119 lines
3.4 KiB
Python
119 lines
3.4 KiB
Python
import os
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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import 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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import novoapi_functions
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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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# mapping from ipa to xsampa
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
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#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
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# ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
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# if not ipa_converted == ipa:
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# print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
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ipas = []
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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').replace(';', ':')
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ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
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ipa = ipa.replace('ː', ':').replace(' ', '')
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ipas.append(ipa)
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else:
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ipas.append('')
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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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# 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 load_transcriptions_novo70(clean_wav_dir):
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""" extract rows of which ipa is written in novo70 phonset. """
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df = load_transcriptions_clean(clean_wav_dir)
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df_novo70 = pd.DataFrame(index=[], columns=list(df.keys()))
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for index, row in df.iterrows():
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not_in_novo70 = novoapi_functions.phones_not_in_novo70(row['ipa'])
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if len(not_in_novo70) == 0:
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df_novo70 = df_novo70.append(row, ignore_index=True)
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return df_novo70
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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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def load_pronunciations(WORD, htk_dic):
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""" load pronunciation variants from HTK dic file.
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Args:
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WORD (str): word in capital letters.
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htk_dic (path): HTK dict file.
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Returns:
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(pronunciations) (list): pronunciation variants of WORD.
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Notes:
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Because this function loads all contents from htk_dic file,
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it is not recommended to use for large lexicon.
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"""
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with open(htk_dic) as f:
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lines = f.read().replace(' sil', '')
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lines = lines.split('\n')
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return [' '.join(line.split(' ')[1:])
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for line in lines if line.split(' ')[0]==WORD] |