488 lines
19 KiB
Python
488 lines
19 KiB
Python
import os
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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import sys
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import csv
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import subprocess
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from collections import Counter
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import re
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn.metrics import confusion_matrix
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import acoustic_model_functions as am_func
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import convert_xsampa2ipa
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import defaultfiles as default
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from forced_alignment import pyhtk
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## ======================= user define =======================
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excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
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data_dir = os.path.join(default.experiments_dir, 'stimmen', 'data')
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wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
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acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
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htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
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fa_dir = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
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result_dir = os.path.join(default.experiments_dir, 'stimmen', 'result')
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kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme')
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kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
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lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
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#lex_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
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#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
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# procedure
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make_htk_dict_files = 0
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do_forced_alignment_htk = 0
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eval_forced_alignment_htk = 0
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make_kaldi_data_files = 0
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make_kaldi_lexicon_txt = 0
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load_forced_alignment_kaldi = 1
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eval_forced_alignment_kaldi = 1
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## ======================= add paths =======================
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sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
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from forced_alignment import convert_phone_set
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from forced_alignment import pyhtk
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sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
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from evaluation import plot_confusion_matrix
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## ======================= convert phones ======================
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
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xls = pd.ExcelFile(excel_file)
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## check conversion
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#df = pd.read_excel(xls, 'frequency')
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#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
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# #ipa_converted = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, xsampa_)
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# ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
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# if not ipa_converted == ipa:
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# print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
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## check phones included in FAME!
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# the phones used in the lexicon.
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#phonelist = am_func.get_phonelist(lex_asr)
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# the lines which include a specific phone.
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#lines = am_func.find_phone(lex_asr, 'x')
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# Filename, Word, Self Xsampa
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df = pd.read_excel(xls, 'original')
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ipas = []
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famehtks = []
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for xsampa in df['Self Xsampa']:
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if not isinstance(xsampa, float): # 'NaN'
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# typo?
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xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
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xsampa = xsampa.replace(';', ':')
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ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
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ipa = ipa.replace('ː', ':')
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ipa = ipa.replace(' ', '')
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ipas.append(ipa)
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famehtk = convert_phone_set.ipa2famehtk(ipa)
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famehtks.append(famehtk)
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else:
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ipas.append('')
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famehtks.append('')
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# extract interesting cols.
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df = pd.DataFrame({'filename': df['Filename'],
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'word': df['Word'],
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'xsampa': df['Self Xsampa'],
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'ipa': pd.Series(ipas),
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'famehtk': pd.Series(famehtks)})
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# cleansing.
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df = df[~df['famehtk'].isin(['/', ''])]
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word_list = np.unique(df['word'])
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## ======================= make dict files used for HTK. ======================
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if make_htk_dict_files:
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output_type = 3
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for word in word_list:
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htk_dict_file = htk_dict_dir + '\\' + word + '.dic'
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# pronunciation variant of the target word.
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pronvar_ = df['famehtk'][df['word'].str.match(word)]
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# make dic file.
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am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
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## ======================= forced alignment using HTK =======================
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if do_forced_alignment_htk:
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#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
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for hmm_num in [256, 512, 1024]:
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hmm_num_str = str(hmm_num)
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acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
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predictions = pd.DataFrame({'filename': [''],
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'word': [''],
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'xsampa': [''],
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'ipa': [''],
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'famehtk': [''],
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'prediction': ['']})
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for i, filename in enumerate(df['filename']):
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print('=== {0}/{1} ==='.format(i, len(df)))
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if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
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wav_file = os.path.join(wav_dir, filename)
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if os.path.exists(wav_file):
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word = df['word'][i]
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WORD = word.upper()
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fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
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#if not os.path.exists(fa_file):
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# make label file.
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label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
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with open(label_file, 'w') as f:
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lines = f.write(WORD)
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htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
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pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite,
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default.phonelist, acoustic_model)
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os.remove(label_file)
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prediction = am_func.read_fileFA(fa_file)
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print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
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else:
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prediction = ''
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print('!!!!! file not found.')
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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)
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predictions = predictions.append(line)
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else:
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prediction = ''
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print('!!!!! invalid entry.')
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predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
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## ======================= make files which is used for forced alignment by Kaldi =======================
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if make_kaldi_data_files:
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wav_scp = os.path.join(kaldi_data_dir, 'wav.scp')
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text_file = os.path.join(kaldi_data_dir, 'text')
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utt2spk = os.path.join(kaldi_data_dir, 'utt2spk')
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# remove previous files.
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if os.path.exists(wav_scp):
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os.remove(wav_scp)
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if os.path.exists(text_file):
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os.remove(text_file)
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if os.path.exists(utt2spk):
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os.remove(utt2spk)
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f_wav_scp = open(wav_scp, 'a', encoding="utf-8", newline='\n')
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f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
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f_utt2spk = open(utt2spk, 'a', encoding="utf-8", newline='\n')
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# make wav.scp, text, and utt2spk files.
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for i in df.index:
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filename = df['filename'][i]
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print('=== {0}: {1} ==='.format(i, filename))
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#if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
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wav_file = os.path.join(wav_dir, filename)
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if os.path.exists(wav_file):
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speaker_id = 'speaker_' + str(i).zfill(4)
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utterance_id = filename.replace('.wav', '')
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utterance_id = utterance_id.replace(' ', '_')
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utterance_id = speaker_id + '-' + utterance_id
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# wav.scp file
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wav_file_unix = wav_file.replace('\\', '/')
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wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/')
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f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
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# text file
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word = df['word'][i].lower()
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f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
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# utt2spk
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f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
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f_wav_scp.close()
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f_text_file.close()
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f_utt2spk.close()
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## ======================= make lexicon txt which is used by Kaldi =======================
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if make_kaldi_lexicon_txt:
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option_num = 6
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# remove previous file.
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if os.path.exists(lexicon_txt):
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os.remove(lexicon_txt)
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lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
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if os.path.exists(lexiconp_txt):
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os.remove(lexiconp_txt)
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# output lexicon.txt
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f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
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pronvar_list_all = []
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for word in word_list:
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# pronunciation variant of the target word.
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pronunciation_variants = df['ipa'][df['word'].str.match(word)]
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c = Counter(pronunciation_variants)
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total_num = sum(c.values())
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#with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
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# for key in c.keys():
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# f.write("{0},{1}\n".format(key,c[key]))
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for key, value in c.most_common(option_num):
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# make possible pronounciation variant list.
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pronvar_list = am_func.fame_pronunciation_variant(key)
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for pronvar_ in pronvar_list:
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split_ipa = convert_phone_set.split_fame_ipa(pronvar_)
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pronvar_out = ' '.join(split_ipa)
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pronvar_list_all.append([word, pronvar_out])
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pronvar_list_all = np.array(pronvar_list_all)
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pronvar_list_all = np.unique(pronvar_list_all, axis=0)
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# output
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f_lexicon_txt.write('<UNK>\tSPN\n')
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for line in pronvar_list_all:
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f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
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f_lexicon_txt.close()
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## ======================= load kaldi forced alignment result =======================
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if load_forced_alignment_kaldi:
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phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
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merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt')
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#filenames = np.load(data_dir + '\\filenames.npy')
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#words = np.load(data_dir + '\\words.npy')
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#pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
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#pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
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#word_list = np.unique(words)
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# load the mapping between phones and ids.
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with open(phones_txt, 'r', encoding="utf-8") as f:
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mapping_phone2id = f.read().split('\n')
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phones = []
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phone_ids = [] # ID of phones
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for m in mapping_phone2id:
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m = m.split(' ')
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if len(m) > 1:
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phones.append(m[0])
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phone_ids.append(int(m[1]))
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# load the result of FA.
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with open(merged_alignment_txt, 'r') as f:
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lines = f.read()
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lines = lines.split('\n')
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predictions = pd.DataFrame({'filename': [''],
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'word': [''],
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'xsampa': [''],
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'ipa': [''],
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'famehtk': [''],
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'prediction': ['']})
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#fa_filenames = []
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#fa_pronunciations = []
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utterance_id_ = ''
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pronunciation = []
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for line in lines:
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line = line.split(' ')
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if len(line) == 5:
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utterance_id = line[0]
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if utterance_id == utterance_id_:
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phone_id = int(line[4])
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#if not phone_id == 1:
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phone_ = phones[phone_ids.index(phone_id)]
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phone = re.sub(r'_[A-Z]', '', phone_)
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if not phone == 'SIL':
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pronunciation.append(phone)
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else:
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filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
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prediction = ''.join(pronunciation)
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df_ = df[df['filename'].str.match(filename)]
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df_idx = df_.index[0]
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prediction_ = pd.Series([#filename,
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#df_['word'][df_idx],
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#df_['xsampa'][df_idx],
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#df_['ipa'][df_idx],
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#df_['famehtk'][df_idx],
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df_.iloc[0,1],
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df_.iloc[0,3],
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df_.iloc[0,4],
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df_.iloc[0,2],
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df_.iloc[0,0],
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prediction],
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index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'],
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name=df_idx)
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predictions = predictions.append(prediction_)
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#fa_filenames.append()
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#fa_pronunciations.append(' '.join(pronunciation))
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pronunciation = []
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utterance_id_ = utterance_id
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predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
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## ======================= evaluate the result of forced alignment =======================
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if eval_forced_alignment_htk:
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htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
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compare_hmm_num = 1
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if compare_hmm_num:
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f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
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f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
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for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
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#for hmm_num in [256]:
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hmm_num_str = str(hmm_num)
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if compare_hmm_num:
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f_result.write("{},".format(hmm_num_str))
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#match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
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#prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
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#prediction = pd.Series(prediction, index=df.index, name='prediction')
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#result = pd.concat([df, prediction], axis=1)
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result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
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# load pronunciation variants
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for word in word_list:
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htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
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with open(htk_dict_file, 'r') as f:
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lines = f.read().split('\n')[:-1]
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pronunciation_variants = [line.split('\t')[1] for line in lines]
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# see only words which appears in top 3.
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result_ = result[result['word'].str.match(word)]
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result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
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match_num = sum(result_['famehtk'] == result_['prediction'])
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total_num = len(result_)
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print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100))
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if compare_hmm_num:
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f_result.write("{0},{1},".format(match_num, total_num))
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else:
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# output confusion matrix
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cm = confusion_matrix(result_['famehtk'], result_['prediction'])
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plt.figure()
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plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
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plt.savefig(result_dir + '\\cm_' + word + '.png')
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if compare_hmm_num:
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f_result.write('\n')
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if compare_hmm_num:
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f_result.close()
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## ======================= evaluate the result of forced alignment of kaldi =======================
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if eval_forced_alignment_kaldi:
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result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
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f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
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f_result.write("word,total,valid,match,[%]\n")
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# load pronunciation variants
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with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f:
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lines = f.read().split('\n')[:-1]
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pronunciation_variants_all = [line.split('\t') for line in lines]
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word_list = np.delete(word_list, [0], 0) # remove 'Oog'
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for word in word_list:
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# load pronunciation variant of the word.
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pronunciation_variants = []
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for line in pronunciation_variants_all:
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if line[0] == word.lower():
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pronunciation_variants.append(line[1].replace(' ', ''))
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# see only words which appears in top 3.
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result_ = result[result['word'].str.match(word)]
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result_tolerant = pd.DataFrame({
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'filename': [''],
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'word': [''],
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'xsampa': [''],
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'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')
|