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@ -9,12 +9,13 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
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ProjectSection(SolutionItems) = preProject
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..\forced_alignment\forced_alignment\__init__.py = ..\forced_alignment\forced_alignment\__init__.py
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..\forced_alignment\forced_alignment\convert_phone_set.py = ..\forced_alignment\forced_alignment\convert_phone_set.py
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..\toolbox\evaluation.py = ..\toolbox\evaluation.py
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..\ipa-xsama-converter\converter.py = ..\ipa-xsama-converter\converter.py
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..\forced_alignment\forced_alignment\defaultfiles.py = ..\forced_alignment\forced_alignment\defaultfiles.py
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..\forced_alignment\forced_alignment\forced_alignment.pyproj = ..\forced_alignment\forced_alignment\forced_alignment.pyproj
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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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..\forced_alignment\forced_alignment\pronunciations.py = ..\forced_alignment\forced_alignment\pronunciations.py
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..\toolbox\pyHTK.py = ..\toolbox\pyHTK.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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..\forced_alignment\forced_alignment\tempfilename.py = ..\forced_alignment\forced_alignment\tempfilename.py
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@ -22,11 +22,12 @@ 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 = 0
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check_lexicon = 1
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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 = 1
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train_model = 0
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forced_alignment = 0
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sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
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@ -287,7 +288,7 @@ 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 [128, 256, 512, 1024]:
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for mix_num in [16, 32, 64, 128]:
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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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@ -314,6 +315,5 @@ 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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@ -28,12 +28,6 @@
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<Compile Include="convert_xsampa2ipa.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="defaultfiles.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="fa_test.py">
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<SubType>Code</SubType>
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</Compile>
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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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@ -1,13 +1,17 @@
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import os
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import sys
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from collections import Counter
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import numpy as np
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import pandas as pd
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import defaultfiles as default
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sys.path.append(default.forced_alignment_module_dir)
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## ======================= user define =======================
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repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
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curr_dir = repo_dir + '\\acoustic_model'
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forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
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sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
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sys.path.append(forced_alignment_module)
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from forced_alignment import convert_phone_set
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@ -38,41 +42,6 @@ def make_filelist(input_dir, output_txt):
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fout.write(input_dir + '\\' + filename + '\n')
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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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WORD = word.upper()
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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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def get_phonelist(lexicon_file):
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""" Make a list of phones which appears in the lexicon. """
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@ -130,22 +99,4 @@ def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
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lex2 = pd.read_table(lexicon_file2, names=['word', 'pronunciation'])
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lex = pd.concat([lex1, lex2])
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lex = lex.sort_values(by='word', ascending=True)
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lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
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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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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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lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
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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\WSL\kaldi-trunk\egs\fame\s5\corpus
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FAME_dir = c:\OneDrive\Research\rug\experiments\friesian\corpus
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@ -7,155 +7,122 @@ import json
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import sys
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import os
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import defaultfiles as default
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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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#sys.path.append(ipa_xsampa_converter_dir)
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#import converter
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def load_converter(source, sink, ipa_xsampa_converter_dir):
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"""load the converter.
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source and sink are either of "ipa", "xsampa" or "sassc".
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"""
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choices = ["ipa", "xsampa", "sassc"]
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"""load the converter.
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source and sink are either of "ipa", "xsampa" or "sassc".
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"""
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choices = ["ipa", "xsampa", "sassc"]
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# Validate params
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try:
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choice1 = choices.index(source)
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choice2 = choices.index(sink)
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if choice1 == choice2:
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print("source and destination format are the same.")
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except ValueError:
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print("source and destination should be one of [ipa xsampa sassc].")
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exit(1)
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# Mappings from disk
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# some may not be used if source or sink is already IPA
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source_to_ipa = {}
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ipa_to_sink = {}
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# Validate params
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try:
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choice1 = choices.index(source)
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choice2 = choices.index(sink)
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if choice1 == choice2:
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print("source and destination format are the same.")
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except ValueError:
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print("source and destination should be one of [ipa xsampa sassc].")
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exit(1)
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# Mappings from disk
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# some may not be used if source or sink is already IPA
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source_to_ipa = {}
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ipa_to_sink = {}
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ipa_xsampa = []
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sassc_ipa = []
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ipa_xsampa = []
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sassc_ipa = []
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# The IPAs that actually occur within SASSC
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sassc_active_ipa = {}
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# The IPAs that actually occur within SASSC
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sassc_active_ipa = {}
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script_dir = os.path.dirname(os.path.realpath(__file__))
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script_dir = os.path.dirname(os.path.realpath(__file__))
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with open(os.path.join(ipa_xsampa_converter_dir, "ipa_xsampa_map.json"), encoding="utf-8") as f:
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ipa_xsampa = json.load(f)
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with open(os.path.join(ipa_xsampa_converter_dir, "ipa_xsampa_map.json"), encoding="utf-8") as f:
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ipa_xsampa = json.load(f)
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sassc_active = source == "sassc" or sink == "sassc"
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if sassc_active:
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with open(os.path.join(script_dir, "./sassc_ipa.json")) as f:
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sassc_ipa = json.load(f)
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for pair in sassc_ipa:
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for char in pair[1]:
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sassc_active_ipa[char] = 1
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sassc_active = source == "sassc" or sink == "sassc"
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if sassc_active:
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with open(os.path.join(script_dir, "./sassc_ipa.json")) as f:
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sassc_ipa = json.load(f)
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for pair in sassc_ipa:
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for char in pair[1]:
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sassc_active_ipa[char] = 1
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if source == "xsampa":
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for pair in ipa_xsampa:
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source_to_ipa[pair[1]] = pair[0]
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elif source == "sassc":
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for pair in sassc_ipa:
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source_to_ipa[pair[0]] = pair[1]
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if source == "xsampa":
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for pair in ipa_xsampa:
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source_to_ipa[pair[1]] = pair[0]
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elif source == "sassc":
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for pair in sassc_ipa:
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source_to_ipa[pair[0]] = pair[1]
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if sink == "xsampa":
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for pair in ipa_xsampa:
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ipa_to_sink[pair[0]] = pair[1]
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elif sink == "sassc":
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for pair in sassc_ipa:
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ipa_to_sink[pair[1]] = pair[0]
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if sink == "xsampa":
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for pair in ipa_xsampa:
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ipa_to_sink[pair[0]] = pair[1]
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elif sink == "sassc":
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for pair in sassc_ipa:
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ipa_to_sink[pair[1]] = pair[0]
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# Combine them into a single mapping
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mapping = {}
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if source == "ipa":
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mapping = ipa_to_sink
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elif sink == "ipa":
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mapping = source_to_ipa
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else:
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for k, ipas in source_to_ipa.iteritems():
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map_out = ""
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failed = False
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for ipa in ipas:
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val = ipa_to_sink.get(ipa)
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if not val:
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failed = True
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break
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map_out += val
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mapping[k] = map_out if not failed else None
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# Combine them into a single mapping
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mapping = {}
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if source == "ipa":
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mapping = ipa_to_sink
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elif sink == "ipa":
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mapping = source_to_ipa
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else:
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for k, ipas in source_to_ipa.iteritems():
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map_out = ""
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failed = False
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for ipa in ipas:
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val = ipa_to_sink.get(ipa)
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if not val:
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failed = True
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break
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map_out += val
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mapping[k] = map_out if not failed else None
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return mapping
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return mapping
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def conversion(source, sink, mapping, line):
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"""
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conversion.
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Args:
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mapping: can be obtained with load_converter().
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line: must be seperated, by default the seperator is whitespace.
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"""
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"""
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conversion.
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Args:
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mapping: can be obtained with load_converter().
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line: must be seperated, by default the seperator is whitespace.
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"""
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# Edit this to change the seperator
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SEPERATOR = " "
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# Edit this to change the seperator
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SEPERATOR = " "
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line = line.strip()
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output = []
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#if sassc_active:
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# tokens = line.split(SEPERATOR)
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#else:
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tokens = line
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for token in tokens:
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if token.isspace():
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output.append(token)
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continue
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# Remove extraneous chars that IPA does not accept
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if sink == "sassc":
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cleaned_token = u""
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for char in token:
|
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if sassc_active_ipa.get(char):
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cleaned_token += char
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token = cleaned_token
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mapped = mapping.get(token)
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if not mapped:
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print("WARNING: could not map token ", token, file=sys.stderr)
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else:
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output.append(mapped)
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#if sassc_active:
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# output = SEPERATOR.join(output)
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#else:
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output = "".join(output)
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return output
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def xsampa2ipa(mapping, xsampa):
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"""
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conversion from xsampa to ipa.
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Args:
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mapping: can be obtained with load_converter().
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xsampa: a line written in xsampa.
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Notes:
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function conversion does not work when:
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- the input is a word.
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- when the line includes '\'.
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- 'ɡ' and 'g' are considered to be different.
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"""
|
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# make a multi_character_list to split 'xsampa'.
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multi_character_list = []
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for i in list(mapping):
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||||
if len(i) > 1:
|
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multi_character_list.append(i)
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# conversion
|
||||
ipa = []
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for phone in convert_phone_set.multi_character_tokenize(xsampa, multi_character_list):
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ipa.append(mapping.get(phone, phone))
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ipa = ''.join(ipa)
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# strange conversion.
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ipa = ipa.replace('ɡ', 'g')
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return ipa
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line = line.strip()
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output = []
|
||||
#if sassc_active:
|
||||
# tokens = line.split(SEPERATOR)
|
||||
#else:
|
||||
tokens = line
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||||
for token in tokens:
|
||||
if token.isspace():
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||||
output.append(token)
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||||
continue
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||||
# Remove extraneous chars that IPA does not accept
|
||||
if sink == "sassc":
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cleaned_token = u""
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||||
for char in token:
|
||||
if sassc_active_ipa.get(char):
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cleaned_token += char
|
||||
token = cleaned_token
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mapped = mapping.get(token)
|
||||
if not mapped:
|
||||
print("WARNING: could not map token ", token, file=sys.stderr)
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else:
|
||||
output.append(mapped)
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||||
#if sassc_active:
|
||||
# output = SEPERATOR.join(output)
|
||||
#else:
|
||||
output = "".join(output)
|
||||
|
||||
return output
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||||
|
|
@ -1,35 +0,0 @@
|
|||
import os
|
||||
|
||||
#default_hvite_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'htk', 'config.HVite')
|
||||
|
||||
cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
|
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#config_hcopy = os.path.join(cygwin_dir, 'config', 'config.HCopy')
|
||||
#config_train = os.path.join(cygwin_dir, 'config', 'config.train')
|
||||
config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
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||||
#mkhmmdefs_pl = os.path.join(cygwin_dir, 'src', 'acoustic_model', 'mkhmmdefs.pl')
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||||
|
||||
#dbLexicon = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\lexicon.accdb
|
||||
#scriptBarbara = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\pronvars_barbara.perl
|
||||
#exeG2P = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\string2phon.exe
|
||||
|
||||
#[pyHTK]
|
||||
#configHVite = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\config.HVite
|
||||
#filePhoneList = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\phonelist_barbara.txt
|
||||
#AcousticModel = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\hmmdefs_16-2_barbara.compo
|
||||
|
||||
#dbLexicon = config['cLexicon']['dbLexicon']
|
||||
#scriptBarbara = config['cLexicon']['scriptBarbara']
|
||||
#exeG2P = config['cLexicon']['exeG2P']
|
||||
|
||||
#configHVite = config['pyHTK']['configHVite']
|
||||
#filePhoneList = config['pyHTK']['filePhoneList']
|
||||
#AcousticModel = config['pyHTK']['AcousticModel']
|
||||
|
||||
repo_dir = r'C:\Users\Aki\source\repos'
|
||||
ipa_xsampa_converter_dir = os.path.join(repo_dir, 'ipa-xsama-converter')
|
||||
forced_alignment_module_dir = os.path.join(repo_dir, 'forced_alignment')
|
||||
|
||||
fame_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus'
|
||||
experiments_dir = r'c:\OneDrive\Research\rug\experiments'
|
||||
|
||||
phonelist = os.path.join(experiments_dir, 'friesian', 'acoustic_model', 'config', 'phonelist_friesian.txt')
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
import os
|
||||
import sys
|
||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
import defaultfiles as default
|
||||
|
||||
sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
|
||||
from forced_alignment import forced_alignment
|
||||
|
||||
wav_file = r'C:\Users\Aki\source\repos\forced_alignment\notebooks\sample\10147-1464186409-1917281.wav'
|
||||
forced_alignment(
|
||||
wav_file,
|
||||
#'Australië'
|
||||
'BUFFETCOUPON COULISSEN DOUANE'
|
||||
)
|
||||
|
||||
|
|
@ -1,437 +1,257 @@
|
|||
import os
|
||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
import sys
|
||||
import csv
|
||||
import subprocess
|
||||
import configparser
|
||||
from collections import Counter
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
#from sklearn.metrics import confusion_matrix
|
||||
|
||||
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')
|
||||
## ======================= functions =======================
|
||||
|
||||
def read_fileFA(fileFA):
|
||||
"""
|
||||
read the result file of HTK forced alignment.
|
||||
this function only works when input is one word.
|
||||
"""
|
||||
with open(fileFA, 'r') as f:
|
||||
lines = f.read()
|
||||
lines = lines.split('\n')
|
||||
|
||||
phones = []
|
||||
for line in lines:
|
||||
line_split = line.split()
|
||||
if len(line_split) > 1:
|
||||
phones.append(line_split[2])
|
||||
|
||||
return ' '.join(phones)
|
||||
|
||||
|
||||
#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')
|
||||
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')
|
||||
|
||||
#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')
|
||||
#####################
|
||||
## USER DEFINE ##
|
||||
#####################
|
||||
curr_dir = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
|
||||
config_ini = curr_dir + '\\config.ini'
|
||||
forced_alignment_module = r'C:\Users\Aki\source\repos\forced_alignment'
|
||||
forced_alignment_module_old = r'C:\OneDrive\Research\rug\code\forced_alignment\forced_alignment'
|
||||
ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
|
||||
|
||||
csvfile = r"C:\OneDrive\Research\rug\stimmen\Frisian Variants Picture Task Stimmen.csv"
|
||||
experiments_dir = r'C:\OneDrive\Research\rug\experiments'
|
||||
data_dir = experiments_dir + '\\stimmen\\data'
|
||||
cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
|
||||
|
||||
# procedure
|
||||
convert_phones = 0
|
||||
make_dic_files = 0
|
||||
do_forced_alignment_htk = 1
|
||||
make_kaldi_data_files = 0
|
||||
make_kaldi_lexicon_txt = 0
|
||||
load_forced_alignment_kaldi = 0
|
||||
eval_forced_alignment = 0
|
||||
make_dic_files_short = 0
|
||||
do_forced_alignment = 0
|
||||
eval_forced_alignment = 1
|
||||
|
||||
|
||||
|
||||
## ======================= add paths =======================
|
||||
|
||||
sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
|
||||
sys.path.append(forced_alignment_module)
|
||||
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
|
||||
# for interactive window
|
||||
sys.path.append(curr_dir)
|
||||
import convert_xsampa2ipa
|
||||
import acoustic_model_functions as am_func
|
||||
|
||||
# for forced-alignment
|
||||
sys.path.append(forced_alignment_module_old)
|
||||
import pyHTK
|
||||
|
||||
|
||||
## ======================= load variables =======================
|
||||
config = configparser.ConfigParser()
|
||||
config.sections()
|
||||
config.read(config_ini)
|
||||
|
||||
FAME_dir = config['Settings']['FAME_dir']
|
||||
|
||||
lex_asr = FAME_dir + '\\lexicon\\lex.asr'
|
||||
lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
|
||||
|
||||
|
||||
## ======================= convert phones ======================
|
||||
if convert_phones:
|
||||
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
|
||||
|
||||
mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
|
||||
## check phones included in FAME!
|
||||
# the phones used in the lexicon.
|
||||
#phonelist = am_func.get_phonelist(lex_htk)
|
||||
|
||||
xls = pd.ExcelFile(excel_file)
|
||||
# the lines which include a specific phone.
|
||||
#lines = am_func.find_phone(lex_asr, 'x')
|
||||
|
||||
## 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))
|
||||
with open(csvfile, encoding="utf-8") as fin:
|
||||
lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
|
||||
next(lines, None) # skip the headers
|
||||
|
||||
filenames = []
|
||||
words = []
|
||||
pronunciations = []
|
||||
for line in lines:
|
||||
if line[1] is not '' and len(line) > 5:
|
||||
filenames.append(line[0])
|
||||
words.append(line[1])
|
||||
pron_xsampa = line[3]
|
||||
pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
|
||||
pron_ipa = pron_ipa.replace('ː', ':')
|
||||
pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
|
||||
|
||||
# adjust to phones used in the acoustic model.
|
||||
pron_famehtk = pron_famehtk.replace('sp', 'sil')
|
||||
pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
|
||||
pron_famehtk = pron_famehtk.replace('w :', 'wh')
|
||||
pron_famehtk = pron_famehtk.replace('e :', 'eh')
|
||||
pron_famehtk = pron_famehtk.replace('eh :', 'eh')
|
||||
pron_famehtk = pron_famehtk.replace('ih :', 'ih')
|
||||
|
||||
## check phones included in FAME!
|
||||
# the phones used in the lexicon.
|
||||
#phonelist = am_func.get_phonelist(lex_asr)
|
||||
#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
|
||||
#pron = []
|
||||
#for phoneme in pron_famehtk.split(' '):
|
||||
# pron.append(translation_key.get(phoneme, phoneme))
|
||||
#pronunciations.append(' '.join(pron_famehtk))
|
||||
pronunciations.append(pron_famehtk)
|
||||
|
||||
# the lines which include a specific phone.
|
||||
#lines = am_func.find_phone(lex_asr, 'x')
|
||||
# check if all phones are in the phonelist of the acoustic model.
|
||||
#phonelist = ' '.join(pronunciations)
|
||||
#np.unique(phonelist.split(' '))
|
||||
#phonelist.find(':')
|
||||
|
||||
filenames = np.array(filenames)
|
||||
words = np.array(words)
|
||||
pronunciations = np.array(pronunciations)
|
||||
|
||||
# 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(';', ':')
|
||||
del line, lines
|
||||
del pron_xsampa, pron_ipa, pron_famehtk
|
||||
|
||||
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(['/', ''])]
|
||||
np.save(data_dir + '\\filenames.npy', filenames)
|
||||
np.save(data_dir + '\\words.npy', words)
|
||||
np.save(data_dir + '\\pronunciations.npy', pronunciations)
|
||||
else:
|
||||
filenames = np.load(data_dir + '\\filenames.npy')
|
||||
words = np.load(data_dir + '\\words.npy')
|
||||
|
||||
pronunciations = np.load(data_dir + '\\pronunciations.npy')
|
||||
word_list = np.unique(words)
|
||||
|
||||
|
||||
## ======================= make dict files used for HTK. ======================
|
||||
if make_dic_files:
|
||||
word_list = np.unique(df['word'])
|
||||
output_dir = experiments_dir + r'\stimmen\dic'
|
||||
|
||||
output_type = 3
|
||||
|
||||
for word in word_list:
|
||||
htk_dict_file = htk_dict_dir + '\\' + word + '.dic'
|
||||
for word in word_list:
|
||||
WORD = word.upper()
|
||||
fileDic = output_dir + '\\' + word + '.dic'
|
||||
|
||||
# pronunciation variant of the target word.
|
||||
pronvar_ = df['famehtk'][df['word'].str.match(word)]
|
||||
# make dic file.
|
||||
pronvar_ = pronunciations[words == word]
|
||||
pronvar = np.unique(pronvar_)
|
||||
|
||||
# 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, 2, 4, 8, 16, 32, 64, 128, 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 = []
|
||||
for i, filename in enumerate(df['filename']):
|
||||
print('=== {0}/{1} ==='.format(i, len(df)))
|
||||
wav_file = os.path.join(wav_dir, filename)
|
||||
|
||||
if os.path.exists(wav_file) and i in df['filename'].keys():
|
||||
word = df['word'][i]
|
||||
WORD = word.upper()
|
||||
|
||||
# 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')
|
||||
fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
|
||||
pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, default.phonelist, acoustic_model)
|
||||
|
||||
prediction = am_func.read_fileFA(fa_file)
|
||||
predictions.append(prediction)
|
||||
|
||||
os.remove(label_file)
|
||||
print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
|
||||
else:
|
||||
predictions.append('')
|
||||
print('!!!!! file not found.')
|
||||
|
||||
predictions = np.array(predictions)
|
||||
#match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
|
||||
np.save(os.path.join(data_dir, 'predictions_hmm' + hmm_num_str + '.npy'), predictions)
|
||||
with open(fileDic, 'w') as f:
|
||||
for pvar in pronvar:
|
||||
f.write('{0}\t{1}\n'.format(WORD, pvar))
|
||||
|
||||
|
||||
## ======================= make files which is used for forced alignment by Kaldi =======================
|
||||
if make_kaldi_data_files:
|
||||
wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
|
||||
kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
|
||||
kaldi_data_dir = os.path.join(kaldi_work_dir, 'data', 'alignme')
|
||||
kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
|
||||
htk_dict_dir = os.path.join(experiments_dir, 'stimmen', 'dic_top3')
|
||||
## ======================= make dict files for most popular words. ======================
|
||||
if make_dic_files_short:
|
||||
output_dir = experiments_dir + r'\stimmen\dic'
|
||||
|
||||
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')
|
||||
#word = word_list[3]
|
||||
for word in word_list:
|
||||
WORD = word.upper()
|
||||
fileStat = output_dir + '\\' + word + '_stat.csv'
|
||||
|
||||
pronvar = pronunciations[words == word]
|
||||
c = Counter(pronvar)
|
||||
total_num = sum(c.values())
|
||||
|
||||
lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
|
||||
|
||||
predictions = []
|
||||
file_num_max = len(filenames)
|
||||
|
||||
# remove previous files.
|
||||
if os.path.exists(wav_scp):
|
||||
os.remove(wav_scp)
|
||||
if os.path.exists(text_file):
|
||||
os.remove(text_file)
|
||||
if os.path.exists(utt2spk):
|
||||
os.remove(utt2spk)
|
||||
|
||||
f_wav_scp = open(wav_scp, 'a', encoding="utf-8", newline='\n')
|
||||
f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
|
||||
f_utt2spk = open(utt2spk, 'a', encoding="utf-8", newline='\n')
|
||||
|
||||
# make wav.scp, text, and utt2spk files.
|
||||
for i in range(0, file_num_max):
|
||||
#for i in range(400, 410):
|
||||
print('=== {0}/{1} ==='.format(i+1, file_num_max))
|
||||
filename = filenames[i]
|
||||
wav_file = wav_dir + '\\' + filename
|
||||
|
||||
if os.path.exists(wav_file):
|
||||
speaker_id = 'speaker_' + str(i).zfill(4)
|
||||
utterance_id = filename.replace('.wav', '')
|
||||
utterance_id = utterance_id.replace(' ', '_')
|
||||
utterance_id = speaker_id + '-' + utterance_id
|
||||
|
||||
# wav.scp file
|
||||
wav_file_unix = wav_file.replace('\\', '/')
|
||||
wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/')
|
||||
|
||||
f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
|
||||
|
||||
# text file
|
||||
word = words[i].lower()
|
||||
f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
|
||||
|
||||
# utt2spk
|
||||
f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
|
||||
|
||||
f_wav_scp.close()
|
||||
f_text_file.close()
|
||||
f_utt2spk.close()
|
||||
with open(fileStat, 'w') as f:
|
||||
for key, value in c.items():
|
||||
f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, value/total_num*100, WORD, key))
|
||||
|
||||
|
||||
## ======================= 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
|
||||
## ======================= forced alignment =======================
|
||||
if do_forced_alignment:
|
||||
configHVite = cygwin_dir + r'\config\config.HVite'
|
||||
filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
|
||||
wav_dir = experiments_dir + r'\stimmen\wav'
|
||||
|
||||
# remove previous file.
|
||||
if os.path.exists(lexicon_txt):
|
||||
os.remove(lexicon_txt)
|
||||
#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'
|
||||
|
||||
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
|
||||
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
|
||||
|
||||
if os.path.exists(fileWav):
|
||||
word = words[i]
|
||||
WORD = word.upper()
|
||||
|
||||
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)
|
||||
# make label file.
|
||||
fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab')
|
||||
with open(fileLab, 'w') as f:
|
||||
lines = f.write(WORD)
|
||||
|
||||
# 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'
|
||||
fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
|
||||
fileFA = experiments_dir + r'\stimmen\FA_short' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
|
||||
|
||||
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:
|
||||
pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
|
||||
prediction = read_fileFA(fileFA)
|
||||
predictions.append(prediction)
|
||||
|
||||
# pronunciation variant of the target word.
|
||||
pronvar_ = pronunciations[words == word]
|
||||
# remove ''
|
||||
pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
|
||||
os.remove(fileLab)
|
||||
print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
|
||||
else:
|
||||
predictions.append('')
|
||||
print('!!!!! file not found.')
|
||||
|
||||
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_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 = 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:
|
||||
match_num = []
|
||||
for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
|
||||
#hmm_num = 256
|
||||
hmm_num_str = str(hmm_num)
|
||||
match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
|
||||
|
||||
# use dic_short?
|
||||
if 1:
|
||||
pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
|
||||
for word in word_list:
|
||||
fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
|
||||
pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
|
||||
|
||||
# see only words which appears in top 3.
|
||||
match_short = []
|
||||
for line in match:
|
||||
word = line[0]
|
||||
WORD = word.upper()
|
||||
pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
|
||||
|
||||
if line[1] in pronvar:
|
||||
match_short.append(line)
|
||||
#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')
|
||||
|
||||
# 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)]
|
||||
|
||||
match_short = np.array(match_short)
|
||||
match = np.copy(match_short)
|
||||
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)
|
||||
|
||||
# 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]])
|
||||
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 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')
|
||||
Loading…
Reference in New Issue