
|
||||
stimmen_wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
|
||||
stimmen_result_novoapi_dir = os.path.join(stimmen_dir, 'result', 'novoapi')
|
||||
|
||||
stimmen_transcription_xlsx = os.path.join(stimmen_data_dir, 'Frisian Variants Picture Task Stimmen.xlsx')
|
||||
stimmen_transcription_xlsx = os.path.join(stimmen_dir, 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
|
||||
phonelist_friesian_txt = os.path.join(experiments_dir, 'friesian', 'acoustic_model', 'config', 'phonelist_friesian.txt')
|
||||
|
||||
novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
|
||||
#novo_api_dir = r'c:\Python36-32\Lib\site-packages\novoapi'
|
||||
novo70_phoneset = os.path.join(novo_api_dir, 'asr', 'phoneset', 'nl', 'novo70.phoneset')
|
||||
|
||||
|
||||
|
||||
#phonelist_txt = os.path.join(htk_dir, 'config', 'phonelist.txt')
|
||||
#fame_s5_dir = os.path.join(fame_dir, 's5')
|
||||
#fame_corpus_dir = os.path.join(fame_dir, 'corpus')
|
||||
#stimmen_result_novoapi_dir = os.path.join(stimmen_dir, 'result', 'novoapi')
|
||||
# novoapi_functions
|
||||
|
||||
|
||||
|
||||
@ -341,3 +341,14 @@ def fix_single_quote(lexicon_file):
|
||||
|
||||
def word2htk(word):
|
||||
return ''.join([fame_asr.translation_key_word2htk.get(i, i) for i in word])
|
||||
|
||||
|
||||
def ipa2htk(ipa):
|
||||
curr_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
|
||||
|
||||
ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
|
||||
ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
|
||||
asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
|
||||
htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
|
||||
return ''.join(htk_splitted)
|
||||
@ -27,7 +27,7 @@ extract_features = 0
|
||||
flat_start = 0
|
||||
train_model_without_sp = 0
|
||||
add_sp = 0
|
||||
train_model_with_sp = 0
|
||||
train_model_with_sp = 1
|
||||
|
||||
|
||||
|
||||
@ -321,7 +321,8 @@ if add_sp:
|
||||
## ======================= train model with short pause =======================
|
||||
if train_model_with_sp:
|
||||
print('==== train model with sp ====')
|
||||
for niter in range(niter_max+1, niter_max*2+1):
|
||||
#for niter in range(niter_max+1, niter_max*2+1):
|
||||
for niter in range(20, 50):
|
||||
timer_start = time.time()
|
||||
hmm_n = 'iter' + str(niter)
|
||||
hmm_n_pre = 'iter' + str(niter-1)
|
||||
|
||||
@ -69,6 +69,10 @@ else:
|
||||
translation_key_ipa2asr['ə:'] = 'ə'
|
||||
translation_key_ipa2asr['r.'] = 'r'
|
||||
translation_key_ipa2asr['r:'] = 'r'
|
||||
# added for stimmen.
|
||||
translation_key_ipa2asr['ɪ:'] = 'ɪ:'
|
||||
translation_key_ipa2asr['y:'] = 'y'
|
||||
|
||||
np.save(os.path.join('phoneset', 'fame_ipa2asr.npy'), translation_key_ipa2asr)
|
||||
|
||||
|
||||
|
||||
@ -1,84 +1,145 @@
|
||||
import os
|
||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
import sys
|
||||
import csv
|
||||
import subprocess
|
||||
from collections import Counter
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
#import csv
|
||||
#import subprocess
|
||||
#from collections import Counter
|
||||
#import re
|
||||
import shutil
|
||||
import glob
|
||||
|
||||
#import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.metrics import confusion_matrix
|
||||
#import matplotlib.pyplot as plt
|
||||
#from sklearn.metrics import confusion_matrix
|
||||
|
||||
import acoustic_model_functions as am_func
|
||||
import convert_xsampa2ipa
|
||||
#import acoustic_model_functions as am_func
|
||||
#import convert_xsampa2ipa
|
||||
import defaultfiles as default
|
||||
|
||||
from forced_alignment import pyhtk
|
||||
#from forced_alignment import pyhtk
|
||||
#sys.path.append(default.forced_alignment_module_dir)
|
||||
#from forced_alignment import convert_phone_set
|
||||
#import acoustic_model_functions as am_func
|
||||
import convert_xsampa2ipa
|
||||
import stimmen_functions
|
||||
import fame_functions
|
||||
import convert_phoneset
|
||||
from phoneset import fame_ipa, fame_asr
|
||||
sys.path.append(default.toolbox_dir)
|
||||
import file_handling as fh
|
||||
from htk import pyhtk
|
||||
|
||||
|
||||
## ======================= user define =======================
|
||||
excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
|
||||
data_dir = os.path.join(default.experiments_dir, 'stimmen', 'data')
|
||||
#excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
|
||||
#data_dir = os.path.join(default.experiments_dir, 'stimmen', 'data')
|
||||
|
||||
wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
|
||||
#wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
|
||||
|
||||
acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
|
||||
htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
|
||||
fa_dir = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
|
||||
result_dir = os.path.join(default.experiments_dir, 'stimmen', 'result')
|
||||
#acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
|
||||
#htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
|
||||
#fa_dir = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
|
||||
#result_dir = os.path.join(default.experiments_dir, 'stimmen', 'result')
|
||||
|
||||
kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme')
|
||||
kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
|
||||
lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
|
||||
#kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme')
|
||||
#kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
|
||||
#lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
|
||||
|
||||
#lex_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
|
||||
#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
|
||||
|
||||
|
||||
# procedure
|
||||
make_htk_dict_files = 0
|
||||
do_forced_alignment_htk = 0
|
||||
eval_forced_alignment_htk = 0
|
||||
make_kaldi_data_files = 0
|
||||
make_kaldi_lexicon_txt = 0
|
||||
load_forced_alignment_kaldi = 1
|
||||
eval_forced_alignment_kaldi = 1
|
||||
## procedure
|
||||
#make_htk_dict_files = 0
|
||||
#do_forced_alignment_htk = 0
|
||||
#eval_forced_alignment_htk = 0
|
||||
#make_kaldi_data_files = 0
|
||||
#make_kaldi_lexicon_txt = 0
|
||||
#load_forced_alignment_kaldi = 1
|
||||
#eval_forced_alignment_kaldi = 1
|
||||
|
||||
|
||||
|
||||
|
||||
## ======================= add paths =======================
|
||||
sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
|
||||
from forced_alignment import convert_phone_set
|
||||
from forced_alignment import pyhtk
|
||||
### ======================= add paths =======================
|
||||
#sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
|
||||
#from forced_alignment import convert_phone_set
|
||||
#from forced_alignment import pyhtk
|
||||
|
||||
sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
|
||||
from evaluation import plot_confusion_matrix
|
||||
#sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
|
||||
#from evaluation import plot_confusion_matrix
|
||||
|
||||
config_dir = os.path.join(default.htk_dir, 'config')
|
||||
model_dir = os.path.join(default.htk_dir, 'model')
|
||||
lattice_file = os.path.join(config_dir, 'stimmen.ltc')
|
||||
#pyhtk.create_word_lattice_file(
|
||||
# os.path.join(config_dir, 'stimmen.net'),
|
||||
# lattice_file)
|
||||
hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp')
|
||||
|
||||
## ======================= make test data ======================
|
||||
# copy wav files which is in the stimmen data.
|
||||
stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
|
||||
fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
|
||||
|
||||
df = stimmen_functions.load_transcriptions()
|
||||
word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
|
||||
word_list = sorted(word_list)
|
||||
|
||||
#for index, row in df.iterrows():
|
||||
# filename = row['filename']
|
||||
# if isinstance(filename, str):
|
||||
# wav_file = os.path.join(default.stimmen_wav_dir, filename)
|
||||
# if os.path.exists(wav_file):
|
||||
# shutil.copy(wav_file, os.path.join(stimmen_test_dir, filename))
|
||||
# pyhtk.create_label_file(
|
||||
# row['word'],
|
||||
# os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
|
||||
|
||||
|
||||
## ======================= convert phones ======================
|
||||
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
|
||||
# after manually removed files which does not contain clear sound,
|
||||
# update df as df_test.
|
||||
#wav_file_list = glob.glob(os.path.join(stimmen_test_dir, '*.wav'))
|
||||
#df_test = pd.DataFrame(index=[], columns=list(df.keys()))
|
||||
#for wav_file in wav_file_list:
|
||||
# filename = os.path.basename(wav_file)
|
||||
# df_ = df[df['filename'].str.match(filename)]
|
||||
# df_test = pd.concat([df_test, df_])
|
||||
|
||||
xls = pd.ExcelFile(excel_file)
|
||||
|
||||
## check conversion
|
||||
#df = pd.read_excel(xls, 'frequency')
|
||||
#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
|
||||
# #ipa_converted = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, xsampa_)
|
||||
# ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
|
||||
# if not ipa_converted == ipa:
|
||||
# print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
|
||||
#output = pyhtk.recognition(
|
||||
# os.path.join(default.htk_dir, 'config', 'config.rec',
|
||||
# lattice_file,
|
||||
# os.path.join(model_dir, 'hmm1', 'iter13'),
|
||||
# dictionary_file,
|
||||
# os.path.join(config_dir, 'phonelist.txt'),
|
||||
# hvite_scp)
|
||||
|
||||
|
||||
## check phones included in FAME!
|
||||
# the phones used in the lexicon.
|
||||
#phonelist = am_func.get_phonelist(lex_asr)
|
||||
## check phones included in stimmen but not in FAME!
|
||||
splitted_ipas = [' '.join(
|
||||
convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones))
|
||||
for ipa in df['ipa']]
|
||||
stimmen_phones = set(' '.join(splitted_ipas))
|
||||
stimmen_phones = list(stimmen_phones)
|
||||
#stimmen_phones = list(set(fame_asr.phone_reduction(list(stimmen_phones))))
|
||||
#fame_phones = fame_asr.phoneset_short
|
||||
fame_phones = fame_ipa.phoneset
|
||||
stimmen_phones.sort()
|
||||
fame_phones.sort()
|
||||
print('phones which are used in stimmen transcription but not in FAME corpus are:\n{}'.format(
|
||||
set(stimmen_phones) - set(fame_phones)
|
||||
))
|
||||
for ipa in df['ipa']:
|
||||
ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
|
||||
if ':' in ipa_splitted:
|
||||
print(ipa_splitted)
|
||||
|
||||
htk = [fame_functions.ipa2htk(ipa) for ipa in df['ipa']]
|
||||
|
||||
ipa = 'e:χ'
|
||||
fame_functions.ipa2htk(ipa)
|
||||
|
||||
# the lines which include a specific phone.
|
||||
#lines = am_func.find_phone(lex_asr, 'x')
|
||||
|
||||
|
||||
# Filename, Word, Self Xsampa
|
||||
|
||||
@ -52,7 +52,7 @@ p = argparse.ArgumentParser()
|
||||
#p.add_argument("--user", default=None)
|
||||
#p.add_argument("--password", default=None)
|
||||
p.add_argument("--user", default='martijn.wieling')
|
||||
p.add_argument("--password", default='fa0Thaic')
|
||||
p.add_argument("--password", default='xxxxxx')
|
||||
args = p.parse_args()
|
||||
|
||||
#wav_file = 'c:\\OneDrive\\WSL\\test\\onetwothree.wav'
|
||||
|
||||
@ -173,7 +173,7 @@ def forced_alignment(wav_file, word, pronunciation_ipa):
|
||||
# username / password cannot be passed as artuments...
|
||||
p = argparse.ArgumentParser()
|
||||
p.add_argument("--user", default='martijn.wieling')
|
||||
p.add_argument("--password", default='fa0Thaic')
|
||||
p.add_argument("--password", default='xxxxxx')
|
||||
args = p.parse_args()
|
||||
|
||||
rec = session.Recognizer(grammar_version="1.0", lang="nl", snodeid=101, user=args.user, password=args.password, keepopen=True) # , modeldir=modeldir)
|
||||
|
||||
@ -73,12 +73,14 @@ reduction_key = {
|
||||
# already removed beforehand in phoneset. Just to be sure.
|
||||
phones_to_be_removed = ['ú', 's:', 'ɔ̈:']
|
||||
|
||||
phoneset_short = [reduction_key.get(i, i) for i in phoneset
|
||||
def phone_reduction(phones):
|
||||
return [reduction_key.get(i, i) for i in phones
|
||||
if not i in phones_to_be_removed]
|
||||
phoneset_short = list(set(phoneset_short))
|
||||
phoneset_short = list(set(phone_reduction(phoneset)))
|
||||
phoneset_short.sort()
|
||||
|
||||
|
||||
|
||||
## translation_key to htk format (ascii).
|
||||
# phones which gives UnicodeEncodeError when phone.encode("ascii")
|
||||
# are replaced with other characters.
|
||||
|
||||
@ -5,6 +5,7 @@ phoneset = [
|
||||
'i̯',
|
||||
'i̯ⁿ',
|
||||
'y',
|
||||
'y:', # not included in lex.ipa, but in stimmen.
|
||||
'i',
|
||||
'i.',
|
||||
'iⁿ',
|
||||
@ -13,7 +14,7 @@ phoneset = [
|
||||
'ɪ',
|
||||
'ɪⁿ',
|
||||
'ɪ.',
|
||||
#'ɪ:', # not included in lex.ipa
|
||||
'ɪ:', # not included in lex.ipa, but in stimmen.
|
||||
'ɪ:ⁿ',
|
||||
'e',
|
||||
'e:',
|
||||
@ -100,6 +101,36 @@ phoneset = [
|
||||
'l'
|
||||
]
|
||||
|
||||
## reduce the number of phones.
|
||||
# the phones which are used in stimmen transcription but not in FAME corpus.
|
||||
# replacements are based on the advice from Jelske Dijkstra on 2018/06/21.
|
||||
stimmen_replacement = {
|
||||
'æ': 'ɛ',
|
||||
'ø': 'ö', # or 'ö:'
|
||||
'ø:': 'ö:', # Aki added.
|
||||
'œ': 'ɔ̈', # or 'ɔ̈:'
|
||||
'œ:': 'ɔ̈:', # Aki added.
|
||||
'ɐ': 'a', # or 'a:'
|
||||
'ɐ:': 'a:', # Aki added.
|
||||
'ɑ': 'a', # or 'a:'
|
||||
'ɑ:': 'a:', # Aki added
|
||||
'ɒ': 'ɔ', # or 'ɔ:'
|
||||
'ɒ:': 'ɔ:', # Aki added.
|
||||
'ɾ': 'r',
|
||||
'ʁ': 'r',
|
||||
'ʊ': 'u',
|
||||
'χ': 'x',
|
||||
|
||||
# aki guessed.
|
||||
'ʀ': 'r',
|
||||
'ɹ': 'r',
|
||||
'w': 'ö'
|
||||
}
|
||||
phoneset.extend(list(stimmen_replacement.keys()))
|
||||
|
||||
def phone_reduction(phones):
|
||||
return [stimmen_replacement.get(i, i) for i in phones]
|
||||
|
||||
|
||||
## the list of multi character phones.
|
||||
# for example, the length of 'i̯ⁿ' is 3, but in the codes it is treated as one letter.
|
||||
|
||||
Binary file not shown.
38
acoustic_model/stimmen_functions.py
Normal file
38
acoustic_model/stimmen_functions.py
Normal file
@ -0,0 +1,38 @@
|
||||
import os
|
||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
import pandas as pd
|
||||
|
||||
import convert_xsampa2ipa
|
||||
import defaultfiles as default
|
||||
|
||||
|
||||
def load_transcriptions():
|
||||
stimmen_transcription = pd.ExcelFile(default.stimmen_transcription_xlsx)
|
||||
df = pd.read_excel(stimmen_transcription, 'original')
|
||||
|
||||
# mapping from ipa to xsampa
|
||||
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
|
||||
#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
|
||||
# ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
|
||||
# if not ipa_converted == ipa:
|
||||
# print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
|
||||
|
||||
ipas = []
|
||||
for xsampa in df['Self Xsampa']:
|
||||
if not isinstance(xsampa, float): # 'NaN'
|
||||
# typo?
|
||||
xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t').replace(';', ':')
|
||||
|
||||
ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
|
||||
ipa = ipa.replace('ː', ':').replace(' ', '')
|
||||
ipas.append(ipa)
|
||||
else:
|
||||
ipas.append('')
|
||||
|
||||
df_ = pd.DataFrame({'filename': df['Filename'],
|
||||
'word': df['Word'],
|
||||
'xsampa': df['Self Xsampa'],
|
||||
'ipa': pd.Series(ipas)})
|
||||
df_ = df_[~df_['ipa'].str.contains('/')]
|
||||
return df_.dropna()
|
||||
Loading…
Reference in New Issue
Block a user