517 lines
19 KiB
Python
517 lines
19 KiB
Python
import os
|
||
import sys
|
||
import csv
|
||
import subprocess
|
||
import configparser
|
||
from collections import Counter
|
||
import re
|
||
|
||
import numpy as np
|
||
import pandas as pd
|
||
import matplotlib.pyplot as plt
|
||
from sklearn.metrics import confusion_matrix
|
||
|
||
|
||
## ======================= functions =======================
|
||
|
||
def read_fileFA(fileFA):
|
||
"""
|
||
read the result file of HTK forced alignment.
|
||
this function only works when input is one word.
|
||
"""
|
||
with open(fileFA, 'r') as f:
|
||
lines = f.read()
|
||
lines = lines.split('\n')
|
||
|
||
phones = []
|
||
for line in lines:
|
||
line_split = line.split()
|
||
if len(line_split) > 1:
|
||
phones.append(line_split[2])
|
||
|
||
return ' '.join(phones)
|
||
|
||
|
||
def make_dic(word, pronvar_, fileDic, output_type):
|
||
"""
|
||
make dict files which can be used for HTK.
|
||
param word: target word.
|
||
param pronvar_: pronunciation variant. nx2 (WORD /t pronunciation) ndarray.
|
||
param fileDic: output dic file.
|
||
param output_type: 0:full, 1:statistics, 2:frequency <2% entries are removed. 3:top 3.
|
||
"""
|
||
#assert(output_type < 4 and output_type >= 0, 'output_type should be an integer between 0 and 3.')
|
||
|
||
if output_type == 0: # full
|
||
pronvar = np.unique(pronvar_)
|
||
|
||
with open(fileDic, 'w') as f:
|
||
for pvar in pronvar:
|
||
f.write('{0}\t{1}\n'.format(WORD, pvar))
|
||
else:
|
||
c = Counter(pronvar_)
|
||
total_num = sum(c.values())
|
||
with open(fileDic, 'w') as f:
|
||
if output_type == 3:
|
||
for key, value in c.most_common(3):
|
||
f.write('{0}\t{1}\n'.format(WORD, key))
|
||
else:
|
||
for key, value in c.items():
|
||
percentage = value/total_num*100
|
||
|
||
if output_type == 1: # all
|
||
f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, percentage, WORD, key))
|
||
elif output_type == 2: # less than 2 percent
|
||
if percentage < 2:
|
||
f.write('{0}\t{1}\n'.format(WORD, key))
|
||
|
||
|
||
## ======================= user define =======================
|
||
curr_dir = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
|
||
config_ini = curr_dir + '\\config.ini'
|
||
forced_alignment_module = r'C:\Users\Aki\source\repos\forced_alignment'
|
||
forced_alignment_module_old = r'C:\OneDrive\Research\rug\code\forced_alignment\forced_alignment'
|
||
ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
|
||
accent_classification_dir = r'C:\Users\Aki\source\repos\accent_classification\accent_classification'
|
||
|
||
|
||
experiments_dir = r'C:\OneDrive\Research\rug\experiments'
|
||
data_dir = experiments_dir + '\\stimmen\\data'
|
||
csvfile = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
|
||
|
||
cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
|
||
|
||
# procedure
|
||
convert_phones = 0
|
||
make_dic_files = 0
|
||
make_dic_files_short = 0
|
||
do_forced_alignment_htk = 0
|
||
make_kaldi_data_files = 0
|
||
make_kaldi_lexicon_txt = 0
|
||
load_forced_alignment_kaldi = 1
|
||
eval_forced_alignment = 0
|
||
|
||
|
||
|
||
## ======================= add paths =======================
|
||
|
||
sys.path.append(forced_alignment_module)
|
||
from forced_alignment import convert_phone_set
|
||
|
||
# for interactive window
|
||
sys.path.append(curr_dir)
|
||
import convert_xsampa2ipa
|
||
import acoustic_model_functions as am_func
|
||
|
||
# for forced-alignment
|
||
sys.path.append(forced_alignment_module_old)
|
||
import pyHTK
|
||
|
||
# to output confusion matrix
|
||
sys.path.append(accent_classification_dir)
|
||
from output_confusion_matrix import plot_confusion_matrix
|
||
|
||
|
||
## ======================= load variables =======================
|
||
config = configparser.ConfigParser()
|
||
config.sections()
|
||
config.read(config_ini)
|
||
|
||
FAME_dir = config['Settings']['FAME_dir']
|
||
|
||
lex_asr = FAME_dir + '\\lexicon\\lex.asr'
|
||
lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
|
||
|
||
|
||
## ======================= convert phones ======================
|
||
if convert_phones:
|
||
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
|
||
|
||
## check phones included in FAME!
|
||
# the phones used in the lexicon.
|
||
#phonelist = am_func.get_phonelist(lex_htk)
|
||
|
||
# the lines which include a specific phone.
|
||
#lines = am_func.find_phone(lex_asr, 'x')
|
||
|
||
with open(csvfile, encoding="utf-8") as fin:
|
||
lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
|
||
next(lines, None) # skip the headers
|
||
|
||
filenames = []
|
||
words = []
|
||
pronunciations = []
|
||
for line in lines:
|
||
if line[1] is not '' and len(line) > 5:
|
||
filenames.append(line[0])
|
||
words.append(line[1])
|
||
pron_xsampa = line[3]
|
||
pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
|
||
pron_ipa = pron_ipa.replace('ː', ':')
|
||
pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
|
||
|
||
# adjust to phones used in the acoustic model.
|
||
pron_famehtk = pron_famehtk.replace('sp', 'sil')
|
||
pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
|
||
pron_famehtk = pron_famehtk.replace('w :', 'wh')
|
||
pron_famehtk = pron_famehtk.replace('e :', 'eh')
|
||
pron_famehtk = pron_famehtk.replace('eh :', 'eh')
|
||
pron_famehtk = pron_famehtk.replace('ih :', 'ih')
|
||
|
||
#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
|
||
#pron = []
|
||
#for phoneme in pron_famehtk.split(' '):
|
||
# pron.append(translation_key.get(phoneme, phoneme))
|
||
#pronunciations.append(' '.join(pron_famehtk))
|
||
pronunciations.append(pron_famehtk)
|
||
|
||
# check if all phones are in the phonelist of the acoustic model.
|
||
#phonelist = ' '.join(pronunciations)
|
||
#np.unique(phonelist.split(' '))
|
||
#phonelist.find(':')
|
||
|
||
filenames = np.array(filenames)
|
||
words = np.array(words)
|
||
pronunciations = np.array(pronunciations)
|
||
|
||
del line, lines
|
||
del pron_xsampa, pron_ipa, pron_famehtk
|
||
|
||
np.save(data_dir + '\\filenames.npy', filenames)
|
||
np.save(data_dir + '\\words.npy', words)
|
||
np.save(data_dir + '\\pronunciations.npy', pronunciations)
|
||
else:
|
||
filenames = np.load(data_dir + '\\filenames.npy')
|
||
words = np.load(data_dir + '\\words.npy')
|
||
|
||
pronunciations = np.load(data_dir + '\\pronunciations.npy')
|
||
word_list = np.unique(words)
|
||
|
||
|
||
## ======================= make dict files used for HTK. ======================
|
||
if make_dic_files:
|
||
output_type = 2
|
||
output_dir = experiments_dir + r'\stimmen\dic_short'
|
||
|
||
for word in word_list:
|
||
WORD = word.upper()
|
||
fileDic = output_dir + '\\' + word + '.dic'
|
||
|
||
# pronunciation variant of the target word.
|
||
pronvar_ = pronunciations[words == word]
|
||
# remove ''
|
||
pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
|
||
|
||
# make dic file.
|
||
make_dic(word, pronvar_, fileDic, output_type)
|
||
|
||
|
||
## ======================= forced alignment using HTK =======================
|
||
if do_forced_alignment_htk:
|
||
configHVite = cygwin_dir + r'\config\config.HVite'
|
||
filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
|
||
wav_dir = experiments_dir + r'\stimmen\wav'
|
||
|
||
#hmm_num = 128
|
||
for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
|
||
hmm_num_str = str(hmm_num)
|
||
AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-2\hmmdefs'
|
||
|
||
predictions = []
|
||
file_num_max = len(filenames)
|
||
for i in range(0, file_num_max):
|
||
#for i in range(500, 502):
|
||
print('=== {0}/{1} ==='.format(i, file_num_max))
|
||
filename = filenames[i]
|
||
fileWav = wav_dir + '\\' + filename
|
||
|
||
if os.path.exists(fileWav):
|
||
word = words[i]
|
||
WORD = word.upper()
|
||
|
||
# make label file.
|
||
fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab')
|
||
with open(fileLab, 'w') as f:
|
||
lines = f.write(WORD)
|
||
|
||
fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
|
||
fileFA = experiments_dir + r'\stimmen\FA' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
|
||
|
||
pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
|
||
prediction = read_fileFA(fileFA)
|
||
predictions.append(prediction)
|
||
|
||
os.remove(fileLab)
|
||
print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
|
||
else:
|
||
predictions.append('')
|
||
print('!!!!! file not found.')
|
||
|
||
predictions = np.array(predictions)
|
||
match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
|
||
np.save(data_dir + '\\match_hmm' + hmm_num_str + '.npy', match)
|
||
|
||
|
||
## ======================= make files which is used for forced alignment by Kaldi =======================
|
||
if make_kaldi_data_files:
|
||
wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
|
||
kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
|
||
kaldi_data_dir = os.path.join(kaldi_work_dir, 'data', 'alignme')
|
||
kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
|
||
htk_dict_dir = os.path.join(experiments_dir, 'stimmen', 'dic_top3')
|
||
|
||
wav_scp = os.path.join(kaldi_data_dir, 'wav.scp')
|
||
text_file = os.path.join(kaldi_data_dir, 'text')
|
||
utt2spk = os.path.join(kaldi_data_dir, 'utt2spk')
|
||
|
||
lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
|
||
|
||
predictions = []
|
||
file_num_max = len(filenames)
|
||
|
||
# remove previous files.
|
||
if os.path.exists(wav_scp):
|
||
os.remove(wav_scp)
|
||
if os.path.exists(text_file):
|
||
os.remove(text_file)
|
||
if os.path.exists(utt2spk):
|
||
os.remove(utt2spk)
|
||
|
||
f_wav_scp = open(wav_scp, 'a', encoding="utf-8", newline='\n')
|
||
f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
|
||
f_utt2spk = open(utt2spk, 'a', encoding="utf-8", newline='\n')
|
||
|
||
# make wav.scp, text, and utt2spk files.
|
||
for i in range(0, file_num_max):
|
||
#for i in range(400, 410):
|
||
print('=== {0}/{1} ==='.format(i+1, file_num_max))
|
||
filename = filenames[i]
|
||
wav_file = wav_dir + '\\' + filename
|
||
|
||
if os.path.exists(wav_file):
|
||
speaker_id = 'speaker_' + str(i).zfill(4)
|
||
utterance_id = filename.replace('.wav', '')
|
||
utterance_id = utterance_id.replace(' ', '_')
|
||
utterance_id = speaker_id + '-' + utterance_id
|
||
|
||
# wav.scp file
|
||
wav_file_unix = wav_file.replace('\\', '/')
|
||
wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/')
|
||
|
||
f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
|
||
|
||
# text file
|
||
word = words[i].lower()
|
||
f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
|
||
|
||
# utt2spk
|
||
f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
|
||
|
||
f_wav_scp.close()
|
||
f_text_file.close()
|
||
f_utt2spk.close()
|
||
|
||
|
||
## ======================= make lexicon txt which is used by Kaldi =======================
|
||
if make_kaldi_lexicon_txt:
|
||
kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
|
||
kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
|
||
lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
|
||
option_num = 5
|
||
|
||
# remove previous file.
|
||
if os.path.exists(lexicon_txt):
|
||
os.remove(lexicon_txt)
|
||
|
||
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
|
||
with open(csvfile, encoding="utf-8") as fin:
|
||
lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
|
||
next(lines, None) # skip the headers
|
||
|
||
filenames = []
|
||
words = []
|
||
pronunciations = []
|
||
p = []
|
||
for line in lines:
|
||
if line[1] is not '' and len(line) > 5:
|
||
filenames.append(line[0])
|
||
words.append(line[1])
|
||
pron_xsampa = line[3]
|
||
pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
|
||
pron_ipa = pron_ipa.replace('ː', ':')
|
||
|
||
# adjust to phones used in the acoustic model.
|
||
pronunciations.append(pron_ipa)
|
||
|
||
# check if all phones are in the phonelist of the acoustic model.
|
||
#'y', 'b', 'ɾ', 'u', 'ɔ:', 'ø', 't', 'œ', 'n', 'ɒ', 'ɐ', 'f', 'o', 'k', 'x', 'ɡ', 'v', 's', 'ɛ:', 'ɪ:', 'ɑ', 'ɛ', 'a', 'd', 'z', 'ɪ', 'ɔ', 'l', 'i:', 'm', 'p', 'a:', 'i', 'e', 'j', 'o:', 'ʁ', 'h', ':', 'e:', 'ə', 'æ', 'χ', 'w', 'r', 'ə:', 'sp', 'ʊ', 'u:', 'ŋ'
|
||
|
||
filenames = np.array(filenames)
|
||
words = np.array(words)
|
||
wordlist = np.unique(words)
|
||
pronunciations = np.array(pronunciations)
|
||
|
||
# output lexicon.txt
|
||
#f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
|
||
pronvar_list_all = []
|
||
for word in word_list:
|
||
|
||
# pronunciation variant of the target word.
|
||
pronvar_ = pronunciations[words == word]
|
||
# remove ''
|
||
pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
|
||
|
||
c = Counter(pronvar_)
|
||
total_num = sum(c.values())
|
||
|
||
for key, value in c.most_common(option_num):
|
||
#print('{0}\t{1}\t{2}\t{3}'.format(word, key, value, total_num))
|
||
key = key.replace('æ', 'ɛ')
|
||
key = key.replace('ɐ', 'a')
|
||
key = key.replace('ɑ', 'a')
|
||
key = key.replace('ɾ', 'r')
|
||
key = key.replace('ʁ', 'r')
|
||
key = key.replace('ʊ', 'u')
|
||
key = key.replace('χ', 'x')
|
||
#print('-->{0}\t{1}\t{2}\t{3}\n'.format(word, key, value, total_num))
|
||
|
||
# make possible pronounciation variant list.
|
||
pronvar_list = [key]
|
||
while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
|
||
pronvar_list_ = []
|
||
for p in pronvar_list:
|
||
if 'ø:' in p:
|
||
pronvar_list_.append(p.replace('ø:', 'ö'))
|
||
pronvar_list_.append(p.replace('ø:', 'ö:'))
|
||
if 'œ' in p:
|
||
pronvar_list_.append(p.replace('œ', 'ɔ̈'))
|
||
pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
|
||
if 'ɒ' in p:
|
||
pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
|
||
pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
|
||
pronvar_list = np.unique(pronvar_list_)
|
||
|
||
for pronvar_ in pronvar_list:
|
||
split_ipa = convert_phone_set.split_ipa_fame(pronvar_)
|
||
pronvar_out = ' '.join(split_ipa)
|
||
pronvar_list_all.append([word, pronvar_out])
|
||
|
||
# output
|
||
pronvar_list_all = np.array(pronvar_list_all)
|
||
pronvar_list_all = np.unique(pronvar_list_all, axis=0)
|
||
#f_lexicon_txt.write('<UNK>\tSPN\n')
|
||
#for line in pronvar_list_all:
|
||
# f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
|
||
|
||
#f_lexicon_txt.close()
|
||
|
||
## ======================= load kaldi forced alignment result =======================
|
||
if load_forced_alignment_kaldi:
|
||
kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
|
||
phones_txt = kaldi_work_dir + '\\data\\lang\\phones.txt'
|
||
merged_alignment_txt = kaldi_work_dir + '\\exp\\tri1_alignme\\merged_alignment.txt'
|
||
|
||
filenames = np.load(data_dir + '\\filenames.npy')
|
||
words = np.load(data_dir + '\\words.npy')
|
||
pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
|
||
pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
|
||
word_list = np.unique(words)
|
||
|
||
# load the mapping between phones and ids.
|
||
with open(phones_txt, 'r', encoding="utf-8") as f:
|
||
mappings = f.read().split('\n')
|
||
|
||
phones = []
|
||
phone_ids = []
|
||
for m in mappings:
|
||
m = m.split(' ')
|
||
if len(m) > 1:
|
||
phones.append(m[0])
|
||
phone_ids.append(int(m[1]))
|
||
|
||
with open(merged_alignment_txt, 'r') as f:
|
||
lines = f.read()
|
||
lines = lines.split('\n')
|
||
|
||
fa_filenames = []
|
||
fa_pronunciations = []
|
||
filename_ = ''
|
||
pron = []
|
||
for line in lines:
|
||
line = line.split(' ')
|
||
if len(line) == 5:
|
||
filename = line[0]
|
||
if filename == filename_:
|
||
phone_id = int(line[4])
|
||
#if not phone_id == 1:
|
||
phone = phones[phone_ids.index(phone_id)]
|
||
pron_ = re.sub(r'_[A-Z]', '', phone)
|
||
if not pron_ == 'SIL':
|
||
pron.append(pron_)
|
||
else:
|
||
fa_filenames.append(re.sub(r'speaker_[0-9]{4}-', '', filename))
|
||
fa_pronunciations.append(' '.join(pron))
|
||
pron = []
|
||
|
||
filename_ = filename
|
||
|
||
# correct or not.
|
||
for filename, fa_pronunciation in zip(fa_filenames, fa_pronunciations):
|
||
|
||
|
||
|
||
## ======================= evaluate the result of forced alignment =======================
|
||
if eval_forced_alignment:
|
||
|
||
match_num = []
|
||
for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
|
||
#hmm_num = 256
|
||
hmm_num_str = str(hmm_num)
|
||
match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
|
||
|
||
# use dic_short?
|
||
if 1:
|
||
pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
|
||
for word in word_list:
|
||
fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
|
||
pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
|
||
|
||
# see only words which appears in top 3.
|
||
match_short = []
|
||
for line in match:
|
||
word = line[0]
|
||
WORD = word.upper()
|
||
pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
|
||
|
||
if line[1] in pronvar:
|
||
match_short.append(line)
|
||
|
||
match_short = np.array(match_short)
|
||
match = np.copy(match_short)
|
||
|
||
# number of match
|
||
total_match = sum(match[:, 1] == match[:, 2])
|
||
print("{}: {}/{}".format(hmm_num_str, total_match, match.shape[0]))
|
||
match_num.append([hmm_num, total_match, match.shape[0]])
|
||
|
||
|
||
# number of mixtures vs accuracy
|
||
match_num = np.array(match_num)
|
||
plt.xscale("log")
|
||
plt.plot(match_num[:, 0], match_num[:, 1]/match_num[0, 2], 'o-')
|
||
plt.xlabel('number of mixtures', fontsize=14, fontweight='bold')
|
||
plt.ylabel('accuracy', fontsize=14, fontweight='bold')
|
||
plt.show()
|
||
|
||
# confusion matrix
|
||
#dir_out = r'C:\OneDrive\Research\rug\experiments\stimmen\result'
|
||
#word_list = np.unique(match[:, 0])
|
||
|
||
#for word in word_list:
|
||
# match_ = match[match[:, 0] == word, :]
|
||
# cm = confusion_matrix(match_[:, 1], match_[:, 2])
|
||
# pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
|
||
|
||
# plt.figure()
|
||
# plot_confusion_matrix(cm, classes=pronvar, normalize=True)
|
||
# plt.savefig(dir_out + '\\cm_' + word + '.png') |