User:Angeliki/Grad-prototyping

GUIDES

Antennas making: https://sat.weblog.mur.at/

Radio to speech: https://towardsdatascience.com/make-amateur-radio-cool-again-said-mr-artificial-intelligence-36cb32978fb2

Speech tools: http://www.speech.cs.cmu.edu/, https://realpython.com/python-speech-recognition/, https://cmusphinx.github.io/, https://github.com/Uberi/speech_recognition, https://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/

Zine-machines

coordinates to lines

zine-machines.kstraat.casa

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()

Mastodon-bot

A way to publish some text or images to Mastodon (a decentralized, open source social network) using a bot account.

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

FM2text

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

Pirates speech to text

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

Web Radio

Net Art radio