def detected_callback(): print("hotword detected") text = "Bonjour monsieur. Comment allez-vous ?" lang = "fr" speech = Speech(text, lang) sox_effects = ("speed", "1.1") speech.play(sox_effects)
def readText(text): """ Lee un texto """ lang = "es" speech = Speech(text, lang) speech.play()
def callback(recognizer, audio): print("callback") # received audio data, now we'll recognize it using Google Speech Recognition try: # for testing purposes, we're just using the default API key # to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")` # instead of `r.recognize_google(audio)` said = recognizer.recognize_google(audio, language="fr-FR") print("Google Speech Recognition thinks you said " + mytext) if said == 'stop': # calling this function requests that the background listener stop listening stop_listening(wait_for_stop=False) print('the end') else: speech = Speech(said, "fr") sox_effects = ("speed", "1.1") speech.play(sox_effects) except sr.UnknownValueError: print("Google Speech Recognition could not understand audio") except sr.RequestError as e: print( "Could not request results from Google Speech Recognition service; {0}" .format(e))
def _hablar(self, mensaje: str) -> None: """ Usa Google Speech para hablar al usuario con una frase aleatoria. """ habla = Speech(mensaje, 'es') habla.play()
def speak_object_changes(speech_flag, object_changes, height, width): global speech_out_array global old_speech_out_array old_speech_out_array = speech_out_array speech_out_array = [] for key, object_change in object_changes.items(): speech_out_str = key if "person_name" in object_change: speech_out_str = object_change["person_name"] if object_change["appeared"] is True: speech_out_str += " entered frame at " speech_out_str += get_object_position_words( object_change["bbox"], height, width) elif object_change["disappeared"] is True: speech_out_str += " left frame" else: speech_out_str += " moved to " speech_out_str += get_object_position_words( object_change["bbox"], height, width) speech_out_array.append(speech_out_str) output_array = [] for speech in speech_out_array: if speech not in old_speech_out_array: output_array.append(speech) if speech_flag is True: speech = Speech(speech_out_str, LANG) speech.play(sox_effects) return output_array
def notice(text, lang='vi'): try: speech = Speech(text, lang) speech.play() except Exception: print(text) print('No network connect! So i can not speak for you!')
def response(sentence, userID='123', show_details=False): results = classify(sentence) # if we have a classification then find the matching intent tag if results: # loop as long as there are matches to process while results: for i in intents['intents']: # find a tag matching the first result if i['tag'] == results[0][0]: # set context for this intent if necessary if 'context_set' in i: if show_details: print('context:', i['context_set']) context[userID] = i['context_set'] # check if this intent is contextual and applies to this user's conversation if not 'context_filter' in i or \ (userID in context and 'context_filter' in i and i['context_filter'] == context[userID]): if show_details: print('tag:', i['tag']) val = random.choice(i['responses']) speech = Speech(val, lang) sox_effects = ("speed", "1.0") speech.play(sox_effects) # a random response from the intent return print(val) results.pop(0)
def textToSpeech(text): lang = 'en-US' lang = lang.replace('-','_') speech = Speech(text, lang) sox_effects = ("speed", "1.0") sox_effects = ("vol", "0.5") speech.play(sox_effects)
def say(text, speed=1.5, lang='en'): try: speech = Speech(text.replace('\n', ''), lang) speech.play() sox_effects = ('speed', speed) speech.play(sox_effects) except TypeError: print('Could not read {}'.format(text))
def warning_notification(self, text): print(dt.datetime.now().strftime('%b %d, %H:%M:%S'), text) while self.warning_notification_output: time.sleep(0.5) self.warning_notification_output = True speech = Speech(text, 'en') speech.play() time.sleep(3) self.warning_notification_output = False
def textToSpeech(text): # lang = 'ko_KR' global lang lang = lang.replace('-','_') speech = Speech(text, lang) sox_effects = ("speed", "1.0") sox_effects = ("vol", "0.05") speech.play(sox_effects)
def update_output(n_clicks, value): vocab_data = data_loader.get_question_answer_vocab("2") qvocab = vocab_data['question_vocab'] q_map = { vocab_data['question_vocab'][qw] : qw for qw in vocab_data['question_vocab']} print('filename::',filen) fc7_features = utils.extract_fc7_features(filen, 'Data/vgg16.tfmodel') model_options = { 'num_lstm_layers' : 2, 'rnn_size' : 512, 'embedding_size' : 512, 'word_emb_dropout' : 0.5, 'image_dropout' : 0.5, 'fc7_feature_length' : 4096, 'lstm_steps' : vocab_data['max_question_length'] + 1, 'q_vocab_size' : len(vocab_data['question_vocab']), 'ans_vocab_size' : len(vocab_data['answer_vocab']) } question_vocab = vocab_data['question_vocab'] word_regex = re.compile(r'\w+') question_ids = np.zeros((1, vocab_data['max_question_length']), dtype = 'int32') print('qst',value) question_words = re.findall(word_regex, value) base = vocab_data['max_question_length'] - len(question_words) for i in range(0, len(question_words)): if question_words[i] in question_vocab: question_ids[0][base + i] = question_vocab[ question_words[i] ] else: question_ids[0][base + i] = question_vocab['UNK'] ans_map = { vocab_data['answer_vocab'][ans] : ans for ans in vocab_data['answer_vocab']} model = vis_lstm_model.Vis_lstm_model(model_options) input_tensors, t_prediction, t_ans_probab = model.build_generator() sess = tf.InteractiveSession() saver = tf.train.Saver() saver.restore(sess, 'Data/Models/modelnew99.ckpt') pred, answer_probab = sess.run([t_prediction, t_ans_probab], feed_dict={ input_tensors['fc7']:fc7_features, input_tensors['sentence']:question_ids, }) print("answerprediction",pred[0]) #model.summary() #plot_model(model,to_file='predictmodel.png') print ("Ans:", ans_map[pred[0]]) answer_probab_tuples = [(-answer_probab[0][idx], idx) for idx in range(len(answer_probab[0]))] answer_probab_tuples.sort() print ("Top Answers") for i in range(1): print (ans_map[ answer_probab_tuples[0][1] ]) #ans=(ans_map[answer_probab_tuples[i][1] ]) lang = "en" text="This is a "+ans_map[ answer_probab_tuples[0][1] ] speech = Speech(text, lang) sox_effects = ("speed", "0.8") speech.play(sox_effects) return ans_map[answer_probab_tuples[0][1]]
def read(message, print_text=True): speech = Speech(message, LANGUAGE) if print_text: cprint('\n' * (rows // 2) + ' ' * ((columns // 2) - (len(message) // 2)) + message, 'red', attrs=['bold']) sox_effects = ("speed", "1.") speech.play(sox_effects)
def wrapper(*args, **kwargs): t1 = time.time() out = fun(*args, **kwargs) t2 = time.time() text = "It took " + str((t2 - t1)) + " to run the function." print(text) speech = Speech(text, lang) speech.play(None) return out
def speach(request, id): phrase = Phrase.objects.get(pk=id) speech = Speech(phrase.original, "en") sox_effects = ("speed", "1") speech.play(sox_effects) return HttpResponse('Speach')
def say(): lang = 'en' if request.method == 'POST': text = request.args.get('text') lang = request.args.get('lang') speech = Speech(text, lang) speech.play() return "pyTalk said {}!".format(text) else: return "pyTalk say works only on POST"
def textToSpeech(request): text = request.GET.get("text", "") lang = "ur" translation = database.child("DataSet").order_by_key().limit_to_first( 3).get() translator = Translator() translatorResult = translator.translate(text, src="en", dest="ur") speech = Speech(translatorResult.text, lang) sox_effects = ("speed", "1.0") speech.play(sox_effects) return HttpResponse("1")
def say(text=None): from google_speech import Speech if text is None: say_fortune() elif text.lower() == "flipkart": say_fk_fortune() else: lang = "en" speech = Speech(text, lang) speech.play()
def speak_and_save(self, text, path, index): speech = Speech(text, self.language) speech.play(sox_effects=("speed", DEFAULT_SPEED)) if not os.path.isdir(path): os.mkdir(path) pass full_path = "{}/{}.mp3".format(path, index) print(full_path) speech.save(full_path)
def shyna_speaks(msg: object) -> object: msgs = random.choice(msg.split("|")) for i in sent_tokenize(msgs): print(i) lang = "en" speech = Speech(i, lang) sox_effects = ( "speed", "1", ) speech.play(sox_effects)
def bookText(self, bookPath): if os.path.exists(bookPath): with open(bookPath, "rb") as file: pdf = PyPDF2.PdfFileReader(file) totalPages = pdf.getNumPages() pg = int(input(f"Enter page no. to read (1-{totalPages}): ")) text = pdf.getPage(pg - 1).extractText() # print(text) if self.isOnline(): tts = Speech(text, "en") tts.play() else: print("Go Online for a better audio experience") self.contentReader(text) else: print(f"Sorry no file found at {bookPath}")
def myfunc(): self.is_speaking = True if self.speech_engine == 'not_set': if sys.platform == 'darwin': self.speech_engine = 'mac' else: self.speech_engine == 'google' if self.speech_engine == 'mac': subprocess.call(['say', '-v', speaker, sentence]) elif self.speech_engine == 'google': speech = Speech(sentence, lang) speech.play(tuple()) self.is_speaking = False
def myfunc(): self.is_speaking = True if self.speech_engine == 'not_set': if sys.platform == 'darwin': self.speech_engine = 'mac' else: self.speech_engine == 'google' if self.speech_engine == 'mac': os.system('say -v {} "{}"'.format(speaker, sentence)) elif self.speech_engine == 'google': speech = Speech(sentence, lang) speech.play(tuple()) self.is_speaking = False
def myspeech(): r = sr.Recognizer() with sr.Microphone() as source: print("Listening...") r.pause_threshold = 1 audio = r.listen(source) try: query = r.recognize_google(audio, language='fr-in') global vocale vocale = query.split() except sr.UnknownValueError: speech = Speech("je ne vous ai pas compris", "fr") speech.play() query = str(speech) return query
def test(text): # reverberance sox_effects = ("reverb", "1.5") speech = Speech(text, "fr") speech.play(( "speed", "1.2", "reverb", "80", "pitch", "-300", "pad", "1", "5", # "oops", #"lowpass", "100", "5" ))
def init_listener(self): try: print("A moment of silence, please...") with self.mic as source: self.r.adjust_for_ambient_noise(source) print("Set minimum energy threshold to {}".format( self.r.energy_threshold)) while True: print("Listening!") with self.mic as source: audio = self.r.listen(source) print("Got it! Now to recognize it...") try: # recognize speech value = self.r.recognize_google(audio) #value = self.r.recognize_sphinx(audio) print('val: ' + value) value = value.lower().split() # recognizing wake word and extracting from command if value.pop(0) == 'hey' and value.pop( 0) == config.wake_word: # we need some special handling here to correctly print unicode characters to standard output value = ' '.join(value) if str is bytes: # this version of Python uses bytes for strings (Python 2) print(u"You said {}".format(value).encode("utf-8")) value = value.encode("utf-8") else: # this version of Python uses unicode for strings (Python 3+) print("You said {}".format(value)) result = self.do_cmd(value) if result: lang = "en" speech = Speech(result, lang) sox_effects = ('speed', '1.0') speech.play(sox_effects) print(result) else: print('not a command') except sr.UnknownValueError: print("Oops! Didn't catch that") except sr.RequestError as e: print( "Uh oh! Couldn't request results from Google Speech Recognition service; {0}" .format(e)) except KeyboardInterrupt: pass
def speak(self): lang = "en" speech = Speech(self.data, lang) # you can also apply audio effects while playing (using SoX) # see http://sox.sourceforge.net/sox.html#EFFECTS for full effect documentation sox_effects = ("tempo", "1.5") return speech.play(sox_effects)
def say(text=None, lang="en", robot=False): import os from google_speech import Speech if text is None: say_fortune() elif text == "flipkart": say_fk_fortune() else: speech = Speech(text, lang) if not robot: sox_effects = ("speed", "1.02", "vol", "0.3") else: sox_effects = ("speed 0.9 overdrive 10 echo 0.8 0.7 " "6 0.7 echo 0.8 0.7 10 0.7 echo 0.8 0.7 " "12 0.7 echo 0.8 0.88 12 0.7 echo 0.8 " "0.88 30 0.7 echo 0.6 0.6 60 0.7").split(" ") speech.play(sox_effects) print(text)
def inner(*args, **kwargs): jk = kwargs.pop('jk', None) # before function call: if jk: text = "LOL! Then why are you asking me to run '" + fun.__name__ + "'? Unbelievable!" speech = Speech(text, lang) speech.play(None) # function call: if run_anyway or not jk: out = fun(*args, **kwargs) else: out = None # after function call: if jk: message_idx = randint(0, len(messages.default) - 1) text = messages.default[message_idx] speech = Speech(text, lang) speech.play(None) return out
def handleKeyEvent(self, symbol, modifiers): if symbol == pyglet.window.key.BACKSPACE: self.currentPage == START_PAGE return if self.currentPage == START_PAGE: if symbol == pyglet.window.key._1: self.initPage(MANAGE_WORDBANK_PAGE) elif symbol == pyglet.window.key._2: self.initPage(START_DICTATION_PAGE) elif symbol == pyglet.window.key._3: self.initPage(SETTINGS_PAGE) elif self.currentPage == MANAGE_WORDBANK_PAGE: pass elif self.currentPage == START_DICTATION_PAGE: if symbol == pyglet.window.key.ENTER: self.initPage(IN_DICTATION_PAGE) #test only if symbol == pyglet.window.key._1: self.toggleSelect(self.wordbank.wordlists()[0]) elif symbol == pyglet.window.key._2: self.toggleSelect(self.wordbank.wordlists()[1]) elif self.currentPage == IN_DICTATION_PAGE: if symbol == pyglet.window.key.RIGHT: print(self.wordsToDictate[self.dictationIndex]) self.dictationIndex = (self.dictationIndex + 1) % len( self.wordsToDictate) speech = Speech(self.wordsToDictate[self.dictationIndex], LANG_CN_MANDARIN) speech.play() elif symbol == pyglet.window.key.R: speech = Speech(self.wordsToDictate[self.dictationIndex], LANG_CN_MANDARIN) speech.play() elif currentPage == SETTINGS_PAGE: pass