Forced alignment by Kaldi is added.
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yemaozi88
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d56ef7f075
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@ -15,6 +15,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
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..\forced_alignment\forced_alignment\htk_dict.py = ..\forced_alignment\forced_alignment\htk_dict.py
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..\forced_alignment\forced_alignment\lexicon.py = ..\forced_alignment\forced_alignment\lexicon.py
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..\forced_alignment\forced_alignment\mlf.py = ..\forced_alignment\forced_alignment\mlf.py
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..\accent_classification\accent_classification\output_confusion_matrix.py = ..\accent_classification\accent_classification\output_confusion_matrix.py
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..\forced_alignment\forced_alignment\pronunciations.py = ..\forced_alignment\forced_alignment\pronunciations.py
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..\forced_alignment\forced_alignment\pyhtk.py = ..\forced_alignment\forced_alignment\pyhtk.py
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..\forced_alignment\forced_alignment\scripts.py = ..\forced_alignment\forced_alignment\scripts.py
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@ -22,12 +22,11 @@ dataset_list = ['devel', 'test', 'train']
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extract_features = 0
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make_feature_list = 0
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conv_lexicon = 0
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check_lexicon = 1
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check_lexicon = 0
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make_mlf = 0
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combine_files = 0
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flat_start = 0
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train_model = 0
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forced_alignment = 0
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train_model = 1
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sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
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@ -288,7 +287,7 @@ if flat_start:
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## ======================= estimate monophones =======================
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if train_model:
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iter_num_max = 3
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for mix_num in [16, 32, 64, 128]:
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for mix_num in [128, 256, 512, 1024]:
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for iter_num in range(1, iter_num_max+1):
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print("===== mix{}, iter{} =====".format(mix_num, iter_num))
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iter_num_pre = iter_num - 1
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@ -315,5 +314,6 @@ if train_model:
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fout.write("MU %d {*.state[2-4].mix}" % (mix_num_next))
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subprocessStr = 'HHEd -T 1 -H ' + modelN_dir + '\\' + hmmdefs_name + ' -M ' + modelN_dir_next + ' ' + header_file + ' ' + phonelist
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subprocess.call(subprocessStr, shell=True)
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@ -31,6 +31,9 @@
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<Compile Include="performance_check.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="pyKaldi.py">
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<SubType>Code</SubType>
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</Compile>
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</ItemGroup>
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<ItemGroup>
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<Content Include="config.ini" />
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@ -2,4 +2,4 @@
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config_hcopy = c:\cygwin64\home\Aki\acoustic_model\config\config.HCopy
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config_train = c:\cygwin64\home\Aki\acoustic_model\config\config.train
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mkhmmdefs_pl = c:\cygwin64\home\Aki\acoustic_model\src\acoustic_model\mkhmmdefs.pl
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FAME_dir = c:\OneDrive\Research\rug\experiments\friesian\corpus
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FAME_dir = C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus
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@ -4,52 +4,92 @@ import csv
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import subprocess
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import configparser
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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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## ======================= functions =======================
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def read_fileFA(fileFA):
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"""
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read the result file of HTK forced alignment.
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this function only works when input is one word.
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"""
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with open(fileFA, 'r') as f:
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lines = f.read()
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lines = lines.split('\n')
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"""
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read the result file of HTK forced alignment.
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this function only works when input is one word.
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"""
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with open(fileFA, 'r') as f:
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lines = f.read()
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lines = lines.split('\n')
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phones = []
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for line in lines:
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line_split = line.split()
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if len(line_split) > 1:
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phones.append(line_split[2])
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phones = []
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for line in lines:
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line_split = line.split()
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if len(line_split) > 1:
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phones.append(line_split[2])
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return ' '.join(phones)
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return ' '.join(phones)
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#####################
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## USER DEFINE ##
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#####################
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def make_dic(word, pronvar_, fileDic, output_type):
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"""
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make dict files which can be used for HTK.
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param word: target word.
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param pronvar_: pronunciation variant. nx2 (WORD /t pronunciation) ndarray.
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param fileDic: output dic file.
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param output_type: 0:full, 1:statistics, 2:frequency <2% entries are removed. 3:top 3.
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"""
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#assert(output_type < 4 and output_type >= 0, 'output_type should be an integer between 0 and 3.')
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if output_type == 0: # full
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pronvar = np.unique(pronvar_)
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with open(fileDic, 'w') as f:
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for pvar in pronvar:
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f.write('{0}\t{1}\n'.format(WORD, pvar))
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else:
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c = Counter(pronvar_)
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total_num = sum(c.values())
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with open(fileDic, 'w') as f:
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if output_type == 3:
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for key, value in c.most_common(3):
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f.write('{0}\t{1}\n'.format(WORD, key))
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else:
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for key, value in c.items():
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percentage = value/total_num*100
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if output_type == 1: # all
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f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, percentage, WORD, key))
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elif output_type == 2: # less than 2 percent
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if percentage < 2:
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f.write('{0}\t{1}\n'.format(WORD, key))
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## ======================= user define =======================
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curr_dir = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
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config_ini = curr_dir + '\\config.ini'
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forced_alignment_module = r'C:\Users\Aki\source\repos\forced_alignment'
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forced_alignment_module_old = r'C:\OneDrive\Research\rug\code\forced_alignment\forced_alignment'
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ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
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ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
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accent_classification_dir = r'C:\Users\Aki\source\repos\accent_classification\accent_classification'
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csvfile = r"C:\OneDrive\Research\rug\stimmen\Frisian Variants Picture Task Stimmen.csv"
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experiments_dir = r'C:\OneDrive\Research\rug\experiments'
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data_dir = experiments_dir + '\\stimmen\\data'
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cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
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data_dir = experiments_dir + '\\stimmen\\data'
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csvfile = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
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cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
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# procedure
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convert_phones = 0
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make_dic_files = 0
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make_dic_files_short = 0
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do_forced_alignment = 0
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eval_forced_alignment = 1
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do_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 = 0
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@ -67,6 +107,10 @@ import acoustic_model_functions as am_func
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sys.path.append(forced_alignment_module_old)
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import pyHTK
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# to output confusion matrix
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sys.path.append(accent_classification_dir)
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from output_confusion_matrix import plot_confusion_matrix
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## ======================= load variables =======================
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config = configparser.ConfigParser()
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@ -81,177 +125,393 @@ lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
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## ======================= convert phones ======================
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if convert_phones:
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
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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_htk)
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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_htk)
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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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# the lines which include a specific phone.
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#lines = am_func.find_phone(lex_asr, 'x')
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with open(csvfile, encoding="utf-8") as fin:
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lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
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next(lines, None) # skip the headers
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with open(csvfile, encoding="utf-8") as fin:
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lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
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next(lines, None) # skip the headers
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filenames = []
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words = []
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pronunciations = []
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for line in lines:
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if line[1] is not '' and len(line) > 5:
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filenames.append(line[0])
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words.append(line[1])
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pron_xsampa = line[3]
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pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
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pron_ipa = pron_ipa.replace('ː', ':')
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pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
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filenames = []
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words = []
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pronunciations = []
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for line in lines:
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if line[1] is not '' and len(line) > 5:
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filenames.append(line[0])
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words.append(line[1])
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pron_xsampa = line[3]
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pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
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pron_ipa = pron_ipa.replace('ː', ':')
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pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
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# adjust to phones used in the acoustic model.
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pron_famehtk = pron_famehtk.replace('sp', 'sil')
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pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
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pron_famehtk = pron_famehtk.replace('w :', 'wh')
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pron_famehtk = pron_famehtk.replace('e :', 'eh')
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pron_famehtk = pron_famehtk.replace('eh :', 'eh')
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pron_famehtk = pron_famehtk.replace('ih :', 'ih')
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# adjust to phones used in the acoustic model.
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pron_famehtk = pron_famehtk.replace('sp', 'sil')
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pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
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pron_famehtk = pron_famehtk.replace('w :', 'wh')
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pron_famehtk = pron_famehtk.replace('e :', 'eh')
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pron_famehtk = pron_famehtk.replace('eh :', 'eh')
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pron_famehtk = pron_famehtk.replace('ih :', 'ih')
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#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
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#pron = []
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#for phoneme in pron_famehtk.split(' '):
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# pron.append(translation_key.get(phoneme, phoneme))
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#pronunciations.append(' '.join(pron_famehtk))
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pronunciations.append(pron_famehtk)
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#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
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#pron = []
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#for phoneme in pron_famehtk.split(' '):
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# pron.append(translation_key.get(phoneme, phoneme))
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#pronunciations.append(' '.join(pron_famehtk))
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pronunciations.append(pron_famehtk)
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# check if all phones are in the phonelist of the acoustic model.
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#phonelist = ' '.join(pronunciations)
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#np.unique(phonelist.split(' '))
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#phonelist.find(':')
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# check if all phones are in the phonelist of the acoustic model.
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#phonelist = ' '.join(pronunciations)
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#np.unique(phonelist.split(' '))
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#phonelist.find(':')
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filenames = np.array(filenames)
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words = np.array(words)
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pronunciations = np.array(pronunciations)
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filenames = np.array(filenames)
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words = np.array(words)
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pronunciations = np.array(pronunciations)
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del line, lines
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del pron_xsampa, pron_ipa, pron_famehtk
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del line, lines
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del pron_xsampa, pron_ipa, pron_famehtk
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np.save(data_dir + '\\filenames.npy', filenames)
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np.save(data_dir + '\\words.npy', words)
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np.save(data_dir + '\\pronunciations.npy', pronunciations)
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np.save(data_dir + '\\filenames.npy', filenames)
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np.save(data_dir + '\\words.npy', words)
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np.save(data_dir + '\\pronunciations.npy', pronunciations)
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else:
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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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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.npy')
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pronunciations = np.load(data_dir + '\\pronunciations.npy')
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word_list = np.unique(words)
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## ======================= make dict files used for HTK. ======================
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if make_dic_files:
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output_dir = experiments_dir + r'\stimmen\dic'
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output_type = 2
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output_dir = experiments_dir + r'\stimmen\dic_short'
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for word in word_list:
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WORD = word.upper()
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fileDic = output_dir + '\\' + word + '.dic'
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for word in word_list:
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WORD = word.upper()
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fileDic = output_dir + '\\' + word + '.dic'
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# make dic file.
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pronvar_ = pronunciations[words == word]
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pronvar = np.unique(pronvar_)
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# pronunciation variant of the target word.
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pronvar_ = pronunciations[words == word]
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# remove ''
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pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
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with open(fileDic, 'w') as f:
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for pvar in pronvar:
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f.write('{0}\t{1}\n'.format(WORD, pvar))
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# make dic file.
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make_dic(word, pronvar_, fileDic, output_type)
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## ======================= make dict files for most popular words. ======================
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if make_dic_files_short:
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output_dir = experiments_dir + r'\stimmen\dic'
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## ======================= forced alignment using HTK =======================
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if do_forced_alignment_htk:
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configHVite = cygwin_dir + r'\config\config.HVite'
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filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
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wav_dir = experiments_dir + r'\stimmen\wav'
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#word = word_list[3]
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for word in word_list:
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WORD = word.upper()
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fileStat = output_dir + '\\' + word + '_stat.csv'
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#hmm_num = 128
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for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
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hmm_num_str = str(hmm_num)
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AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-2\hmmdefs'
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pronvar = pronunciations[words == word]
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c = Counter(pronvar)
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total_num = sum(c.values())
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predictions = []
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file_num_max = len(filenames)
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for i in range(0, file_num_max):
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#for i in range(500, 502):
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print('=== {0}/{1} ==='.format(i, file_num_max))
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filename = filenames[i]
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fileWav = wav_dir + '\\' + filename
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with open(fileStat, 'w') as f:
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for key, value in c.items():
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f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, value/total_num*100, WORD, key))
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if os.path.exists(fileWav):
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word = words[i]
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WORD = word.upper()
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# make label file.
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fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab')
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with open(fileLab, 'w') as f:
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lines = f.write(WORD)
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fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
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fileFA = experiments_dir + r'\stimmen\FA' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
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pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
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prediction = read_fileFA(fileFA)
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predictions.append(prediction)
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os.remove(fileLab)
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print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
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else:
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predictions.append('')
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print('!!!!! file not found.')
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predictions = np.array(predictions)
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match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
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np.save(data_dir + '\\match_hmm' + hmm_num_str + '.npy', match)
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## ======================= forced alignment =======================
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if do_forced_alignment:
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configHVite = cygwin_dir + r'\config\config.HVite'
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filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
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wav_dir = experiments_dir + r'\stimmen\wav'
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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_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
|
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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')
|
||||
|
||||
#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128]:
|
||||
for hmm_num in [64]:
|
||||
hmm_num_str = str(hmm_num)
|
||||
AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-3\hmmdefs'
|
||||
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)
|
||||
for i in range(0, file_num_max):
|
||||
print('=== {0}/{1} ==='.format(i, file_num_max))
|
||||
filename = filenames[i]
|
||||
fileWav = wav_dir + '\\' + filename
|
||||
lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
|
||||
|
||||
if os.path.exists(fileWav):
|
||||
word = words[i]
|
||||
WORD = word.upper()
|
||||
predictions = []
|
||||
file_num_max = len(filenames)
|
||||
|
||||
# make label file.
|
||||
fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab')
|
||||
with open(fileLab, 'w') as f:
|
||||
lines = f.write(WORD)
|
||||
# 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)
|
||||
|
||||
fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
|
||||
fileFA = experiments_dir + r'\stimmen\FA_short' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
|
||||
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')
|
||||
|
||||
pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
|
||||
prediction = read_fileFA(fileFA)
|
||||
predictions.append(prediction)
|
||||
# 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
|
||||
|
||||
os.remove(fileLab)
|
||||
print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
|
||||
else:
|
||||
predictions.append('')
|
||||
print('!!!!! file not found.')
|
||||
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):
|
||||
|
||||
predictions = np.array(predictions)
|
||||
match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
|
||||
np.save(data_dir + '\\match_hmm' + hmm_num_str + '.npy', match)
|
||||
|
||||
|
||||
## ======================= evaluate the result of forced alignment =======================
|
||||
if eval_forced_alignment:
|
||||
|
||||
#for hmm_num in [1, 2, 4, 8, 16, 32, 64]:
|
||||
hmm_num = 64
|
||||
hmm_num_str = str(hmm_num)
|
||||
match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
|
||||
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_short' + '\\' + word + '.dic'
|
||||
pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
|
||||
# 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)]
|
||||
|
||||
match_short = []
|
||||
for line in match:
|
||||
word = line[0]
|
||||
WORD = word.upper()
|
||||
pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
|
||||
# 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)
|
||||
if line[1] in pronvar:
|
||||
match_short.append(line)
|
||||
|
||||
match_short = np.array(match_short)
|
||||
match = np.copy(match_short)
|
||||
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]))
|
||||
# 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')
|
||||
26
acoustic_model/pyKaldi.py
Normal file
26
acoustic_model/pyKaldi.py
Normal file
@ -0,0 +1,26 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
forced_alignment_module = r'C:\Users\Aki\source\repos\forced_alignment'
|
||||
|
||||
## ======================= add paths =======================
|
||||
|
||||
sys.path.append(forced_alignment_module)
|
||||
from forced_alignment import convert_phone_set
|
||||
|
||||
|
||||
htk_dict_file = r'C:\OneDrive\Research\rug\experiments\stimmen\dic_top3\Reus.dic'
|
||||
#kaldi_lexicon = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\data\lang\phones\'
|
||||
alignment_txt = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\exp\tri1_alignme\merged_alignment.txt'
|
||||
phones_txt = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\exp\tri1_alignme\phones.txt'
|
||||
phone_map_txt = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\data\local\lang\phone_map.txt'
|
||||
|
||||
with open(phone_map_txt, 'r', encoding="utf-8") as f:
|
||||
lines = f.read()
|
||||
lines = lines.split('\n')
|
||||
|
||||
with open(alignment_txt, 'r', encoding="utf-8") as f:
|
||||
lines =
|
||||
#phone_in = [line for line in lines if 'SIL' in line]
|
||||
#if len(phone_in) == 1:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user