acoustic_model.sln

@@ -10,6 +10,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
 		..\forced_alignment\forced_alignment\__init__.py = ..\forced_alignment\forced_alignment\__init__.py
 		..\forced_alignment\forced_alignment\convert_phone_set.py = ..\forced_alignment\forced_alignment\convert_phone_set.py
 		..\toolbox\evaluation.py = ..\toolbox\evaluation.py
+		..\forced_alignment\forced_alignment\forced_alignment.pyproj = ..\forced_alignment\forced_alignment\forced_alignment.pyproj
 		..\forced_alignment\forced_alignment\htk_dict.py = ..\forced_alignment\forced_alignment\htk_dict.py
 		..\forced_alignment\forced_alignment\lexicon.py = ..\forced_alignment\forced_alignment\lexicon.py
 		..\forced_alignment\forced_alignment\mlf.py = ..\forced_alignment\forced_alignment\mlf.py
@@ -17,7 +18,6 @@ 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
-		..\..\..\..\..\Python36-32\Lib\site-packages\novoapi\backend\session.py = ..\..\..\..\..\Python36-32\Lib\site-packages\novoapi\backend\session.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
 	EndProjectSection

acoustic_model/acoustic_model.pyproj

@@ -4,8 +4,7 @@
     <SchemaVersion>2.0</SchemaVersion>
     <ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
     <ProjectHome>.</ProjectHome>
-    <StartupFile>
-    </StartupFile>
+    <StartupFile>check_novoapi.py</StartupFile>
     <SearchPath>
     </SearchPath>
     <WorkingDirectory>.</WorkingDirectory>

acoustic_model/check_novoapi.py

@@ -3,31 +3,23 @@ os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
 
 import sys
 import csv
-from collections import Counter
-import random
-import shutil
+#import subprocess
+#from collections import Counter
+#import re
 
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
+#from sklearn.metrics import confusion_matrix
 
-from sklearn.metrics import confusion_matrix
-from sklearn.metrics import accuracy_score
-import novoapi 
-
-import defaultfiles as default
-sys.path.append(default.forced_alignment_module_dir)
-from forced_alignment import pyhtk, convert_phone_set
-#import acoustic_model_functions as am_func
+import acoustic_model_functions as am_func
 import convert_xsampa2ipa
+import defaultfiles as default
+
+from forced_alignment import pyhtk, convert_phone_set
+
+import novoapi 
 import novoapi_functions
-sys.path.append(default.accent_classification_dir)
-import output_confusion_matrix
-
-## procedure 
-forced_alignment_novo70 = True
-balance_sample_numbers  = False
-
 
 ## ===== load novo phoneset =====
 phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa = novoapi_functions.load_phonset()
@@ -147,106 +139,6 @@ df_per_word = pd.DataFrame(index=[], columns=df_samples.keys())
 
 for word in word_list:
 	df_samples_ = df_samples[df_samples['word']==word]
-	df_samples_ = df_samples_[df_samples_['frequency']>2]
+	df_samples_ = df_samples_[df_samples_['frequency']>1]
 	df_per_word = df_per_word.append(df_samples_, ignore_index=True)
-#df_per_word.to_excel(os.path.join(default.stimmen_dir, 'pronunciation_variants_novo70.xlsx'), encoding="utf-8")
-
-
-## ===== forced alignment =====
-reus_dir = r'C:\OneDrive\Desktop\Reus'
-if forced_alignment_novo70:
-	Results = pd.DataFrame(index=[],
-		columns=['filename', 'word', 'xsampa', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
-	#for word in word_list:
-	for word in ['Reus']:
-		# pronunciation variants top 3
-		df_per_word_ = df_per_word[df_per_word['word']==word]
-		df_per_word_ = df_per_word_.sort_values('frequency', ascending=False)
-		if len(df_per_word_) < 3: # pauw, rozen
-			pronunciation_ipa = list(df_per_word_['ipa'])
-		elif word=='Reuzenrad':
-			pronunciation_ipa = [
-				df_per_word_.iloc[0]['ipa'], 
-				df_per_word_.iloc[1]['ipa'], 
-				df_per_word_.iloc[2]['ipa'],
-				df_per_word_.iloc[3]['ipa']]
-		else:
-			# oog, oor, reus, roeiboot
-			pronunciation_ipa = [
-				df_per_word_.iloc[0]['ipa'], 
-				df_per_word_.iloc[1]['ipa'], 
-				df_per_word_.iloc[2]['ipa']]
-		#print("{0}: {1}".format(word, pronunciation_ipa))
-
-		# samples for the word
-		df_ = df[df['word']==word]
-
-		# samples in which all pronunciations are written in novo70.
-		samples = df_.query("ipa in @pronunciation_ipa")
-		
-
-		## ===== balance sample numbers =====
-		if balance_sample_numbers:
-			c = Counter(samples['ipa'])
-			sample_num_list = [c[key] for key in c.keys()]
-			sample_num		= np.min(sample_num_list)
-
-			samples_balanced = pd.DataFrame(index=[], columns=list(samples.keys()))
-			for key in c.keys():
-				samples_ = samples[samples['ipa'] == key]
-				samples_balanced = samples_balanced.append(samples_.sample(sample_num), ignore_index = True)
-
-			samples = samples_balanced
-
-
-		results = pd.DataFrame(index=[],
-			columns=['filename', 'word', 'xsampa', 'ipa', 'result_ipa', 'result_novo70', 'llh'])
-	
-		for i in range(0, len(samples)):
-			sample = samples.iloc[i]
-			filename = sample['filename']
-			wav_file = os.path.join(default.stimmen_wav_dir, filename)
-			if os.path.exists(wav_file):
-				# for Martijn
-				#shutil.copy(wav_file, os.path.join(reus_dir, filename))
-
-				pronunciation_ipa_ = [ipa.replace(':', 'ː') for ipa in pronunciation_ipa]
-				result = novoapi_functions.forced_alignment(wav_file, word, pronunciation_ipa_)
-				result_ipa, result_novo70, llh = novoapi_functions.result2pronunciation(result, word)
-				result_ = pd.Series([
-					sample['filename'],
-					sample['word'],
-					sample['xsampa'],
-					sample['ipa'],
-					' '.join(result_ipa),
-					' '.join(result_novo70),
-					llh
-					], index=results.columns)
-				results = results.append(result_, ignore_index = True)
-				print('{0}/{1}: answer {2} - prediction {3}'.format( 
-			 i+1, len(samples), result_['ipa'], result_['result_ipa']))
-			results.to_excel(os.path.join(reus_dir, 'results.xlsx'), encoding="utf-8")
-		if len(results) > 0:
-			Results = Results.append(results, ignore_index = True)
-		Results.to_excel(os.path.join(default.stimmen_dir, 'Results.xlsx'), encoding="utf-8")
-else:
-	Results_xlsx = pd.ExcelFile(os.path.join(default.stimmen_dir, 'Results.xlsx'), encoding="utf-8")
-	Results = pd.read_excel(Results_xlsx, 'Sheet1')
-
-
-## ===== analysis =====
-#result_novoapi_dir = os.path.join(default.stimmen_dir, 'result', 'novoapi')
-#for word in word_list:
-#	if not word == 'Oog':
-
-#		Results_ = Results[Results['word'] == word]
-#		y_true  = list(Results_['ipa'])
-#		y_pred_ = [ipa.replace(' ', '') for ipa in list(Results_['result_ipa'])]
-#		y_pred  = [ipa.replace('ː', ':') for ipa in y_pred_]
-#		pronunciation_variants = list(set(y_true))
-#		cm = confusion_matrix(y_true, y_pred, labels=pronunciation_variants)
-
-#		plt.figure()
-#		output_confusion_matrix.plot_confusion_matrix(cm, pronunciation_variants, normalize=False)
-#		#plt.show()
-#		plt.savefig(os.path.join(result_novoapi_dir, word + '.png'))
+df_per_word.to_excel(os.path.join(default.stimmen_dir, 'pronunciation_variants_novo70.xlsx'), encoding="utf-8")

acoustic_model/defaultfiles.py

@@ -29,7 +29,6 @@ config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
 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')
-accent_classification_dir   = os.path.join(repo_dir, 'accent_classification', 'accent_classification')
 
 WSL_dir   = r'C:\OneDrive\WSL'
 fame_dir        = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')

acoustic_model/novoapi_functions.py

@@ -6,8 +6,6 @@ import json
 
 from novoapi.backend import session
 
-import os
-os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
 import defaultfiles as default
 
 
@@ -38,29 +36,10 @@ def load_phonset():
 				phoneset_novo70.append(novo70)
 				translation_key_ipa2novo70[ipa] = novo70
 				translation_key_novo702ipa[novo70] = ipa
-
-	# As per Nederlandse phoneset_aki.xlsx recieved from David
-	# [ɔː] oh / ohr # from ipa->novo70, only oh is used.
-	# [ɪː] ih / ihr # from ipa->novo70, only ih is used.
-	# [iː] iy
-	# [œː] uh
-	# [ɛː] eh
-	# [w] wv in IPA written as ʋ. 
-	extra_ipa	 = ['ɔː', 'ɪː', 'iː', 'œː', 'ɛː', 'ʋ']
-	extra_novo70 = ['oh', 'ih', 'iy', 'uh', 'eh', 'wv'] 
-	for ipa, novo70 in zip(extra_ipa, extra_novo70):
-		phoneset_ipa.append(ipa)
-		phoneset_novo70.append(novo70)
-		translation_key_ipa2novo70[ipa] = novo70
-		translation_key_novo702ipa[novo70] = ipa
-
-	translation_key_novo702ipa['ohr'] = 'ɔː'
-	translation_key_novo702ipa['ihr'] = 'ɪː'
-
 	phoneset_ipa    = np.unique(phoneset_ipa)
 	phoneset_novo70 = np.unique(phoneset_novo70)
 
-	return phoneset_ipa, phoneset_novo70, translation_key_ipa2novo70, translation_key_novo702ipa
+	return 
 
 
 def multi_character_tokenize(line, multi_character_tokens):
@@ -191,9 +170,4 @@ def result2pronunciation(result, word):
 	phones = result_[0]['phones']
 	pronunciation_novo70 = [phone['label'] for phone in phones]
 	pronunciation_ipa = [novo702ipa(phone) for phone in pronunciation_novo70]
-	return pronunciation_ipa, pronunciation_novo70, llh
-
-
-if __name__ == 'main':
-	pronunciation_ipa = ['rø:s', 'mɑn', 'mɑntsjə']
-	grammar = make_grammar('reus', pronunciation_ipa)
+	return pronunciation_ipa, pronunciation_novo70, llh

novoapi/asr/segments/praat.py

@@ -28,6 +28,7 @@ def print_info_tier(output, title, begin, end, label):
     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"'
@@ -69,11 +70,8 @@ def seg2tg(fname, segments):
 
     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))
-    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)
+    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/asr/spraaklab/schema.py

@@ -266,7 +266,7 @@ def test(data=None):
         print("{0} validated not OK {1}".format(data, e.message))
     else:
         #print data, "validated OK"
-        print("{0} validated OK".format(data))
+        print("{} validated OK".format(data))
 
 
 if __name__ == "__main__":

novoapi/backend/session.py

@@ -188,8 +188,7 @@ class Recognizer(object):
         nbytes_sent = 0
         start = time.time()
         for j in range(0, len(buf), buffer_size):
-            #audio_packet = str(buf[j:j + buffer_size])
-            audio_packet = buf[j:j + 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()}))

novoapi_for_python3x/readme

@@ -1,64 +0,0 @@
-novoapi( https://bitbucket.org/novolanguage/python-novo-api ) is written in Python 2.7. 
-To install it on Python 3.x the following points should be modified. 
-- basestring --> str
-- print xxx --> print({}.format(xxx)).
-- import xxx --> from . import xxx
-- except Exception, e --> except Exception as e
-- remove tuples from input arguments of a function.
-Concretely...
-
-=== novoapi\backend\__init__.py
-#import session
-from . import session
-
-
-=== novoapi\backend\session.py
-#except Exception, e:
-except Exception as e:
-
-#print self.last_message
-print(self.last_message)
-
-
-=== novoapi\asr\__init__.py
-#import segments
-#import spraaklab
-from . import segments
-from . import spraaklab
-
-
-=== novoapi\asr\segments\praat.py
-#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))
-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)
-
-
-=== novoapi\asr\spraaklab\__init__.py ===
-#import schema
-from . import schema
-
-
-=== novoapi\asr\spraaklab\schema.py ===
-#if isinstance(object, basestring):
-if isinstance(object, str):
-
-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("{0} validated OK".format(data))
-
-
-Then to make it correctly work, few more modification is needed.
-When the wav file is read using the wave module, the output (named buf) is a string of bytes on Python 2.7 while buf is a byte object on Python 3.6. 
-Therefore... 
-
-=== novoapi\backend\session.py
-#audio_packet = str(buf[j:j + buffer_size])
-audio_packet = buf[j:j + buffer_size]
-
-Also, because of this difference, Segment.__repr__ (novoapi\asr\segments\segments.py) does not work.

reus-test/reus-test.py

@@ -1,119 +0,0 @@
-#!/usr/bin/env python
-import os
-os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
-
-import argparse
-import json
-
-from novoapi.backend import session
-
-p = argparse.ArgumentParser()
-p.add_argument("--user", default='martijn.wieling')
-p.add_argument("--password", default='xxxxx')
-args = p.parse_args()
-
-rec = session.Recognizer(grammar_version="1.0", lang="nl", snodeid=101, user=args.user, password=args.password, keepopen=True)
-
-grammar = {
-  "type": "confusion_network",
-  "version": "1.0",
-  "data": {
-    "kind": "sequence",
-    "elements": [
-      {
-        "kind": "word",
-        "pronunciation": [
-          {
-            "phones": [
-              "r",
-              "eu0",
-              "s"
-            ],
-            "id": 0
-          }
-          ,
-          {
-            "phones": [
-              "m",
-              "a0",
-              "n"
-            ],
-            "id": 1
-          }
-		   ,
-          {
-            "phones": [
-              "m",
-              "a0",
-              "n",
-              "t",
-              "s",
-              "y",
-              "ax"
-            ],
-            "id": 2
-          }
-        ],
-        "label": "reus"
-      }
-    ]
-  },
-  "return_objects": [
-    "grammar"
-  ],
-  "phoneset": "novo70"
-}
-
-res = rec.setgrammar(grammar)
-#print "Set grammar result", res
-
-
-## === novoapi/backend/session.py ===
-#import wave
-#import time
-#from novoapi.backend.session import rpcid, segmentation
-
-#wavf = "reus1008-reus.wav"
-#w = wave.open(wavf, 'r')
-#nchannels, sampwidth, framerate, nframes, comptype, compname = w.getparams()
-#buf = w.readframes(nframes)
-#w.close()
-
-#buffer_size = 4096
-#nbytes_sent = 0
-#start = time.time()
-#for j in range(0, len(buf), buffer_size):
-#    audio_packet = buf[j:j + buffer_size]
-#    nbytes_sent += len(audio_packet)
-#    rec.conn.send_binary(audio_packet)
-#rec.conn.send(json.dumps({"jsonrpc": "2.0", "method": "get_result", "id": rpcid.next()}))
-#print(rpcid.next())
-#rec.last_message = rec.conn.recv() 
-#message = json.loads(rec.last_message)
-#result = session.segmentation(message["result"]["words"])
-#result.export()
-## ====================================
-
-def result2pronunciation(result, word):
-	#result_ = res.export()[1]
-	result_ = [result[i] for i in range(len(result)) if result[i]['label'] == word] 
-	llh = result_[0]['llh']
-	phones = result_[0]['phones']
-	pronunciation = [phone['label'] for phone in phones]
-	return pronunciation, llh
-
-
-res = rec.recognize_wav("reus1008-reus.wav")
-#print "\n\n\nThe pronounced word in reus1008-reus.wav is: REUS\n\n"
-#print "Recognition result:", json.dumps(res.export(), indent=4)
-result2pronunciation(res.export(), 'reus')
-
-#print "\n\n\nThe pronounced word in reus1167-man.wav is: MAN\n\n"
-res2 = rec.recognize_wav("reus1167-man.wav")
-#print "Recognition result:", json.dumps(res2.export(), indent=4)
-result2pronunciation(res2.export(), 'reus')
-
-#print "\n\n\nThe pronounced word in reus3768-mantsje.wav is: MANTSJE\n\n"
-res3 = rec.recognize_wav("reus3768-mantsje.wav")
-#print "Recognition result:", json.dumps(res3.export(), indent=4)
-result2pronunciation(res3.export(), 'reus')