136 lines
4.2 KiB
Python
136 lines
4.2 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 sys
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import csv
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#import subprocess
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#from collections import Counter
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#import re
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import numpy as np
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import pandas as pd
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#import matplotlib.pyplot as plt
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#from sklearn.metrics import confusion_matrix
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import acoustic_model_functions as am_func
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import convert_xsampa2ipa
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import defaultfiles as default
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from forced_alignment import pyhtk, convert_phone_set
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import novoapi
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import novoapi_functions
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## ======================= novo phoneset ======================
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phoneset_ipa, phoneset_novo70, translation_key = novoapi_functions.load_phonset()
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# As per Nederlandse phoneset_aki.xlsx recieved from David
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# [ɔː] oh / ohr
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# [ɪː] ih / ihr
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# [iː] iy
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# [œː] uh
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# [ɛː] eh
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# [w] wv in IPA written as ʋ.
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david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'w']
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## ======================= extract words which is written only with novo70 ======================
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
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stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx)
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df = pd.read_excel(stimmen_transcription_, 'frequency')
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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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transcription_ipa = list(df['IPA'])
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# transcription mistake?
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transcription_ipa = [ipa.replace(';', 'ː') for ipa in transcription_ipa if not ipa=='pypɪl' and not pd.isnull(ipa)]
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transcription_ipa = [ipa.replace('ˑ', '') for ipa in transcription_ipa] # only one case.
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not_in_novo70 = []
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all_in_novo70 = []
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for ipa in transcription_ipa:
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ipa = ipa.replace(':', 'ː')
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ipa = convert_phone_set.split_ipa(ipa)
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not_in_novo70_ = [phone for phone in ipa
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if not phone in phoneset_ipa and not phone in david_suggestion]
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not_in_novo70_ = [phone.replace('sp', '') for phone in not_in_novo70_]
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not_in_novo70_ = [phone.replace(':', '') for phone in not_in_novo70_]
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not_in_novo70_ = [phone.replace('ː', '') for phone in not_in_novo70_]
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if len(not_in_novo70_) == 0:
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all_in_novo70.append(''.join(ipa))
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#translation_key.get(phone, phone)
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not_in_novo70.extend(not_in_novo70_)
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not_in_novo70_list = list(set(not_in_novo70))
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## check which phone is used in stimmen but not in novo70
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# 'ʀ', 'ʁ',
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# 'ɒ', 'ɐ',
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# 'o', 'a' (o:, a:?)
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# [e] 'nyːver mɑntsjə' (1)
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# [ɾ] 'ɪːɾ'(1)
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# [ɹ] 'iːjəɹ' (1), 'ɪ:ɹ' (1)
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# [ø] 'gʀøtəpi:r'(1), 'grøtəpi:r'(1)
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# [æ] 'røːzəʀæt'(2), 'røːzəræt'(1)
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# [ʊ] 'ʊ'(1) --> can be ʏ (uh)??
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# [χ] --> can be x??
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def search_phone_ipa(x, phone_list):
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x_in_item = []
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for ipa in phone_list:
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ipa_original = ipa
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ipa = ipa.replace(':', 'ː')
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ipa = convert_phone_set.split_ipa(ipa)
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if x in ipa and not x+':' in ipa:
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x_in_item.append(ipa_original)
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return x_in_item
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#search_phone_ipa('ø', transcription_ipa)
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df = pd.read_excel(stimmen_transcription_, '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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else:
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ipas.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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# find options which all phones are in novo70.
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#word_list = list(set(df['word']))
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#word_list = [word for word in word_list if not pd.isnull(word)]
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#word = word_list[1]
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## pronunciation variants of 'word'
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#df_ = df[df['word'] == word]['xsampa']
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##pronunciation_variant = list(set(df_))
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cols = ['word', 'ipa', 'frequency']
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df_samples = pd.DataFrame(index=[], columns=cols)
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for ipa in all_in_novo70:
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ipa = ipa.replace('ː', ':')
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samples = df[df['ipa'] == ipa]
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word = list(set(samples['word']))[0]
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samples_Series = pd.Series([word, ipa, len(samples)], index=df_samples.columns)
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df_samples = df_samples.append(samples_Series, ignore_index=True) |