101 lines
3.1 KiB
Python
101 lines
3.1 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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## ======================= novo phoneset ======================
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translation_key = dict()
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#phonelist_novo70_ = pd.ExcelFile(default.phonelist_novo70_xlsx)
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#df = pd.read_excel(phonelist_novo70_, 'list')
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## *_simple includes columns which has only one phone in.
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#for ipa, novo70 in zip(df['IPA_simple'], df['novo70_simple']):
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# if not pd.isnull(ipa):
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# print('{0}:{1}'.format(ipa, novo70))
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# translation_key[ipa] = novo70
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#phonelist_novo70 = np.unique(list(df['novo70_simple']))
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phoneset_ipa = []
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phoneset_novo70 = []
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with open(default.novo70_phoneset, "rt", encoding="utf-8") as fin:
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lines = fin.read()
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lines = lines.split('\n')
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for line in lines:
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words = line.split('\t')
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if len(words) > 1:
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novo70 = words[0]
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ipa = words[1]
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phoneset_ipa.append(ipa)
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phoneset_novo70.append(novo70)
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translation_key[ipa] = novo70
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phoneset_ipa = np.unique(phoneset_ipa)
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phoneset_novo70 = np.unique(phoneset_novo70)
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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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david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː']
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## ======================= convert phones ======================
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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_, 'check')
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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(';', ':') 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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for ipa in transcription_ipa:
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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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#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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def search_phone_ipa(x, phone_list):
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return [phone for phone in phone_list if x in convert_phone_set.split_ipa(phone)]
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# 'ɐ', 'ɒ', 'w', 'æ', 'ʀ', 'ʁ',
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# 'œː', 'ɾ',
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# 'o', 'a'
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# [e] 'nyːver mɑntsjə' (1)
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# [ɹ] 'iːjəɹ' (2)
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search_phone_ipa('ˑ', transcription_ipa) |