moved testing parts in htk_vs_kaldi into stimmen_test.py

acoustic_model/acoustic_model.pyproj

@@ -52,6 +52,7 @@
     <Compile Include="phoneset\fame_asr.py" />
     <Compile Include="phoneset\fame_ipa.py" />
     <Compile Include="stimmen_functions.py" />
+    <Compile Include="stimmen_test.py" />
   </ItemGroup>
   <ItemGroup>
     <Content Include="config.ini" />

acoustic_model/htk_vs_kaldi.py

@@ -59,10 +59,6 @@ from htk import pyhtk
 #load_forced_alignment_kaldi = 1
 #eval_forced_alignment_kaldi = 1
 
-
-
-
-### ======================= add paths =======================
 #sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
 #from forced_alignment import convert_phone_set
 #from forced_alignment import pyhtk
@@ -78,6 +74,7 @@ lattice_file = os.path.join(config_dir, 'stimmen.ltc')
 #	lattice_file)
 hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp')
 
+
 ## ======================= make test data ======================
 # copy wav files which is in the stimmen data.
 stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
@@ -87,25 +84,15 @@ df = stimmen_functions.load_transcriptions()
 word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
 word_list = sorted(word_list)
 
-#for index, row in df.iterrows():
-#	filename = row['filename']
-#	if isinstance(filename, str):
-#		wav_file = os.path.join(default.stimmen_wav_dir, filename)
-#		if os.path.exists(wav_file):
-#			shutil.copy(wav_file, os.path.join(stimmen_test_dir, filename))
-#			pyhtk.create_label_file(
-#				row['word'], 
-#				os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
-
 
 # after manually removed files which does not contain clear sound,
 # update df as df_test.
-#wav_file_list = glob.glob(os.path.join(stimmen_test_dir, '*.wav'))
+wav_file_list = glob.glob(os.path.join(stimmen_test_dir, '*.wav'))
-#df_test = pd.DataFrame(index=[], columns=list(df.keys()))
+df_test = pd.DataFrame(index=[], columns=list(df.keys()))
-#for wav_file in wav_file_list:
+for wav_file in wav_file_list:
-#	filename = os.path.basename(wav_file)
+	filename = os.path.basename(wav_file)
-#	df_ = df[df['filename'].str.match(filename)]
+	df_ = df[df['filename'].str.match(filename)]
-#	df_test = pd.concat([df_test, df_])
+	df_test = pd.concat([df_test, df_])
 
 #output = pyhtk.recognition(
 #	os.path.join(default.htk_dir, 'config', 'config.rec', 
@@ -115,26 +102,6 @@ word_list = sorted(word_list)
 #	os.path.join(config_dir, 'phonelist.txt'), 
 #	hvite_scp)
 
-
-## check phones included in stimmen but not in FAME!
-splitted_ipas = [' '.join(
-		convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)) 
-		for ipa in df['ipa']]
-stimmen_phones = set(' '.join(splitted_ipas))
-stimmen_phones = list(stimmen_phones)
-#stimmen_phones = list(set(fame_asr.phone_reduction(list(stimmen_phones))))
-#fame_phones    = fame_asr.phoneset_short
-fame_phones = fame_ipa.phoneset
-stimmen_phones.sort()
-fame_phones.sort()
-print('phones which are used in stimmen transcription but not in FAME corpus are:\n{}'.format(
-	set(stimmen_phones) - set(fame_phones)
-	))
-for ipa in df['ipa']:
-	ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
-	if ':' in ipa_splitted:
-		print(ipa_splitted)
-
 htk = [fame_functions.ipa2htk(ipa) for ipa in df['ipa']]
 
 ipa = 'e:χ'

acoustic_model/stimmen_test.py

@@ -0,0 +1,61 @@
+import os
+os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
+import sys
+
+import shutil
+import glob
+
+#import numpy as np
+import pandas as pd
+
+import defaultfiles as default
+import convert_xsampa2ipa
+import stimmen_functions
+import fame_functions
+import convert_phoneset
+from phoneset import fame_ipa, fame_asr
+sys.path.append(default.toolbox_dir)
+import file_handling as fh
+from htk import pyhtk
+
+
+## ======================= user define =======================
+
+
+
+## ======================= make test data ======================
+# copy wav files which is in the stimmen data.
+stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
+fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
+
+df = stimmen_functions.load_transcriptions()
+#word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
+#word_list = sorted(word_list)
+
+#for index, row in df.iterrows():
+#	filename = row['filename']
+#	if isinstance(filename, str):
+#		wav_file = os.path.join(default.stimmen_wav_dir, filename)
+#		if os.path.exists(wav_file):
+#			shutil.copy(wav_file, os.path.join(stimmen_test_dir, filename))
+#			pyhtk.create_label_file(
+#				row['word'], 
+#				os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
+
+
+## check phones included in stimmen but not in FAME!
+splitted_ipas = [' '.join(
+		convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)) 
+		for ipa in df['ipa']]
+stimmen_phones = set(' '.join(splitted_ipas))
+stimmen_phones = list(stimmen_phones)
+fame_phones = fame_ipa.phoneset
+stimmen_phones.sort()
+fame_phones.sort()
+print('phones which are used in stimmen transcription but not in FAME corpus are:\n{}'.format(
+	set(stimmen_phones) - set(fame_phones)
+	))
+for ipa in df['ipa']:
+	ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
+	if ':' in ipa_splitted:
+		print(ipa_splitted)