based on the recommendation from linguists, the total number of phones is reduced.
This commit is contained in:
yemaozi88
parent
5a587e0422
commit
bbed340228
Binary file not shown.
@ -5,7 +5,21 @@ VisualStudioVersion = 15.0.26730.12
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MinimumVisualStudioVersion = 10.0.40219.1
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Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "acoustic_model", "acoustic_model\acoustic_model.pyproj", "{4D8C8573-32F0-4A62-9E62-3CE5CC680390}"
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EndProject
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Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "forced_alignment", "..\forced_alignment\forced_alignment\forced_alignment.pyproj", "{92E4D819-38D0-467A-ABEE-09662EEAA084}"
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution Items", "{3DCEA49A-8FD7-4255-A223-573DCD2595E0}"
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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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..\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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..\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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..\forced_alignment\forced_alignment\test_environment.py = ..\forced_alignment\forced_alignment\test_environment.py
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EndProjectSection
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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@ -15,8 +29,6 @@ Global
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GlobalSection(ProjectConfigurationPlatforms) = postSolution
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{4D8C8573-32F0-4A62-9E62-3CE5CC680390}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{4D8C8573-32F0-4A62-9E62-3CE5CC680390}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{92E4D819-38D0-467A-ABEE-09662EEAA084}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{92E4D819-38D0-467A-ABEE-09662EEAA084}.Release|Any CPU.ActiveCfg = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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Binary file not shown.
@ -3,7 +3,9 @@ import sys
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import tempfile
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import configparser
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import subprocess
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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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@ -12,13 +14,26 @@ repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
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curr_dir = repo_dir + '\\acoustic_model'
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config_ini = curr_dir + '\\config.ini'
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output_dir = 'd:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
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forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced-alignment'
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forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
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dataset_list = ['devel', 'test', 'train']
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# procedure
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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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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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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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sys.path.append(forced_alignment_module)
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from forced_alignment import convert_phone_set
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import acoustic_model_functions as am_func
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@ -30,88 +45,294 @@ config.read(config_ini)
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config_hcopy = config['Settings']['config_hcopy']
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config_train = config['Settings']['config_train']
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mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl']
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FAME_dir = config['Settings']['FAME_dir']
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lexicon_file = FAME_dir + '\\lexicon\\lex.asr'
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dataset_list = ['devel', 'test', 'train']
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lex_asr = FAME_dir + '\\lexicon\\lex.asr'
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lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
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lex_oov = FAME_dir + '\\lexicon\\lex.oov'
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lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk'
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#lex_ipa = FAME_dir + '\\lexicon\\lex.ipa'
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#lex_ipa_ = FAME_dir + '\\lexicon\\lex.ipa_'
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#lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk'
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lex_htk = FAME_dir + '\\lexicon\\lex_original.htk'
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lex_htk_ = FAME_dir + '\\lexicon\\lex.htk'
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hcompv_scp = output_dir + '\\scp\\combined.scp'
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combined_mlf = output_dir + '\\label\\combined.mlf'
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model_dir = output_dir + '\\model'
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model0_dir = model_dir + '\\hmm0'
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proto_init = model_dir + '\\proto38'
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proto_name = 'proto'
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phonelist = output_dir + '\\config\\phonelist_friesian.txt'
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hmmdefs_name = 'hmmdefs'
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## ======================= extract features =======================
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##dataset = dataset_list[0]
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#for dataset in dataset_list:
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# print(dataset)
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if extract_features:
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print("==== extract features ====\n")
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## make a script file for HCopy
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#hcopy_scp = tempfile.NamedTemporaryFile(mode='w', delete=False)
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#hcopy_scp.close()
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for dataset in dataset_list:
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print(dataset)
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## using the filelist in FAME! corpus
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#feature_dir = output_dir + '\\mfc\\' + dataset
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#am_func.make_hcopy_scp_from_filelist_in_fame(FAME_dir, dataset, feature_dir, hcopy_scp.name)
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# a script file for HCopy
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hcopy_scp = tempfile.NamedTemporaryFile(mode='w', delete=False)
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hcopy_scp.close()
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## extract features
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#subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name
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#subprocess.call(subprocessStr, shell=True)
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# get a list of features (hcopy.scp) from the filelist in FAME! corpus
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feature_dir = output_dir + '\\mfc\\' + dataset
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am_func.make_hcopy_scp_from_filelist_in_fame(FAME_dir, dataset, feature_dir, hcopy_scp.name)
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#os.remove(hcopy_scp.name)
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# extract features
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subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name
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subprocess.call(subprocessStr, shell=True)
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## ======================= make a list of features =======================
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##dataset = dataset_list[2]
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#for dataset in dataset_list:
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# print(dataset)
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if make_feature_list:
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print("==== make a list of features ====\n")
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# feature_dir = output_dir + '\\mfc\\' + dataset
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# hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp'
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for dataset in dataset_list:
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print(dataset)
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# am_func.make_filelist(feature_dir, hcompv_scp)
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feature_dir = output_dir + '\\mfc\\' + dataset
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hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp'
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am_func.make_filelist(feature_dir, hcompv_scp)
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## ======================= check the phonemes used in the lexicon =======================
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phonelist = am_func.get_phonelist(lexicon_file) # 49
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phonelist_list = list(phonelist)
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## ======================= convert lexicon from ipa to fame_htk =======================
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if conv_lexicon:
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print('==== convert lexicon from ipa 2 fame ====\n')
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#lines_g1 = am_func.find_phone(lexicon_file, 'g')
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#lines_g2 = am_func.find_phone(lexicon_file, 'ɡ')
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# lex.asr is Kaldi compatible version of lex.ipa.
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# to check...
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#lexicon_ipa = pd.read_table(lex_ipa, names=['word', 'pronunciation'])
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#with open(lex_ipa_, "w", encoding="utf-8") as fout:
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# for word, pronunciation in zip(lexicon_ipa['word'], lexicon_ipa['pronunciation']):
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# # ignore nasalization and '.'
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# pronunciation_ = pronunciation.replace(u'ⁿ', '')
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# pronunciation_ = pronunciation_.replace('.', '')
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# pronunciation_split = convert_phone_set.split_ipa_fame(pronunciation_)
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# fout.write("{0}\t{1}\n".format(word, ' '.join(pronunciation_split)))
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# convert each lexicon from ipa description to fame_htk phoneset.
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am_func.ipa2famehtk_lexicon(lex_oov, lex_oov_htk)
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am_func.ipa2famehtk_lexicon(lex_asr, lex_asr_htk)
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# combine lexicon
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# pronunciations which is not found in lex.asr are generated using G2P and listed in lex.oov.
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# therefore there is no overlap between lex_asr and lex_oov.
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am_func.combine_lexicon(lex_asr_htk, lex_oov_htk, lex_htk)
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## ======================= check if all the phones are successfully converted =======================
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if check_lexicon:
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print("==== check if all the phones are successfully converted. ====\n")
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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, 'g')
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# statistics over the lexicon
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lexicon_htk = pd.read_table(lex_htk, names=['word', 'pronunciation'])
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pronunciation = lexicon_htk['pronunciation']
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phones_all = []
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for word in pronunciation:
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phones_all = phones_all + word.split()
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c = Counter(phones_all)
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## =======================
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## manually make changes to the pronunciation dictionary and save it as lex.htk
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## =======================
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# (1) Replace all tabs with single space;
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# (2) Put a '\' before any dictionary entry beginning with single quote
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#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
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## ======================= make label file =======================
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dataset = 'train'
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hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp'
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script_list = FAME_dir + '\\data\\' + dataset + '\\text'
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if make_mlf:
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print("==== make mlf ====\n")
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lexicon = pd.read_table(lexicon_file, names=['word', 'pronunciation'])
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print("generating word level transcription...\n")
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for dataset in dataset_list:
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hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp'
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hcompv_scp2 = output_dir + '\\scp\\' + dataset + '_all_words_in_lexicon.scp'
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script_list = FAME_dir + '\\data\\' + dataset + '\\text'
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mlf_word = output_dir + '\\label\\' + dataset + '_word.mlf'
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mlf_phone = output_dir + '\\label\\' + dataset + '_phone.mlf'
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with open(hcompv_scp) as fin:
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# lexicon
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lexicon_htk = pd.read_table(lex_htk, names=['word', 'pronunciation'])
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# list of features
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with open(hcompv_scp) as fin:
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features = fin.read()
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features = features.split('\n')
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with open(script_list, "rt", encoding="utf-8") as fin:
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# list of scripts
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with open(script_list, "rt", encoding="utf-8") as fin:
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scripts = fin.read()
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scripts = pd.Series(scripts.split('\n'))
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i = 0
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missing_words = []
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fscp = open(hcompv_scp2, 'wt')
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fmlf = open(mlf_word, "wt", encoding="utf-8")
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fmlf.write("#!MLF!#\n")
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feature_nr = 1
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for feature in features:
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sys.stdout.write("\r%d/%d" % (feature_nr, len(features)))
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sys.stdout.flush()
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feature_nr += 1
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file_basename = os.path.basename(feature).replace('.mfc', '')
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feature = features[0]
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file_basename = os.path.basename(feature).replace('.mfc', '')
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# get words from scripts.
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try:
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script = scripts[scripts.str.contains(file_basename)]
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except IndexError:
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script = []
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# get words from scripts.
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script = scripts[scripts.str.contains(file_basename)]
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script_id = script.index[0]
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script_txt = script.get(script_id)
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script_words = script_txt.split(' ')
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del script_words[0]
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if len(script) != 0:
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script_id = script.index[0]
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script_txt = script.get(script_id)
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script_words = script_txt.split(' ')
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del script_words[0]
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# make the label file.
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SCRIPT_WORDS = []
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script_prons = []
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all_prons_found = 1
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for word in script_words:
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SCRIPT_WORDS.append(word.upper())
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extracted = lexicon[lexicon['word']==word]
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# check if all words can be found in the lexicon.
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SCRIPT_WORDS = []
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script_prons = []
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is_in_lexicon = 1
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for word in script_words:
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WORD = word.upper()
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SCRIPT_WORDS.append(WORD)
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extracted = lexicon_htk[lexicon_htk['word']==WORD]
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if len(extracted) == 0:
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missing_words.append(word)
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script_prons.append(extracted)
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all_prons_found *= len(extracted)
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# make the dict file.
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is_in_lexicon *= len(extracted)
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convert_phone_set.ipa2fame(phonelist_list)
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phonelist_list
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# if all pronunciations are found in the lexicon, update scp and mlf files.
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if is_in_lexicon:
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# add the feature filename into the .scp file.
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fscp.write("{}\n".format(feature))
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i += 1
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# add the words to the mlf file.
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fmlf.write('\"*/{}.lab\"\n'.format(file_basename))
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#fmlf.write('{}'.format('\n'.join(SCRIPT_WORDS)))
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for word_ in SCRIPT_WORDS:
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if word_[0] == '\'':
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word_ = '\\' + word_
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fmlf.write('{}\n'.format(word_))
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fmlf.write('.\n')
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print("\n{0} has {1} samples.\n".format(dataset, i))
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np.save(output_dir + '\\missing_words' + '_' + dataset + '.npy', missing_words)
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fscp.close()
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fmlf.close()
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## generate phone level transcription
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print("generating phone level transcription...\n")
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mkphones = output_dir + '\\label\\mkphones0.txt'
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subprocessStr = r"HLEd -l * -d " + lex_htk_ + ' -i ' + mlf_phone + ' ' + mkphones + ' ' + mlf_word
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subprocess.call(subprocessStr, shell=True)
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## ======================= combined scps and mlfs =======================
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if combine_files:
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print("==== combine scps and mlfs ====\n")
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fscp = open(hcompv_scp, 'wt')
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fmlf = open(combined_mlf, 'wt')
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for dataset in dataset_list:
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fmlf.write("#!MLF!#\n")
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for dataset in dataset_list:
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each_mlf = output_dir + '\\label\\' + dataset + '_phone.mlf'
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each_scp = output_dir + '\\scp\\' + dataset + '_all_words_in_lexicon.scp'
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with open(each_mlf, 'r') as fin:
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lines = fin.read()
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lines = lines.split('\n')
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fmlf.write('\n'.join(lines[1:]))
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with open(each_scp, 'r') as fin:
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lines = fin.read()
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fscp.write(lines)
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fscp.close()
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fmlf.close()
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## ======================= flat start monophones =======================
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if flat_start:
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subprocessStr = 'HCompV -T 1 -C ' + config_train + ' -m -v 0.01 -S ' + hcompv_scp + ' -M ' + model0_dir + ' ' + proto_init
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subprocess.call(subprocessStr, shell=True)
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# allocate mean & variance to all phones in the phone list
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subprocessStr = 'perl ' + mkhmmdefs_pl + ' ' + model0_dir + '\\proto38' + ' ' + phonelist + ' > ' + model0_dir + '\\' + hmmdefs_name
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subprocess.call(subprocessStr, shell=True)
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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 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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modelN_dir = model_dir + '\\hmm' + str(mix_num) + '-' + str(iter_num)
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if not os.path.exists(modelN_dir):
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os.makedirs(modelN_dir)
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if iter_num == 1 and mix_num == 1:
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modelN_dir_pre = model0_dir
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else:
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modelN_dir_pre = model_dir + '\\hmm' + str(mix_num) + '-' + str(iter_num_pre)
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## re-estimation
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subprocessStr = 'HERest -T 1 -C ' + config_train + ' -v 0.01 -I ' + combined_mlf + ' -H ' + modelN_dir_pre + '\\' + hmmdefs_name + ' -M ' + modelN_dir + ' ' + phonelist + ' -S ' + hcompv_scp
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subprocess.call(subprocessStr, shell=True)
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mix_num_next = mix_num * 2
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modelN_dir_next = model_dir + '\\hmm' + str(mix_num_next) + '-0'
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if not os.path.exists(modelN_dir_next):
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os.makedirs(modelN_dir_next)
|
||||
|
||||
header_file = modelN_dir + '\\mix' + str(mix_num_next) + '.hed'
|
||||
with open(header_file, 'w') as fout:
|
||||
fout.write("MU %d {*.state[2-4].mix}" % (mix_num_next))
|
||||
|
||||
subprocessStr = 'HHEd -T 1 -H ' + modelN_dir + '\\' + hmmdefs_name + ' -M ' + modelN_dir_next + ' ' + header_file + ' ' + phonelist
|
||||
subprocess.call(subprocessStr, shell=True)
|
||||
|
||||
|
||||
### ======================= forced alignment =======================
|
||||
#if forced_alignment:
|
||||
# try:
|
||||
# scripts.run_command([
|
||||
# 'HVite','-T', '1', '-a', '-C', configHVite,
|
||||
# '-H', AcousticModel, '-m', '-I',
|
||||
# mlf_file, '-i', fa_file, '-S',
|
||||
# script_file, htk_dict_file, filePhoneList
|
||||
# ])
|
||||
# except:
|
||||
# print("\033[91mHVite command failed with these input files:\033[0m")
|
||||
# print(_debug_show_file('HVite config', configHVite))
|
||||
# print(_debug_show_file('Accoustic model', AcousticModel))
|
||||
# print(_debug_show_file('Master Label file', mlf_file))
|
||||
# print(_debug_show_file('Output', fa_file))
|
||||
# print(_debug_show_file('Script file', script_file))
|
||||
# print(_debug_show_file('HTK dictionary', htk_dict_file))
|
||||
# print(_debug_show_file('Phoneme list', filePhoneList))
|
||||
# raise
|
||||
|
||||
|
||||
##os.remove(hcopy_scp.name)
|
||||
|
||||
@ -1,9 +1,18 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
## ======================= user define =======================
|
||||
repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
|
||||
curr_dir = repo_dir + '\\acoustic_model'
|
||||
forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
|
||||
|
||||
|
||||
sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
|
||||
sys.path.append(forced_alignment_module)
|
||||
from forced_alignment import convert_phone_set
|
||||
|
||||
|
||||
def make_hcopy_scp_from_filelist_in_fame(FAME_dir, dataset, feature_dir, hcopy_scp):
|
||||
@ -62,3 +71,32 @@ def find_phone(lexicon_file, phone):
|
||||
if phone in pron:
|
||||
extracted.append(line)
|
||||
return extracted
|
||||
|
||||
|
||||
def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
|
||||
""" Convert a lexicon file from IPA to HTK format for FAME! corpus. """
|
||||
|
||||
lexicon_in = pd.read_table(lexicon_file_in, names=['word', 'pronunciation'])
|
||||
with open(lexicon_file_out, "w", encoding="utf-8") as fout:
|
||||
for word, pronunciation in zip(lexicon_in['word'], lexicon_in['pronunciation']):
|
||||
pronunciation_no_space = pronunciation.replace(' ', '')
|
||||
pronunciation_famehtk = convert_phone_set.ipa2famehtk(pronunciation_no_space)
|
||||
if 'ceh' not in pronunciation_famehtk and 'sh' not in pronunciation_famehtk:
|
||||
fout.write("{0}\t{1}\n".format(word.upper(), pronunciation_famehtk))
|
||||
|
||||
|
||||
def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
|
||||
""" Combine two lexicon files and sort by words. """
|
||||
|
||||
with open(lexicon_file1, "rt", encoding="utf-8") as fin:
|
||||
lines1 = fin.read()
|
||||
lines1 = lines1.split('\n')
|
||||
with open(lexicon_file2, "rt", encoding="utf-8") as fin:
|
||||
lines2 = fin.read()
|
||||
lines2 = lines2.split('\n')
|
||||
|
||||
lex1 = pd.read_table(lexicon_file1, names=['word', 'pronunciation'])
|
||||
lex2 = pd.read_table(lexicon_file2, names=['word', 'pronunciation'])
|
||||
lex = pd.concat([lex1, lex2])
|
||||
lex = lex.sort_values(by='word', ascending=True)
|
||||
lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
|
||||
@ -1,4 +1,5 @@
|
||||
[Settings]
|
||||
config_hcopy = c:\cygwin64\home\Aki\acoustic_model\config\config.HCopy
|
||||
config_train = c:\cygwin64\home\Aki\acoustic_model\config\config.train
|
||||
mkhmmdefs_pl = c:\cygwin64\home\Aki\acoustic_model\src\acoustic_model\mkhmmdefs.pl
|
||||
FAME_dir = d:\OneDrive\Research\rug\experiments\friesian\corpus
|
||||
22
acoustic_model/performance_check.py
Normal file
22
acoustic_model/performance_check.py
Normal file
@ -0,0 +1,22 @@
|
||||
### ======================= forced alignment =======================
|
||||
#if forced_alignment:
|
||||
# try:
|
||||
# scripts.run_command([
|
||||
# 'HVite','-T', '1', '-a', '-C', configHVite,
|
||||
# '-H', AcousticModel, '-m', '-I',
|
||||
# mlf_file, '-i', fa_file, '-S',
|
||||
# script_file, htk_dict_file, filePhoneList
|
||||
# ])
|
||||
# except:
|
||||
# print("\033[91mHVite command failed with these input files:\033[0m")
|
||||
# print(_debug_show_file('HVite config', configHVite))
|
||||
# print(_debug_show_file('Accoustic model', AcousticModel))
|
||||
# print(_debug_show_file('Master Label file', mlf_file))
|
||||
# print(_debug_show_file('Output', fa_file))
|
||||
# print(_debug_show_file('Script file', script_file))
|
||||
# print(_debug_show_file('HTK dictionary', htk_dict_file))
|
||||
# print(_debug_show_file('Phoneme list', filePhoneList))
|
||||
# raise
|
||||
|
||||
|
||||
##os.remove(hcopy_scp.name)
|
||||
Loading…
Reference in New Issue
Block a user