load novo phoneset.

.gitignore

@@ -0,0 +1,264 @@
+## Ignore Visual Studio temporary files, build results, and
+## files generated by popular Visual Studio add-ons.
+
+## important ##
+.acoustic_model/forced_alignment_novo.py
+
+# User-specific files
+*.suo
+*.user
+*.userosscache
+*.sln.docstates
+
+# User-specific files (MonoDevelop/Xamarin Studio)
+*.userprefs
+
+# Build results
+[Dd]ebug/
+[Dd]ebugPublic/
+[Rr]elease/
+[Rr]eleases/
+x64/
+x86/
+bld/
+[Bb]in/
+[Oo]bj/
+[Ll]og/
+
+# Visual Studio 2015 cache/options directory
+.vs/
+# Uncomment if you have tasks that create the project's static files in wwwroot
+#wwwroot/
+
+# MSTest test Results
+[Tt]est[Rr]esult*/
+[Bb]uild[Ll]og.*
+
+# NUNIT
+*.VisualState.xml
+TestResult.xml
+
+# Build Results of an ATL Project
+[Dd]ebugPS/
+[Rr]eleasePS/
+dlldata.c
+
+# DNX
+project.lock.json
+project.fragment.lock.json
+artifacts/
+
+*_i.c
+*_p.c
+*_i.h
+*.ilk
+*.meta
+*.obj
+*.pch
+*.pdb
+*.pgc
+*.pgd
+*.rsp
+*.sbr
+*.tlb
+*.tli
+*.tlh
+*.tmp
+*.tmp_proj
+*.log
+*.vspscc
+*.vssscc
+.builds
+*.pidb
+*.svclog
+*.scc
+
+# Chutzpah Test files
+_Chutzpah*
+
+# Visual C++ cache files
+ipch/
+*.aps
+*.ncb
+*.opendb
+*.opensdf
+*.sdf
+*.cachefile
+*.VC.db
+*.VC.VC.opendb
+
+# Visual Studio profiler
+*.psess
+*.vsp
+*.vspx
+*.sap
+
+# TFS 2012 Local Workspace
+$tf/
+
+# Guidance Automation Toolkit
+*.gpState
+
+# ReSharper is a .NET coding add-in
+_ReSharper*/
+*.[Rr]e[Ss]harper
+*.DotSettings.user
+
+# JustCode is a .NET coding add-in
+.JustCode
+
+# TeamCity is a build add-in
+_TeamCity*
+
+# DotCover is a Code Coverage Tool
+*.dotCover
+
+# NCrunch
+_NCrunch_*
+.*crunch*.local.xml
+nCrunchTemp_*
+
+# MightyMoose
+*.mm.*
+AutoTest.Net/
+
+# Web workbench (sass)
+.sass-cache/
+
+# Installshield output folder
+[Ee]xpress/
+
+# DocProject is a documentation generator add-in
+DocProject/buildhelp/
+DocProject/Help/*.HxT
+DocProject/Help/*.HxC
+DocProject/Help/*.hhc
+DocProject/Help/*.hhk
+DocProject/Help/*.hhp
+DocProject/Help/Html2
+DocProject/Help/html
+
+# Click-Once directory
+publish/
+
+# Publish Web Output
+*.[Pp]ublish.xml
+*.azurePubxml
+# TODO: Comment the next line if you want to checkin your web deploy settings
+# but database connection strings (with potential passwords) will be unencrypted
+#*.pubxml
+*.publishproj
+
+# Microsoft Azure Web App publish settings. Comment the next line if you want to
+# checkin your Azure Web App publish settings, but sensitive information contained
+# in these scripts will be unencrypted
+PublishScripts/
+
+# NuGet Packages
+*.nupkg
+# The packages folder can be ignored because of Package Restore
+**/packages/*
+# except build/, which is used as an MSBuild target.
+!**/packages/build/
+# Uncomment if necessary however generally it will be regenerated when needed
+#!**/packages/repositories.config
+# NuGet v3's project.json files produces more ignoreable files
+*.nuget.props
+*.nuget.targets
+
+# Microsoft Azure Build Output
+csx/
+*.build.csdef
+
+# Microsoft Azure Emulator
+ecf/
+rcf/
+
+# Windows Store app package directories and files
+AppPackages/
+BundleArtifacts/
+Package.StoreAssociation.xml
+_pkginfo.txt
+
+# Visual Studio cache files
+# files ending in .cache can be ignored
+*.[Cc]ache
+# but keep track of directories ending in .cache
+!*.[Cc]ache/
+
+# Others
+ClientBin/
+~$*
+*~
+*.dbmdl
+*.dbproj.schemaview
+*.jfm
+*.pfx
+*.publishsettings
+node_modules/
+orleans.codegen.cs
+
+# Since there are multiple workflows, uncomment next line to ignore bower_components
+# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)
+#bower_components/
+
+# RIA/Silverlight projects
+Generated_Code/
+
+# Backup & report files from converting an old project file
+# to a newer Visual Studio version. Backup files are not needed,
+# because we have git ;-)
+_UpgradeReport_Files/
+Backup*/
+UpgradeLog*.XML
+UpgradeLog*.htm
+
+# SQL Server files
+*.mdf
+*.ldf
+
+# Business Intelligence projects
+*.rdl.data
+*.bim.layout
+*.bim_*.settings
+
+# Microsoft Fakes
+FakesAssemblies/
+
+# GhostDoc plugin setting file
+*.GhostDoc.xml
+
+# Node.js Tools for Visual Studio
+.ntvs_analysis.dat
+
+# Visual Studio 6 build log
+*.plg
+
+# Visual Studio 6 workspace options file
+*.opt
+
+# Visual Studio LightSwitch build output
+**/*.HTMLClient/GeneratedArtifacts
+**/*.DesktopClient/GeneratedArtifacts
+**/*.DesktopClient/ModelManifest.xml
+**/*.Server/GeneratedArtifacts
+**/*.Server/ModelManifest.xml
+_Pvt_Extensions
+
+# Paket dependency manager
+.paket/paket.exe
+paket-files/
+
+# FAKE - F# Make
+.fake/
+
+# JetBrains Rider
+.idea/
+*.sln.iml
+
+# CodeRush
+.cr/
+
+# Python Tools for Visual Studio (PTVS)
+__pycache__/
+*.pyc

acoustic_model.sln

@@ -18,8 +18,8 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
 		..\toolbox\pyHTK.py = ..\toolbox\pyHTK.py
 		..\forced_alignment\forced_alignment\pyhtk.py = ..\forced_alignment\forced_alignment\pyhtk.py
 		..\forced_alignment\forced_alignment\scripts.py = ..\forced_alignment\forced_alignment\scripts.py
-		..\forced_alignment\forced_alignment\tempfilename.py = ..\forced_alignment\forced_alignment\tempfilename.py
 		..\forced_alignment\forced_alignment\test_environment.py = ..\forced_alignment\forced_alignment\test_environment.py
+		..\..\..\..\..\OneDrive\WSL\python-novo-api\test\testgrammar.py = ..\..\..\..\..\OneDrive\WSL\python-novo-api\test\testgrammar.py
 	EndProjectSection
 EndProject
 Global

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 +1,40 @@
+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')
+
+
+## novo phoneset
+#translation_key = dict()
+## *_simple includes columns which has only one phone in.
+#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
+#phonelist_novo70 = np.unique(list(df['novo70_simple']))
+

acoustic_model/defaultfiles.py

@@ -3,7 +3,7 @@ import os
 #default_hvite_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'htk', 'config.HVite')
 
 cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
-kaldi_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
+
 #config_hcopy = os.path.join(cygwin_dir, 'config', 'config.HCopy')
 #config_train = os.path.join(cygwin_dir, 'config', 'config.train')
 config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
@@ -30,7 +30,16 @@ repo_dir = r'C:\Users\Aki\source\repos'
 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'
+WSL_dir   = r'C:\OneDrive\WSL'
+fame_dir        = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
+fame_s5_dir     = os.path.join(fame_dir, 's5')
+fame_corpus_dir = os.path.join(fame_dir, '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')
+
+novo_api_dir = os.path.join(WSL_dir, 'python-novo-api')
+cmu69_phoneset = os.path.join(novo_api_dir, 'novoapi', 'asr', 'phoneset', 'en', 'cmu69.phoneset')
 
-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
 
@@ -30,12 +28,12 @@ htk_dict_dir       = os.path.join(default.experiments_dir, 'stimmen', 'dic_short
 fa_dir             = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
 result_dir         = os.path.join(default.experiments_dir, 'stimmen', 'result')
 
-kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme') 
-kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
+kaldi_data_dir = os.path.join(default.fame_s5_dir, 'data', 'alignme') 
+kaldi_dict_dir = os.path.join(default.fame_s5_dir, 'data', 'local', 'dict')
 lexicon_txt    = os.path.join(kaldi_dict_dir, 'lexicon.txt')
 
-#lex_asr	 = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
-#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
+#lex_asr	 = os.path.join(default.fame_corpus_dir, 'lexicon', 'lex.asr')
+#lex_asr_htk = os.path.join(default.fame_corpus_dir, 'lexicon', 'lex.asr_htk')
 
 
 # procedure
@@ -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
@@ -62,12 +58,12 @@ 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_)
+#    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))
@@ -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):
@@ -278,13 +274,13 @@ if make_kaldi_lexicon_txt:
 
 ## ======================= load kaldi forced alignment result =======================
 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')
+    phones_txt = os.path.join(default.fame_s5_dir, 'data', 'lang', 'phones.txt')
+    merged_alignment_txt = os.path.join(default.fame_s5_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)

novoapi_for_python3x/__init__.py

@@ -0,0 +1,5 @@
+#!/usr/bin/env python
+
+__version__ = "0.2"
+
+import backend

novoapi_for_python3x/asr/__init__.py

@@ -0,0 +1,6 @@
+#!/usr/bin/env python
+
+#import segments
+#import spraaklab
+from . import segments
+from . import spraaklab

novoapi_for_python3x/asr/segments/__init__.py

@@ -0,0 +1,4 @@
+#!/usr/bin/env python
+
+from .segments import Segmentation
+from .praat import seg2tg

novoapi_for_python3x/asr/segments/praat.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+# (c) 2015--2018 NovoLanguage, author: David A. van Leeuwen
+
+import codecs
+
+def print_header(output, begin, end, nr_tiers):
+    print >> output, 'File type = "ooTextFile"'
+    print >> output, 'Object class = "TextGrid"'
+    print >> output, ''
+    print >> output, 'xmin = %s' % begin
+    print >> output, 'xmax = %s' % end
+    print >> output, 'tiers? <exists>'
+    print >> output, 'size = %d' % nr_tiers
+    print >> output, 'item []:'
+
+
+def print_info_tier(output, title, begin, end, label):
+    print >> output, '\titem [%d]:' % 0
+    print >> output, '\t\tclass = "IntervalTier"'
+    print >> output, '\t\tname = "%s"' % title
+    print >> output, '\t\txmin = %s' % begin
+    print >> output, '\t\txmax = %s' % end
+    print >> output, '\t\tintervals: size = %d' % 1
+
+    print >> output, '\t\tintervals [1]:'
+    print >> output, '\t\t\txmin = %s' % begin
+    print >> output, '\t\t\txmax = %s' % end
+    print >> output, '\t\t\ttext = "%s"' % label
+
+
+#def print_tier(output, title, begin, end, segs, (format, formatter)):
+def print_tier(output, title, begin, end, segs, format, formatter):
+    print >> output, '\titem [%d]:' % 0
+    print >> output, '\t\tclass = "IntervalTier"'
+    print >> output, '\t\tname = "%s"' % title
+    print >> output, '\t\txmin = %s' % begin
+    print >> output, '\t\txmax = %s' % end
+    print >> output, '\t\tintervals: size = %d' % len(segs)
+
+    count = 1
+    for seg in segs:
+        #print seg
+        print >> output, '\t\tintervals [%d]:' % count
+        print >> output, '\t\t\txmin = %s' % repr(int(seg['begin']) / 100.0)
+        print >> output, '\t\t\txmax = %s' % repr(int(seg['end']) / 100.0)
+        string = '\t\t\ttext = "' + format + '"'
+        print >> output, string % formatter(seg['label'])
+        count += 1
+
+
+def seg2tg(fname, segments):
+    if not segments:
+        return
+    output = codecs.open(fname, "w", encoding="utf-8")
+
+    confidences = []
+    word_labels = []
+    phones = []
+
+    for s in segments:
+        conf = s.llh if hasattr(s, "llh") else s.score
+        confidences.append({'begin': s.begin, 'end': s.end, 'label': conf})
+        word_labels.append({'begin': s.begin, 'end': s.end, 'label': s.label})
+        for p in s.phones:
+            phones.append({'begin': p.begin, 'end': p.end, 'label': p.label})
+
+
+    begin = repr(int(segments[0].begin) / 100.0)
+    end = repr(int(segments[-1].end) / 100.0)
+
+    nr_tiers = 3
+    print_header(output, begin, end, nr_tiers)
+    print_tier(output, "confidence", begin, end, confidences, ('%.3f', lambda x: x))
+    print_tier(output, "words", begin, end, word_labels, ('%s', lambda x: x))
+    print_tier(output, "phones", begin, end, phones, ('%s', lambda x: x))
+
+    output.close()

novoapi_for_python3x/asr/segments/segments.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# (c) 2015--2018 NovoLanguage, author: David A. van Leeuwen
+
+## These classes can be initialized with dictionaries, as they are returned by the python spraaklab recognition system.
+
+class Segment(object):
+    def __init__(self, segment):
+        self.begin = segment["begin"]
+        self.end = segment["end"]
+        self.begintime = segment.get("beginTime", self.begin / 100.0)
+        self.endtime = segment.get("endTime", self.end / 100.0)
+        self.label = segment["label"]
+        self.score = segment["score"]
+        if "llh" in segment:
+            self.llh = segment["llh"]
+        if "phones" in segment:
+            self.type = "word"
+            self.phones = Segmentation(segment["phones"], ["sil"])
+            if hasattr(self.phones[0], "llh"):
+                self.minllh = min([s.llh for s in self.phones]) ## the current word llh for error detection
+        else:
+            self.type = "phone"
+
+    def __repr__(self):
+        res = "%8.3f -- %8.3f score %8.3f " % (self.begintime, self.endtime, self.score)
+        if hasattr(self, "llh"):
+            res += "llh %8.3f " % self.llh
+        res += self.label.encode("utf8")
+        return res
+
+    def export(self):
+        r = {"begin": self.begin, "end": self.end, "label": self.label, "score": self.score, "type": self.type}
+        if hasattr(self, "llh"):
+            r["llh"] = self.llh
+        if hasattr(self, "phones"):
+            r["phones"] = self.phones.export()
+        return r
+
+class Segmentation(object):
+    def __init__(self, segments, sils=["<s>", "</s>", "!sil"]):
+        """Create a segmentation from a spraaklab recognition structure.
+        segments: an array of words (or phones), represented by a dict with
+        "begin", "end", "label", "score", and "llh" keys.  Words can also have
+        "phones" which is another array of segments."""
+        self.segments = [Segment(s) for s in segments]
+        if self.segments:
+            self.type = self.segments[0].type
+        else:
+            self.type = None
+        self.sils = sils
+        self.orig = segments ## in case we want to have access to the original recognition structure
+
+    def __getitem__(self, item):
+        return self.segments[item]
+
+    def __repr__(self):
+        ns = len(self.segments)
+        res = "Segmentation with %d %s%s" % (ns, self.type, "" if ns==1 else "s")
+        for seg in self.segments:
+            res += "\n " + repr(seg)
+        return res
+
+    def __len__(self):
+        return len(self.segments)
+
+    def score(self, skip=None):
+        if not skip:
+            skip = self.sils
+        s = 0.0
+        for seg in self.segments:
+            if seg.label not in skip:
+                s += seg.score
+        return s
+
+    def llhs(self, skip=None):
+        if not skip:
+            skip = self.sils
+        return [seg.llh for seg in self.segments if hasattr(seg, "llh") and seg.label not in skip]
+
+    def llh(self, skip=None):
+        return sum(self.llhs(skip))
+
+    def minllh(self, skip=None):
+        llhs = self.llhs(skip)
+        if llhs:
+            return min(llhs)
+        else:
+            return None
+
+    def labels(self, skip=None):
+        if not skip:
+            skip = self.sils
+        return [seg.label for seg in self.segments if seg.label not in skip]
+
+    def sentence(self, skip=None):
+        return " ".join(self.labels(skip))
+
+    def export(self):
+        return [seg.export() for seg in self.segments]

novoapi_for_python3x/asr/spraaklab/__init__.py

@@ -0,0 +1,4 @@
+#!/usr/bin/env python
+
+#import schema
+from . import schema

novoapi_for_python3x/asr/spraaklab/schema.py

@@ -0,0 +1,273 @@
+#!/usr/bin/env python
+## (c) 2017 NovoLanguage, author: David A. van Leeuwen
+
+## The purpose of this to define the grammar structure in a json schema, so that it can be validated,
+## (de)serialized, and perhaps even automatically converted to a Python class structure.
+
+import json
+import jsonschema
+
+grammar_schema_v10 = {
+    "$schema": "http://json-schema.org/schema#",
+    "title": "NovoLanguage grammar",
+    "description": "A grammar specification for the NovoLanguage Automatic Speech Recognition",
+    "$ref": "#/definitions/group",
+    "definitions": {
+        "phones": {
+            "type": "array",
+            "items": {
+                "type": "string"
+            },
+            "minItems": 1
+        },
+        "pronunciation": {
+            "type": "object",
+            "properties": {
+                "phones": {
+                    "$ref": "#/definitions/phones"
+                },
+                "syllables": {
+                    "type": "array",
+                    "items": {
+                        "$ref": "#/definitions/syllable"
+                    },
+                    "minItems": 1
+                },
+                "id": {
+                    "type": "integer",
+                    "description": "ID to distinguish this pronunciation from other variants"
+                },
+                "meta": {
+                    "type": "object"
+                }
+            },
+            "required": ["phones"]
+        },
+        "syllable": {
+            "type": "object",
+            "properties": {
+                "begin": {
+                    "type": "integer",
+                    "minimum": 0
+                },
+                "end": {
+                    "type": "integer",
+                    "minimum": 0
+                },
+                "stress": {
+                    "type": "integer",
+                    "minimum": 0
+                },
+                "tone": {
+                    "type": "integer",
+                    "minimum": 0
+                }
+            },
+            "required": ["begin", "end"]
+        },
+        "word": {
+            "type": "object",
+            "properties": {
+                "kind": {
+                    "type": "string",
+                    "enum": ["word"]
+                },
+                "label": {
+                    "type": "string"
+                },
+                "pronunciation": {
+                    "anyOf": [
+                        {
+                            "$ref": "#/definitions/pronunciation"
+                        },
+                        {
+                            "type": "array",
+                            "items": {
+                                "anyOf": [
+                                    {
+                                        "$ref": "#/definitions/pronunciation"
+                                    },
+                                    {
+                                        "$ref": "#/definitions/phones"
+                                    }
+                                ]
+                            },
+                            "minItems": 1
+                        },
+                        {
+                            "$ref": "#/definitions/phones"
+                        }
+
+                    ]
+                },
+                "syllables": {
+                    "type": "array",
+                    "items": {
+                        "$ref": "#/definitions/syllable"
+                    }
+                },
+                "graphemes": {
+                    "type": "array",
+                    "items": {
+                        "type": "string"
+                    }
+                },
+                "id": {
+                    "type": "integer",
+                    "description": "ID to distinguish this word from other words (with possibly the same label)"
+                },
+                "meta": {
+                    "type": "object"
+                }
+            },
+            "required": ["label"]
+        },
+        "element": {
+            "title": "element",
+            "oneOf": [
+                {
+                    "$ref": "#/definitions/word"
+                },
+                {
+                    "$ref": "#/definitions/group"
+                },
+                {
+                    "type": ["string", "null"]
+                }
+            ]
+        },
+        "group": {
+            "title": "element group",
+            "type": "object",
+            "properties": {
+                "kind": {
+                    "type": "string",
+                    "enum": ["sequence", "alternatives", "order"]
+                },
+                "elements": {
+                    "type": "array",
+                    "items": {
+                        "$ref": "#/definitions/element"
+                    },
+                    "minItems": 1,
+                },
+                "meta": {
+                    "type": "object"
+                }
+            },
+            "required": ["kind", "elements"]
+        }
+    }
+}
+
+grammar_schema_v01 = {
+    "$schema": "http://json-schema.org/schema#",
+    "title": "NovoLanguage grammar v0.1",
+    "description": "A grammar specification for the NovoLanguage Automatic Speech Recognition",
+    "type": "object",
+    "properties": {
+        "type": {
+            "type": "string",
+            "enum": ["multiple_choice", "word_order"]
+        },
+        "parts": {
+            "type": "array",
+            "minItems": 1,
+            "maxItems": 5,
+            "items": {
+                "type": ["string", "array"],
+                "items": {
+                    "type": ["string"]
+                }
+            }
+        }
+    }
+}
+
+grammar_rpc_schema = {
+    "$schema": "http://json-schema.org/schema#",
+    "title": "NovoLanguage RPC grammar",
+    "type": "object",
+    "properties": {
+        "type": {
+            "type": "string",
+            "enum": ["confusion_network"]
+        },
+        "version": {
+            "type": "string",
+            "default": "v0.1"
+        },
+        "data": {
+            "type": "object"
+        },
+        "return_dict": {
+            "type": "boolean"
+        },
+        "return_objects": {
+            "type": "array",
+            "items": {
+                "type": "string",
+                "enum": ["dict", "grammar"]
+            }
+        },
+        "phoneset": {
+            "type": "string",
+            "enum": ["cmu69", "novo70", "mdbg115"]
+        },
+        "parallel_silence": {
+            "type": "boolean"
+        }
+    },
+    "required": ["type", "data"]
+}
+
+def validate(object, schema=grammar_schema_v10):
+    #if isinstance(object, basestring):
+    if isinstance(object, str):
+        object = json.loads(object)
+    if not isinstance(object, dict):
+        raise TypeError("Expected dict or json string")
+    try:
+        jsonschema.validate(object, schema)
+    except jsonschema.ValidationError:
+        return False
+    except Exception:
+        raise
+    else:
+        return True
+
+def validate_rpc_grammar(message):
+    """validate an rpc grammar message"""
+    if not validate(message, grammar_rpc_schema):
+        raise ValueError("Not a valid RPC grammar")
+    version = message.get("version", "0.1")
+    data = message["data"]
+    if version == "0.1":
+        if not validate(data, grammar_schema_v01):
+            raise ValueError("Not a valid grammar v0.1")
+    elif version == "1.0":
+        if not validate(data, grammar_schema_v10):
+            raise ValueError("Not a valid grammar v1.0")
+    else:
+        raise ValueError("Unsupported schema version")
+
+
+## test
+def test(data=None):
+    if not data:
+        data = {"kind": "sequence", "elements": [
+            {"kind": "alternatives", "elements": ["a plain string", "an alternative string"]},
+            {"kind": "word", "label": "a word", "pronunciation": {"phones": ["ah", "w", "er", "d"]}},
+            {"kind": "order", "elements": [{"kind": "word", "label": "another word", "visible": False}, "last word"]}]}
+    try:
+        jsonschema.validate(data, schema)
+    except jsonschema.ValidationError as e:
+        #print data, "validated not OK", e.message
+        print("{0} validated not OK {1}".format(data, e.message))
+    else:
+        #print data, "validated OK"
+        print("{} validated OK".format(data))
+
+
+if __name__ == "__main__":
+    test()

novoapi_for_python3x/backend/__init__.py

@@ -0,0 +1,4 @@
+#!/usr/bin/env python
+
+#import session
+from . import session

novoapi_for_python3x/backend/session.py

@@ -0,0 +1,254 @@
+#!/usr/bin/env python
+# (c) 2015--2018 NovoLanguage, author: David A. van Leeuwen
+
+## Recognition interface for actual backend.  Adapted from player.asr.debug.
+
+import json
+import sys
+import wave
+import requests
+import websocket
+import logging
+import collections
+
+import time
+
+from .. import asr
+
+logger = logging.getLogger(__name__)
+
+## turn off annoying warnings
+requests.packages.urllib3.disable_warnings()
+logging.getLogger("requests.packages.urllib3.connectionpool").setLevel(logging.WARN)
+
+buffer_size = 4096
+gm = "gm.novolanguage.com" ## dev
+protocol = "https"
+port = 443
+apiversion = 0
+
+sessions = collections.Counter()
+
+def segmentation(result):
+    """converts a raw backend recognition result to a segment of novo.asr.segments class Segmentation"""
+    for w in result:
+        w["score"] = w["confidence"]["prob"]
+        w["llh"] = w["confidence"]["llr"]
+        w["label"] = w["label"]["raw"]
+        w["begin"] /= 10
+        w["end"] /= 10
+        for p in w["phones"]:
+            p["score"] = p["confidence"]["prob"]
+            p["llh"] = p["confidence"]["llr"]
+            p["begin"] /= 10
+            p["end"] /= 10
+    return asr.segments.Segmentation(result)
+
+class rpcid:
+    id = 0
+    @staticmethod
+    def next():
+        rpcid.id += 1
+        return rpcid.id
+
+class Recognizer(object):
+    def __init__(self, lang="en", gm=gm, grammar_version="0.1", user=None, password=None, snodeid=None, keepopen=False):
+        self.lang = lang
+        self.keepopen = keepopen
+        self.api_url = "%s://%s:%d/v%d" % (protocol, gm, port, apiversion)
+        self.verify = False
+        self.headers = {"Content-Type": "application/json"}
+        self.login_user(user, password)
+        data = {"l2": lang, "local": False, "skipupload": True}
+        if snodeid:
+            data["snodeid"] = snodeid
+        self.conn = None
+        self.init_session(data)
+        self.grammar_version = grammar_version
+        self.last_message = None
+
+    def login_user(self, username, password):
+        # obtain authentication token of user
+        logger.info('obtain auth token at %s', self.api_url)
+        data = {
+            'username': username,
+            'password': password
+        }
+        try:
+            r = requests.post(self.api_url + '/publishers/1/login', headers=self.headers, data=json.dumps(data), verify=self.verify)
+        except Exception as e:
+            logger.error("Cannot post request to GM API for user login: %s", e.message)
+            sys.exit(-1)
+        assert r.ok, r.reason
+        result = r.json()
+        if "errors" in result["response"]:
+            logger.info("Error in logging in: %s", result["response"]["errors"])
+            sys.exit(-1)
+
+        user_auth_token = result['response']['user']['authentication_token']
+        logger.info("User auth token is: %s", user_auth_token)
+
+        # set auth token in header
+        self.headers['Authentication-Token'] = user_auth_token
+
+    def init_session(self, data, direct=False, use_ip=False):
+        logger.info('Request new session: %s', data)
+        r = requests.post(self.api_url + '/sessions', headers=self.headers, data=json.dumps(data), verify=self.verify)
+        if not r.ok:
+            logger.error("New session request failed: %s", r.text)
+            return
+
+        status_url = r.headers.get("location")
+        if status_url:
+            ## we got a redirect
+            status = {}
+            while True:
+                logger.debug("Checking %s", status_url)
+                s = requests.get(status_url, verify=self.verify)
+                if not s.ok:
+                    logger.error('Checking Failed: %s', s.text)
+                    return
+
+                status = s.json()
+                if status['status'] == 'PENDING':
+                    logger.debug("Status: %s", status['status'])
+                    time.sleep(1)
+                else:
+                    break
+            session = status['result'][0] ## [1] is another status code...
+            if "error" in session:
+                logger.error("Error in getting a snode: %s", session["error"])
+                raise Exception
+        else:
+            session = r.json()
+
+        try:
+            logger.info("Session: %r", session)
+            if direct:
+                snode_ip = session["snode"]["ip"]
+                proxy_url = snode_ip
+                snode_port = session["port"]
+                ws_url = "%s://%s:%d/" % ("ws", snode_ip, snode_port)
+            else:
+                field = "ip" if use_ip else "hostname"
+                proxy_url = session['snode']['datacentre']['proxy'][field]
+                ws_url = 'wss://' + proxy_url + '/' + session['uuid']
+            logger.info("Connecting to websocket: %s", ws_url)
+            conn = websocket.create_connection(ws_url, sslopt={"check_hostname": self.verify})
+            logger.info("Connected.")
+        #except Exception, e:
+        except Exception as e:
+            logger.error("Unable to connect to websocket: %s", e.message)
+            raise e
+
+        self.session_id = session['id']
+        self.proxy_url = proxy_url
+        self.conn = conn
+        self.session = session
+        sessions[session["uuid"]] += 1
+
+    def setgrammar(self, grammar): ## backend grammar object: {"data": {...}, "type": "confusion_network"}
+        data = {"jsonrpc": "2.0",
+                'type': 'jsonrpc',
+                'method': 'set_grammar',
+                'params': grammar,
+                "id": rpcid.next()}
+        asr.spraaklab.schema.validate_rpc_grammar(grammar)
+        self.conn.send(json.dumps(data))
+        result = json.loads(self.conn.recv())
+        if result.get("error"):
+            logger.error("Exercise validation error: %s", result)
+        return result
+
+    def set_alternatives_grammar(self, *args, **kwargs):
+        if not "version" in kwargs:
+            kwargs["version"] = self.grammar_version
+        return self.setgrammar(alternatives_grammar(*args, **kwargs))
+
+    def recognize_wav(self, wavf):
+        w = wave.open(wavf, 'r')
+        nchannels, sampwidth, framerate, nframes, comptype, compname = w.getparams()
+        if nchannels > 1:
+            logging.error("Please use .wav with only 1 channel, found %d channels in %s", nchannels, wavf)
+            return
+        if (sampwidth != 2):
+            logging.error("Please use .wav with 2-byte PCM data, found %d bytes in %s", sampwidth, wavf)
+            return
+        if (framerate != 16000.0):
+            logging.error("Please use .wav sampled at 16000 Hz, found %1.0f in %s", framerate, wavf)
+            return
+        if (comptype != 'NONE'):
+            logging.error("Please use .wav with uncompressed data, found %s in %s", compname, wavf)
+            return
+        buf = w.readframes(nframes)
+        w.close()
+        return self.recognize_data(buf)
+
+    def recognize_data(self, buf):
+        nbytes_sent = 0
+        start = time.time()
+        for j in range(0, len(buf), buffer_size):
+            audio_packet = str(buf[j:j + buffer_size])
+            nbytes_sent += len(audio_packet)
+            self.conn.send_binary(audio_packet)
+        self.conn.send(json.dumps({"jsonrpc": "2.0", "method": "get_result", "id": rpcid.next()}))
+        logger.info("Waiting for recognition result...")
+        self.last_message = self.conn.recv() ## keep result for the interested applications
+        message = json.loads(self.last_message)
+        dur = time.time() - start
+        logger.info("Recognition took %5.3f seconds", dur)
+        if "error" in message:
+            raise RuntimeError("Error from recognition backend: %r" % message.get("error"))
+        return segmentation(message["result"]["words"])
+
+    def recognize_url(self, url):
+        start = time.time()
+        data = json.dumps({"jsonrpc": "2.0", "method": "send_audio", "id": rpcid.next(), "params": {"type": "url", "data": url, "details": ["word", "utterance"]}})
+        self.conn.send(data)
+        logger.info("Waiting for recognition result...")
+        self.last_message = self.conn.recv() ## keep result for the interested applications
+        #print self.last_message
+        print(self.last_message)
+        message = json.loads(self.last_message)
+        dur = time.time() - start
+        logger.info("Recognition took %5.3f seconds", dur)
+        if "error" in message:
+            raise RuntimeError("Error from recognition backend: %r" % message.get("error"))
+        return segmentation(message["result"]["words"])
+
+    def __del__(self):
+        sessions[self.session["uuid"]] -= 1
+        if self.conn and sessions[self.session["uuid"]] <= 0:
+            self.conn.close()
+            url = self.api_url + '/sessions/%d' % self.session_id
+            if self.keepopen:
+                logger.info("Keeping session open...")
+            else:
+                logger.info("Closing session: %s", url)
+                r = requests.delete(url, headers=self.headers, verify=self.verify)
+                assert r.ok, r.reason
+
+def alternatives_grammar(parts, version="0.1", ret=None):
+    """Make a grammar of alternatives, as array(sequence)-of-array(alternatives)-of-strings"""
+    r = {"type": "confusion_network", "version": version}
+    if version=="0.1":
+        r["data"] = {"type": "multiple_choice", "parts": parts}
+        if isinstance(ret, list) and "dict" in ret:
+            r["return_dict"] = True
+    elif version=="1.0":
+        seqels = []
+        for part in parts:
+            altels = []
+            for alt in part:
+                words = alt.split(" ")
+                if len(words) > 1:
+                    alt = {"kind": "sequence", "elements": words}
+                altels.append(alt)
+            seqels.append({"kind": "alternatives", "elements": altels})
+        r["data"] = {"kind": "sequence", "elements": seqels}
+        if isinstance(ret, list):
+            r["return_objects"] = ret
+    else:
+        raise ValueError("Unsupported version: %s" % version)
+    asr.spraaklab.schema.validate_rpc_grammar(r)
+    return r

novoapi_for_python3x/utils/json/__init__.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+
+## from https://stackoverflow.com/questions/1447287/format-floats-with-standard-json-module
+class PrettyFloat(float):
+    def __repr__(self):
+        return '%.15g' % self
+
+def pretty_floats(obj):
+    if isinstance(obj, float):
+        return PrettyFloat(obj)
+    elif isinstance(obj, dict):
+        return dict((k, pretty_floats(v)) for k, v in obj.items())
+    elif isinstance(obj, (list, tuple)):
+        return map(pretty_floats, obj)
+    return obj
+
+def rounded_floats(obj, ndigits=15):
+    if isinstance(obj, float):
+        return PrettyFloat(round(obj, ndigits))
+    elif isinstance(obj, dict):
+        return dict((k, rounded_floats(v, ndigits)) for k, v in obj.items())
+    elif isinstance(obj, (list, tuple)):
+        return map(lambda o: rounded_floats(o, ndigits), obj)
+    return obj
+