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May 19, 2020 05:48
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Voice Chatbot in Python using Speech Recognition, NLTK, Google Text-to-Speech & Scikit-learn
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| #sudo apt-get install portaudio19-dev python-all-dev python3-all-dev | |
| #sudo apt-get install portaudio19-dev | |
| #pip install SpeechRecognition numpy gTTs sklearn | |
| #pip install gTTS | |
| #sudo apt-get install mpg123 | |
| import io | |
| import random | |
| import string | |
| import warnings | |
| import numpy as np | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import warnings | |
| from gtts import gTTS | |
| import os | |
| warnings.filterwarnings('ignore') | |
| import speech_recognition as sr | |
| import nltk | |
| from nltk.stem import WordNetLemmatizer | |
| # for downloading package files can be commented after First run | |
| nltk.download('popular', quiet=True) | |
| nltk.download('nps_chat',quiet=True) | |
| nltk.download('punkt') | |
| nltk.download('wordnet') | |
| posts = nltk.corpus.nps_chat.xml_posts()[:10000] | |
| # To Recognise input type as QUES. | |
| def dialogue_act_features(post): | |
| features = {} | |
| for word in nltk.word_tokenize(post): | |
| features['contains({})'.format(word.lower())] = True | |
| return features | |
| featuresets = [(dialogue_act_features(post.text), post.get('class')) for post in posts] | |
| size = int(len(featuresets) * 0.1) | |
| train_set, test_set = featuresets[size:], featuresets[:size] | |
| classifier = nltk.NaiveBayesClassifier.train(train_set) | |
| # Recognised input types | |
| #Greet | |
| #"Bye"/> | |
| #"Clarify"/> | |
| #"Continuer"/> | |
| #"Emotion"/> | |
| #"Emphasis"/> | |
| #"Greet"/> | |
| #"Reject"/> | |
| #"Statement"/> | |
| #"System"/> | |
| #"nAnswer"/> | |
| #"whQuestion"/> | |
| #"yAnswer"/> | |
| #"ynQuestion"/> | |
| #"Other" | |
| #colour palet | |
| def prRed(skk): print("\033[91m {}\033[00m" .format(skk)) | |
| def prGreen(skk): print("\033[92m {}\033[00m" .format(skk)) | |
| def prYellow(skk): print("\033[93m {}\033[00m" .format(skk)) | |
| def prLightPurple(skk): print("\033[94m {}\033[00m" .format(skk)) | |
| def prPurple(skk): print("\033[95m {}\033[00m" .format(skk)) | |
| def prCyan(skk): print("\033[96m {}\033[00m" .format(skk)) | |
| def prLightGray(skk): print("\033[97m {}\033[00m" .format(skk)) | |
| def prBlack(skk): print("\033[98m {}\033[00m" .format(skk)) | |
| #Reading in the input_corpus | |
| with open('intro_join','r', encoding='utf8', errors ='ignore') as fin: | |
| raw = fin.read().lower() | |
| #TOkenisation | |
| sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences | |
| word_tokens = nltk.word_tokenize(raw)# converts to list of words | |
| # Preprocessing | |
| lemmer = WordNetLemmatizer() | |
| def LemTokens(tokens): | |
| return [lemmer.lemmatize(token) for token in tokens] | |
| remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation) | |
| def LemNormalize(text): | |
| return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict))) | |
| # Keyword Matching | |
| GREETING_INPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey",) | |
| GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"] | |
| def greeting(sentence): | |
| """If user's input is a greeting, return a greeting response""" | |
| for word in sentence.split(): | |
| if word.lower() in GREETING_INPUTS: | |
| return random.choice(GREETING_RESPONSES) | |
| # Generating response and processing | |
| def response(user_response): | |
| robo_response='' | |
| sent_tokens.append(user_response) | |
| TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english') | |
| tfidf = TfidfVec.fit_transform(sent_tokens) | |
| vals = cosine_similarity(tfidf[-1], tfidf) | |
| idx=vals.argsort()[0][-2] | |
| flat = vals.flatten() | |
| flat.sort() | |
| req_tfidf = flat[-2] | |
| if(req_tfidf==0): | |
| robo_response=robo_response+"I am sorry! I don't understand you" | |
| return robo_response | |
| else: | |
| robo_response = robo_response+sent_tokens[idx] | |
| return robo_response | |
| #Recording voice input using microphone | |
| file = "file.mp3" | |
| flag=True | |
| fst="My name is Jarvis. I will answer your queries about Science. If you want to exit, say Bye" | |
| tts = gTTS(fst, 'en') | |
| tts.save(file) | |
| os.system("mpg123 " + file ) | |
| r = sr.Recognizer() | |
| prYellow(fst) | |
| # Taking voice input and processing | |
| while(flag==True): | |
| with sr.Microphone() as source: | |
| audio= r.listen(source) | |
| try: | |
| user_response = format(r.recognize(audio)) | |
| print("\033[91m {}\033[00m" .format("YOU SAID : "+user_response)) | |
| except sr.UnknownValueError: | |
| prYellow("Oops! Didn't catch that") | |
| pass | |
| #user_response = input() | |
| #user_response=user_response.lower() | |
| clas=classifier.classify(dialogue_act_features(user_response)) | |
| if(clas!='Bye'): | |
| if(clas=='Emotion'): | |
| flag=False | |
| prYellow("Jarvis: You are welcome..") | |
| else: | |
| if(greeting(user_response)!=None): | |
| print("\033[93m {}\033[00m" .format("Jarvis: "+greeting(user_response))) | |
| else: | |
| print("\033[93m {}\033[00m" .format("Jarvis: ",end="")) | |
| res=(response(user_response)) | |
| prYellow(res) | |
| sent_tokens.remove(user_response) | |
| tts = gTTS(res, 'en') | |
| tts.save(file) | |
| os.system("mpg123 " + file) | |
| else: | |
| flag=False | |
| prYellow("Jarvis: Bye! take care..") |
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