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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 shutil
import glob
import numpy as np
import pandas as pd
from collections import Counter
#import matplotlib.pyplot as plt
#from sklearn.metrics import confusion_matrix
#import acoustic_model_functions as am_func
#import convert_xsampa2ipa
import defaultfiles as default
#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')
#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')
#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_dic_file = 0
make_HTK_files = 0
extract_features = 0
#make_htk_dict_files = 0
#do_forced_alignment_htk = 0
#eval_forced_alignment_htk = 0
make_kaldi_files = 0
#make_kaldi_lexicon_txt = 0
#load_forced_alignment_kaldi = 1
#eval_forced_alignment_kaldi = 1
#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
## HTK related files.
config_dir = os.path.join(default.htk_dir, 'config')
model_dir = os.path.join(default.htk_dir, 'model')
feature_dir = os.path.join(default.htk_dir, 'mfc', 'stimmen')
config_hcopy = os.path.join(config_dir, 'config.HCopy')
# files to be made.
lattice_file = os.path.join(config_dir, 'stimmen.ltc')
phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
stimmen_dic = os.path.join(default.htk_dir, 'lexicon', 'stimmen_recognition.dic')
hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hcopy.scp')
hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hvite.scp')
hresult_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_result.scp')
## Kaldi related files.
kaldi_data_dir = os.path.join(default.kaldi_dir, 'data')
# files to be made.
wav_scp = os.path.join(kaldi_data_dir, 'test', 'wav.scp')
text_file = os.path.join(kaldi_data_dir, 'test', 'text')
utt2spk = os.path.join(kaldi_data_dir, 'test', 'utt2spk')
corpus_txt = os.path.join(kaldi_data_dir, 'local', 'corpus.txt')
lexicon_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'lexicon.txt')
nonsilence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'nonsilence_phones.txt')
silence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'silence_phones.txt')
optional_silence_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'optional_silence.txt')
## ======================= load test data ======================
stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
df = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
df = stimmen_functions.add_row_asr(df)
df = stimmen_functions.add_row_htk(df)
word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
word_list = sorted(word_list)
## ======================= make dic file to check pronunciation variants ======================
# dic file should be manually modified depends on the task - recognition / forced-alignemnt.
if make_dic_file:
# for HTK.
with open(stimmen_dic, mode='wb') as f:
for word in word_list:
df_ = df[df['word']==word]
pronunciations = list(np.unique(df_['htk']))
pronunciations_ = [word.upper() + ' sil ' + ' '.join(convert_phoneset.split_word(
htk, fame_asr.multi_character_phones_htk)) + ' sil'
for htk in pronunciations]
f.write(bytes('\n'.join(pronunciations_) + '\n', 'ascii'))
f.write(bytes('SILENCE sil\n', 'ascii'))
# for Kaldi.
fh.make_new_directory(os.path.join(kaldi_data_dir, 'local', 'dict'))
with open(lexicon_txt, mode='wb') as f:
f.write(bytes('!SIL sil\n', 'utf-8'))
f.write(bytes('<UNK> spn\n', 'utf-8'))
for word in word_list:
df_ = df[df['word']==word]
pronunciations = list(np.unique(df_['asr']))
pronunciations_ = [word.lower() + ' ' + ' '.join(convert_phoneset.split_word(
asr, fame_asr.multi_character_phones))
for asr in pronunciations]
f.write(bytes('\n'.join(pronunciations_) + '\n', 'utf-8'))
## ======================= test data for recognition ======================
# only target pronunciation variants.
df_rec = pd.DataFrame(index=[], columns=list(df.keys()))
for word in word_list:
variants = [htk.replace(' ', '')
for htk in stimmen_functions.load_pronunciations(word.upper(), stimmen_dic)]
df_ = df[df['word'] == word]
for index, row in df_.iterrows():
if row['htk'] in variants:
df_rec = df_rec.append(row, ignore_index=True)
## ======================= make files required for HTK ======================
if make_HTK_files:
# make a word lattice file.
pyhtk.create_word_lattice_file(
os.path.join(config_dir, 'stimmen.net'),
lattice_file)
# extract features.
with open(hcopy_scp, 'wb') as f:
filelist = [os.path.join(stimmen_test_dir, filename) + '\t'
+ os.path.join(feature_dir, os.path.basename(filename).replace('.wav', '.mfc'))
for filename in df['filename']]
f.write(bytes('\n'.join(filelist), 'ascii'))
pyhtk.wav2mfc(config_hcopy, hcopy_scp)
# make label files.
for index, row in df.iterrows():
filename = row['filename'].replace('.wav', '.lab')
label_file = os.path.join(feature_dir, filename)
with open(label_file, 'wb') as f:
label_string = 'SILENCE\n' + row['word'].upper() + '\nSILENCE\n'
f.write(bytes(label_string, 'ascii'))
## ======================= make files required for Kaldi =======================
if make_kaldi_files:
fh.make_new_directory(os.path.join(kaldi_data_dir, 'test'))
fh.make_new_directory(os.path.join(kaldi_data_dir, 'test', 'local'))
fh.make_new_directory(os.path.join(kaldi_data_dir, 'conf'))
# 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, row in df_rec.iterrows():
filename = row['filename']
print('=== {0}: {1} ==='.format(i, filename))
wav_file = os.path.join(stimmen_test_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
# output
f_wav_scp.write('{0} {1}\n'.format(
utterance_id,
wav_file.replace('c:/', '/mnt/c/').replace('\\', '/'))) # convert path to unix format.
f_text_file.write('{0}\t{1}\n'.format(utterance_id, df_rec['word'][i].lower()))
f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
f_wav_scp.close()
f_text_file.close()
f_utt2spk.close()
with open(corpus_txt, 'wb') as f:
f.write(bytes('\n'.join([word.lower() for word in word_list]) + '\n', 'utf-8'))
with open(nonsilence_phones_txt, 'wb') as f:
f.write(bytes('\n'.join(fame_asr.phoneset_short) + '\n', 'utf-8'))
with open(silence_phones_txt, 'wb') as f:
f.write(bytes('sil\nspn\n', 'utf-8'))
with open(optional_silence_txt, 'wb') as f:
f.write(bytes('sil\n', 'utf-8'))
with open(os.path.join(kaldi_data_dir, 'conf', 'decode.config'), 'wb') as f:
f.write(bytes('first_beam=10.0\n', 'utf-8'))
f.write(bytes('beam=13.0\n', 'utf-8'))
f.write(bytes('lattice_beam=6.0\n', 'utf-8'))
with open(os.path.join(kaldi_data_dir, 'conf', 'mfcc.conf'), 'wb') as f:
f.write(bytes('--use-energy=false', 'utf-8'))
## ======================= recognition ======================
listdir = glob.glob(os.path.join(feature_dir, '*.mfc'))
with open(hvite_scp, 'wb') as f:
f.write(bytes('\n'.join(listdir), 'ascii'))
with open(hresult_scp, 'wb') as f:
f.write(bytes('\n'.join(listdir).replace('.mfc', '.rec'), 'ascii'))
# calculate result
performance = np.zeros((1, 2))
for niter in range(50, 60):
output = pyhtk.recognition(
os.path.join(config_dir, 'config.rec'),
lattice_file,
os.path.join(default.htk_dir, 'model', 'hmm1', 'iter' + str(niter), 'hmmdefs'),
stimmen_dic, phonelist_txt, hvite_scp)
output = pyhtk.calc_recognition_performance(
stimmen_dic, hresult_scp)
per_sentence, per_word = pyhtk.load_recognition_output_all(output)
performance_ = np.array([niter, per_sentence['accuracy']]).reshape(1, 2)
performance = np.r_[performance, performance_]
print('{0}: {1}[%]'.format(niter, per_sentence['accuracy']))
#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:
#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
for hmm_num in [256, 512, 1024]:
hmm_num_str = str(hmm_num)
acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
predictions = pd.DataFrame({'filename': [''],
'word': [''],
'xsampa': [''],
'ipa': [''],
'famehtk': [''],
'prediction': ['']})
for i, filename in enumerate(df['filename']):
print('=== {0}/{1} ==='.format(i, len(df)))
if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
wav_file = os.path.join(wav_dir, filename)
if os.path.exists(wav_file):
word = df['word'][i]
WORD = word.upper()
fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
#if not os.path.exists(fa_file):
# make label file.
label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
with open(label_file, 'w') as f:
lines = f.write(WORD)
htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite,
default.phonelist, acoustic_model)
os.remove(label_file)
prediction = am_func.read_fileFA(fa_file)
print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
else:
prediction = ''
print('!!!!! file not found.')
line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i)
predictions = predictions.append(line)
else:
prediction = ''
print('!!!!! invalid entry.')
predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
## ======================= make lexicon txt which is used by Kaldi =======================
if make_kaldi_lexicon_txt:
option_num = 6
# remove previous file.
if os.path.exists(lexicon_txt):
os.remove(lexicon_txt)
lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
if os.path.exists(lexiconp_txt):
os.remove(lexiconp_txt)
# 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.
pronunciation_variants = df['ipa'][df['word'].str.match(word)]
c = Counter(pronunciation_variants)
total_num = sum(c.values())
#with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
# for key in c.keys():
# f.write("{0},{1}\n".format(key,c[key]))
for key, value in c.most_common(option_num):
# make possible pronunciation variant list.
pronvar_list = am_func.fame_pronunciation_variant(key)
for pronvar_ in pronvar_list:
split_ipa = convert_phone_set.split_fame_ipa(pronvar_)
pronvar_out = ' '.join(split_ipa)
pronvar_list_all.append([word, pronvar_out])
pronvar_list_all = np.array(pronvar_list_all)
pronvar_list_all = np.unique(pronvar_list_all, axis=0)
# output
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:
phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
merged_alignment_txt = os.path.join(default.kaldi_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:
mapping_phone2id = f.read().split('\n')
phones = []
phone_ids = [] # ID of phones
for m in mapping_phone2id:
m = m.split(' ')
if len(m) > 1:
phones.append(m[0])
phone_ids.append(int(m[1]))
# load the result of FA.
with open(merged_alignment_txt, 'r') as f:
lines = f.read()
lines = lines.split('\n')
predictions = pd.DataFrame({'filename': [''],
'word': [''],
'xsampa': [''],
'ipa': [''],
'famehtk': [''],
'prediction': ['']})
#fa_filenames = []
#fa_pronunciations = []
utterance_id_ = ''
pronunciation = []
for line in lines:
line = line.split(' ')
if len(line) == 5:
utterance_id = line[0]
if utterance_id == utterance_id_:
phone_id = int(line[4])
#if not phone_id == 1:
phone_ = phones[phone_ids.index(phone_id)]
phone = re.sub(r'_[A-Z]', '', phone_)
if not phone == 'SIL':
pronunciation.append(phone)
else:
filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
prediction = ''.join(pronunciation)
df_ = df[df['filename'].str.match(filename)]
df_idx = df_.index[0]
prediction_ = pd.Series([#filename,
#df_['word'][df_idx],
#df_['xsampa'][df_idx],
#df_['ipa'][df_idx],
#df_['famehtk'][df_idx],
df_.iloc[0,1],
df_.iloc[0,3],
df_.iloc[0,4],
df_.iloc[0,2],
df_.iloc[0,0],
prediction],
index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'],
name=df_idx)
predictions = predictions.append(prediction_)
#fa_filenames.append()
#fa_pronunciations.append(' '.join(pronunciation))
pronunciation = []
utterance_id_ = utterance_id
predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
## ======================= evaluate the result of forced alignment =======================
if eval_forced_alignment_htk:
htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
compare_hmm_num = 1
if compare_hmm_num:
f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
#for hmm_num in [256]:
hmm_num_str = str(hmm_num)
if compare_hmm_num:
f_result.write("{},".format(hmm_num_str))
#match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
#prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
#prediction = pd.Series(prediction, index=df.index, name='prediction')
#result = pd.concat([df, prediction], axis=1)
result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
# load pronunciation variants
for word in word_list:
htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
with open(htk_dict_file, 'r') as f:
lines = f.read().split('\n')[:-1]
pronunciation_variants = [line.split('\t')[1] for line in lines]
# see only words which appears in top 3.
result_ = result[result['word'].str.match(word)]
result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
match_num = sum(result_['famehtk'] == result_['prediction'])
total_num = len(result_)
print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100))
if compare_hmm_num:
f_result.write("{0},{1},".format(match_num, total_num))
else:
# output confusion matrix
cm = confusion_matrix(result_['famehtk'], result_['prediction'])
plt.figure()
plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
plt.savefig(result_dir + '\\cm_' + word + '.png')
if compare_hmm_num:
f_result.write('\n')
if compare_hmm_num:
f_result.close()
## ======================= evaluate the result of forced alignment of kaldi =======================
if eval_forced_alignment_kaldi:
result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
f_result.write("word,total,valid,match,[%]\n")
# load pronunciation variants
with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f:
lines = f.read().split('\n')[:-1]
pronunciation_variants_all = [line.split('\t') for line in lines]
word_list = np.delete(word_list, [0], 0) # remove 'Oog'
for word in word_list:
# load pronunciation variant of the word.
pronunciation_variants = []
for line in pronunciation_variants_all:
if line[0] == word.lower():
pronunciation_variants.append(line[1].replace(' ', ''))
# see only words which appears in top 3.
result_ = result[result['word'].str.match(word)]
result_tolerant = pd.DataFrame({
'filename': [''],
'word': [''],
'xsampa': [''],
'ipa': [''],
'prediction': [''],
'match': ['']})
for i in range(0, len(result_)):
line = result_.iloc[i]
# make a list of all possible pronunciation variants of ipa description.
# i.e. possible answers from forced alignment.
ipa = line['ipa']
pronvar_list = [ipa]
pronvar_list_ = am_func.fame_pronunciation_variant(ipa)
if not pronvar_list_ is None:
pronvar_list += list(pronvar_list_)
# only focus on pronunciations which can be estimated from ipa.
if len(set(pronvar_list) & set(pronunciation_variants)) > 0:
if line['prediction'] in pronvar_list:
ismatch = True
else:
ismatch = False
line_df = pd.DataFrame(result_.iloc[i]).T
df_idx = line_df.index[0]
result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'],
line_df.loc[df_idx, 'word'],
line_df.loc[df_idx, 'xsampa'],
line_df.loc[df_idx, 'ipa'],
line_df.loc[df_idx, 'prediction'],
ismatch],
index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'],
name=df_idx)
result_tolerant = result_tolerant.append(result_tolerant_)
# remove the first entry (dummy)
result_tolerant = result_tolerant.drop(0, axis=0)
total_num = len(result_)
valid_num = len(result_tolerant)
match_num = np.sum(result_tolerant['match'])
print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num))
f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100))
f_result.close()
## output confusion matrix
#cm = confusion_matrix(result_['ipa'], result_['prediction'])
#plt.figure()
#plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
#plt.savefig(result_dir + '\\cm_' + word + '.png')