Beagle Board - beagleboard.org

Things used in this project

Hardware components:
Seeed beaglebonegreen
BeagleBoard.org SeeedStudio BeagleBone Green
Or any Beaglebone board.
×1
USB Audio Adapter
×1
Microphone
Any microphone with a 3.5mm connector
×1
Speakers
To simplify things must be powered by 5V
×1
Te connectivity 4 103741 0 image 75px
Male Header 40 Position 1 Row (0.1")
×1
5V Power Supply
×1
SeeedStudio Temperature and Humidity Grove Sensor
Easy to use sensor that uses SeeeStudio standard sensor connector.
×1
Software apps and online services:
Avs med 3 22
Amazon Alexa Alexa Voice Service
Wit.ai
Beaglebone Debian
Hand tools and fabrication machines:
09507 01
Soldering iron (generic)

Schematics

Schematic Overview
Unfortunately Fritzing didn't have all the components I used so I tried to pick components that were as close as possible so you can get an idea.
Untitled%20sketch%202 bb3

Code

BeagleManPython
Software to turn your Beagle into a Alexa like system that you can also create custom actions to react to.
import alsaaudio
import json
import os.path
import os
import pycurl
import requests
import re
import sys
import time

from creds import *
from hdc1000 import getTemperature
from pocketsphinx.pocketsphinx import *
from requests.packages.urllib3.exceptions import *
from sphinxbase.sphinxbase import *
from StringIO import StringIO
from threading import Thread

# Avoid warning about insure request
requests.packages.urllib3.disable_warnings(InsecurePlatformWarning)

# ------ Start User configuration settings --------
sphinx_data_path = "/root/pocketsphinx/"
modeldir = sphinx_data_path+"/model/"
datadir = sphinx_data_path+"/test/data"

recording_file_path = "/root/beagleman/"
filename=recording_file_path+"/myfile.wav"
filename_raw=recording_file_path+"/myfile.pcm"

# Personalize the robot :)
username = "Franklin"

# Trigger phrase. Pick a phrase that is easy to save repeatedly the SAME way
# seems by default a single syllable word is better
trigger_phrase = "dog"

wit_token = "<Wit AI Token>"

# ----- End User Configuration -----

wit_ai_authorization = "Authorization: Bearer "+wit_token

# PocketSphinx configuration
config = Decoder.default_config()

# Set recognition model to US
config.set_string('-hmm', os.path.join(modeldir, 'en-us/en-us'))
config.set_string('-dict', os.path.join(modeldir, 'en-us/cmudict-en-us.dict'))

#Specify recognition key phrase
config.set_string('-keyphrase', trigger_phrase)
config.set_float('-kws_threshold',3)

# Hide the VERY verbose logging information
config.set_string('-logfn', '/dev/null')

path = os.path.realpath(__file__).rstrip(os.path.basename(__file__))

# Read microphone at 16 kHz. Data is signed 16 bit little endian format.
inp = alsaaudio.PCM(alsaaudio.PCM_CAPTURE)
inp.setchannels(1)
inp.setrate(16000)
inp.setformat(alsaaudio.PCM_FORMAT_S16_LE)
inp.setperiodsize(1024)

token = None
recording_file = None

start = time.time()

# Determine if trigger word/phrase has been detected
record_audio = False
wit_ai_received = False

# Process audio chunk by chunk. On keyword detected perform action and restart search
decoder = Decoder(config)
decoder.start_utt()

# Using slightly outdated urlib3 software by default. But disable harmless warning
requests.packages.urllib3.disable_warnings(InsecurePlatformWarning)

# All Alexa code based on awesome code from AlexaPi
# https://github.com/sammachin/AlexaPi

# Verify that the user is connected to the internet
def internet_on():
	print "Checking Internet Connection"
	try:
		r =requests.get('https://api.amazon.com/auth/o2/token')
		print "Connection OK"
		return True
	except:
		print "Connection Failed"
		return False

#Get Alexa Token
def gettoken():
	global token
	refresh = refresh_token
	if token:
		return token
	elif refresh:
		payload = {"client_id" : Client_ID, "client_secret" : Client_Secret, "refresh_token" : refresh, "grant_type" : "refresh_token", }
		url = "https://api.amazon.com/auth/o2/token"
		r = requests.post(url, data = payload)
		resp = json.loads(r.text)
		token = resp['access_token']
		return token
	else:
		return False
		
def alexa():
	url = 'https://access-alexa-na.amazon.com/v1/avs/speechrecognizer/recognize'
	headers = {'Authorization' : 'Bearer %s' % gettoken()}
	# Set parameters to Alexa request for our audio recording
	d = {
		"messageHeader": {
			"deviceContext": [{
				"name": "playbackState",
				"namespace": "AudioPlayer",
				"payload": {
					"streamId": "",
					"offsetInMilliseconds": "0",
					"playerActivity": "IDLE"
				}
			}]
		},
		"messageBody": {
			"profile": "alexa-close-talk",
			"locale": "en-us",
			"format": "audio/L16; rate=44100; channels=1"
		}
	}

	# Send our recording audio to Alexa
	with open(filename_raw) as inf:
		files = [
				('file', ('request', json.dumps(d), 'application/json; charset=UTF-8')),
				('file', ('audio', inf, 'audio/L16; rate=44100; channels=1'))
				]	
		r = requests.post(url, headers=headers, files=files)

	if r.status_code == 200:
		print "Debug: Alexa provided a response"

		for v in r.headers['content-type'].split(";"):
			if re.match('.*boundary.*', v):
				boundary =  v.split("=")[1]
		data = r.content.split(boundary)
		for d in data:
			if (len(d) >= 1024):
				audio = d.split('\r\n\r\n')[1].rstrip('--')

		# Write response audio to response.mp3 may or may not be played later
		with open(path+"response.mp3", 'wb') as f:
			f.write(audio)
	else:
		print "Debug: Alexa threw an error with code: ",r.status_code

def offline_speak(string):
	os.system('espeak -ven-uk -p50 -s140 "'+string+'" > /dev/null 2>&1')


# Code based on examples from Facebook's wit.ai
# https://wit.ai/docs/http/20141022
def handle_intent(response):
	intent = response[0]["intent"]

	if intent == "alarm":
		offline_speak("Your alarm has been set")
		return True

	elif intent == "seeedstudio":
		offline_speak("Seeed Studio is located in Shenzhen, China")
		return True

	elif intent == "temperature":
		offline_speak("Measuring room temperature")
		temp = getTemperature()
		temp = "{0:.2f}".format(temp)
		offline_speak("The current room temperature is "+temp+" degrees fahrenheit")
		return True

	return False

def wit_ai():
	global wit_ai_received

	#Make an HTTP request via python curl using our saved audio recording
	#Api document https://wit.ai/docs/http/20141022
	output = StringIO()
	c = pycurl.Curl()

	c.setopt(c.URL, 'https://api.wit.ai/speech?v=20141022')

	# Send authorization string along with indicate that we are sending audio
	c.setopt(c.HTTPHEADER, [wit_ai_authorization,
						'Content-Type: audio/wav'])
	c.setopt(c.FOLLOWLOCATION, True)

	# Specify that we are doing a POST request
	c.setopt(pycurl.POST, 1)

	# Get size of our audio file
	filesize = os.path.getsize(filename)
	c.setopt(c.POSTFIELDSIZE, filesize)

	# Pass a function that will read the audio file
	fin = open(filename, 'rb')
	c.setopt(c.READFUNCTION, fin.read)

	c.setopt(c.WRITEFUNCTION, output.write)

	# Ignore SSL verification
	c.setopt(pycurl.SSL_VERIFYPEER, 0)   
	c.setopt(pycurl.SSL_VERIFYHOST, 0)

	# Send our Web Service request
	c.perform()

	c.close()

	# Get our response
	response =  json.loads(output.getvalue())

	wit_ai_received = False
   
	# Check if we got an error
	if 'error' not in response.keys():
		print "Debug: Wit.ai believe the audio said: ", response["_text"]

		# See if our code handles the specified intent
		if response["outcomes"][0]["intent"] == "UNKNOWN" or not handle_intent(response["outcomes"]):
			print "Debug: Unrecognized Wit.ai intent. Let Alexa handle it"
		else:
			wit_ai_received = True
			print "Debug: Wit.ai handled response ignore response from Alexa"
	else:
		print "Debug: Wit.ai returned an error"

def web_service():
	global wit_ai_received

	# Call the two speech recognitions services in parallel
	alexa_thread = Thread( target=alexa, args=() )
	wit_ai_thread = Thread( target=wit_ai, args=( ) )

	alexa_thread.start()
	wit_ai_thread.start()

	# Prioritize a response from Wit.ai
	wit_ai_thread.join()

	# See if Wit.ai code handled response
	if wit_ai_received != True:
		# Wait until Alexa code handles response
		alexa_thread.join()

		# Play Alexa response
		os.system('play  -c 1 -r 24000 -q {}response.mp3  > /dev/null 2>&1'.format(path))
		time.sleep(.5)
		

while internet_on() == False:
	print "."

offline_speak("Hello "+username+", Ask me any question")

print "Debug: Ready to receive request"
while True:
	try:
		# Read from microphone
		l,buf = inp.read()
	except:
                # Hopefully we read fast enough to avoid overflow errors
		print "Debug: Overflow"
		continue

	#Process microphone audio via PocketSphinx only when trigger word
	# hasn't been detected
	if buf and record_audio == False:
		decoder.process_raw(buf, False, False)

	# Detect if keyword/trigger word was said
	if record_audio == False and decoder.hyp() != None:
		# Trigger phrase has been detected
		record_audio = True
		start = time.time()

		# To avoid overflows close the microphone connection
		inp.close()

		# Open file that will be used to save raw micrphone recording
		recording_file = open(filename_raw, 'w')
		recording_file.truncate()

		# Indicate that the system is listening to request
		offline_speak("Yes")

		# Reenable reading microphone raw data
		inp = alsaaudio.PCM(alsaaudio.PCM_CAPTURE)
		inp.setchannels(1)
		inp.setrate(16000)
		inp.setformat(alsaaudio.PCM_FORMAT_S16_LE)
		inp.setperiodsize(1024)

		print ("Debug: Start recording")


	# Only write if we are recording
	if record_audio == True:
		recording_file.write(buf)

	# Stop recording after 5 seconds
	if record_audio == True and time.time() - start > 5:
		print ("Debug: End recording")
		record_audio = False

		# Close file we are saving microphone data to
		recording_file.close()

		# Convert raw PCM to wav file (includes audio headers)
		os.system("sox -t raw -r 16000 -e signed -b 16 -c 1 "+filename_raw+" "+filename+" && sync");

		print "Debug: Sending audio to services to be processed"
		# Send recording to our speech recognition web services
		web_service()

		# Now that request is handled restart audio decoding
		decoder.end_utt()
		decoder.start_utt()
Sphinx4
Base library used for Pocket Sphinx
AlexaPi
Used as a reference to interact with Amazon's Alexa Service.
BeagleMan Repository
Includes the main BeagleMan source file along with the various helper source files.

Credits

Photo
Franklin Cooper Jr.
2 projects • 5 followers
Contact
Thanks to Carnegie Mellon University, Robert Nelson, Sam Machin, Wit.AI team, and Alexa Voice Service Team and the Amazon Echo Team.

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