def test_nltk_cfg_qtype(self):
print("test_nltk_cfg_qtype")
gfile = os.path.join(curdir, os.path.pardir, "config",
"grammar.question-type.cfg")
question_grammar = nltk.data.load('file:%s' % gfile)
def get_missing_words(grammar, tokens):
"""
Find list of missing tokens not covered by grammar
"""
missing = [
tok for tok in tokens if not grammar._lexical_index.get(tok)
]
return missing
sentence = "what is your name"
sent = sentence.split()
missing = get_missing_words(question_grammar, sent)
target = []
for x in sent:
if x in missing:
continue
target.append(x)
rd_parser = RecursiveDescentParser(question_grammar)
result = []
print("target: ", target)
for tree in rd_parser.parse(target):
result.append(x)
print("Question Type\n", tree)
if len(result) == 0:
print("Not Question Type")
def cfg_zh(self):
grammar = nltk.CFG.fromstring("""
S -> N VP
VP -> V NP | V NP | V N
V -> "尊敬"
N -> "我们" | "老师"
""")
sent = "我们 尊敬 老师".split()
rd_parser = RecursiveDescentParser(grammar)
result = []
for i, tree in enumerate(rd_parser.parse(sent)):
result.append(tree)
print("Tree [%s]: %s" % (i + 1, tree))
assert len(result) > 0, "Can not recognize CFG tree."
if len(result) == 1:
print("Draw tree with Display ...")
result[0].draw()
else:
print("WARN: Get more then one trees.")
print(result)
def cfg_en(self):
print("test_nltk_cfg_en") # 定义英文语法规则
grammar = nltk.CFG.fromstring("""
S -> NP VP
VP -> V NP | V NP PP
V -> "saw" | "ate"
NP -> "John" | "Mary" | "Bob" | Det N | Det N PP
Det -> "a" | "an" | "the" | "my"
N -> "dog" | "cat" | "cookie" | "park"
PP -> P NP
P -> "in" | "on" | "by" | "with"
""")
sent = "Mary saw Bob".split()
rd_parser = RecursiveDescentParser(grammar)
result = []
for i, tree in enumerate(rd_parser.parse(sent)):
result.append(tree)
assert len(result) > 0, " CFG tree parse fail."
print(result)
def Process(str, file):
sr = RecursiveDescentParser(pgrammar)
r = list(sr.parse(str.split()))
if len(r) > 0:
cadResult = GenerateCadFile(ParentedTree.convert(r[0]))
cadResult.write(file)
else:
print("************* " + str)
def test2():
from nltk.parse import RecursiveDescentParser
rd = RecursiveDescentParser(grammar)
sentence1 = 'the cat chased the dog'.split()
sentence2 = 'the cat chased the dog on the rug'.split()
print rd.parse(sentence2)
def check_syntax(text):
# here we list all possible languages that can be used in the grammar
lang_pos = {}
for l in pycountry.languages:
p = pycountry.languages.get(name=l.name)
try:
alpha_kind = p.alpha_2
lang_pos[p.name]=alpha_kind
except:
pass
lang_command = '''LANG ->'''
for lg in lang_pos:
if list(lang_pos.keys()).index(lg) == 0:
lang_command+=' '+str(lang_pos[lg])+' '
else:
lang_command+='''| '{x}' '''.format(x=lang_pos[lg])
# here we list all possible functions , given that the system was already re-written to accomodate
# the changes
func_command = '''FUNC ->'''
for attr in dir(node_func):
if dir(node_func).index(attr) == 0:
func_command+=' '+str(attr)+' '
else:
func_command+='''| '{x}' '''.format(x=attr)
# here we substitute the pre made rules in the grammar itself
grammar = CFG.fromstring(('''
S -> 'plug' '<' FUNC '>' 'as' LANG | 'unplug' '<' LANG '>'
command1
command2
'''.replace('command1',lang_command)).replace('command2',func_command))
grammar_rd = RecursiveDescentParser(grammar)
# here we check the syntax and the lexical using the already described cfg
for t in text.split('\n'):
parsed = []
try:
for tree in grammar_rd.parse(t.split()):
parsed.append(tree)
if len(parsed) != 0:
print(parsed)
pass
else:
return 'syntax error'
return 'parsed'
except:
return 'syntax/lexical error'
def sensibility_test(transcribeText, backdoor):
if backdoor:
print('Sentence is sensible')
return 1
else:
grammar = nltk.data.load('grammars/book_grammars/drt.cfg')
# sr = ShiftReduceParser(grammar=grammar)
rd = RecursiveDescentParser()
try:
for t in rd.parse(transcribeText):
print(t)
print('Sentence is sensible')
except:
print('Sentence is not sensible')
class GridGen():
def __init__(self):
PrWd_rules = []
PrWd_rules_old = ['']
for i in range(7): # set maximum level-1 length
PrWd_rules_new = [x +' '+ y for x in PrWd_rules_old \
for y in ['x', 'o']] # level-1 grid
PrWd_rules += PrWd_rules_new
PrWd_rules_old = PrWd_rules_new[:]
PrWd_rules = ['PrWd -> ' + x for x in PrWd_rules]
# Culminativity (at least one level-1 grid mark)
PrWd_rules = [x for x in PrWd_rules if re.search('x', x)]
# Expansions of syllable preterminals
Term_rules = ['x -> "σ"', 'o -> "σ"']
grammar_rules = PrWd_rules + Term_rules
grammar_rulestr = '\n'.join(grammar_rules)
grammar = CFG.fromstring(grammar_rulestr)
print(f'# of productions in grammar: {len(grammar.productions())}')
self.grammar = grammar
self.parser = RecursiveDescentParser(grammar)
def parses(self, inpt):
T = [t for t in self.parser.parse(inpt.split())]
T = [ParentedTree.convert(t) for t in T]
return T
def test_sample(self):
print("test_sample")
# This is a CFG grammar, where:
# Start Symbol : S
# Nonterminal : NP,VP,DT,NN,VB
# Terminal : "I", "a" ,"saw" ,"dog"
grammar = nltk.grammar.CFG.fromstring("""
S -> NP VP
NP -> DT NN | NN
VP -> VB NP
DT -> "a"
NN -> "I" | "dog"
VB -> "saw"
""")
sentence = "I saw a dog".split()
parser = RecursiveDescentParser(grammar)
final_tree = parser.parse(sentence)
for i in final_tree:
print(i)
def __init__(self):
# Expansions of PrWd
PrWd_rules = []
PrWd_rules_old = ['']
for i in range(5):
PrWd_rules_new = [x+' '+y for x in PrWd_rules_old \
for y in ['MainFt', 'Ft', 'Syll']]
PrWd_rules += PrWd_rules_new
PrWd_rules_old = PrWd_rules_new[:]
PrWd_rules = ['PrWd -> ' + x for x in PrWd_rules]
# Culminativity (exactly one main-stress foot)
PrWd_rules = [x for x in PrWd_rules if re.search('Main', x) \
and not re.search('Main.*Main', x)]
#print(len(PrWd_rules))
# Expansions of (Main)Ft
MainFt_rules = ['MainFt -> '+y for y in \
['MainStressSyll', 'MainStressSyll Syll', 'Syll MainStressSyll']]
Ft_rules = ['Ft -> '+y for y in \
['StressSyll', 'StressSyll Syll', 'Syll StressSyll']]
# Expansions of (Main)(Stress)Syll
Syll_rules = ['MainStressSyll -> s1', 'StressSyll -> s2', 'Syll -> s0']
# Expansions of syllable preterminals
Term_rules = ['s1 -> "σ"', 's2 -> "σ"', 's0 -> "σ"']
grammar_rules = PrWd_rules + MainFt_rules \
+ Ft_rules + Syll_rules + Term_rules
grammar_rulestr = '\n'.join(grammar_rules)
grammar = CFG.fromstring(grammar_rulestr)
print(f'# of productions in grammar: {len(grammar.productions())}')
self.grammar = grammar
self.parser = RecursiveDescentParser(grammar)
def callback(self, text):
"""this function is to process the recognized text and send the processed information to Arduino which controls some devices"""
global text_last
global xm_speech_req
global xm_speech_res
global sentences
#if len(text) != 0 :
# if text[28] == '=':
# text = text[29:]
# elif text[29] == '=':
# text = text[30:]
if text != "you are right" and text != "no" and len(text) != 0:
text_last = text
if type(text) == str and len(text) != 0:
print("recognized : " + text)
if xm_speech_req.command == 1: #Answer a question
if text == "you are right":
print('question: ' + text_last)
file_handle_c = open(
'/home/domistic/catkin_ws/src/xm_speech_for_linux/xm_speech/msc/bnf/c.txt',
mode='rb')
file_handle_d = open(
'/home/domistic/catkin_ws/src/xm_speech_for_linux/xm_speech/msc/bnf/d.txt',
mode='rb')
c_contents = file_handle_c.readlines()
d_contents = file_handle_d.readlines()
t = '\"' + text_last + '\"' + '\n'
if t in c_contents:
c_index = c_contents.index(t)
d = d_contents[c_index]
print('answer: ' + d)
xm_speech_res.num = 1
self.tts(d)
elif xm_speech_req.command == 2: #GPSR
if text != "you are right" and text != "no" and text != "you can stop here":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
self.tts("OK")
sen = text_last
if "tell the day of the month" in sen:
sentences = "tell the day of the month"
sen = sen.replace("tell the day of the month",
"sentence")
if "tell the day of the week" in sen:
sentences = "tell the day of the week"
sen = sen.replace("tell the day of the week",
"sentence")
if "tell the date" in sen:
sentences = "tell the date"
sen = sen.replace("tell the date", "sentence")
for i in double_words:
sen = sen.replace(i.replace("_", " "), i)
#print(sen)
rd = RecursiveDescentParser(grammar)
t = rd.parse_one(sen.split())
#print(t)
search(t)
text_last = ''
elif xm_speech_req.command == 3: #WhoIsWho
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
print("k")
self.tts("OK")
print("text_last:" + text_last)
#name
for t in people:
if t in text_last:
xm_speech_res.name.append(t)
xm_speech_res.num += 1
for i in objectno:
if i in text_last:
xm_speech_res.object.append(i)
xm_speech_res.num += 1
text_last = ''
elif xm_speech_req.command == 4: #SPR
#print('question: ' + text)
file_handle_a = open(
'/home/domistic/catkin_ws/src/xm_speech_for_linux/xm_speech/msc/bnf/a.txt',
mode='rb')
file_handle_b = open(
'/home/domistic/catkin_ws/src/xm_speech_for_linux/xm_speech/msc/bnf/b.txt',
mode='rb')
a_contents = file_handle_a.readlines()
b_contents = file_handle_b.readlines()
t = text + '?\n'
if t == a_contents[0]:
print('finally')
if t in a_contents:
a_index = a_contents.index(t)
b = b_contents[a_index]
xm_speech_res.answer = b
print('answer: ' + b)
xm_speech_res.num = 1
self.tts(b)
elif text == "number of people standing":
xm_speech_res.num = 2
time.sleep(1)
elif text == "number of people sitting":
xm_speech_res.num = 3
time.sleep(1)
elif text == "what is the number of the people who waving arms":
xm_speech_res.num = 4
time.sleep(1)
elif (xm_speech_req.command == 5): #help me carry
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
if text_last.find("stop") >= 0:
xm_speech_res.action.append('stop')
elif text_last.find("follow") >= 0:
xm_speech_res.action.append('follow')
elif text_last.find("take") >= 0:
xm_speech_res.action.append('take')
if text_last.find("kitchen") >= 0:
xm_speech_res.target.append('kitchen')
elif text_last.find("livingroom") >= 0:
xm_speech_res.target.append('livingroom')
elif text_last.find("bedroom") >= 0:
xm_speech_res.target.append('bedroom')
elif text_last.find("diningroom") >= 0:
xm_speech_res.target.append('diningroom')
xm_speech_res.num = 1
print("text_last:" + text_last)
text_last = ''
if text == "you can stop here":
xm_speech_res.action.append('stop')
elif (xm_speech_req.command == 6): #shopping
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
self.tts("OK")
print("text_last:" + text_last)
if text_last == "you can stop here":
xm_speech_res.action.append('stop')
xm_speech_res.num += 1
#follow
elif text_last.find('follow') >= 0:
xm_speech_res.action.append('follow')
xm_speech_res.num += 1
else:
for i in objectno:
if i in text_last:
xm_speech_res.object.append(i)
xm_speech_res.num += 1
#方向
if text_last.find('right') >= 0:
xm_speech_res.target.append('right')
xm_speech_res.num += 1
if text_last.find('left') >= 0:
xm_speech_res.target.append('left')
xm_speech_res.num += 1
if text_last.find('front') >= 0:
xm_speech_res.target.append('front')
xm_speech_res.num += 1
text_last = ''
def Theona():
intro1, intro2, intro3 = sentence_generation('open')
audio_play('boost.wav')
os.system(intro1)
train_sents = conll2000.chunked_sents('train.txt', chunk_types=['NP'])
print('Training data... It will take 2-4 minutes.')
chunker = ConsecutiveNPChunker(train_sents)
os.system(intro2)
# Theona Introduction
audio_play('start_up.wav')
os.system(intro3)
# Step1. ASR
# Use recognizer to record the speech.
recorder = sr.Recognizer()
starting = sentence_generation('hello')
with sr.Microphone() as mike:
print('Hello. Please speaking.')
audio_play('pong.wav')
os.system(starting)
my_sound = recorder.listen(mike)
print('Processing...')
# Speech signal to text. Supported by google Speech api: Internet needs to be connected.
tmp_words = recorder.recognize_google(my_sound)
words = str(tmp_words)
# test printing...
print(words)
# Step2. SLU
# 1. find the specific places to users.
#words = 'show me starbucks'
# Tokenize the sentence.
tokenized = word_tokenize(words)
# Parsing the sentence to find out goal and entity clearly.
pos_tagged = nltk.pos_tag(tokenized)
chunk_words = chunker.parse(pos_tagged)
reorder_words = tree_reconstruct(chunk_words)
# Build the grammar for parsing.
GOAL_FIND,ENTITY_PLACE = nonterminals('GOAL_FIND,ENTITY_PLACE')
usr_goal = ENTITY_PLACE
usr_find = GOAL_FIND
VP,NP,O = nonterminals('VP,NP,O')
grammar = CFG_grammar()
rd_parser = RecursiveDescentParser(grammar)
# Parsing the sentence.
parsed_words = []
for parsing in rd_parser.parse(reorder_words):
print(parsing)
# Find GOAL and ENTITY
for detect in parsing:
if detect.label() == 'GOAL_FIND':
usr_goal = detect.leaves()[0]
if detect.label() == 'ENTITY_PLACE':
usr_place = detect.leaves()[0]
finding = sentence_generation('finding')
finding = re.sub('<place>',usr_place,finding)
audio_play('tone.wav')
os.system(finding)
# 2. Provide weather information to users.
# Step3. DM
# Collect information from the internet.
# Location
google_url = "https://www.google.co.kr/?gfe_rd=cr&ei=8YoTV-OdF8WL8AWGp5DgDg&gws_rd=ssl#newwindow=1&q="
daum_url = 'http://search.daum.net/search?w=tot&DA=YZR&t__nil_searchbox=btn&sug=&sugo=&sq=&o=&q='
# Connect to the internet to proceed the users' request: goal and entity.
if usr_goal == 'find':
# Searching in Daum.
usr_request_url = daum_url + usr_place + '&tltm=1'
request = requests.get(usr_request_url)
soup = BeautifulSoup(request.content,'html.parser')
# Searching in Google.
#usr_request_url = google_url + usr_place
#request = requests.get(usr_request_url)
#soup = BeautifulSoup(request)
# Collect information.
# Find the closest 5 places around the location in which you start to request.
all_data = soup.find_all('div',{'class','cont_place'})
first_data = all_data[0]
# Address
address_info = all_data[0].find_all('a',{'class','more_address'})[0].text
# Phone Number
phone_info = all_data[0].find_all('span',{'class','f_url'})[0].text
# Location (map)
map_info = all_data[0].find('a').get('href')
# Weather
# Step4. NLG
# Generate an appropriate sentence.
answer_text = NLG_transoformation('find')
# Adjust the words if it is Korean.
address_info = lang_adjust(address_info)
# Substitude the markers to proper words
answer_text = re.sub('<place>',usr_place,answer_text)
answer_text = re.sub('<address>',address_info,answer_text)
answer_text = re.sub('<phone>',phone_info,answer_text)
# Step5. TTS
audio_play('tone.wav')
os.system('say ' + answer_text)
grammar_reduced = CFG.fromstring("""
S -> NS|VS
NS -> N N
VS -> V0 N | V1 N N | V2 N N N
PP -> P N
N -> 'الماء'|'الكريم'|'الكثير'|'الشجاع'|'الرسول'|'محمد'|'الطفل'|'العدو'|'الصاحب'|'الطالب'|'الدرس'|'الصلاة'|'الحق'|'المسافر'|'الطريق'|'الرجل'|'الخطأ'|'الأرض'|'كل'|'ثوب'|'جديد'|'درس'|'سهل'|'ماء'|'كريم'|'كثير'|'شجاع'|'رسول'|'طفل'|'عدو'|'صاحب'|'طالب'|'صلاة'|'حق'|'مسافر'|'طريق'|'رجل'|'خطأ'|'أرض'|'صاحبه'|'حقه'|'الامتحان'
P -> 'في'|'الى'|'من'|'عن'|'على'
V0 -> 'تفتح'|'فاض'|'ثار'|'هبت'|'جلس'|'ضاع'|'خرج'|'نام'|'وقعد'|'سافر'|'صدق'
V1 -> 'طوى'|'أكل'|'بلل'|'زرع'|'أطفأ'|'يركب'|'يستجيب'|'حفظ'|'كتب'|'شاهد'|'قال'
V2 -> 'يسقي'|'كسا'|'أعطى'|'ظن'|'حسب'|'جعل'|'خال'|'منح'|'منع'|'ألبس'
""")
#####RecursiveDescentParser######
tdParser = RecursiveDescentParser(grammar)
def rdp(s):
for w in tdParser.parse(s.split()):
print (w)
#####ShiftReduceParser#####
srPraser = ShiftReduceParser(grammar_reduced,2)
def srp(s):
for w in srPraser.parse(s.split()):
print (w)
#####LeftCornerParser#####
lcPraser = LeftCornerChartParser(grammar)
def lcp(s):
def createTree2(self):
grammar1 = self.makeGrammar()
rd = RecursiveDescentParser(grammar1)
for tree in rd.parse(self.myWords):
print(tree)
def callback(self, text):
"""this function is to process the recognized text and send the processed information to Arduino which controls some devices"""
global text_last
global xm_speech_req
global xm_speech_res
flag = 0
if len(text) != 0 :
if text[28] == '=':
text = text[29:]
elif text[29] == '=':
text = text[30:]
if text != "you are right" and text != "no" and len(text) != 0:
text_last = text
if type(text) == str and len(text) != 0:
print("recognized : " + text)
if xm_speech_req.command == 1:#Answer a question
if text == "what is the capital of china":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('Beijing')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","bei jing"])
xm_speech_res.num = 1
elif text == "how many hours in a day":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('twenty four')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","24"])
xm_speech_res.num = 1
elif text == "how many season are there in one year":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('four')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","4"])
xm_speech_res.num = 1
elif text == "how many seconds in one minute":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('sixty')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","60"])
xm_speech_res.num = 1
elif text == "what is the world biggest island":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('green land')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","green land"])
xm_speech_res.num = 1
elif text == "what is the biggest province of china":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('xin jiang')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","xin jiang"])
xm_speech_res.num = 1
elif text == "how large is the area of china":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('nine million and six hundred thousand saquare kilometers')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","nine million and six hundred thousand saquare kilometers"])
xm_speech_res.num = 1
elif text == "who was the first president of the usa":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('george washington')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","george washington"])
xm_speech_res.num = 1
elif text == "what is china's national animal":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('panda')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","panda"])
xm_speech_res.num = 1
elif text == "how many children did queen victoria have":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('nine children')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","nine children"])
xm_speech_res.num = 1
elif text == "what was the former name of new york":
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('new amsterdam')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","new amsterdam"])
xm_speech_res.num = 1
elif xm_speech_req.command == 2:#GPSR
if text != "you are right" and text != "no":
if len(text_last) != 0:
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call(text_last)
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130",text_last])
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('am i right?')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","am i right?"])
if text == "you are right":
if len(text_last) != 0:
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('OK')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","OK"])
sen = text_last
rd = RecursiveDescentParser(grammar)
t = rd.parse_one(sen.split())
search(t)
elif xm_speech_req.command == 3:#WhoIsWho
if text != "you are right" and text != "no":
if len(text_last) != 0:
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call(text_last)
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130",text_last])
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('am i right?')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","am i right?"])
if text == "you are right":
if len(text_last) != 0:
tts_test = rospy.ServiceProxy('tts',xm_Speech_tts)
tts_res = tts_test.call('OK')
if tts_res.flag == 1:
subprocess.call(["play","tts_sample.wav"])
else:
subprocess.call(["espeak","-v","f3+en_us","-s","130","OK"])
print("text_last:"+text_last)
#name
if text_last.find('Tom') >= 0:
xm_speech_res.name.append('Tom')
xm_speech_res.num += 1
elif text_last.find('Paul') >= 0:
xm_speech_res.name.append('Paul')
xm_speech_res.num += 1
elif text_last.find('Green') >= 0:
xm_speech_res.name.append('Green')
xm_speech_res.num += 1
elif text_last.find('Kevin') >= 0:
xm_speech_res.name.append('Kevin')
xm_speech_res.num += 1
elif text_last.find('Tracy') >= 0:
xm_speech_res.name.append('Tracy')
xm_speech_res.num += 1
elif text_last.find('John') >= 0:
xm_speech_res.name.append('John')
xm_speech_res.num += 1
elif text_last.find('Angel') >= 0:
xm_speech_res.name.append('Angel')
xm_speech_res.num += 1
elif text_last.find('Jamie') >= 0:
xm_speech_res.name.append('Jamie')
xm_speech_res.num += 1
elif text_last.find('Fisher') >= 0:
xm_speech_res.name.append('Fisher')
xm_speech_res.num += 1
elif text_last.find('Shirley') >= 0:
xm_speech_res.name.append('Shirley')
xm_speech_res.num += 1
elif text_last.find('Robin') >= 0:
xm_speech_res.name.append('Robin')
xm_speech_res.num += 1
elif text_last.find('Daniel') >= 0:
xm_speech_res.name.append('Daniel')
xm_speech_res.num += 1
#object
if text_last.find('paper') >= 0:
xm_speech_res.object.append('paper')
xm_speech_res.num += 1
elif text_last.find('noodles') >= 0:
xm_speech_res.object.append('noodles')
xm_speech_res.num += 1
elif text_last.find('pie') >= 0:
xm_speech_res.object.append('pie')
xm_speech_res.num += 1
elif text_last.find('tea') >= 0:
xm_speech_res.object.append('tea')
xm_speech_res.num += 1
elif text_last.find('biscuit') >= 0:
xm_speech_res.object.append('biscuit')
xm_speech_res.num += 1
elif text_last.find('chip') >= 0:
xm_speech_res.object.append('chip')
xm_speech_res.num += 1
elif text_last.find('soap') >= 0:
xm_speech_res.object.append('soap')
xm_speech_res.num += 1
elif text_last.find('shampoo') >= 0:
xm_speech_res.object.append('shampoo')
xm_speech_res.num += 1
elif text_last.find('milk tea') >= 0:
xm_speech_res.object.append('milk tea')
xm_speech_res.num += 1
elif text_last.find('water') >= 0:
xm_speech_res.object.append('water')
xm_speech_res.num += 1
elif text_last.find('toothpaste') >= 0:
xm_speech_res.object.append('toothpaste')
xm_speech_res.num += 1
elif text_last.find('coffee') >= 0:
xm_speech_res.object.append('coffee')
xm_speech_res.num += 1
elif text_last.find('cola') >= 0:
xm_speech_res.object.append('cola')
xm_speech_res.num += 1
elif text_last.find('red bull') >= 0:
xm_speech_res.object.append('red bull')
xm_speech_res.num += 1
def parser(plain_text,
set_name={
'name': ['x', 'y', 'z'],
'iten': ['a', 'b', 'c'],
'def': ['m', 'n', 'o']
}):
#first we define the cfg that will generate all possible sentences for the language
# based on the names of set's and ite's name the user have choosen
# formatting the functions names
line_grammar = "MEM_FUNC -> "
counter = 0
while counter < len(set_name['def']):
if counter == 0:
formated_newstring = " '{a}' ".format(
a=set_name['def'][counter])
else:
formated_newstring = " | '{a}' ".format(
a=set_name['def'][counter])
line_grammar += formated_newstring
counter += 1
# formatting for set names
line_grammar_0 = "NAME -> "
counter = 0
while counter < len(set_name['name']):
if counter == 0:
formated_newstring = " '{a}' ".format(
a=set_name['name'][counter])
else:
formated_newstring = " | '{a}' ".format(
a=set_name['name'][counter])
line_grammar_0 += formated_newstring
counter += 1
line_grammar_1 = "NAME_I -> "
counter = 0
while counter < len(set_name['iten']):
if counter == 0:
formated_newstring = " '{a}' ".format(
a=set_name['iten'][counter])
else:
formated_newstring = " | '{a}' ".format(
a=set_name['iten'][counter])
line_grammar_1 += formated_newstring
counter += 1
prime_cloudy_grammar = ((("""
T -> COM_D END | INIT_A COM_A END | 'start_cloud{' | '}end_cloud'
COM_D -> 'name::=' NAME '{' ITEN ';' MEM ';'
lacune_1
lacune_2
ATTR -> ITEN ';' MEM ';'
ITEN -> 'iten::=' NAME_I
MEM -> 'membership::=' '(' MEM_FUNC ')'
lacune_3
INIT_A -> 'active=>' '{'
COM_A -> NAME Q NAME | NAME_I O NAME |'plot=>' CONJ 'using:' PLOT_S
PLOT_S -> 'line' | 'venn'
CONJ -> NAME Q NAME | NAME
Q -> '-u' | '-i' | '-c'
O -> 'in' | 'out' | '<m>'
END -> '}end'
""".replace('lacune_1', line_grammar_0)).replace(
'lacune_2', line_grammar_1)).replace('lacune_3', line_grammar))
# using the nltk's tool to generate , we create the formal cfg
_cloudy_grammar = CFG.fromstring(prime_cloudy_grammar)
#for sentences_test in generate(_cloudy_grammar,n=200):
#print(' '.join(sentences_test))
# then we create the parser for this grammar
cloudy_rd = RecursiveDescentParser(_cloudy_grammar)
# split the input text into lines
code_total = plain_text.split('\n')
counter = 0
while counter < len(code_total):
test = code_total[counter].split()
# all code must start and end with specific sample of code as follows
if counter == 0 and 'start_cloud{' in test:
print("starting parsing")
pass
elif counter != 0:
pass
else:
return 'start_cloud statment not found'
# the end cloud statment determines where the parser will stop parsing the code
if "}end_cloud" in test:
print('end of parsing')
return 'end of parsing'
else:
pass
try:
parsed_check = []
for parsed in cloudy_rd.parse(test):
parsed_check.append(parsed)
# if the length of the list which contain the parsed sentences is equal to 0 then
# it means that the sentence wasnt well, so there's a some syntax error
if len(parsed_check) != 0:
pass
else:
return 'Syntax error on: (' + str(
code_total[counter]) + ' ) at line : ' + str(counter)
except:
# if some lexical component not allowed is used then the system can recognize it faster
return 'Lexical error on : (' + str(
code_total[counter]) + ') at line : ' + str(counter)
counter += 1
def callback(recognizer, audio):
# received audio data, now we'll recognize it using xunfei Speech Recognition
global xm_speech_req
global xm_speech_res
global text_last
audio_wav_data = audio.get_wav_data()
with open("microphone-results.wav", "wb") as f:
f.write(audio.get_wav_data())
# print("audio_len : " + str(len(audio_wav_data))) # audio data length
# print("Got it! Now to recognize it...")
print("detecting...")
if len(audio_wav_data) > 50000 and len(audio_wav_data) < 300000:
print("pcm_len : " + str(len(audio_wav_data))) # audio data length
rospy.wait_for_service('xm_speech')
try:
xm_speech = rospy.ServiceProxy('xm_speech', xm_Speech)
resp1 = xm_speech(audio_wav_data)
text = resp1.sr_text
if text != "yes" and text != "no" and len(text) != 0:
text_last = text
if type(text) == str and len(text) != 0:
print("recognized : " + text)
if xm_speech_req.command == 0:
if text == "what is the captital of china":
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130", "bei jing"
])
xm_speech_res.num = 1
elif text == "how many hours in a day":
subprocess.call(
["espeak", "-v", "f3+en_us", "-s", "130", "24"])
xm_speech_res.num = 1
elif text == "how many season are there in one year":
subprocess.call(
["espeak", "-v", "f3+en_us", "-s", "130", "4"])
xm_speech_res.num = 1
elif text == "how many seconds in one minute":
subprocess.call(
["espeak", "-v", "f3+en_us", "-s", "130", "60"])
xm_speech_res.num = 1
elif text == "what is the world biggest island":
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130",
"green land"
])
xm_speech_res.num = 1
elif text == "what is the biggest province of china":
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130",
"xin jiang"
])
xm_speech_res.num = 1
elif text == "how large is the area of china":
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130",
"nine million and six hundred thousand saquare kilometers"
])
xm_speech_res.num = 1
elif text == "who was the first president of the usa":
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130",
"george washington"
])
xm_speech_res.num = 1
elif text == "what is china's national animal":
subprocess.call(
["espeak", "-v", "f3+en_us", "-s", "130", "panda"])
xm_speech_res.num = 1
elif xm_speech_req.command == 1: #GPSR
if text != "yes" and text != "no":
if len(text_last) != 0:
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130",
text_last
])
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130",
"yes or no"
])
if text == "yes":
if len(text_last) != 0:
sen = text_last
rd = RecursiveDescentParser(grammar)
t = rd.parse_one(sen.split())
search(t)
elif xm_speech_req.command == 2: #HelpMeCarry
if text != "yes" and text != "no":
if len(text_last) != 0:
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130",
text_last
])
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130",
"yes or no"
])
if text == "yes":
if len(text_last) != 0:
subprocess.call([
"espeak", "-v", "f3+en_us", "-s", "130", "OK"
])
print("text_last:" + text_last)
if text_last == "follow me":
xm_speech_res.action.append('follow')
xm_speech_res.num += 1
elif text_last == "remember location":
xm_speech_res.action.append('remember')
xm_speech_res.num += 1
elif text_last == "take it":
xm_speech_res.action.append('grasp')
xm_speech_res.num += 1
elif text_last == "carry to the kitchen":
xm_speech_res.target.append('kitchen')
xm_speech_res.num += 1
elif text_last == "carry to the bedroom":
xm_speech_res.target.append('bedroom')
xm_speech_res.num += 1
elif text_last == "take to the dining room":
xm_speech_res.target.append('diningroom')
xm_speech_res.num += 1
elif text_last == "carry to the living room":
xm_speech_res.target.append('livingroom')
xm_speech_res.num += 1
else:
pass
except rospy.ServiceException, e:
print "Service call failed: %s" % e
N -> 'tree'
N -> 'fish'
Adj -> 'angry'
Adj -> 'frightened'
Adj -> 'little'
Adj -> 'tall'
V -> 'chased'
V -> 'said'
V -> 'thought'
V -> 'was'
V -> 'put'
P -> 'on'
""")
# In[4]:
rd = RecursiveDescentParser(grammar1)
sentence1 = 'mary saw a telescope in the park'.split()
for t in rd.parse(sentence1):
print(t)
t.draw()
# In[ ]:
#before executing this line restart the kernel and clear all outputs
rd = RecursiveDescentParser(grammar2)
sentence2 = 'the bear chased the frightened squirrel'.split()
for s in rd.parse(sentence2):
print(s)
s.draw()
def parse_word_tag(word, tag, sentence):
rule_perphrase_c = """S -> DP | PP | AP | VP | CP | ADVP
DP -> Dprime | Dprime QP | Dprime AP | Dprime CP
Dprime -> D | NP | D NP | D CP
NP -> Nprime | Nprime DP | Nprime PP | Nprime AP | Nprime VP | Nprime CP | Nprime ADVP
Nprime -> N | N PP | PP N | N QP
PP -> Pprime | Pprime ADVP | Pprime VP
Pprime -> P | P DP
AP -> Aprime | Aprime ADVP | Aprime AP | Aprime CP
Aprime -> A | A DP
VP -> Vprime | Vprime ADVP | Vprime DP | Vprime CP
Vprime -> V | V DP | V PRN
CP -> Cprime | Cprime VP | Cprime DP | Cprime NP | Cprime AP | Cprime QP | Cprime ADVP
Cprime -> C | C Cprime
QP -> Qprime | Qprime CP
Qprime -> Q | Q NP
ADVP -> ADVprime | ADVprime QP | ADVprime DP | ADVprime AP | ADVprime CP | ADVprime VP
ADVprime -> ADV | ADV ADVP""" + '\n'
rule_perphrase_b = """S -> DP | PP | AP | VP | CP | ADV
DP -> Dprime | Dprime QP | Dprime AP | Dprime CP
Dprime -> D | D NP | NP | D CP
NP -> Nprime | Nprime DP | Nprime PP | Nprime AP | Nprime VP | Nprime CP
Nprime -> N | N PP | PP N
PP -> Pprime | Pprime ADV | Pprime VP
Pprime -> P | P DP
AP -> Aprime | Aprime ADV | Aprime AP | Aprime CP
Aprime -> A | A DP
VP -> Vprime | Vprime ADV| Vprime DP | Vprime CP
Vprime -> V | V DP | V PRN
CP -> Cprime | Cprime VP | Cprime DP | Cprime NP | Cprime QP | Cprime ADV
Cprime -> C
QP -> Qprime | Qprime CP
Qprime -> Q""" + '\n'
rule_perphrase_a = """S -> DP | PP | AP | VP | CP | ADV
DP -> Dprime | Dprime QP | Dprime AP | Dprime CP
Dprime -> D NP | NP | D CP
NP -> Nprime | Nprime DP | Nprime PP | Nprime AP | Nprime VP | Nprime CP
Nprime -> N | N PP | PP N
PP -> Pprime | Pprime ADV | Pprime VP
Pprime -> P | P DP
AP -> Aprime | Aprime ADV
Aprime -> A | A DP
VP -> Vprime | Vprime ADV | Vprime DP
Vprime -> V | V DP | V PRN | Vprime CP
CP -> Cprime | Cprime VP | Cprime DP | Cprime NP | Cprime QP
Cprime -> C """ + '\n'
rule_test_c = """S -> DP Period | VP Period
DP -> Dprime | Dprime QP | Dprime AP | Dprime CP
Dprime -> D NP | NP | D CP
NP -> Nprime | Nprime DP | Nprime PP | Nprime AP | Nprime VP | Nprime CP
Nprime -> N | N PP | PP N
PP -> Pprime | Pprime ADV | Pprime VP
Pprime -> P | P DP
AP -> Aprime | Aprime ADV
Aprime -> A | A DP
VP -> Vprime | Vprime ADV | Vprime DP
Vprime -> V | V DP | V PRN | Vprime CP
CP -> Cprime | Cprime VP | Cprime DP | Cprime NP
Cprime -> C """ + '\n'
rule_test = """S -> DP Period | VP Period
DP -> Dprime | Dprime QP | Dprime AP
Dprime -> D NP | NP
NP -> Nprime | Nprime DP | Nprime PP | Nprime AP | Nprime VP
Nprime -> N | N PP | PP N | N CP PP | PP CP N
PP -> Pprime | Pprime ADV | Pprime VP
Pprime -> P | P DP
AP -> Aprime | Aprime ADV
Aprime -> A | A DP
VP -> Vprime | Vprime ADV | Vprime DP
Vprime -> V | V DP | V PRN | Vprime CP
CP -> Cprime | Cprime VP
Cprime -> C | C VP | C NP """ + '\n'
rule_test_b = """S -> DP VP
DP -> Dprime QP | Dprime AP
Dprime -> D NP
PP -> Pprime ADV | Pprime VP
Pprime -> P DP
AP -> Aprime ADV
Aprime -> A DP
VP -> Vprime ADV | Vprime DP
Vprime -> V DP | V PRN | V CP
NP -> Nprime DP | Nprime PP | Nprime AP | Nprime VP
Nprime -> N PP | PP N
CP -> Cprime VP
Cprime -> C VP | C NP """ + '\n'
rule_abc = """S -> DP Period
DP -> Dprime QP | Dprime AP
Dprime -> D NP
NP -> Nprime DP | Nprime PP | Nprime AP | Nprime VP
Nprime -> N PP | PP N | N CP PP | PP CP N
PP -> Pprime ADV | Pprime VP
Pprime -> P DP
AP -> Aprime ADV
Aprime -> A DP
VP -> Vprime ADV | Vprime DP
Vprime -> V DP | V PRN | Vprime CP
CP -> Cprime VP
Cprime -> C VP | C NP """ + '\n'
rule_test_b = """S -> DP VP
DP -> Dprime QP | Dprime AP
Dprime -> D NP
PP -> Pprime ADV | Pprime VP
Pprime -> P DP
AP -> Aprime ADV
Aprime -> A DP
VP -> Vprime ADV | Vprime DP
Vprime -> V DP | V PRN | V CP
NP -> Nprime DP | Nprime PP | Nprime AP | Nprime VP
Nprime -> N PP | PP N
CP -> Cprime VP
Cprime -> C VP | C NP """ + '\n'
rule = """S -> NP VP Sym | VP NP Sym | VP Comma NP | NP Comma VP
DP -> Dprime QP | Dprime AP
Dprime -> D NP
PP -> Pprime ADV | Pprime TP
Pprime -> P DP
AP -> Aprime ADV
Aprime -> A DP
VP -> Vprime ADV | Vprime DP
Vprime -> V DP | V PRN | Vprime CP | V comma DP | V comma PRN | comma Vprime CP
NP -> Nprime DP | Nprime PP | Nprime AP | Nprime VP
Nprime -> N PP | PP N | N Comma PP | PP Comma N | N CP PP | PP CP N
TP -> Tprime DP | Tprime Q
Tprime -> Tum VP | Tin VP
Tprime -> Tma AP
Tprime -> Tna- PP
Tprime -> Tmay VP
Tprime -> Ttaga VP
CP -> Cprime TP
Cprime -> C TP | C NP | comma C TP | comma C NP""" + '\n'
rule_backup = """S -> NP VP | VP NP
DP -> Dprime QP | Dprime AP
Dprime -> D NP
PP -> Pprime ADV | Pprime TP
Pprime -> P DP
AP -> Aprime ADV
Aprime -> A DP
VP -> Vprime ADV | Vprime DP
Vprime -> V DP | V PRN | Vprime CP
NP -> Nprime DP | Nprime PP | Nprime AP | Nprime VP
Nprime -> N PP | PP N | N CP PP | PP CP N
TP -> Tprime DP | Tprime Q
Tprime -> Tum VP | Tin VP
Tprime -> Tma AP
Tprime -> Tna- PP
Tprime -> Tmay VP
Tprime -> Ttaga VP
CP -> Cprime TP
Cprime -> C TP | C NP """ + '\n'
i_tag = 0
tag_rule = []
sentence_word_tag = ''
#print('tag length: ', len(tag))
while i_tag < len(tag):
if "NN" in tag[i_tag]:
tag_rule.append('N')
elif "PR" in tag[i_tag]:
tag_rule.append('N')
elif "DT" in tag[i_tag]:
tag_rule.append('D')
elif "LM" in tag[i_tag]:
tag_rule.append('C')
elif "CCU" in tag[i_tag]:
tag_rule.append('P')
elif "CC" in tag[i_tag]:
tag_rule.append('C')
elif "VB" in tag[i_tag]:
tag_rule.append('V')
elif "JJ" in tag[i_tag]:
tag_rule.append('A')
elif "RB" in tag[i_tag]:
tag_rule.append('ADV')
elif "CD" in tag[i_tag]:
tag_rule.append('Q')
elif "TS" in tag[i_tag]:
tag_rule.append('D')
elif "FW" in tag[i_tag]:
tag_rule.append('N')
elif "PMP" in tag[i_tag]:
tag_rule.append('Period')
elif "PMC" in tag[i_tag]:
tag_rule.append('C')
elif "PM" in tag[i_tag]:
tag_rule.append('Sym')
i_word = 0
word_repeated = False
while i_word < i_tag:
if word[i_tag] == word[i_word]:
word_repeated = True
i_word += 1
#print('i_tag: ', i_tag)
if not word_repeated:
sentence_word_tag += tag_rule[i_tag] + " -> " + "'" + word[i_tag] + "'" + '\n'
i_tag += 1
# DP = D' + QP | D' + AP
# D' = D + NP
#
# PP = P' + ADV | P' + TP
# P' = P + DP
#
# AP = A' + ADV
# A' = A + DP
#
# VP = V' + ADV | V' + DP
# V' = V + DP ¦ V + PRN ¦ V' + CP
#
# NP = N' + attribute phrase
# N' = N + PP
sentence_split = sentence.split()
grammar = CFG.fromstring(rule_perphrase_c + sentence_word_tag)
# #test uncomment to test english structure
# grammar = CFG.fromstring("""
# S -> NP VP
# PP -> P NP
# NP -> 'the' N | N PP | 'the' N PP
# VP -> V NP | V PP | V NP PP
# N -> 'cat'
# N -> 'dog'
# N -> 'rug'
# V -> 'chased'
# V -> 'sat'
# P -> 'in'
# P -> 'on'""")
# sentence_split = 'the cat chased the dog on the rug'.split()
rd = RecursiveDescentParser(grammar)
sr = ShiftReduceParser(grammar)
chart_parser = nltk.ChartParser(grammar)
earley_chart_parser = nltk.EarleyChartParser(grammar)
chart_parser = earley_chart_parser
print(tag_rule)
parse_tree = []
print('Parse')
for tree in chart_parser.parse(sentence_split):
parse_tree.append(tree)
if len(parse_tree) > 0:
print(parse_tree[0])
else:
print('NO TREE')
def callback(self, text):
"""this function is to process the recognized text and send the processed information to Arduino which controls some devices"""
global text_last
global xm_speech_req
global xm_speech_res
#if len(text) != 0 :
# if text[28] == '=':
# text = text[29:]
# elif text[29] == '=':
# text = text[30:]
if text != "you are right" and text != "no" and len(text) != 0:
text_last = text
print(text)
t = type(text)
print(t)
if type(text) == str and len(text) != 0:
print("recognized : " + text)
if xm_speech_req.command == 1: #Answer a question
if text == "what is the capital of china":
print("Beijing")
self.tts("Beijing")
xm_speech_res.num = 1
elif text == "how many hours in a day":
print("twenty four")
self.tts("twenty four")
xm_speech_res.num = 1
elif text == "how many season are there in one year":
print("four")
self.tts("four")
xm_speech_res.num = 1
elif text == "how many seconds in one minute":
print("sixty")
self.tts("sixty")
xm_speech_res.num = 1
elif text == "what is the world biggest island":
print("green land")
self.tts("green land")
xm_speech_res.num = 1
elif text == "what is the biggest province of china":
print("xin jiang")
self.tts("xin jiang")
xm_speech_res.num = 1
elif text == "how large is the area of china":
print(
"nine million and six hundred thousand saquare kilometers"
)
self.tts(
"nine million and six hundred thousand saquare kilometers"
)
xm_speech_res.num = 1
elif text == "who was the first president of the usa":
print("george washington")
self.tts("george washington")
xm_speech_res.num = 1
elif text == "what is china's national animal":
print("panda")
self.tts("panda")
xm_speech_res.num = 1
elif text == "how many children did queen victoria have":
print("nine children")
self.tts("nine children")
xm_speech_res.num = 1
elif text == "what was the former name of new york":
print("new amsterdam")
self.tts("new amsterdam")
xm_speech_res.num = 1
elif xm_speech_req.command == 2: #GPSR
if text != "you are right" and text != "no" and text != "stop here now":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
self.tts("OK")
sen = text_last
rd = RecursiveDescentParser(grammar)
t = rd.parse_one(sen.split())
search(t)
elif text == "stop here now":
xm_speech_res.action.append('stop')
xm_speech_res.num = 1
self.tts("OK")
elif xm_speech_req.command == 3: #WhoIsWho
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
self.tts("OK")
print("text_last:" + text_last)
#name
if text_last.find('Tom') >= 0:
xm_speech_res.name.append('Tom')
xm_speech_res.num += 1
elif text_last.find('Micheal') >= 0:
xm_speech_res.name.append('Micheal')
xm_speech_res.num += 1
elif text_last.find('Jack') >= 0:
xm_speech_res.name.append('Jack')
xm_speech_res.num += 1
elif text_last.find('Kevin') >= 0:
xm_speech_res.name.append('Kevin')
xm_speech_res.num += 1
elif text_last.find('Rose') >= 0:
xm_speech_res.name.append('Rose')
xm_speech_res.num += 1
elif text_last.find('John') >= 0:
xm_speech_res.name.append('John')
xm_speech_res.num += 1
elif text_last.find('Mary') >= 0:
xm_speech_res.name.append('Mary')
xm_speech_res.num += 1
elif text_last.find('Fisher') >= 0:
xm_speech_res.name.append('Fisher')
xm_speech_res.num += 1
elif text_last.find('Adam') >= 0:
xm_speech_res.name.append('Adam')
xm_speech_res.num += 1
elif text_last.find('Daniel') >= 0:
xm_speech_res.name.append('Daniel')
xm_speech_res.num += 1
#object
if text_last.find('ice tea') >= 0:
xm_speech_res.object.append('ice tea')
xm_speech_res.num += 1
elif text_last.find('safeguard') >= 0:
xm_speech_res.object.append('safeguard')
xm_speech_res.num += 1
elif text_last.find('napkin') >= 0:
xm_speech_res.object.append('napkin')
xm_speech_res.num += 1
elif text_last.find('chips') >= 0:
xm_speech_res.object.append('chips')
xm_speech_res.num += 1
elif text_last.find('laoganma') >= 0:
xm_speech_res.object.append('laoganma')
xm_speech_res.num += 1
elif text_last.find('porridge') >= 0:
xm_speech_res.object.append('porridge')
xm_speech_res.num += 1
elif text_last.find('milk') >= 0:
xm_speech_res.object.append('milk')
xm_speech_res.num += 1
elif text_last.find('water') >= 0:
xm_speech_res.object.append('water')
xm_speech_res.num += 1
elif text_last.find('cola') >= 0:
xm_speech_res.object.append('cola')
xm_speech_res.num += 1
elif text_last.find('sprite') >= 0:
xm_speech_res.object.append('sprite')
xm_speech_res.num += 1
elif xm_speech_req.command == 4: #SPR
print(text)
#ArenaQ
if text == "How many bookshelf are in the living room":
xm_speech_res.num = 1
self.tts("three")
elif text == "In which room is the fridge":
xm_speech_res.num = 1
self.tts("kitchen")
elif text == "How many doors has the Arena":
xm_speech_res.num = 1
self.tts("2")
elif text == "In which room is the dining table":
xm_speech_res.num = 1
tself.tts("diningroom")
elif text == "Where is located the bed":
xm_speech_res.num = 1
self.tts("bedroom")
elif text == "How many fireplace are in the bedroom":
xm_speech_res.num = 1
self.tts("zero")
elif text == "In which room is the sofa":
xm_speech_res.num = 1
self.tts("livingroom")
elif text == "Where is located the fridge":
xm_speech_res.num = 1
self.tts("kitchen")
elif text == "Where is located the cupboard":
xm_speech_res.num = 1
self.tts("diningroom")
elif text == "In which room is the bookshelf":
xm_speech_res.num = 1
self.tts("bedroom")
#ObjectQ
elif text == "how many hours in a day":
xm_speech_res.num = 1
self.tts("twenty four")
elif text == "how many season are there in one year":
xm_speech_res.num = 1
self.tts("four")
elif text == "how many seconds in one minute":
xm_speech_res.num = 1
self.tts("sixty")
elif text == "what is the world biggest island":
xm_speech_res.num = 1
self.tts("green land")
elif text == "what is the biggest province of china":
xm_speech_res.num = 1
self.tts("shin junk")
elif text == "how large is the area of china":
xm_speech_res.num = 1
self.tts(
"Nine million and six hundred thousand saquare kilometers"
)
elif text == "Who was the first president of the USA":
xm_speech_res.num = 1
self.tts("George Washington")
elif text == "What is China's national animal":
xm_speech_res.num = 1
self.tts("panda")
elif text == "How many children did Queen Victoria have":
xm_speech_res.num = 1
self.tts("Nine children")
elif text == "What was the former name of New York":
xm_speech_res.num = 1
self.tts("New Amsterdam")
#PredefinedQ
elif text == "How many people live in the Japan":
xm_speech_res.num = 1
self.tts("A little over 80 million")
elif text == "what is the capital of the united states":
xm_speech_res.num = 1
self.tts("Washington DC")
elif text == "what day is today":
xm_speech_res.num = 1
self.tts("Saturday")
elif text == "What city is the capital of the Japan":
xm_speech_res.num = 1
self.tts("Tokyo")
elif text == "Where does the term computer bug come from":
xm_speech_res.num = 1
self.tts("From a moth trapped in a relay")
elif text == "When was invented the B programming language":
xm_speech_res.num = 1
self.tts("B was developed circa 1969 at Bell Labs")
elif text == "Who invented the C programming language":
xm_speech_res.num = 1
self.tts("Ken Thompson and Dennis Ritchie")
elif text == "What is the highest point in Japan":
xm_speech_res.num = 1
self.tts("The highest point in Japan is Mount Fuji")
elif text == "What are the colours of the Japanese flag":
xm_speech_res.num = 1
self.tts(
"Japanese flag is a red circle centred over white")
elif text == "Who invented the first compiler":
xm_speech_res.num = 1
self.tts("Grace Brewster Murray Hopper invented it")
#CrowdOperatorQ
'''elif text == "number of male people sitting":
xm_speech_res.num = 2
time.sleep(1)
elif text == "number of female people sitting":
xm_speech_res.num = 3
time.sleep(1)
elif text == "number of male people standing":
xm_speech_res.num = 4
time.sleep(1)
elif text == "number of female people standing":
xm_speech_res.num = 5
time.sleep(1)
elif text == "number of people standing":
xm_speech_res.num = 6
time.sleep(1)
elif text == "number of people sitting":
xm_speech_res.num = 7
time.sleep(1)
'''
if text == 'number of male people':
xm_speech_res.num = 2
time.sleep(1)
if text == 'number of female people':
xm_speech_res.num = 3
time.sleep(1)
if text == 'number of people':
xm_speech_res.num = 4
time.sleep(1)
elif (xm_speech_req.command == 5): #help me carry
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
if text_last.find("stop") >= 0:
xm_speech_res.action.append('stop')
elif text_last.find("follow") >= 0:
xm_speech_res.action.append('follow')
elif text_last.find("take") >= 0:
xm_speech_res.action.append('take')
if text_last.find("kitchen") >= 0:
xm_speech_res.target.append('kitchen')
elif text_last.find("livingroom") >= 0:
xm_speech_res.target.append('livingroom')
elif text_last.find("bedroom") >= 0:
xm_speech_res.target.append('bedroom')
elif text_last.find("diningroom") >= 0:
xm_speech_res.target.append('diningroom')
if text_last.find("bag") >= 0:
xm_speech_res.object.append('bag')
self.tts("OK")
xm_speech_res.num = 1
print("text_last:" + text_last)
elif (xm_speech_req.command == 6): #shopping
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
self.tts("OK")
print("text_last:" + text_last)
num = 0
#follow
if text_last.find('follow') >= 0:
xm_speech_res.action.append('follow')
num += 1
#object
if text_last.find('ice tea') >= 0:
xm_speech_res.object.append('ice tea')
num += 1
if text_last.find('safeguard') >= 0:
xm_speech_res.object.append('safeguard')
num += 1
if text_last.find('napkin') >= 0:
xm_speech_res.object.append('napkin')
num += 1
if text_last.find('chips') >= 0:
xm_speech_res.object.append('chips')
num += 1
if text_last.find('laoganma') >= 0:
xm_speech_res.object.append('laoganma')
num += 1
if text_last.find('milk') >= 0:
xm_speech_res.object.append('milk')
num += 1
if text_last.find('water') >= 0:
xm_speech_res.object.append('water')
num += 1
if text_last.find('cola') >= 0:
xm_speech_res.object.append('cola')
num += 1
if text_last.find('porridge') >= 0:
xm_speech_res.object.append('porridge')
num += 1
if text_last.find('sprite') >= 0:
xm_speech_res.object.append('sprite')
num += 1
#方向
if text_last.find('right') >= 0:
xm_speech_res.target.append('right')
num += 1
if text_last.find('left') >= 0:
xm_speech_res.target.append('left')
num += 1
xm_speech_res.num = num
grammar = CFG.fromstring("""
VP -> GOAL_FIND O ENTITY_PLACE | GOAL_FIND ENTITY_PLACE
NP -> P ENTITY_PLACE | ENTITY_PLACE
GOAL_FIND -> 'find'
GOAL_FIND -> 'show'
GOAL_FIND -> 'tell'
O -> 'me'
P -> 'in'
ENTITY_PLACE -> 'starbucks'
ENTITY_PLACE -> 'Starbucks'
ENTITY_PLACE -> 'Coffee Bean'
ENTITY_PLACE -> 'Coffeebean'
""")
rd_parser = RecursiveDescentParser(grammar)
# Parsing the sentence.
parsed_words = []
for parsing in rd_parser.parse(tokenized):
print(parsing)
# Find GOAL and ENTITY
for detect in parsing:
if detect.label() == 'GOAL_FIND':
usr_goal = detect.leaves()[0]
if detect.label() == 'ENTITY_PLACE':
usr_place = detect.leaves()[0]
finding = sentence_generation('finding')
finding = re.sub('<place>',usr_place,finding)
def callback(self, text):
"""this function is to process the recognized text and send the processed information to Arduino which controls some devices"""
global text_last
global xm_speech_req
global xm_speech_res
global sentences
#if len(text) != 0 :
# if text[28] == '=':
# text = text[29:]
# elif text[29] == '=':
# text = text[30:]
if text != "you are right" and text != "no" and len(text) != 0:
text_last = text
if type(text) == str and len(text) != 0:
print("recognized : " + text)
if xm_speech_req.command == 1: #Answer a question
print('question: ' + text)
file_handle_c = open(
'/home/moonknight/catkin_ws/src/xm_speech/msc/bnf/c.txt',
mode='rb')
file_handle_d = open(
'/home/moonknight/catkin_ws/src/xm_speech/msc/bnf/d.txt',
mode='rb')
c_contents = file_handle_c.readlines()
d_contents = file_handle_d.readlines()
t = '\"' + text + '\"' + '\n'
if t in c_contents:
c_index = c_contents.index(t)
d = d_contents[c_index]
print('answer: ' + d)
xm_speech_res.num = 1
self.tts(d)
elif xm_speech_req.command == 2: #GPSR
if text != "you are right" and text != "no" and text != "you can stop here":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
self.tts("OK")
sen = text_last
for t in kitchen:
sen = sen.replace(t, "kitchen")
for t in livingroom:
sen = sen.replace(t, "livingroom")
for t in bedroom:
sen = sen.replace(t, "bedroom")
for t in hallway:
sen = sen.replace(t, "hallway")
if "bed" in sen and "bedroom" not in sen:
sen = sen.replace("bed", "bedroom")
if "tell the day of the month" in sen:
sentences = "tell the day of the month"
sen = sen.replace("tell the day of the month",
"sentence")
if "tell the day of the week" in sen:
sentences = "tell the day of the week"
sen = sen.replace("tell the day of the week",
"sentence")
if "tell the date" in sen:
sentences = "tell the date"
sen = sen.replace("tell the date", "sentence")
for i in double_words:
sen = sen.replace(i.replace("_", " "), i)
print(sen)
rd = RecursiveDescentParser(grammar)
t = rd.parse_one(sen.split())
print(t)
search(t)
text_last = ''
elif xm_speech_req.command == 3: #WhoIsWho
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
self.tts("OK")
print("text_last:" + text_last)
#name
for t in people:
if t in text_last:
xm_speech_res.name.append(t)
xm_speech_res.num += 1
#object
if text_last.find('ice tea') >= 0:
xm_speech_res.object.append('ice_tea')
xm_speech_res.num += 1
elif text_last.find('water') >= 0:
xm_speech_res.object.append('water')
xm_speech_res.num += 1
elif text_last.find('cracker') >= 0:
xm_speech_res.object.append('cracker')
xm_speech_res.num += 1
elif text_last.find('duck') >= 0:
xm_speech_res.object.append('duck')
xm_speech_res.num += 1
elif text_last.find('orange juice') >= 0:
xm_speech_res.object.append('orange_juice')
xm_speech_res.num += 1
elif text_last.find('porridge') >= 0:
xm_speech_res.object.append('porridge')
xm_speech_res.num += 1
elif text_last.find('milk') >= 0:
xm_speech_res.object.append('milk')
xm_speech_res.num += 1
elif text_last.find('milk tea') >= 0:
xm_speech_res.object.append('milk_tea')
xm_speech_res.num += 1
elif text_last.find('tissue') >= 0:
xm_speech_res.object.append('tissue')
xm_speech_res.num += 1
elif text_last.find('cola') >= 0:
xm_speech_res.object.append('cola')
xm_speech_res.num += 1
elif text_last.find('sprite') >= 0:
xm_speech_res.object.append('sprite')
xm_speech_res.num += 1
elif text_last.find('powder juice') >= 0:
xm_speech_res.object.append('powder_juice')
xm_speech_res.num += 1
elif text_last.find('green tea') >= 0:
xm_speech_res.object.append('green_tea')
xm_speech_res.num += 1
elif text_last.find('gum') >= 0:
xm_speech_res.object.append('gum')
xm_speech_res.num += 1
elif text_last.find('fanta') >= 0:
xm_speech_res.object.append('fanta')
xm_speech_res.num += 1
elif text_last.find('french fries') >= 0:
xm_speech_res.object.append('french_fries')
xm_speech_res.num += 1
elif text_last.find('yoghurt') >= 0:
xm_speech_res.object.append('yoghurt')
xm_speech_res.num += 1
elif text_last.find('noodle') >= 0:
xm_speech_res.object.append('noodle')
xm_speech_res.num += 1
elif text_last.find('crisp') >= 0:
xm_speech_res.object.append('crisp')
xm_speech_res.num += 1
elif text_last.find('herbal tea') >= 0:
xm_speech_res.object.append('herbal_tea')
xm_speech_res.num += 1
elif text_last.find('coconut') >= 0:
xm_speech_res.object.append('coconut')
xm_speech_res.num += 1
elif text_last.find('peartea') >= 0:
xm_speech_res.object.append('pear_tea')
xm_speech_res.num += 1
elif text_last.find('tea') >= 0:
xm_speech_res.object.append('tea')
xm_speech_res.num += 1
elif text_last.find('melon seeds') >= 0:
xm_speech_res.object.append('melon_seeds')
xm_speech_res.num += 1
elif text_last.find('redbull') >= 0:
xm_speech_res.object.append('redbull')
xm_speech_res.num += 1
elif text_last.find('toothpaste') >= 0:
xm_speech_res.object.append('toothpaste')
xm_speech_res.num += 1
elif text_last.find('grapejuice') >= 0:
xm_speech_res.object.append('grape_juice')
xm_speech_res.num += 1
elif text_last.find('soap') >= 0:
xm_speech_res.object.append('soap')
xm_speech_res.num += 1
elif text_last.find('shampoo') >= 0:
xm_speech_res.object.append('shampoo')
xm_speech_res.num += 1
text_last = ''
elif xm_speech_req.command == 4: #SPR
print('question: ' + text)
file_handle_a = open(
'/home/domistic/catkin_ws/src/xm_speech_for_linux/xm_speech/msc/bnf/a.txt',
mode='rb')
file_handle_b = open(
'/home/domistic/catkin_ws/src/xm_speech_for_linux/xm_speech/msc/bnf/b.txt',
mode='rb')
a_contents = file_handle_a.readlines()
b_contents = file_handle_b.readlines()
t = text + '?\n'
if t == a_contents[0]:
print('finally')
if t in a_contents:
a_index = a_contents.index(t)
b = b_contents[a_index]
xm_speech_res.answer = b
print('answer: ' + b)
xm_speech_res.num = 1
self.tts(b)
elif text == "number of people standing":
xm_speech_res.num = 2
time.sleep(1)
elif text == "number of people sitting":
xm_speech_res.num = 3
time.sleep(1)
elif text == "what is the number of the people who waving arms":
xm_speech_res.num = 4
time.sleep(1)
elif (xm_speech_req.command == 5): #help me carry
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
if text_last.find("stop") >= 0:
xm_speech_res.action.append('stop')
elif text_last.find("follow") >= 0:
xm_speech_res.action.append('follow')
elif text_last.find("take") >= 0:
xm_speech_res.action.append('take')
if text_last.find("kitchen") >= 0:
xm_speech_res.target.append('kitchen')
elif text_last.find("livingroom") >= 0:
xm_speech_res.target.append('livingroom')
elif text_last.find("bedroom") >= 0:
xm_speech_res.target.append('bedroom')
elif text_last.find("diningroom") >= 0:
xm_speech_res.target.append('diningroom')
xm_speech_res.num = 1
print("text_last:" + text_last)
text_last = ''
if text == "you can stop here":
xm_speech_res.action.append('stop')
elif (xm_speech_req.command == 6): #shopping
if text != "you are right" and text != "no":
if len(text_last) != 0:
self.tts(text_last)
self.tts("am i right")
if text == "you are right":
if len(text_last) != 0:
self.tts("OK")
print("text_last:" + text_last)
if text_last == "you can stop here":
xm_speech_res.action.append('stop')
xm_speech_res.num += 1
#follow
elif text_last.find('follow') >= 0:
xm_speech_res.action.append('follow')
xm_speech_res.num += 1
#object
elif text_last.find('ice tea') >= 0:
xm_speech_res.object.append('ice_tea')
xm_speech_res.num += 1
elif text_last.find('redbull') >= 0:
xm_speech_res.object.append('redbull')
xm_speech_res.num += 1
elif text_last.find('toothpaste') >= 0:
xm_speech_res.object.append('toothpaste')
xm_speech_res.num += 1
elif text_last.find(' Chips ') >= 0:
xm_speech_res.object.append(' Chips ')
xm_speech_res.num += 1
elif text_last.find('laoganma') >= 0:
xm_speech_res.object.append('laoganma')
xm_speech_res.num += 1
elif text_last.find('milk tea') >= 0:
xm_speech_res.object.append('milk_tea')
xm_speech_res.num += 1
elif text_last.find('water') >= 0:
xm_speech_res.object.append('water')
xm_speech_res.num += 1
elif text_last.find('cola') >= 0:
xm_speech_res.object.append('cola')
xm_speech_res.num += 1
elif text_last.find('porridge') >= 0:
xm_speech_res.object.append('porridge')
xm_speech_res.num += 1
elif text_last.find('sprite') >= 0:
xm_speech_res.object.append('sprite')
xm_speech_res.num += 1
elif text_last.find('shampoo') >= 0:
xm_speech_res.object.append('shampoo')
xm_speech_res.num += 1
elif text_last.find('water') >= 0:
xm_speech_res.object.append('water')
xm_speech_res.num += 1
elif text_last.find('cracker') >= 0:
xm_speech_res.object.append('cracker')
xm_speech_res.num += 1
elif text_last.find('duck') >= 0:
xm_speech_res.object.append('duck')
xm_speech_res.num += 1
elif text_last.find('orange juice') >= 0:
xm_speech_res.object.append('orange_juice')
xm_speech_res.num += 1
elif text_last.find('porridge') >= 0:
xm_speech_res.object.append('porridge')
xm_speech_res.num += 1
elif text_last.find('milk') >= 0:
xm_speech_res.object.append('milk')
xm_speech_res.num += 1
elif text_last.find('tissue') >= 0:
xm_speech_res.object.append('tissue')
xm_speech_res.num += 1
elif text_last.find('cola') >= 0:
xm_speech_res.object.append('cola')
xm_speech_res.num += 1
elif text_last.find('sprite') >= 0:
xm_speech_res.object.append('sprite')
xm_speech_res.num += 1
elif text_last.find('powder juice') >= 0:
xm_speech_res.object.append('powder_juice')
xm_speech_res.num += 1
elif text_last.find('green tea') >= 0:
xm_speech_res.object.append('green_tea')
xm_speech_res.num += 1
elif text_last.find('gum') >= 0:
xm_speech_res.object.append('gum')
xm_speech_res.num += 1
elif text_last.find('fanta') >= 0:
xm_speech_res.object.append('fanta')
xm_speech_res.num += 1
elif text_last.find('french fries') >= 0:
xm_speech_res.object.append('french_fries')
xm_speech_res.num += 1
elif text_last.find('yoghurt') >= 0:
xm_speech_res.object.append('yoghurt')
xm_speech_res.num += 1
elif text_last.find('noodle') >= 0:
xm_speech_res.object.append('noodle')
xm_speech_res.num += 1
elif text_last.find('crisp') >= 0:
xm_speech_res.object.append('crisp')
xm_speech_res.num += 1
elif text_last.find('herbal tea') >= 0:
xm_speech_res.object.append('herbal_tea')
xm_speech_res.num += 1
elif text_last.find('coconut') >= 0:
xm_speech_res.object.append('coconut')
xm_speech_res.num += 1
elif text_last.find('pear tea') >= 0:
xm_speech_res.object.append('pear_tea')
xm_speech_res.num += 1
elif text_last.find('tea') >= 0:
xm_speech_res.object.append('tea')
xm_speech_res.num += 1
elif text_last.find('melon seeds') >= 0:
xm_speech_res.object.append('melon_seeds')
xm_speech_res.num += 1
elif text_last.find('grape juice') >= 0:
xm_speech_res.object.append('grape_juice')
xm_speech_res.num += 1
elif text_last.find('soap') >= 0:
xm_speech_res.object.append('soap')
xm_speech_res.num += 1
#方向
if text_last.find('right') >= 0:
xm_speech_res.target.append('right')
xm_speech_res.num += 1
if text_last.find('left') >= 0:
xm_speech_res.target.append('left')
xm_speech_res.num += 1
if text_last.find('front') >= 0:
xm_speech_res.target.append('front')
xm_speech_res.num += 1
text_last = ''
from nltk.corpus import treebank
from nltk import PCFG, CFG
cfg_grammar = CFG.fromstring("""
S -> NP VP
NP -> ART N | N N | N | NP PP
VP -> V | V NP | V NP PP
PP -> P NP
ART -> 'a'
N -> 'flower' | 'a' | 'blooms'
V -> 'blooms' | 'flower'
""")
pcfg_grammar = PCFG.fromstring("""
S -> NP VP [1.0]
NP -> ART N [0.53] | N N [0.09] | N [0.14] | NP PP [0.24]
VP -> V [0.386] | V NP [0.393] | V NP PP [0.22]
PP -> P NP [1.0]
ART -> 'a' [1.0]
N -> 'flower' [0.8] | 'a' [0.1] | 'blooms' [0.1]
V -> 'blooms' [0.8] | 'flower' [0.2]
""")
from nltk.parse import RecursiveDescentParser
print(cfg_grammar)
rd = RecursiveDescentParser(pcfg_grammar)
text = "a flower blooms".split()
for t in rd.parse(text):
print(t)
#rd.draw()
