206 lines
7.0 KiB
Python
206 lines
7.0 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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from collections import Counter
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import random
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import shutil
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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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from sklearn.metrics import accuracy_score
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import novoapi
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import defaultfiles as default
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sys.path.append(default.forced_alignment_module_dir)
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from forced_alignment import convert_phone_set
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#import acoustic_model_functions as am_func
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import convert_xsampa2ipa
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import novoapi_functions
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import stimmen_functions
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sys.path.append(default.accent_classification_dir)
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import output_confusion_matrix
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## procedure
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forced_alignment_novo70 = True
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## ===== load novo phoneset =====
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phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa = novoapi_functions.load_phonset()
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## ===== extract pronunciations written in novo70 only (not_in_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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# [w] wv in IPA written as ʋ.
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david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'w']
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## read pronunciation variants.
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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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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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# list of phones not in novo70 phoneset.
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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 phones 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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## ===== load all transcriptions (df) =====
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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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## check frequency of each pronunciation variants
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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)
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# each word
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df_per_word = pd.DataFrame(index=[], columns=df_samples.keys())
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for word in word_list:
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df_samples_ = df_samples[df_samples['word']==word]
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df_samples_ = df_samples_[df_samples_['frequency']>2]
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df_per_word = df_per_word.append(df_samples_, ignore_index=True)
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#df_per_word.to_excel(os.path.join(default.stimmen_dir, 'pronunciation_variants_novo70.xlsx'), encoding="utf-8")
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## ===== forced alignment =====
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rozen_dir = r'c:\Users\Aki\source\repos\acoustic_model\rozen-test'
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if forced_alignment_novo70:
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Results = pd.DataFrame(index=[],
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columns=['filename', 'word', 'xsampa', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
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#for word in word_list:
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for word in ['Rozen']:
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# pronunciation variants top 3
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df_per_word_ = df_per_word[df_per_word['word']==word]
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df_per_word_ = df_per_word_.sort_values('frequency', ascending=False)
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if len(df_per_word_) < 3: # pauw, rozen
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pronunciation_ipa = list(df_per_word_['ipa'])
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elif word=='Reuzenrad':
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pronunciation_ipa = [
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df_per_word_.iloc[0]['ipa'],
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df_per_word_.iloc[1]['ipa'],
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df_per_word_.iloc[2]['ipa'],
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df_per_word_.iloc[3]['ipa']]
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else:
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# oog, oor, reus, roeiboot
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pronunciation_ipa = [
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df_per_word_.iloc[0]['ipa'],
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df_per_word_.iloc[1]['ipa'],
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df_per_word_.iloc[2]['ipa']]
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#print("{0}: {1}".format(word, pronunciation_ipa))
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# samples for the word
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df_ = df[df['word']==word]
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# samples in which all pronunciations are written in novo70.
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samples = df_.query("ipa in @pronunciation_ipa")
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results = pd.DataFrame(index=[],
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columns=['filename', 'word', 'xsampa', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
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for i in range(0, len(samples)):
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sample = samples.iloc[i]
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filename = sample['filename']
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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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# for Martijn
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shutil.copy(wav_file, os.path.join(rozen_dir, filename))
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# pronunciation_ipa_ = [ipa.replace(':', 'ː') for ipa in pronunciation_ipa]
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# result = novoapi_functions.forced_alignment(wav_file, word, pronunciation_ipa_)
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# result_ipa, result_novo70, llh = novoapi_functions.result2pronunciation(result, word)
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# result_ = pd.Series([
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# sample['filename'],
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# sample['word'],
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# sample['xsampa'],
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# sample['ipa'],
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# ' '.join(result_ipa),
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# ' '.join(result_novo70),
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# llh
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# ], index=results.columns)
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# results = results.append(result_, ignore_index = True)
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# print('{0}/{1}: answer {2} - prediction {3}'.format(
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# i+1, len(samples), result_['ipa'], result_['result_ipa']))
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# #results.to_excel(os.path.join(default.stimmen_dir, 'results.xlsx'), encoding="utf-8")
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#if len(results) > 0:
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# Results = Results.append(results, ignore_index = True)
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#Results.to_excel(os.path.join(default.stimmen_result_novoapi_dir, 'Results.xlsx'), encoding="utf-8")
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else:
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Results_xlsx = pd.ExcelFile(os.path.join(default.stimmen_result_novoapi_dir, 'Results.xlsx'), encoding="utf-8")
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Results = pd.read_excel(Results_xlsx, 'Sheet1')
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## ===== analysis =====
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#for word in word_list:
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# if not word == 'Oog':
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# Results_ = Results[Results['word'] == word]
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# y_true = list(Results_['ipa'])
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# y_pred_ = [ipa.replace(' ', '') for ipa in list(Results_['result_ipa'])]
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# y_pred = [ipa.replace('ː', ':') for ipa in y_pred_]
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# pronunciation_variants = list(set(y_true))
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# cm = confusion_matrix(y_true, y_pred, labels=pronunciation_variants)
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# plt.figure()
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# output_confusion_matrix.plot_confusion_matrix(cm, pronunciation_variants, normalize=False)
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# #plt.show()
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# plt.savefig(os.path.join(default.stimmen_result_novoapi_dir, word + '.png')) |