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• Using ffmpeg to convert voice recording captured by HTML5

  8 juillet 2014, par user3789242

  I’m building an HTML5 voice recording software with visualizer.I want the user when recording the voice, and after uploading the file as wave in a blob (server-side), the user should be able to select the audio format of that file using ffmpeg. what I Have achieved so far is uploading the file as wave.what I still want to do is :

  • On the server
  • side pick your preferable web programming framework
  • The web programming framework accepts the upload and stores the file on the server
  • The web programming framework runs a ffmpeg (command line) which processes the file
  • The user can download the processed file

  here is my code so far :

  // variables
  
  var leftchannel = [];
  
  var rightchannel = [];
  
  var recorder = null;
  
  var recording = false;
  
  var recordingLength = 0;
  
  var volume = null;
  
  var audioInput = null;
  
  var sampleRate = 44100;
  
  var audioContext = null;
  
  var context = null;
  
  var outputString;
  
  
  
  
  
  
  
  if (!navigator.getUserMedia)
  
      navigator.getUserMedia = navigator.getUserMedia ||
  
      navigator.webkitGetUserMedia ||
  
      navigator.mozGetUserMedia ||
  
      navigator.msGetUserMedia;
  
  
  
  if (navigator.getUserMedia) {
  
      navigator.getUserMedia({
  
          audio: true
  
      }, success, function (e) {
  
          alert('Error capturing audio.');
  
      });
  
  } else alert('getUserMedia not supported in this browser.');
  
  
  
  
  
  
  
  function getVal(value) {
  
  
  
      // if R is pressed, we start recording
  
      if (value == "record") {
  
          recording = true;
  
          // reset the buffers for the new recording
  
          leftchannel.length = rightchannel.length = 0;
  
          recordingLength = 0;
  
          document.getElementById('output').innerHTML = "Recording now...";
  
  
  
          // if S is pressed, we stop the recording and package the WAV file
  
      } else if (value == "stop") {
  
  
  
          // we stop recording
  
          recording = false;
  
          document.getElementById('output').innerHTML = "Building wav file...";
  
  
  
          // we flat the left and right channels down
  
          var leftBuffer = mergeBuffers(leftchannel, recordingLength);
  
          var rightBuffer = mergeBuffers(rightchannel, recordingLength);
  
          // we interleave both channels together
  
          var interleaved = interleave(leftBuffer, rightBuffer);
  
  
  
  
  
  
  
          var buffer = new ArrayBuffer(44 + interleaved.length * 2);
  
          var view = new DataView(buffer);
  
  
  
          // RIFF chunk descriptor
  
          writeUTFBytes(view, 0, 'RIFF');
  
          view.setUint32(4, 44 + interleaved.length * 2, true);
  
          writeUTFBytes(view, 8, 'WAVE');
  
          // FMT sub-chunk
  
          writeUTFBytes(view, 12, 'fmt ');
  
          view.setUint32(16, 16, true);
  
          view.setUint16(20, 1, true);
  
          // stereo (2 channels)
  
          view.setUint16(22, 2, true);
  
          view.setUint32(24, sampleRate, true);
  
          view.setUint32(28, sampleRate * 4, true);
  
          view.setUint16(32, 4, true);
  
          view.setUint16(34, 16, true);
  
          // data sub-chunk
  
          writeUTFBytes(view, 36, 'data');
  
          view.setUint32(40, interleaved.length * 2, true);
  
  
  
  
  
          var lng = interleaved.length;
  
          var index = 44;
  
          var volume = 1;
  
          for (var i = 0; i < lng; i++) {
  
              view.setInt16(index, interleaved[i] * (0x7FFF * volume), true);
  
              index += 2;
  
          }
  
  
  
          var blob = new Blob([view], {
  
              type: 'audio/wav'
  
          });
  
  
  
          // let's save it locally
  
  
  
          document.getElementById('output').innerHTML = 'Handing off the file now...';
  
          var url = (window.URL || window.webkitURL).createObjectURL(blob);
  
  
  
          var li = document.createElement('li');
  
          var au = document.createElement('audio');
  
          var hf = document.createElement('a');
  
  
  
          au.controls = true;
  
          au.src = url;
  
          hf.href = url;
  
          hf.download = 'audio_recording_' + new Date().getTime() + '.wav';
  
          hf.innerHTML = hf.download;
  
          li.appendChild(au);
  
          li.appendChild(hf);
  
          recordingList.appendChild(li);
  
  
  
      }
  
  }
  
  
  
  
  
  function success(e) {
  
  
  
      audioContext = window.AudioContext || window.webkitAudioContext;
  
      context = new audioContext();
  
  
  
  
  
      volume = context.createGain();
  
  
  
      // creates an audio node from the microphone incoming stream(source)
  
      source = context.createMediaStreamSource(e);
  
  
  
      // connect the stream(source) to the gain node
  
      source.connect(volume);
  
  
  
      var bufferSize = 2048;
  
  
  
      recorder = context.createScriptProcessor(bufferSize, 2, 2);
  
  
  
      //node for the visualizer
  
      analyser = context.createAnalyser();
  
      analyser.smoothingTimeConstant = 0.3;
  
      analyser.fftSize = 512;
  
  
  
      splitter = context.createChannelSplitter();
  
      //when recording happens
  
      recorder.onaudioprocess = function (e) {
  
  
  
          if (!recording) return;
  
          var left = e.inputBuffer.getChannelData(0);
  
          var right = e.inputBuffer.getChannelData(1);
  
  
  
          leftchannel.push(new Float32Array(left));
  
          rightchannel.push(new Float32Array(right));
  
          recordingLength += bufferSize;
  
  
  
          // get the average for the first channel
  
          var array = new Uint8Array(analyser.frequencyBinCount);
  
          analyser.getByteFrequencyData(array);
  
  
  
          var c = document.getElementById("myCanvas");
  
          var ctx = c.getContext("2d");
  
          // clear the current state
  
          ctx.clearRect(0, 0, 1000, 325);
  
          var gradient = ctx.createLinearGradient(0, 0, 0, 300);
  
          gradient.addColorStop(1, '#000000');
  
          gradient.addColorStop(0.75, '#ff0000');
  
          gradient.addColorStop(0.25, '#ffff00');
  
          gradient.addColorStop(0, '#ffffff');
  
          // set the fill style
  
          ctx.fillStyle = gradient;
  
          drawSpectrum(array);
  
  
  
          function drawSpectrum(array) {
  
              for (var i = 0; i < (array.length); i++) {
  
                  var value = array[i];
  
                  ctx.fillRect(i * 5, 325 - value, 3, 325);
  
              }
  
  
  
          }
  
      }
  
  
  
      function getAverageVolume(array) {
  
          var values = 0;
  
          var average;
  
  
  
          var length = array.length;
  
  
  
          // get all the frequency amplitudes
  
          for (var i = 0; i < length; i++) {
  
              values += array[i];
  
          }
  
  
  
          average = values / length;
  
          return average;
  
      }
  
  
  
      // we connect the recorder(node to destination(speakers))
  
      volume.connect(splitter);
  
      splitter.connect(analyser, 0, 0);
  
  
  
      analyser.connect(recorder);
  
      recorder.connect(context.destination);
  
  
  
  }
  
  
  
  
  
  
  
  
  
  function mergeBuffers(channelBuffer, recordingLength) {
  
      var result = new Float32Array(recordingLength);
  
      var offset = 0;
  
      var lng = channelBuffer.length;
  
      for (var i = 0; i < lng; i++) {
  
          var buffer = channelBuffer[i];
  
          result.set(buffer, offset);
  
          offset += buffer.length;
  
      }
  
      return result;
  
  }
  
  
  
  function interleave(leftChannel, rightChannel) {
  
      var length = leftChannel.length + rightChannel.length;
  
      var result = new Float32Array(length);
  
  
  
      var inputIndex = 0;
  
  
  
      for (var index = 0; index < length;) {
  
          result[index++] = leftChannel[inputIndex];
  
          result[index++] = rightChannel[inputIndex];
  
          inputIndex++;
  
      }
  
      return result;
  
  }
  
  
  
  
  
  function writeUTFBytes(view, offset, string) {
  
      var lng = string.length;
  
      for (var i = 0; i < lng; i++) {
  
  
  
          view.setUint8(offset + i, string.charCodeAt(i));
  
      }
  
  }

• ffmpeg issue with Jupyter notebook

  16 novembre 2022, par Nanda

  I am trying to extract frames from a video file using ffmpeg in Python. I installed ffmpeg using Homebrew and ffmpeg-python on the Anaconda-Navigator. Yet when I call ffmpeg on Jupyter notebook as follows

  


  !ffmpeg -i "$file" "$rootdir"/"$folder_name"/frame%04d.png


  


  I get an error saying

  


  zsh:1: command not found: ffmpeg


  


  I clearly see ffmpeg in my usr/local/bin. Can someone please assist me in sorting this ? I am able to use ffmpeg in Google Colab, though.

  


• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Matomo (Piwik) shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Matomo (Piwik) marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Matomo (Piwik) only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Matomo (Piwik) shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Matomo (Piwik) is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Matomo (Piwik) does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution

  The post Make better marketing decisions with attribution modeling appeared first on Analytics Platform - Matomo.