def play(song): player = MPyg321Player() player.play_song(song) print('enter sleep') sleep(10) player.stop() print('stop player')
def MusicPlay(RareInput): MusicName, text = RareInput.split("()") print(MusicName, text) player = MPyg321Player() # instanciate the player player.play_song(MusicName + ".mp3") # play a song sleep(FindHowMuchToPlay(MusicName, text)) player.quit()
def main(): text = story.text full_replace = story.full_replace for x in range(len(full_replace)): full_replace[x] = full_replace[x].strip() unique_replace = list(OrderedDict.fromkeys(full_replace)) print( "Welcome Story Class!! Below I will ask a series of questions to help make Tim's story. Shout out answers " "as you see fit!") for term in unique_replace: temp = input(term + ": ") if temp == "break": break for item in full_replace: if item is term: full_replace[full_replace.index(item)] = temp final_text = "" for count in range(len(text)): final_text += text[count] if count <= 123: final_text += full_replace[count] count += 1 wrapper = textwrap.TextWrapper(width=140) word_list = wrapper.fill(text=final_text) to_print = open("one_run.txt", "w+") to_print.write(word_list) print(word_list) language = "en" myobj = gTTS(text=final_text, lang=language, slow=False) myobj.save("one_audio.mp3") player = MPyg321Player() player.play_song("one_audio.mp3")
def browse(): global player file = filedialog.askopenfilename(initialdir='/home/jpolak/Muzyka') player = MPyg321Player() player.play_song(file) global napis napis = Label(okno) napis.config(text=file.strip('/home/jpolak/Muzyka')) napis.grid(column=0, row=1)
import wit import pyaudio import pvporcupine from gtts import gTTS from logmmse import logmmse_from_file from mpyg321.mpyg321 import MPyg321Player from responder import Responder KEYWORDS = ["jarvis", "bumblebee"] rp = Responder() pa = pyaudio.PyAudio() pl = MPyg321Player() ai = wit.Wit(os.getenv("WITAI_TOKEN")) porcupine = pvporcupine.create(keywords=KEYWORDS) sample_rate = porcupine.sample_rate frames_per_buffer = porcupine.frame_length DURATION = 4.5 audio_stream = pa.open( rate=sample_rate, channels=1, format=pyaudio.paInt16, input=True, frames_per_buffer=frames_per_buffer, )
def main(): """Do the magic""" player = MPyg321Player() do_some_play_pause(player) do_some_jumps(player) player.quit()
import signal import speech_recognition as sr import os import re import asyncio from gtts import gTTS from io import BytesIO from pygame import mixer as Player import pygame # Music Player Setup from mpyg321.mpyg321 import MPyg321Player from time import sleep musicPlayer = MPyg321Player() #___________________ from datetime import datetime from wit import Wit witClient = Wit("JTUTA7EED6JP2VNJPJJOQKT3P7UPQ2HK") import json import random import pyowm owm = pyowm.OWM( 'dcac096e8c94a58b502991795e61f6d4') # You MUST provide a valid API key
import requests import pyttsx3 from gtts import gTTS from mpyg321.mpyg321 import MPyg321Player import os from time import sleep engine = pyttsx3.init() player = MPyg321Player() connected = False response = requests.get("https://google.com") debug_option = True def debug(text): if (debug_option): print(str(text)) if (response.status_code == 200): connected = True debug("Connection established") def disconnectedVoice(text): engine.say(text) engine.runAndWait() def connectedVoice(text): tts = gTTS(text=text, lang='en')
def play(self, filepath): player = MPyg321Player() player.play_song(filepath)
# -*- coding: utf-8 -*- import random from mpyg321.mpyg321 import MPyg321Player from pkg_resources import resource_listdir, resource_filename PLAYER = MPyg321Player() PACKAGE = 'vendingmachine.resources.sounds' # TODO: get rid of this / switch to enum: BUTTON_PRESS = 'button_press' COIN_INSERT = 'coin_insert' COIN_REJECT = 'coin_reject' MUSIC = "music" NOISE = "noise" def get_random_mp3_file(subpackage): """ Example: get_random_mp3_file(COIN_REJECT) """ mp3s = [] subpackage = "{}.{}".format(PACKAGE, subpackage) for n in resource_listdir(subpackage, ''): filename = resource_filename(subpackage, n) if filename.endswith('.mp3'): mp3s.append(filename) return random.choice(mp3s) def play_random(subpackage): PLAYER.play_song(get_random_mp3_file(subpackage))
def face_recon(): # Initialize some variables face_locations = [] face_names = [] data = {} xl_encoding = [] process_this_frame = True global known_face_encodings, known_face_names player = MPyg321Player() already_recognized_face_names = [] try: face_records = pd.read_excel('face_codes.xlsx') for cols in face_records: xl_encoding.append(np.array(face_records[cols])) xl_name = list(face_records.columns) known_face_encodings = xl_encoding known_face_names = xl_name print("4", known_face_encodings) except: pass while True: # Grab a single frame of video ret, frame = video_capture.read() # Resize frame of video to 1/4 size for faster face recognition processing small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) rgb_small_frame = small_frame[:, :, ::-1] # Only process every other frame of video to save time if process_this_frame: # Find all the faces and face encodings in the current frame of video face_locations = face_recognition.face_locations(rgb_small_frame) face_encodings = face_recognition.face_encodings( rgb_small_frame, face_locations) face_names = [] for face_encoding in face_encodings: # See if the face is a match for the known face(s) matches = face_recognition.compare_faces( known_face_encodings, face_encoding) name = "Unknown" face_distances = face_recognition.face_distance( known_face_encodings, face_encoding) best_match_index = np.argmin(face_distances) if matches[best_match_index]: name = known_face_names[best_match_index] if name not in already_recognized_face_names: speak_name = gTTS(text="Hello " + name + " you are handsome", lang='en', slow=False) speak_name.save('name.mp3') player.play_song("name.mp3") print(face_names, name) already_recognized_face_names.append(name) else: new_embedding = face_recognition.face_encodings(frame)[0] speak_name = gTTS(text="Hello there, what's your name?", lang='en', slow=False) speak_name.save('name.mp3') player.play_song("name.mp3") print("Enter : ") time.sleep(4) name = hear() known_face_encodings.append(new_embedding) known_face_names.append(name) face_names.append(name) process_this_frame = not process_this_frame # Display the results for (top, right, bottom, left), name in zip(face_locations, face_names): # Scale back up face locations since the frame we detected in was scaled to 1/4 size top *= 4 right *= 4 bottom *= 4 left *= 4 # Draw a box around the face cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) # Draw a label with a name below the face cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1) # Display the resulting image cv2.imshow('Video', frame) # Hit 'q' on the keyboard to quit! if cv2.waitKey(1) & 0xFF == ord('q'): for keys in known_face_names: for values in known_face_encodings: data[keys] = values known_face_encodings.remove(values) break print(data) df_k = pd.DataFrame(data) print("df", df_k) df_k.to_excel('face_codes.xlsx', index=False) break # Release handle to the webcam video_capture.release() cv2.destroyAllWindows()