find pronunciation variants which all phones are in novo70.

acoustic_model/acoustic_model.pyproj

@@ -35,6 +35,9 @@
     <Compile Include="fa_test.py">
       <SubType>Code</SubType>
     </Compile>
+    <Compile Include="forced_alignment_novo.py">
+      <SubType>Code</SubType>
+    </Compile>
     <Compile Include="htk_vs_kaldi.py">
       <SubType>Code</SubType>
     </Compile>

acoustic_model/check_novoapi.py

@@ -55,14 +55,15 @@ phoneset_novo70 = np.unique(phoneset_novo70)
 # [iː] iy
 # [œː] uh
 # [ɛː] eh
-david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː']
+# [w] wv in IPA written as ʋ. 
+david_suggestion = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'w']
 
 
-## ======================= convert phones ======================
+## ======================= extract words which is written only with novo70 ======================
 mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
 
 stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx)
-df = pd.read_excel(stimmen_transcription_, 'check')
+df = pd.read_excel(stimmen_transcription_, 'frequency')
 #for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
 #    ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
 #    if not ipa_converted == ipa:
@@ -70,11 +71,13 @@ df = pd.read_excel(stimmen_transcription_, 'check')
 transcription_ipa = list(df['IPA'])
 
 # transcription mistake?
-transcription_ipa = [ipa.replace(';', ':') for ipa in transcription_ipa if not ipa=='pypɪl' and not pd.isnull(ipa)]
+transcription_ipa = [ipa.replace(';', 'ː') for ipa in transcription_ipa if not ipa=='pypɪl' and not pd.isnull(ipa)]
 transcription_ipa = [ipa.replace('ˑ', '') for ipa in transcription_ipa] # only one case.
 
 not_in_novo70 = []
+all_in_novo70 = []
 for ipa in transcription_ipa:
+	ipa = ipa.replace(':', 'ː')
 	ipa = convert_phone_set.split_ipa(ipa)
 
 	not_in_novo70_ = [phone for phone in ipa 
@@ -83,19 +86,76 @@ for ipa in transcription_ipa:
 	not_in_novo70_ = [phone.replace(':', '') for phone in not_in_novo70_]
 	not_in_novo70_ = [phone.replace('ː', '') for phone in not_in_novo70_]
 
+	if len(not_in_novo70_) == 0:
+		all_in_novo70.append(''.join(ipa))
+
 	#translation_key.get(phone, phone)
 	not_in_novo70.extend(not_in_novo70_)
 not_in_novo70_list = list(set(not_in_novo70))
 
+## check which phone is used in stimmen but not in novo70
+# 'ʀ', 'ʁ',
+# 'ɒ', 'ɐ', 
+# 'o', 'a' (o:, a:?)
+# [e] 'nyːver mɑntsjə' (1)
+# [ɾ] 'ɪːɾ'(1)
+# [ɹ] 'iːjəɹ' (1), 'ɪ:ɹ' (1)
+# [ø] 'gʀøtəpi:r'(1), 'grøtəpi:r'(1)
+# [æ] 'røːzəʀæt'(2), 'røːzəræt'(1)
+# [ʊ] 'ʊ'(1) --> can be ʏ (uh)??
+# [χ] --> can be x??
 
 def search_phone_ipa(x, phone_list):
-	return [phone for phone in phone_list if x in convert_phone_set.split_ipa(phone)]
+	x_in_item = []
+	for ipa in phone_list:
+		ipa_original = ipa
+		ipa = ipa.replace(':', 'ː')
+		ipa = convert_phone_set.split_ipa(ipa)
+		if x in ipa and not x+':' in ipa:
+			x_in_item.append(ipa_original)
+	return x_in_item
+#search_phone_ipa('ø', transcription_ipa)
 
 
-# 'ɐ', 'ɒ', 'w', 'æ', 'ʀ', 'ʁ', 
-# 'œː', 'ɾ', 
-# 'o', 'a'
-# [e] 'nyːver mɑntsjə' (1)
-# [ɹ] 'iːjəɹ' (2)
 
-search_phone_ipa('ˑ', transcription_ipa)
+
+df = pd.read_excel(stimmen_transcription_, 'original')
+
+ipas     = []
+famehtks = []
+for xsampa in df['Self Xsampa']:
+    if not isinstance(xsampa, float): # 'NaN'
+        # typo?
+        xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
+        xsampa = xsampa.replace(';', ':')
+
+        ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
+        ipa = ipa.replace('ː', ':')
+        ipa = ipa.replace(' ', '')
+        ipas.append(ipa)
+    else:
+        ipas.append('')
+        
+# extract interesting cols.
+df = pd.DataFrame({'filename': df['Filename'], 
+                    'word': df['Word'], 
+                    'xsampa': df['Self Xsampa'],
+                    'ipa': pd.Series(ipas)})
+
+# find options which all phones are in novo70.
+#word_list = list(set(df['word']))
+#word_list = [word for word in word_list if not pd.isnull(word)]
+#word = word_list[1]
+
+## pronunciation variants of 'word' 
+#df_ = df[df['word'] == word]['xsampa']
+##pronunciation_variant = list(set(df_))
+
+cols = ['word', 'ipa', 'frequency']
+df_samples = pd.DataFrame(index=[], columns=cols)
+for ipa in all_in_novo70:
+	ipa = ipa.replace('ː', ':')
+	samples = df[df['ipa'] == ipa]
+	word = list(set(samples['word']))[0]
+	samples_Series = pd.Series([word, ipa, len(samples)], index=df_samples.columns)
+	df_samples = df_samples.append(samples_Series, ignore_index=True)

acoustic_model/forced_alignment_novo.py

@@ -0,0 +1,112 @@
+#
+# forced alignment using novo-api.
+#
+# *** IMPORTANT ***
+# This file should be treated as confidencial.
+# This file should not be copied or uploaded to public sites.
+#
+# NOTES:
+# The usage of novo api: https://bitbucket.org/novolanguage/python-novo-api
+# I couldn't make it work as I described in the mail to Martijn Bartelds on
+# 2018/12/03.
+# As per the advice from him, I modified testgrammer.py and made it a function.
+#
+# In order to run on Python 3.6, the following points are changed in novo-api.
+# (1) backend/__init__.py
+# - #import session
+#   from .  import session
+# (2) backend/session.py
+# - #except Exception, e:
+#   except Exception as e:
+# - #print self.last_message
+#   print(self.last_message)
+# (3) asr/segment/praat.py
+# - def print_tier(output, title, begin, end, segs, (format, formatter))
+#   def print_tier(output, title, begin, end, segs, format, formatter):
+# (4) asr/spraaklab/__init.py
+# - #import session
+#   from .  import session
+# (5) asr/spraaklab/schema.py
+# - #print data, "validated not OK", e.message
+#   print("{0} validated not OK {1}".format(data, e.message))
+# - #print data, "validated OK"
+#   print("{} validated OK".format(data))
+# - #if isinstance(object, basestring):
+#	if isinstance(object, str)
+#
+# Aki Kunikoshi
+# 428968@gmail.com
+#
+import argparse
+import json
+
+from novoapi.backend import session
+
+# username / password cannot be passed as artuments...
+p = argparse.ArgumentParser()
+#p.add_argument("--user", default=None)
+#p.add_argument("--password", default=None)
+p.add_argument("--user", default='martijn.wieling')
+p.add_argument("--password", default='fa0Thaic')
+args = p.parse_args()
+
+wav_file = 'c:\\OneDrive\\WSL\\test\\onetwothree.wav'
+
+rec = session.Recognizer(grammar_version="1.0", lang="nl", snodeid=101, user=args.user, password=args.password, keepopen=True) # , modeldir=modeldir)
+grammar = {
+  "type": "confusion_network",
+  "version": "1.0",
+  "data": {
+	"kind": "sequence",
+	"elements": [{
+		"kind": "word",
+		"pronunciation": [{
+			"phones": ["wv",
+			  "a1",
+			  "n"],
+			"id": 0
+		  },
+		  {
+			"phones": ["wv",
+			  "uh1",
+			  "n"],
+			"id": 1
+		  }],
+		"label": "one"
+	  },
+	  {
+		"kind": "word",
+		"pronunciation": [{
+			"phones": ["t",
+			  "uw1"],
+			"id": 0
+		  }],
+		"label": "two"
+	  },
+	  {
+		"kind": "word",
+		"pronunciation": [{
+			"phones": ["t",
+			  "r",
+			  "iy1"],
+			"id": 0
+		  },
+		  {
+			"phones": ["s",
+			  "r",
+			  "iy1"],
+			"id": 1
+		  }],
+		"label": "three"
+	  }]
+  },
+  "return_objects": ["grammar"],
+  "phoneset": "novo70"
+}
+
+res = rec.setgrammar(grammar)
+#print "Set grammar result", res
+
+#res = rec.recognize_wav("test/onetwothree.wav")
+res = rec.recognize_wav(wav_file)
+#print "Recognition result:", json.dumps(res.export(), indent=4)