User:Angeliki/Grad-prototyping
GUIDES
Zine-machines
export GPS data, publish routes/video frames to raw data-publish
import pykml
from pykml import parser
import csv
import re
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
def translate(value, leftMin, leftMax, rightMin, rightMax):
# Figure out how 'wide' each range is
leftSpan = leftMax - leftMin
rightSpan = rightMax - rightMin
# Convert the left range into a 0-1 range (float)
valueScaled = float(value - leftMin) / float(leftSpan)
# Convert the 0-1 range into a value in the right range.
return rightMin + (valueScaled * rightSpan)
kml_file='prlll.kml'
all_routes = {}
with open(kml_file) as f:
doc = parser.parse(f).getroot().Document.Folder
for pm in doc.iterchildren():
if hasattr(pm, 'LineString'):
name = pm.name.text
coordinates = pm.LineString.coordinates.text
cordinatesinline = coordinates.split('\n')
cordinatesasarray = []
for line in cordinatesinline:
pattern = re.compile("^\s+|\s*,\s*|\s+$")
array = [x for x in pattern.split(line) if x]
if array:
cordinatesasarray.append(array)
all_routes[name]= cordinatesasarray
canvas=canvas.Canvas("route.pdf", pagesize=letter)
canvas.setLineWidth(.8)
canvas.setFont('Helvetica', 6)
for name in all_routes:
x=[]
y=[]
for points in all_routes[name]:
x.append(points[0])
y.append(points[1])
i=0
for a in x:
if i < len(x)-1:
x_new=translate(float(x[i]),float(min(x)), float(max(x)),50,500)
y_new=translate(float(y[i]),float(min(y)),float(max(y)),50,500)
x_new2=translate(float(x[i+1]),float(min(x)), float(max(x)),50,500)
y_new2=translate(float(y[i+1]),float(min(y)),float(max(y)),50,500)
print x_new,y_new,x_new2,y_new2
canvas.line(x_new,y_new,x_new2,y_new2)
i=i+1
canvas.drawString(10,45,name)
canvas.showPage()
canvas.save()
Walking/annotating/listening/transcribing/present voice
Walking the text
Los Banos(PH) voices/ Nomidu Nuna Exhibition
The secrets of pocketsphinx
Acoustic model/training
0 46797 She had your dark suit in greasy wash water all year.
File:
1 Η ο At AtDf Fe|Sg|Nm 2 Atr _ _
2 Σίφνος Σίφνος No NoPr Fe|Sg|Nm 3 Sb _ _
3 φημίζεται φημίζομαι Vb VbMn Id|Pr|03|Sg|Xx|Ip|Pv|Xx 0 Pred _ _
4 και και Cj CjCo _ 5 AuxY _ _
5 για για AsPp AsPpSp _ 3 AuxP _ _
6 τα ο At AtDf Ne|Pl|Ac 8 Atr _ _
7 καταγάλανα καταγάλανος Aj Aj Ba|Ne|Pl|Ac 8 Atr _ _
8 νερά νερό No NoCm Ne|Pl|Ac 5 Obj _ _
9 των ο At AtDf Fe|Pl|Ge 11 Atr _ _
10 πανέμορφων πανέμορφος Aj Aj Ba|Fe|Pl|Ge 11 Atr _ _
11 ακτών ακτή No NoCm Fe|Pl|Ge 8 Atr _ _
12 της μου Pn PnPo Fe|03|Sg|Ge|Xx 11 Atr _ _
13 . . PUNCT PUNCT _ 0 AuxK _ _
1 Πιστεύω πιστεύω Vb VbMn Id|Pr|01|Sg|Xx|Ip|Av|Xx 0 Pred _ _
2 ότι ότι Cj CjSb _ 1 AuxC _ _
3 είναι είμαι Vb VbMn Id|Pr|03|Sg|Xx|Ip|Pv|Xx 2 Obj _ _
4 δίκαιο δίκαιο No NoCm Ne|Sg|Nm 3 Pnom _ _
5 να να Pt PtSj _ 7 AuxV _ _
6 το εγώ Pn PnPe Ne|03|Sg|Ac|We 7 Obj _ _
7 αναγνωρίσουμε αναγνωρίζω Vb VbMn Id|Xx|01|Pl|Xx|Pe|Av|Xx 3 Sb _ _
8 αυτό αυτός Pn PnDm Ne|03|Sg|Ac|Xx 7 Obj _ _
9 . . PUNCT PUNCT _ 0 AuxK _ _
1 Η ο At AtDf Fe|Sg|Nm 2 Atr _ _
Radio2text
I want to use speech_recognition to transcribe speech from radio (FM, AM, FSK,...)
I made a test prototyping and here are the instructions
python listen.py --freq=93400000
from typing import List
from rtlsdr import RtlSdr
import argparse
import datetime
import numpy as np
import pyaudio
import scipy.signal as signal
import speech_recognition as sr
import threading
SampleStream = List[float]
AudioStream = List[int]
stream_buf = bytes()
stream_counter = 0
audio_rate = 48000
recognizer = sr.Recognizer()
audio_output = pyaudio.PyAudio().open(format=pyaudio.paInt16, channels=1, rate=audio_rate, output=True)
def recognize(stream_text):
global args
def logger(s):
f = open('radio_log.txt', 'a+', encoding='utf-8')
f.write(datetime.datetime.now().strftime("[ %d-%b-%Y %H:%M:%S ] "))
f.write(s)
f.write("\x0A")
f.close()
# print('sync')
audio_data = sr.AudioData(stream_text, audio_rate, 2)
try:
# result = recognizer.recognize_sphinx(audio_data)
result = recognizer.recognize_google(audio_data, language=args.lang)
print(result)
logger(result)
except sr.UnknownValueError:
pass
except sr.RequestError as e:
print("Could not request results from GSR service; {0}".format(e))
# print('done')
def stream_audio(data: AudioStream):
global args
global stream_buf
global stream_counter
if not args.verbose:
audio_output.write(data)
if stream_counter < args.buf:
stream_buf += data
stream_counter += 1
else:
threading.Thread(target=recognize, args=(stream_buf,)).start()
stream_buf = bytes()
stream_counter = 0
def process(samples: SampleStream, sdr: RtlSdr) -> None:
sample_rate_fm = 240000
iq_comercial = signal.decimate(samples, int(sdr.get_sample_rate()) // sample_rate_fm)
angle_comercial = np.unwrap(np.angle(iq_comercial))
demodulated_comercial = np.diff(angle_comercial)
audio_signal = signal.decimate(demodulated_comercial, sample_rate_fm // audio_rate, zero_phase=True)
audio_signal = np.int16(14000 * audio_signal)
stream_audio(audio_signal.astype("int16").tobytes())
def read_callback(samples, rtl_sdr_obj):
process(samples, rtl_sdr_obj)
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--ppm', type=int, default=0,
help='ppm error correction')
parser.add_argument('--gain', type=int, default=20,
help='RF gain level')
parser.add_argument('--freq', type=int, default=92900000,
help='frequency to listen to, in Hertz')
parser.add_argument('--lang', type=str, default='en-US',
help='language to recognize, en-US, ru-RU, fi-FI or any other supported')
parser.add_argument('--buf', type=int, default=100,
help='buffer size to recognize, 100 = 6.25 seconds')
parser.add_argument('--verbose', action='store_true',
help='mute audio output')
args = parser.parse_args()
sdr = RtlSdr()
sdr.rs = 2400000
sdr.fc = args.freq
sdr.gain = args.gain
sdr.err_ppm = args.ppm
sdr.read_samples_async(read_callback, int(sdr.get_sample_rate()) // 16)
NEXT: scanning through several frequencies and speech recognition
Collect radio sounds
An attempt to collect radio sounds depending on what text accompanies them, on time, on frequencies or other factors. For example every time the software listens to streaming and recognizes a sound of voice (300 and 3000 Hz) or a word then it records down/collects.
Antennas
