the script 'forced_alignment_novo.py' which is to run novo_api on Python 3.6 environment is added.

acoustic_model/acoustic_model.pyproj

@@ -4,7 +4,7 @@
     <SchemaVersion>2.0</SchemaVersion>
     <ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
     <ProjectHome>.</ProjectHome>
-    <StartupFile>performance_check.py</StartupFile>
+    <StartupFile>check_novoapi.py</StartupFile>
     <SearchPath>
     </SearchPath>
     <WorkingDirectory>.</WorkingDirectory>
@@ -25,6 +25,9 @@
     <Compile Include="acoustic_model_functions.py">
       <SubType>Code</SubType>
     </Compile>
+    <Compile Include="check_novoapi.py">
+      <SubType>Code</SubType>
+    </Compile>
     <Compile Include="convert_xsampa2ipa.py">
       <SubType>Code</SubType>
     </Compile>
@@ -34,7 +37,10 @@
     <Compile Include="fa_test.py">
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="performance_check.py">
+    <Compile Include="forced_alignment_novo.py">
+      <SubType>Code</SubType>
+    </Compile>
+    <Compile Include="htk_vs_kaldi.py">
       <SubType>Code</SubType>
     </Compile>
   </ItemGroup>

acoustic_model/check_novoapi.py

@@ -0,0 +1,38 @@
+import os
+os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
+
+import sys
+import csv
+#import subprocess
+#from collections import Counter
+#import re
+
+import numpy as np
+import pandas as pd
+#import matplotlib.pyplot as plt
+#from sklearn.metrics import confusion_matrix
+
+import acoustic_model_functions as am_func
+import convert_xsampa2ipa
+import defaultfiles as default
+
+from forced_alignment import pyhtk
+
+import novoapi 
+
+## ======================= convert phones ======================
+mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
+
+stimmen_transcription_ = pd.ExcelFile(default.stimmen_transcription_xlsx)
+
+phonelist_novo70_      = pd.ExcelFile(default.phonelist_novo70_xlsx)
+df = pd.read_excel(phonelist_novo70_, 'list')
+
+translation_key = dict()
+for ipa, novo70 in zip(df['IPA_simple'], df['novo70_simple']):
+    if not pd.isnull(ipa):
+        print('{0}:{1}'.format(ipa, novo70))
+        translation_key[ipa] = novo70
+
+#df = pd.read_excel(stimmen_transcription, 'check')
+

acoustic_model/defaultfiles.py

@@ -27,10 +27,14 @@ config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
 #AcousticModel = config['pyHTK']['AcousticModel']
 
 repo_dir = r'C:\Users\Aki\source\repos'
-ipa_xsampa_converter_dir = os.path.join(repo_dir, 'ipa-xsama-converter')
+ipa_xsampa_converter_dir    = os.path.join(repo_dir, 'ipa-xsama-converter')
 forced_alignment_module_dir = os.path.join(repo_dir, 'forced_alignment')
 
-fame_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus'
-experiments_dir = r'c:\OneDrive\Research\rug\experiments'
+fame_dir        = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus'
+
+experiments_dir = r'c:\OneDrive\Research\rug\experiments'
+stimmen_transcription_xlsx = os.path.join(experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
+stimmen_data_dir           = os.path.join(experiments_dir, 'stimmen', 'data')
+phonelist_friesian_txt     = os.path.join(experiments_dir, 'friesian', 'acoustic_model', 'config', 'phonelist_friesian.txt')
+phonelist_novo70_xlsx      = os.path.join(experiments_dir, 'Nederlandse phonesets_aki.xlsx')
 
-phonelist = os.path.join(experiments_dir, 'friesian', 'acoustic_model', 'config', 'phonelist_friesian.txt')

acoustic_model/fa_test.py

@@ -2,15 +2,52 @@ import os
 import sys
 os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
 
+import numpy as np
+
 import defaultfiles as default
 
 sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
-from forced_alignment import forced_alignment
+from forced_alignment import forced_alignment, lexicon, convert_phone_set
 
-wav_file = r'C:\Users\Aki\source\repos\forced_alignment\notebooks\sample\10147-1464186409-1917281.wav'
-forced_alignment(
-    wav_file,
-    #'Australië'
-    'BUFFETCOUPON COULISSEN DOUANE'
-    )
+#wav_file = r'C:\Users\Aki\source\repos\forced_alignment\notebooks\sample\10147-1464186409-1917281.wav'
+#forced_alignment(
+#    wav_file,
+#    'Australië'
+#    #'BUFFETCOUPON COULISSEN DOUANE'
+#    )
 
+# according to: http://lands.let.ru.nl/cgn/doc_Dutch/topics/version_1.0/annot/phonetics/fon_prot.pdf
+phone_list_cgn = ['p', 'd', 't', 'd', 'k', 'g', # plosives
+                 'f', 'v', 's', 'z', 'S', 'Z', 'x', 'G', 'h', # fricatives
+                 'N', 'm', 'n', 'J', 'l', 'r', 'w', 'j', # sonorant 
+                 'I', 'E', 'A', 'O', 'Y', # short vowels
+                 'i', 'y', 'e', '2', 'a', 'o', 'u', # long vowels
+                 '@', # schwa 
+                 'E+', 'Y+', 'A+', # Diftongen
+                 'E:', 'Y:', 'O:', # Leenvocalen
+                 'E~', 'A~', 'O~', 'Y~' # Nasale vocalen
+                 ]
+
+# load word in the lexicon.
+lexicon_file = r'C:\cygwin64\home\Aki\acoustic_model\material\barbara\2010_2510_lexicon_pronvars_HTK.txt'
+with open(lexicon_file, 'r') as f:
+    lines = f.readlines()
+
+words = []
+for line in lines:
+    line_split = line.split()
+    if len(line_split) > 0:
+        word = line_split[0]
+        word.replace('+s', '')
+        word = word.split('-')
+        words.append(word)
+words = list(np.unique(words))
+
+pronunciations = lexicon._grapheme_to_phoneme(words)
+htks = []
+phone_list = set()
+for word in pronunciations.keys():
+    ipa = pronunciations[word]
+    htk = convert_phone_set.split_ipa(ipa)
+    htks.append(htk)
+    phone_list = phone_list | set(htk)

acoustic_model/forced_alignment_novo.py

@@ -0,0 +1,133 @@
+#
+# 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)

acoustic_model/htk_vs_kaldi.py

@@ -3,7 +3,7 @@ os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
 
 import sys
 import csv
-import subprocess
+#import subprocess
 from collections import Counter
 import re
 
@@ -20,8 +20,6 @@ from forced_alignment import pyhtk
 
 
 ## ======================= user define =======================
-excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
-data_dir   = os.path.join(default.experiments_dir, 'stimmen', 'data')
 
 wav_dir	= r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
 
@@ -48,8 +46,6 @@ 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
@@ -62,15 +58,15 @@ from evaluation import plot_confusion_matrix
 ## ======================= convert phones ======================
 mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
 
-xls = pd.ExcelFile(excel_file)
+xls = pd.ExcelFile(default.stimmen_transcription_xlsx)
 
 ## check conversion
-#df = pd.read_excel(xls, 'frequency')
+#df = pd.read_excel(xls, 'check')
 #for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
-#    #ipa_converted = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, xsampa_)
-#    ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
-#    if not ipa_converted == ipa:
-#        print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
+#    if xsampa is not '/':
+#        ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
+#        if not ipa_converted == ipa:
+#            print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
 
 
 ## check phones included in FAME!
@@ -160,7 +156,7 @@ if do_forced_alignment_htk:
                     htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
 
                     pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, 
-                                default.phonelist, acoustic_model)
+                                default.phonelist_friesian_txt, acoustic_model)
                     os.remove(label_file)
 
                     prediction = am_func.read_fileFA(fa_file)
@@ -231,7 +227,7 @@ if make_kaldi_data_files:
 
 ## ======================= make lexicon txt which is used by Kaldi =======================
 if make_kaldi_lexicon_txt:
-    option_num = 6
+    option_num = 7
 
     # remove previous file.
     if os.path.exists(lexicon_txt):
@@ -281,10 +277,10 @@ if load_forced_alignment_kaldi:
     phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
     merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt')
     
-    #filenames	   = np.load(data_dir + '\\filenames.npy')
-    #words		   = np.load(data_dir + '\\words.npy')
-    #pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
-    #pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
+    #filenames	   = np.load(stimmen_data_dir + '\\filenames.npy')
+    #words		   = np.load(stimmen_data_dir + '\\words.npy')
+    #pronunciations = np.load(stimmen_data_dir + '\\pronunciations_ipa.npy')
+    #pronvar_list_all = np.load(stimmen_data_dir + '\\pronvar_list_all.npy')
     #word_list = np.unique(words)    
 
     # load the mapping between phones and ids.
@@ -369,7 +365,7 @@ if eval_forced_alignment_htk:
         if compare_hmm_num:
             f_result.write("{},".format(hmm_num_str))
 
-        #match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
+        #match = np.load(stimmen_data_dir + '\\match_hmm' + hmm_num_str + '.npy')
         #prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
         #prediction = pd.Series(prediction, index=df.index, name='prediction')
         #result = pd.concat([df, prediction], axis=1)