57 lines
1.5 KiB
Python
57 lines
1.5 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
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import novoapi
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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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## 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.cmu69_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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