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• Methods For Retaining State

  26 décembre 2011, par Multimedia Mike — General, evernote, organization

  I jump around between projects. A lot. Over the years, I have employed various methods for retaining state or context as I switch to a different project. Here’s a quick survey and a general classification of their effectiveness.

  Good

  • Evernote : This is a cloud-based note-taking service that has a web client, Mac and Windows clients, and clients for just about ever mobile platform out there. I have an account and access it via the web interface as as the Windows, iOS, and Android clients. I really like it.

  
  
  

  Okay

  • Series of text files : I have been doing this for a very long time. I have many little note-filled directories here and there that are consistently migrated to new machines but generally forgotten about. This isn’t a terrible method but can be unwieldy when you work on lots of different machines. I’m still tracking down all these directories and importing them into Evernote.

  Bad

  • Layout of desktop windows : I have a habit of working on one project in a set of windows on one desktop space and another project in a second set of windows in another space, etc. Oh, this makes me shudder just thinking about it, mostly because of living in constant fear of a power failure or some other inadvertent reset (darn you, default config’d Windows Update) that wipes the state clean (sure, all of the work might have been saved, but I was relying on those windows to be set up in just the right manner to remind me of all the things I was working on). These days, I force myself to reboot at least once a week so I can’t get too deep into this habit. When it’s time to change projects, I write up exactly what I was doing and where I left off and stick it in Evernote.
  • Open browser windows : I guess it’s common to have many, many tabs open in one’s web browser in this day and age. Like many, I use open tabs as a stack of items to read. The state problem comes when a few of the open tabs represent TODO items. Then I start living in fear that the browser might crash or be restarted in an unexpected way and I struggle to recall what 3-5 important TODO items were that I had opened in separate tabs (on top of a stack of less important items). Again, I try to shut down the browser frequently in order to break this tendency. TODO items are better filed in Evernote.
  • Unsaved data in a text editor : Okay, this is just sloppy on my part, shoving temporary data into a text editor window thinking it’s supremely ephemeral. The problem comes when it’s linked to one of the many tasks on my desktop that might be bumped down a few priority levels ; when finally returning to the context-free data, I’m at a loss to explain what it’s for. Evernote gets it, once more, with a more thorough description of what was going on.
  • Email inbox : I make an effort to ensure that my email inbox has the fewest number of messages possible. Once things are dealt with, they get filed away elsewhere. This implies that things in my inbox require action. Some things have a habit of hanging around, though. Longer items now get described in better detail and filed away in Evernote.
  • Classic paper : Thanks to Derek in the comments for reminding me of this one. Paper is a reliable standby but it can get unwieldy when Post-It Notes litter your work area. Further, it can be problematic when you have multiple physical work areas.
  • Shell history : Another method I rely on entirely too often. This is when I count on a recipe of command line incantations living on in the history buffer of my Unix shell (generally Bash). What sequence of git commands allowed me to do XYZ ? Let’s check the shell history– I sure hope it’s still in there.

  Conclusion
  I guess what I’m trying to say here is that I really like Evernote. If you have similar troubles with retaining state, try it out. I hear there are many other services similar to it with slightly varying feature sets (people rave about Microsoft OneNote). So there are plenty of options and something out there is surely a fit.

  Evernote has a free tier and a premium tier. For my meager note-taking needs, I don’t come anywhere close to the free tier’s limit but I decided to pay for a premium subscription simply because I feel like I derive so much value from the service.

  One downside, however, is that I seem to be doing a lot less blogging since I got on Evernote earlier this year (though it is where I author most of these posts nowadays ; I especially like that I have a notebook labeled “Posted” whose incrementing count reminds me that I am getting some stuff out there). I originally started this blog as a sort of technical journal in order to organize notes and projects in a central location. It’s strange to think that if Evernote existed in 2005, I might never have had a reason to start this blog.

• Detect volume via mic, start recording, end on silence, transcribe and sent to endpoint

  15 juin 2023, par alphadmon

  I have been attempting to get this to work in many ways but I can't seem to get it right. Most of the time I get a part of it to work and then when I try to make other parts work, I generally break other things.

  


  I am intercepting the volume coming from the mic and if it is louder than 50, I start a recording. I then keep recording until there is a silence, if the silence is equal to 5 seconds I then stop the recording.

  


  I then send the recording to be transcribed by whisper using OpenAI API.

  


  Once that is returned, I then want to send it to the open ai chat end point and get the response.

  


  After that, I would like to start listening again.

  


  Here is what I have that is sort of working so far, but the recording is an empty file always :

  


  // DETECT SPEECH
const recorder = require('node-record-lpcm16');

// TRANSCRIBE
const fs = require("fs");
const ffmpeg = require("fluent-ffmpeg");
const mic = require("mic");
const { Readable } = require("stream");
const ffmpegPath = require("@ffmpeg-installer/ffmpeg").path;
require('dotenv').config();

// CHAT
const { Configuration, OpenAIApi } = require("openai");

// OPEN AI
const configuration = new Configuration({
    organization: process.env.OPENAI_ORG,
    apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);

// SETUP
ffmpeg.setFfmpegPath(ffmpegPath);

// VARS
let isRecording = false;
const audioFilename = 'recorded_audio.wav';
const micInstance = mic({
    rate: '16000',
    channels: '1',
    fileType: 'wav',
});

// DETECT SPEECH
const file = fs.createWriteStream('determine_speech.wav', { encoding: 'binary' });
const recording = recorder.record();
recording.stream().pipe(file);


recording.stream().on('data', async (data) => {
    let volume = parseInt(calculateVolume(data));
    if (volume > 50 && !isRecording) {
        console.log('You are talking.');
        await recordAudio(audioFilename);
    } else {
        setTimeout(async () => {
            console.log('You are quiet.');
            micInstance.stop();
            console.log('Finished recording');
            const transcription = await transcribeAudio(audioFilename);
            console.log('Transcription:', transcription);
            setTimeout(async () => {
                await askAI(transcription);
            }, 5000);
        }, 5000);
    }
});

function calculateVolume(data) {
    let sum = 0;

    for (let i = 0; i < data.length; i += 2) {
        const sample = data.readInt16LE(i);
        sum += sample * sample;
    }

    const rms = Math.sqrt(sum / (data.length / 2));

    return rms;
}

// TRANSCRIBE
function recordAudio(filename) {
    const micInputStream = micInstance.getAudioStream();
    const output = fs.createWriteStream(filename);
    const writable = new Readable().wrap(micInputStream);

    console.log('Listening...');

    writable.pipe(output);

    micInstance.start();

    micInputStream.on('error', (err) => {
        console.error(err);
    });
}

// Transcribe audio
async function transcribeAudio(filename) {
    const transcript = await openai.createTranscription(
        fs.createReadStream(filename),
        "whisper-1",
    );
    return transcript.data.text;
}

// CHAT
async function askAI(text) {
    let completion = await openai.createChatCompletion({
        model: "gpt-4",
        temperature: 0.2,
        stream: false,
        messages: [
            { role: "user", content: text },
            { role: "system", content: "Act like you are a rude person." }
        ],
    });

    completion = JSON.stringify(completion.data, null, 2);
    console.log(completion);
}


  


• mp4 Vj Animation video lagging hi res video

  21 février 2020, par Ryan Stone

  I am trying to get a video to play inside a video tag at the top left hand corner of my page, it loads ok, the resolution is good and it seems to be looping but it is lagging very much, definatly not achieving 60fps it is in mp4 format and the resolution on the original mp4 is 1920x1080 it is a hi resolution vj free loop called GlassVein, you can see it if you search on youtube. On right clicking properties it comes up with the following inforamtion ;

  Bitrate:127kbs
  Data rate:11270kbps
  Total bitrate:11398kbs
  Audio sample rate is : 44khz
  filetype is:VLC media file(.mp4)
  (but i do not want or need the audio)

  & it also says 30fps, but I’m not sure i believe this as it runs smooth as butter on vlc media player no lagging, just smooth loop animation

  I have searched on :https://trac.ffmpeg.org/wiki/Encode/AAC for encoding information but it is complete gobbldygook to me, I don’t understand a word its saying

  My code is so far as follows ;

      <video src="GlassVeinColorful.mp4" autoplay="1" preload="auto" class="Vid" width="640" height="360" loop="1" viewport="" faststart="faststart" mpeg4="mpeg4" 320x240="320x240" 1080="1080" 128k="128k">  
  
      </video>

  Does anyone know why this is lagging so much, or what I could do about it.
  it is a quality animation and I don’t really want to loose an of its resolution or crispness.. the -s section was originally set to 1920x1080 as this is what the original file is but i have changed it to try and render it quicker...

  Any helpful sites, articles or answers would be great..

  2020 Update

  The Solution to this problem was to convert the Video to WebM, then use Javascript & a Html5 Canvas Element to render the Video to the page instead of using the video tag to embed the video.

  Html

  <section>
  
          <video src="Imgs/Vid/PurpGlassVein.webm" type="video/webm" width="684" height="auto" muted="muted" loop="loop" autoplay="autoplay">
  
                 <source>
  
                 <source>
  
                 <source>
  
          </source></source></source></video>
  
          <canvas style="filter:opacity(0);"></canvas>
  
  </section>

  Css

  video{
  
     display:none !important;
  
     visibility:hidden;
  
  }

  Javascript

      const Canv = document.querySelector("canvas");
  
      const Video = document.querySelector("video");
  
      const Ctx = Canv.getContext("2d");
  
  
  
      Video.addEventListener('play',()=>{
  
        function step() {
  
          Ctx.drawImage(Video, 0, 0, Canv.width, Canv.height)
  
          requestAnimationFrame(step)
  
        }
  
        requestAnimationFrame(step);
  
      })
  
  
  
      Canv.animate({
  
          filter: ['opacity(0) blur(5.28px)','opacity(1) blur(8.20px)']
  
      },{
  
          duration: 7288,
  
          fill: 'forwards',
  
          easing: 'ease-in',
  
          iterations: 1,
  
          delay: 728
  
      })

  I’ve Also Used the Vanilla Javascript .animate() API to fade the element into the page when the page loads. But one Caveat is that both the Canvas and the off-screen Video Tag must match the original videos resolution otherwise it starts to lag again, however you can use Css to scale it down via transform:scale(0.5) ; which doesn’t seem to effect performance at all.

  runs smooth as butter, and doesn’t loose any of the high resolution image.
  Added a slight blur 0.34px onto it aswell to smooth it even more.

  Possibly could of still used ffmpeg to get a better[Smaller File Size] WebM Output file but thats something I’ll have to look into at a later date.