Speech Recognition Software with Ai 

The language barrier has been removed.

Imagine tuning into a QSO between stations in Russian, only to find yourself unable to understand a word due to the language barrier.

What if your SDR transceiver could automatically translate the conversation into your native language with Speech Recognition Software with Ai

We’ll explore an innovative project idea that leverages a speech recognition software to translate any language instantly in real time.

While the concept is exciting, please note that it hasn’t been fully tested by the author. If you decide to implement it, you do so at your own risk. This is an open idea for you to try and adapt to your needs.

Here is the way to get started Step By Step translate any language with Speech Recognition Software using Ai aplication.

That’s a fascinating project! Here’s a step-by-step guide to integrate a speech recognition software to translate any language into your Raspberry Pi SDR transceiver. This setup will capture audio from the SDR, transcribe the speech, translate it, and then display the translated text on either an OLED or 1602 LCD display.

Step 1: Set Up Your Raspberry Pi

1. Install Raspberry Pi OS:
   • Flash Raspberry Pi OS onto your SD card using tools like Balena Etcher.
   • Insert the SD card into your Raspberry Pi and power it up.
   • Connect to your network and open a terminal.
2. Update Your System:
   • Run the following commands to ensure your system is up to date:

     bash

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     sudo apt-get update
sudo apt-get upgrade


Step 2: Install SDR Software

1. Install RTL-SDR:
   • Install the necessary tools to interface with your SDR:

     bash

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     sudo apt-get install rtl-sdr


2. Install GQRX or CubicSDR:

   • These programs will allow you to tune into frequencies and capture audio.

     bash

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     sudo apt-get install gqrx-sdr


Step 3: Capture Audio from SDR

1. Set Up GQRX:
   • Open GQRX and configure it to listen to the frequency of the station you want to monitor.
2. Redirect Audio to a File:
   • Use a tool like sox to capture the audio output:

     bash

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     sudo apt-get install sox
sox -t pulseaudio default output.wav


Step 4: Install and Configure Speech-to-Text (STT)

1. Install Vosk or Google Speech Recognition:
   • Vosk is an offline STT library that works well on Raspberry Pi:

     bash

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     pip3 install vosk

   • Alternatively, you can use Google’s online STT service.

2. Set Up Speech Recognition:

   • Create a Python script (stt.py) to capture the audio and transcribe it:

     python

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     import wave
import json
from vosk import Model, KaldiRecognizermodel = Model(“model”)
     rec = KaldiRecognizer(model, 16000)wf = wave.open(“output.wav”, “rb”)
     while True:
     data = wf.readframes(4000)
     if len(data) == 0:
     break
     if rec.AcceptWaveform(data):
     print(rec.Result())

Step 5: Integrate AI Translation

1. Install Google Translate API:
   • Install the necessary libraries for translation:

     bash

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     pip3 install googletrans==4.0.0-rc1

   • Update your stt.py script to include translation:

     python

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     from googletrans import Translator
translator = Translator()result = rec.Result()
     text = json.loads(result)[“text”]translated = translator.translate(text, src=‘ru’, dest=‘en’)
     print(translated.text)

Step 6: Display Translated Text on OLED or 1602 Display

1. Install OLED Libraries:
   • If using an OLED display, install the necessary libraries:

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     pip3 install luma.oled

2. Configure the OLED Display:
   • Use the following code to display text on the OLED:

     python

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     from luma.core.interface.serial import i2c
from luma.oled.device import ssd1306
from PIL import Image, ImageDraw, ImageFontserial = i2c(port=1, address=0x3C)
     device = ssd1306(serial)# Display the translated text
     with canvas(device) as draw:
     draw.text((0, 0), translated.text, fill=“white”)
3. 1602 LCD Display:
   • If using a 1602 LCD, use this code to display text:

     python

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     import I2C_LCD_driver

     mylcd = I2C_LCD_driver.lcd()
     mylcd.lcd_display_string(translated.text, 1)

Step 7: Test and Deploy

1. Run the Script:
   • Run the stt.py script and monitor the translated text on your display.
   • Fine-tune the script based on your specific needs.
2. Integrate with SDR Software:
   • Integrate the script to run in parallel with your SDR software to automate the translation process.
3. Deploy:
   • Deploy the setup for real-time translation during your QSOs.

This guide should help you get started with integrating AI translation into your Raspberry Pi SDR setup. 

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