Text to Speech.ipynb

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   "source": [
    "# Text To Speech\n",
    "\n",
    "## Text-to-Speech Technology\n",
    "\n",
    "- Text-to-speech (TTS) technology reads aloud digital text—the words on computers, smartphones and tablets\n",
    "- TTS can help kids who struggle with reading\n",
    "- There are TTS tools available for nearly every digital device\n",
    "\n",
    "#### Reference:-\n",
    " - [TTS](https://www.understood.org/en/school-learning/assistive-technology/assistive-technologies-basics/text-to-speech-technology-what-it-is-and-how-it-works)\n",
    " - [Speech Synthesis](https://en.wikipedia.org/wiki/Speech_synthesis)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Libraries to install\n",
    "\n",
    " - `!pip install gTTS`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Speech Synthesis - Process\n",
    "\n",
    "<img align = \"left\" width=\"700\" height =\"700\" src = \"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/TTS_System.svg/825px-TTS_System.svg.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading libraries\n",
    "\n",
    "from gtts import gTTS \n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- gTTS module needs internet and depends on google to get the audio data/conversion\n",
    "- [gTTS Documentation](https://gtts.readthedocs.io/en/latest/module.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text to audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"Text-to-speech or TTS is a type of  assistive technology that reads digital text aloud. It’s sometimes called “read aloud” technology.With a click of a button or the touch of a finger, TTS can take words on a computer or other digital device and convert them into audio\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Selecting language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "language = 'en'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Creating a variable and passing the text & language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech = gTTS(text = text, lang = language, slow = False) \n",
    "#\" slow = False\" says the module that after conversion the audio shoule have a high/normal speed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Saving the converted sudio as a .mp3 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech.save(\"text.mp3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Playback the mp3 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      "text/plain": [
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     "execution_count": 6,
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   ],
   "source": [
    "os.system(\"start text.mp3\") # start command initiates to play the audio from your local windows media player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text file to audio\n",
    "\n",
    "##### Reading the text file and store it to a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"Test-1.txt\", \"r\").read().replace(\"\\n\", \" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Language selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "language = 'en'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Passing the text file to the module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech = gTTS(text = str(file), lang = language, slow = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Saving the converted sudio as a .mp3 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech.save(\"Text1.mp3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Playback the mp3 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "0"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "os.system(\"start Text1.mp3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details refer [KDnuggets](https://www.kdnuggets.com/2020/05/easy-text-speech-python.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text from an online source to audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading libraries\n",
    "from newspaper import Article\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Input online article link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "article = Article('https://bernardmarr.com/default.asp?contentID=2066')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Download & Parse the article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "article.download()\n",
    "article.parse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Download the ‘punkt’ package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\arock.000\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
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     "data": {
      "text/plain": [
       "True"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Implement Natural Language Processing (NLP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "article.nlp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Creating a variable and storing the article's text in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "art_text = article.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Language selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "language = 'en' #English"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Passing the text file to the module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "aiarticle = gTTS(text=art_text, lang=language, slow=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "aiarticle .save(\"ai_article.mp3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Playing the converted file\n",
    "os.system(\"start ai_article.mp3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Reference](https://medium.com/@randerson112358/build-a-text-to-speech-program-using-python-b70de7105383)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Speech to Text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Speech Recognition](https://en.wikipedia.org/wiki/Speech_recognition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Libraries to install\n",
    "\n",
    " - `!pip install speechrecognition`\n",
    " - `!pip install pyttsx3`\n",
    " - `!pip install pipwin`\n",
    " - `!pipwin install pyaudio`\n",
    "\n",
    "- [Documentation for SpeechRecognition Library](https://pypi.org/project/SpeechRecognition)\n",
    " \n",
    "##### Errors\n",
    "- [Pyaudio Installation Errors](https://stackoverflow.com/questions/53866104/pyaudio-failed-to-install-windows-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Talk\n",
      "Time over, thanks\n",
      "Text: speech recognition system basically translate spoken languages into text one of the classic example is Apple Siri thank you\n"
     ]
    }
   ],
   "source": [
    "#import library\n",
    "\n",
    "import speech_recognition as sr\n",
    "\n",
    "# Initialize recognizer to recognize the speech\n",
    "\n",
    "r = sr.Recognizer()\n",
    "\n",
    "# Reading microphone, listeing and storing it to a variable\n",
    "\n",
    "with sr.Microphone() as source:\n",
    "    print(\"Talk\")\n",
    "    audio_text = r.listen(source)\n",
    "    print(\"Time over, thanks\")\n",
    "# recoginize_() method will throw a request error if the API is unreachable, hence using exception handling\n",
    "    \n",
    "    try:\n",
    "        # using google speech recognition\n",
    "        print(\"Text: \"+r.recognize_google(audio_text))\n",
    "    except:\n",
    "         print(\"Sorry, I did not get that\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reference:\n",
    "- [SpeechtoText](https://www.geeksforgeeks.org/python-convert-speech-to-text-and-text-to-speech/)\n",
    "- [Google Language Support](https://cloud.google.com/speech-to-text/docs/languages)\n",
    "- [More](https://stackabuse.com/introduction-to-speech-recognition-with-python/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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