acoustic_model/acoustic_model/performance_check.py

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 pandas as pd
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


## ======================= user define =======================
#curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
#config_ini = 'config.ini'
#repo_dir   = r'C:\Users\Aki\source\repos'
#forced_alignment_module     = repo_dir + '\\forced_alignment'
#forced_alignment_module_old = repo_dir + '\\aki_tools'
#ipa_xsampa_converter_dir    = repo_dir + '\\ipa-xsama-converter'
#accent_classification_dir   = repo_dir + '\\accent_classification\accent_classification'
excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')


#experiments_dir = r'C:\OneDrive\Research\rug\experiments'
data_dir        = os.path.join(default.experiments_dir, 'stimmen', 'data')
#csvfile         = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
#wav_dir	= os.path.join(default.experiments_dir, 'stimmen', 'wav_44k') # 44.1k
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')

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')

#cygwin_dir      = r'C:\cygwin64\home\Aki\acoustic_model'
#lex_asr		= os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')

from forced_alignment import pyhtk

# procedure
make_dic_files = 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(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'))
#import pyHTK
from evaluation import plot_confusion_matrix


## ======================= convert phones ======================

mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)

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))


## check phones included in FAME!
# the phones used in the lexicon.
#phonelist = am_func.get_phonelist(lex_asr)

# the lines which include a specific phone.
#lines = am_func.find_phone(lex_asr, 'x')


# Filename, Word, Self Xsampa
df = pd.read_excel(xls, 'original')
    
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)
        famehtk = convert_phone_set.ipa2famehtk(ipa)
        famehtks.append(famehtk)
    else:
        ipas.append('')
        famehtks.append('')
        
# extract interesting cols.
df = pd.DataFrame({'filename': df['Filename'], 
                    'word': df['Word'], 
                    'xsampa': df['Self Xsampa'],
                    'ipa': pd.Series(ipas),
                    'famehtk': pd.Series(famehtks)})
# cleansing. 
df = df[~df['famehtk'].isin(['/', ''])]

word_list = np.unique(df['word'])


## ======================= make dict files used for HTK. ======================
if make_dic_files:
    output_type = 3
    
    for word in word_list:
        htk_dict_file = htk_dict_dir + '\\' + word + '.dic'

        # pronunciation variant of the target word.
        pronvar_ = df['famehtk'][df['word'].str.match(word)]

        # make dic file.
        am_func.make_dic(word, pronvar_, htk_dict_file, output_type)
        

## ======================= forced alignment using HTK =======================
if do_forced_alignment_htk:
    
    #hmm_num = 2
    #for hmm_num in [1]:
    #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)
                    #predictions.append(prediction)

                    print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
                else:
                    prediction = ''
                    #predictions.append('')
                    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 = ''
                #predictions.append('')
                print('!!!!! invalid entry.')


        #predictions = np.array(predictions)
        #np.save(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'), predictions)
        predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))


## ======================= make files which is used for forced alignment by Kaldi =======================
if make_kaldi_data_files:

    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')

    #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.
    predictions = pd.DataFrame({'filename': [''], 
                'word': [''], 
                'xsampa': [''],
                'ipa': [''],
                'famehtk': [''],
                'prediction': ['']})
    #for i in range(0, file_num_max):
    #for i in range(400, 410):
    for i, filename in enumerate(df['filename']):

        #print('=== {0}/{1} ==='.format(i+1, file_num_max))
        #filename = filenames[i]

        print('=== {0}/{1} ==='.format(i, len(df)))
        wav_file = wav_dir + '\\' + filename
        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):
                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()
                word = df['word'][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:
    #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)
    lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
    if os.path.exists(lexiconp_txt):
        os.remove(lexiconp_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]
        pronunciation_variants = df['ipa'][df['word'].str.match(word)]
        #pronunciation_variants = np.unique(pronunciation_variants)
        # remove ''
        #pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))

        c = Counter(pronunciation_variants)
        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('ɹ', 'r') # ???
            key = key.replace('ʁ', 'r')
            key = key.replace('ʀ', '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_fame_ipa(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 = os.path.join(kaldi_work_dir, 'data', 'lang', 'phones.txt')
    merged_alignment_txt = os.path.join(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):
        
 
    #    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)
    #                #predictions.append(prediction)

    #                print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
    #            else:
    #                prediction = ''
    #                #predictions.append('')
    #                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 = ''
    #            #predictions.append('')
    #            print('!!!!! invalid entry.')


    #    #predictions = np.array(predictions)
    #    #np.save(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'), predictions)
    #    predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))


## ======================= evaluate the result of forced alignment =======================
if eval_forced_alignment:
    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()