dataset is made.
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yemaozi88
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@ -343,6 +343,16 @@ def word2htk(word):
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return ''.join([fame_asr.translation_key_word2htk.get(i, i) for i in word])
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def ipa2asr(ipa):
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curr_dir = os.path.dirname(os.path.abspath(__file__))
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translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
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ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
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ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
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asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
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return ''.join(asr_splitted)
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def ipa2htk(ipa):
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curr_dir = os.path.dirname(os.path.abspath(__file__))
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translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
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@ -9,7 +9,7 @@ import sys
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import shutil
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import glob
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#import numpy as np
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import numpy as np
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import pandas as pd
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#import matplotlib.pyplot as plt
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#from sklearn.metrics import confusion_matrix
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@ -75,24 +75,22 @@ lattice_file = os.path.join(config_dir, 'stimmen.ltc')
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hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp')
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## ======================= make test data ======================
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# copy wav files which is in the stimmen data.
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## ======================= load test data ======================
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stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
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fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
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df = stimmen_functions.load_transcriptions()
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df = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
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df = stimmen_functions.add_row_asr(df)
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df = stimmen_functions.add_row_htk(df)
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word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
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word_list = sorted(word_list)
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# pronunciation variants
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for word in word_list:
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df_ = df[df['word']==word]
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print('{0} has {1} variants'.format(word, len(np.unique(df_['htk'])))
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# after manually removed files which does not contain clear sound,
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# update df as df_test.
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wav_file_list = glob.glob(os.path.join(stimmen_test_dir, '*.wav'))
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df_test = pd.DataFrame(index=[], columns=list(df.keys()))
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for wav_file in wav_file_list:
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filename = os.path.basename(wav_file)
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df_ = df[df['filename'].str.match(filename)]
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df_test = pd.concat([df_test, df_])
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#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
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#output = pyhtk.recognition(
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# os.path.join(default.htk_dir, 'config', 'config.rec',
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@ -102,58 +100,21 @@ for wav_file in wav_file_list:
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# os.path.join(config_dir, 'phonelist.txt'),
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# hvite_scp)
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htk = [fame_functions.ipa2htk(ipa) for ipa in df['ipa']]
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#pyhtk.create_label_file(
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# row['word'],
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# os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
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ipa = 'e:χ'
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fame_functions.ipa2htk(ipa)
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# Filename, Word, Self Xsampa
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df = pd.read_excel(xls, 'original')
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ipas = []
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famehtks = []
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for xsampa in df['Self Xsampa']:
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if not isinstance(xsampa, float): # 'NaN'
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# typo?
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xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
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xsampa = xsampa.replace(';', ':')
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ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
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ipa = ipa.replace('ː', ':')
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ipa = ipa.replace(' ', '')
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ipas.append(ipa)
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famehtk = convert_phone_set.ipa2famehtk(ipa)
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famehtks.append(famehtk)
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else:
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ipas.append('')
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famehtks.append('')
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# extract interesting cols.
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df = pd.DataFrame({'filename': df['Filename'],
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'word': df['Word'],
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'xsampa': df['Self Xsampa'],
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'ipa': pd.Series(ipas),
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'famehtk': pd.Series(famehtks)})
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# cleansing.
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df = df[~df['famehtk'].isin(['/', ''])]
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word_list = np.unique(df['word'])
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## ======================= make dict files used for HTK. ======================
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if make_htk_dict_files:
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output_type = 3
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for word in word_list:
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htk_dict_file = htk_dict_dir + '\\' + word + '.dic'
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# pronunciation variant of the target word.
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pronvar_ = df['famehtk'][df['word'].str.match(word)]
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## ======================= make a HTK dic file ======================
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#if make_htk_dic_file:
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# output_type = 3
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dictionary_txt = os.path.join(default.htk_dir, 'lexicon', 'stimmen.dic')
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#for word in word_list:
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word = word_list[2]
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# pronunciation variant of the target word.
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pronunciations = df_test['asr'][df_test['word'].str.match(word)]
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# make dic file.
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am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
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#am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
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## ======================= forced alignment using HTK =======================
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@ -1,13 +1,15 @@
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import os
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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import glob
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import pandas as pd
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import convert_xsampa2ipa
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import defaultfiles as default
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import fame_functions
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def load_transcriptions():
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def _load_transcriptions():
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stimmen_transcription = pd.ExcelFile(default.stimmen_transcription_xlsx)
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df = pd.read_excel(stimmen_transcription, 'original')
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@ -34,5 +36,48 @@ def load_transcriptions():
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'word': df['Word'],
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'xsampa': df['Self Xsampa'],
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'ipa': pd.Series(ipas)})
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df_ = df_[~df_['ipa'].str.contains('/')]
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# not valid inputs, but seperator.
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df_ = df_[~df_['ipa'].str.contains('/')]
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return df_.dropna()
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def load_transcriptions():
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""" in default.stimmen_transcription_xlsx
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rows of which wav files can be easily found"""
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df = _load_transcriptions()
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df_ = pd.DataFrame(index=[], columns=list(df.keys()))
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for index, row in df.iterrows():
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filename = row['filename']
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if isinstance(filename, str):
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wav_file = os.path.join(default.stimmen_wav_dir, filename)
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if os.path.exists(wav_file):
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df_ = df_.append(row, ignore_index=True)
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return df_
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def load_transcriptions_clean(clean_wav_dir):
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df = _load_transcriptions()
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wav_file_list = glob.glob(os.path.join(clean_wav_dir, '*.wav'))
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df_clean = pd.DataFrame(index=[], columns=list(df.keys()))
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for wav_file in wav_file_list:
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filename = os.path.basename(wav_file)
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df_ = df[df['filename'].str.match(filename)]
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df_clean = pd.concat([df_clean, df_])
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return df_clean
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def add_row_htk(df):
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""" df['htk'] is made from df['ipa'] and added. """
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htk = []
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for index, row in df.iterrows():
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htk.append(fame_functions.ipa2htk(row['ipa']))
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return df.assign(htk=htk)
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def add_row_asr(df):
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""" df['asr'] is made from df['ipa'] and added. """
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asr = []
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for index, row in df.iterrows():
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asr.append(fame_functions.ipa2asr(row['ipa']))
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return df.assign(asr=asr)
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@ -1,9 +1,7 @@
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import os
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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import sys
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import shutil
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import glob
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#import numpy as np
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import pandas as pd
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@ -24,23 +22,27 @@ from htk import pyhtk
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## ======================= make test data ======================
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# copy wav files which is in the stimmen data.
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stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
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fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
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## copy wav files which is in the stimmen data.
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df = stimmen_functions.load_transcriptions()
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#word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
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#word_list = sorted(word_list)
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#for index, row in df.iterrows():
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# filename = row['filename']
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# if isinstance(filename, str):
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# wav_file = os.path.join(default.stimmen_wav_dir, filename)
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# if os.path.exists(wav_file):
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# shutil.copy(wav_file, os.path.join(stimmen_test_dir, filename))
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# pyhtk.create_label_file(
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# row['word'],
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# os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
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# wav_file = os.path.join(default.stimmen_wav_dir, filename)
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# shutil.copy(wav_file, os.path.join(stimmen_test_dir, filename))
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# after manually removed files which has too much noise and multiple words...
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# update the info.
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df_clean = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
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# count how many files are removed due to the quality.
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word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
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word_list = sorted(word_list)
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for word in word_list:
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df_ = df[df['word']==word]
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df_clean_ = df_clean[df_clean['word']==word]
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print('word {0} has {1} clean files among {2} files ({3:.2f} [%]).'.format(
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word, len(df_clean_), len(df_), len(df_clean_)/len(df_)*100))
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## check phones included in stimmen but not in FAME!
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@ -59,3 +61,4 @@ for ipa in df['ipa']:
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ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
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if ':' in ipa_splitted:
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print(ipa_splitted)
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