START OF PREVIOUS DISCUSSION
user:recognition = new webkitSpeechRecognition();
    recognition.continuous = true;
    recognition.interimResults = true;

    let fullTranscript = "\n";

    recognition.onresult = function (event) {
        var interimTranscript = '';

        for (var i = event.resultIndex; i < event.results.length; ++i) {
            if (event.results[i].isFinal) {
                fullTranscript += event.results[i][0].transcript;
            } else {
                interimTranscript += event.results[i][0].transcript;
            }
        }

        console.log('Full Transcript:', fullTranscript);

        let inp = document.querySelector('.overlay-section p');
        inp.textContent = fullTranscript + interimTranscript;
    };

    recognition.onend = () => {
        startRecognition();
    };

    function startRecognition() {
        recognition.start();
    }

make it so every it saves every fullTranscript/isFinal in a list the following:
"start":"TIME IT STARTED"
"tts":"FULL TRANSCRIPT"
"audio":"THE AUDIO CORRESPONDING TO THE EXACT FULL TRANSCRIPT SECTION"
"end":"TIME IT ENDED"
robot:To achieve this, you would need to modify the code to capture timestamps and additionally, you would need a way to record and match the audio to the corresponding transcript. Unfortunately, the Web Speech API doesn’t directly provide a means to capture audio blobs tied to interim results. One workaround could be to simultaneously record the entire audio session using the MediaRecorder API and then, post-processing, split the audio according to the timestamps you’ve captured for each transcript section.<br/><br/>Here is a basic outline of how you can modify the JavaScript code to capture the timestamps:<br/><pre><code class="javascript"><br/>const recognition = new webkitSpeechRecognition();<br/>recognition.continuous = true;<br/>recognition.interimResults = true;<br/><br/>let fullTranscript = “”;<br/>let transcriptList = [];<br/><br/>// Placeholder function to capture audio. You’ll need to replace this with actual audio capture logic<br/>function recordAudio() {<br/>  console.log(“Start audio recording here”);<br/>}<br/><br/>recognition.onresult = function (event) {<br/>    let interimTranscript = ‘’;<br/>    let startTime = null;<br/>    let endTime = null;<br/>    let isFinal = false;<br/><br/>    for (let i = event.resultIndex; i &lt; event.results.length; ++i) {<br/>        endTime = new Date().toISOString(); // Update the end time to the current timestamp<br/><br/>        if (event.results[i].isFinal) {<br/>            startTime = startTime || endTime; // Set the start time if not already set<br/>            fullTranscript += event.results[i][0].transcript;<br/>            isFinal = true;<br/>        } else {<br/>            interimTranscript += event.results[i][0].transcript;<br/>            startTime = startTime || new Date().toISOString(); // Set the start time on the first interim result<br/>        }<br/>    }<br/><br/>    console.log(‘Full Transcript:’, fullTranscript);<br/><br/>    let inp = document.querySelector(‘.overlay-section p’);<br/>    inp.textContent = fullTranscript + interimTranscript;<br/><br/>    // Only save to the list if we have a final transcript<br/>    if (isFinal) {<br/>        transcriptList.push({<br/>            “start”: startTime,<br/>            “tts”: fullTranscript,<br/>            “audio”: null, // Placeholder. Implement audio extraction logic<br/>            “end”: endTime<br/>        });<br/>        // Record and reset the start time for the next final result<br/>        startTime = null;<br/>    }<br/>};<br/><br/>recognition.onend = () =&gt; {<br/>    startRecognition();<br/>};<br/><br/>function startRecognition() {<br/>    recognition.start();<br/>    recordAudio(); // Call audio recording when starting recognition<br/>}<br/><br/>// Call <code>startRecognition</code> to kick things off<br/>startRecognition();<br/><br/>// Implementation of audio logic will be required here</code></pre><br/><br/>It’s important to note that this code does not yet record audio. To implement audio recording, you would use the MediaRecorder API, which involves requesting permission to use the user’s microphone, creating a new MediaRecorder instance, starting it at the same time as you start the speech recognition, and handling the <code>dataavailable</code> event to save the audio data parts into an array.<br/><br/>After you’ve recorded the full audio session, you can use the timestamps to try and split the audio. However, synchronizing the exact audio with each “final” recognition result can be quite challenging and might require audio analysis or some advanced buffer manipulation because the <code>webkitSpeechRecognition</code> API does not provide event-specific audio chunks.<br/><br/>This is a fairly complex problem and the provided solution only outlines the starting point for tracking timestamps with recognized speech, without solving the audio synchronization aspect. If audio syncing is a critical feature, consider using dedicated speech-to-text services that provide transcriptions alongside the corresponding audio.
user:ive already use the media player for something, so can u adapt this idea to my code? here it is:
let recognition;
let audioContext;
let analyser;
let microphone;

function awakePandoraEars() {
    recognition = new webkitSpeechRecognition();
    recognition.continuous = true;
    recognition.interimResults = true;

    let fullTranscript = "\n";

    recognition.onresult = function (event) {
        var interimTranscript = '';

        for (var i = event.resultIndex; i < event.results.length; ++i) {
            if (event.results[i].isFinal) {
                fullTranscript += event.results[i][0].transcript;
            } else {
                interimTranscript += event.results[i][0].transcript;
            }
        }

        console.log('Full Transcript:', fullTranscript);

        let inp = document.querySelector('.overlay-section p');
        inp.textContent = fullTranscript + interimTranscript;
    };

    recognition.onend = () => {
        startRecognition();
    };

    function startRecognition() {
        recognition.start();
    }

    startRecognition();

    audioContext = new (window.AudioContext || window.webkitAudioContext)();
    analyser = audioContext.createAnalyser();

    navigator.mediaDevices.getUserMedia({ audio: true })
        .then((stream) => {
            handleSuccess(stream);
            startRecognition(); // Start speech recognition after getting user media
        })
        .catch((error) => {
            console.error('Error accessing microphone:', error);
        });

    function handleSuccess(stream) {
        microphone = audioContext.createMediaStreamSource(stream);
        microphone.connect(analyser);

        analyser.fftSize = 256;
        const bufferLength = analyser.frequencyBinCount;
        const dataArray = new Uint8Array(bufferLength);

        function back(stream) {
            draw('visualizer');
            draw('mirroredVisualizer');
            draw('visualizerdown', down = true);
            draw('mirroredVisualizerdown', down = true);

            const visualizerCanvas = document.getElementById('visualizer');
            const mirroredCanvas = document.getElementById('mirroredVisualizer');
            const visualizerCanvasDown = document.getElementById('visualizerdown');
            const mirroredCanvasDown = document.getElementById('mirroredVisualizerdown');
            resizeCanvas();

            window.addEventListener('resize', resizeCanvas);

            function resizeCanvas() {
                const containerWidth = window.innerWidth / 2;
                const containerHeight = window.innerHeight;

                visualizerCanvas.width = containerWidth;
                visualizerCanvas.height = containerHeight;
                mirroredCanvas.width = containerWidth;
                mirroredCanvas.height = containerHeight;
                visualizerCanvasDown.width = containerWidth;
                visualizerCanvasDown.height = containerHeight;
                mirroredCanvasDown.width = containerWidth;
                mirroredCanvasDown.height = containerHeight;
            }

            function draw(canvasId, down = false) {
                const canvas = document.getElementById(canvasId);
                const ctx = canvas.getContext('2d');

                function drawFrame() {
                    analyser.getByteFrequencyData(dataArray);

                    ctx.clearRect(0, 0, canvas.width, canvas.height);

                    const barWidth = (canvas.width / bufferLength);

                    dataArray.forEach((value, index) => {
                        const barHeight = 2 * value;
                        let x;

                        x = index * barWidth;

                        ctx.fillStyle = `rgba(${barHeight / 2}, 0, ${-barHeight / 2 + 256}, 0.2)`;
                        if (down) {
                            ctx.fillRect(x, 0, barWidth, barHeight);
                        } else {
                            ctx.fillRect(x, canvas.height - barHeight, barWidth, barHeight);
                        }

                    });

                    requestAnimationFrame(drawFrame);
                }

                drawFrame();
            }

            window.onbeforeunload = function () {
                if (audioContext.state === 'running') {
                    audioContext.close();
                }
            };
        }

        back();
    }
}

function putToSleep() {
    clearCanvas('visualizer');
    clearCanvas('mirroredVisualizer');
    clearCanvas('visualizerdown');
    clearCanvas('mirroredVisualizerdown');

    if (audioContext && audioContext.state === 'running') {
        audioContext.close();
    }
    if (recognition && recognition.abort) {
        recognition.abort();
    }
    if (microphone) {
        microphone.disconnect();
    }
}

function clearCanvas(canvasId) {
    const canvas = document.getElementById(canvasId);
    const ctx = canvas.getContext('2d');

    function drawFrame() {
        ctx.clearRect(0, 0, canvas.width, canvas.height);
        requestAnimationFrame(drawFrame);
    }
    drawFrame();
}


robot:To incorporate the desired feature of timestamping and recording audio corresponding to each transcript, you’re already halfway there with the implementation of your visual audio analytics. <br/><br/>Here’s a brief outline of steps you’ll need to follow to integrate both functionalities:<br/><br/>1. Use two functionalities side by side: Speech recognition to get the transcript and MediaRecorder to record the entire session audio.<br/>2. Collect start and end times for each final transcript and save this metadata alongside the final transcript text.<br/>3. Once the recording is finished, use this metadata to slice the audio accordingly.<br/><br/>Here is an updated version of your code that includes the necessary changes to capture the start and end times for your transcriptions as well as create a list for storing the transcriptions with metadata:<br/><pre><code class="javascript"><br/>let recognition;<br/>let audioContext;<br/>let analyser;<br/>let microphone;<br/>let mediaRecorder;<br/>let audioBlobs = [];<br/>let transcriptList = [];<br/>let startTime;<br/>let audioChunks = [];<br/><br/>function awakePandoraEars() {<br/>  recognition = new webkitSpeechRecognition();<br/>  recognition.continuous = true;<br/>  recognition.interimResults = true;<br/><br/>  let fullTranscript = “\n”;<br/><br/>  recognition.onresult = function (event) {<br/>    var interimTranscript = ‘’;<br/>    let isFinal = false;<br/><br/>    for (var i = event.resultIndex; i &lt; event.results.length; ++i) {<br/>      if (event.results[i].isFinal) {<br/>        startTime = startTime || new Date().getTime(); // Capture the start time for the final result<br/>        fullTranscript += event.results[i][0].transcript;<br/>        isFinal = true;<br/>      } else {<br/>        interimTranscript += event.results[i][0].transcript;<br/>        if (!startTime) {<br/>          startTime = new Date().getTime(); // Update the start time for the first interim result<br/>        }<br/>      }<br/>    }<br/><br/>    console.log(‘Full Transcript:’, fullTranscript);<br/><br/>    let inp = document.querySelector(‘.overlay-section p’);<br/>    inp.textContent = fullTranscript + interimTranscript;<br/><br/>    if (isFinal &amp;&amp; startTime) {<br/>      const endTime = new Date().getTime(); // Capture the end time for the final result<br/>      transcriptList.push({<br/>        start: new Date(startTime).toISOString(),<br/>        tts: fullTranscript,<br/>        audio: null, // Placeholder, for now, to be updated later<br/>        end: new Date(endTime).toISOString()<br/>      });<br/>      startTime = null; // Reset the start time for the next transcript<br/>    }<br/>  };<br/><br/>  recognition.onend = () =&gt; {<br/>    startRecognition();<br/>  };<br/><br/>  function startRecognition() {<br/>    recognition.start();<br/>  }<br/><br/>  startRecognition();<br/><br/>  audioContext = new (window.AudioContext || window.webkitAudioContext)();<br/>  analyser = audioContext.createAnalyser();<br/><br/>  const handleSuccess = (stream) =&gt; {<br/>    microphone = audioContext.createMediaStreamSource(stream);<br/>    microphone.connect(analyser);<br/><br/>    mediaRecorder = new MediaRecorder(stream);<br/>    mediaRecorder.start();<br/><br/>    mediaRecorder.ondataavailable = function (e) {<br/>      audioChunks.push(e.data);<br/>    };<br/><br/>    mediaRecorder.onstop = () =&gt; {<br/>      const audioBlob = new Blob(audioChunks, { type: ‘audio/wav’ });<br/>      audioChunks = []; // Reset the chunks for the next session<br/>      <br/>      // Placeholder for saving or processing the audio data, e.g., upload to server to retrieve the audio URL or cut the audio based on timestamps<br/>    };<br/><br/>    // Remaining code for audio analytics visualization…<br/>  };<br/><br/>  navigator.mediaDevices.getUserMedia({ audio: true }).then(handleSuccess);<br/><br/>  // Remaining code for audio analytics visualization…<br/>}<br/><br/>// Place the rest of the code here.<br/><br/>function putToSleep() {<br/>  // Other cleanup operations like disconnecting the microphone, etc.<br/>  <br/>  if (mediaRecorder &amp;&amp; mediaRecorder.state !== ‘inactive’) {<br/>    mediaRecorder.stop(); // Stop recording<br/>  }<br/>}<br/><br/>// Place the rest of the code here.</code></pre><br/><br/>Please note that the above code prepares the infrastructure for recording, timestamping, and transcribing the audio. However, to extract and sync specific audio segments with their respective transcriptions still requires significant work.<br/><br/>One approach is to save the recorded blob somewhere accessible (client-side, server-side) and handle syncing afterward. This syncing process can become complex, involving audio processing and cutting based on timings, and may not always align perfectly due to possible delays between the speech recognition API timings and the actual recorded audio. <br/>For server-side processing, you would typically upload your recording to the server and then process it using audio processing libraries to cut and align it with your timestamped transcriptions. <br/><br/>If exact synchronization is needed, looking into more advanced services (paid APIs or custom machine learning models) that offer more fine-grained audio analysis might be necessary.<br/><br/>This addition gets more complex, and you may need to experiment to find the right solution for your specific needs.
user:give me the entire code
robot:Certainly! Below is the complete code integrating the previous discussion. It uses the <code>webkitSpeechRecognition</code> for speech recognition and <code>MediaRecorder</code> for capturing audio. This code also maintains a log of transcription segments with timestamps and associated audio chunks. However, it does not include logic for splitting the final audio according to transcript timestamps or syncing it—these tasks would have to be handled in post-processing.<br/><pre><code class="javascript"><br/>let recognition;<br/>let audioContext;<br/>let analyser;<br/>let microphone;<br/>let mediaRecorder;<br/>let audioChunks = [];<br/>let transcriptList = [];<br/>let startTime;<br/>let fullTranscript = “\n”;<br/><br/>function awakePandoraEars() {<br/>    recognition = new webkitSpeechRecognition();<br/>    recognition.continuous = true;<br/>    recognition.interimResults = true;<br/><br/>    recognition.onresult = function (event) {<br/>        let interimTranscript = ‘’;<br/>        let isFinal = false;<br/><br/>        for (let i = event.resultIndex; i &lt; event.results.length; ++i) {<br/>            if (event.results[i].isFinal) {<br/>                startTime = startTime || new Date().getTime();<br/>                fullTranscript += event.results[i][0].transcript;<br/>                isFinal = true;<br/>            } else {<br/>                interimTranscript += event.results[i][0].transcript;<br/>                if (!startTime) {<br/>                    startTime = new Date().getTime();<br/>                }<br/>            }<br/>        }<br/><br/>        console.log(‘Full Transcript:’, fullTranscript);<br/><br/>        let inp = document.querySelector(‘.overlay-section p’);<br/>        inp.textContent = fullTranscript + interimTranscript;<br/><br/>        if (isFinal &amp;&amp; startTime) {<br/>            const endTime = new Date().getTime();<br/>            transcriptList.push({<br/>                start: new Date(startTime).toISOString(),<br/>                tts: event.results[event.resultIndex][0].transcript,<br/>                audio: null,<br/>                end: new Date(endTime).toISOString()<br/>            });<br/>            startTime = null;<br/>        }<br/>    };<br/><br/>    recognition.onend = () =&gt; {<br/>        startRecognition();<br/>    };<br/><br/>    function startRecognition() {<br/>        recognition.start();<br/>    }<br/><br/>    startRecognition();<br/><br/>    audioContext = new (window.AudioContext || window.webkitAudioContext)();<br/>    analyser = audioContext.createAnalyser();<br/><br/>    const handleSuccess = (stream) =&gt; {<br/>        microphone = audioContext.createMediaStreamSource(stream);<br/>        microphone.connect(analyser);<br/><br/>        mediaRecorder = new MediaRecorder(stream);<br/>        mediaRecorder.start();<br/><br/>        mediaRecorder.ondataavailable = function (e) {<br/>            audioChunks.push(e.data);<br/>        };<br/><br/>        mediaRecorder.onstop = () =&gt; {<br/>            const audioBlob = new Blob(audioChunks, { type: ‘audio/wav’ });<br/>            audioChunks = []; // Reset the chunks for the next session<br/><br/>            // Placeholder for saving or processing the audio data<br/>            // You would implement logic here to upload the audio to a server or locally,<br/>            // and associate it with your transcript list entries as needed.<br/>        };<br/><br/>        // Define your visualization drawing function here (not included for brevity)<br/>        // …<br/><br/>        back(); // Starts the visualization function<br/>    };<br/><br/>    navigator.mediaDevices.getUserMedia({ audio: true })<br/>        .then(handleSuccess)<br/>        .catch((error) =&gt; {<br/>            console.error(‘Error accessing microphone:’, error);<br/>        });<br/>}<br/><br/>// Additional functions, such as back(), putToSleep(), clearCanvas(), etc.<br/>// These would be the ones you have defined already in your original code.<br/><br/>// Call awakePandoraEars to start everything<br/>awakePandoraEars();<br/><br/>function putToSleep() {<br/>    // Clean up your visualizers as you defined in clearCanvas<br/>    // …<br/><br/>    if (audioContext &amp;&amp; audioContext.state === ‘running’) {<br/>        audioContext.close();<br/>    }<br/>    if (recognition &amp;&amp; recognition.abort) {<br/>        recognition.abort();<br/>    }<br/>    if (microphone) {<br/>        microphone.disconnect();<br/>    }<br/>    <br/>    if (mediaRecorder &amp;&amp; mediaRecorder.state !== ‘inactive’) {<br/>        mediaRecorder.stop(); // Stop recording<br/>    }<br/>}<br/><br/>// Existing clearCanvas, back and drawing functions associated with the visualizers go here…</code></pre><br/><br/><strong>Notes:</strong><br/>- You will need to implement the visualization functions mentioned as placeholders (<code>visualizeAudio()</code>, <code>draw()</code>, and others) based on the logic you have provided.<br/>- The code is designed to start speech recognition and audio recording after the microphone access is granted.<br/>- The <code>transcriptList</code> stores the trascriptions along with the start and end timestamps. The placeholder <code>null</code> for the <code>audio</code> property is where you would store the reference to the audio that corresponds to the transcript.<br/>- To sync the audio chunks with the transcript, you will need to create and implement an audio processing logic that slices the recording after the session is done or in real-time, depending on your requirements.<br/>- This code doesn’t handle the actual audio slicing and syncing; additional implementation is needed for post-processing the audio.<br/><br/>Due to the complexities involved in processing audio data accurately and the need to work with possibly large files, this often requires server-side handling with appropriate audio processing libraries, and possibly experimenting with different methods to achieve the desired accuracy. Depending on the project’s specifics, you might need to consider using professional transcription and synchronization services.
user:cant u already make a new list with the section of the audio matching the transcript and timestamps? do that please
robot:Sure, creating a new list with sections of the audio matching each transcript and its timestamps requires handling the audio blob produced by the <code>MediaRecorder</code>. The audio is recorded continuously, and in order to match it with the transcripts, you will need to slice it into sections after the recording session is complete.<br/><br/>Below I’ll demonstrate how you could do this in principle using the code from the previous example. However, you need to be aware that due to timing inaccuracies, the resulting audio sections might not align perfectly with the transcripts. This example assumes that you will be processing the entire audio at the end of the session, rather than processing audio chunks in real-time.<br/><br/>Here’s how you could adapt the existing code to produce a list of transcript segments with associated audio files after the recording session is over:<br/><br/>Modify the <code>mediaRecorder.onstop</code> function to process <code>audioChunks</code> and create sliced audio blobs to match the timestamps. You would have to write a separate function to perform this slicing operation based on the timestamps.<br/><pre><code class="javascript"><br/>let audioBlob; // A single Blob that includes the entire recording session<br/><br/>mediaRecorder.onstop = () =&gt; {<br/>    audioBlob = new Blob(audioChunks, { type: ‘audio/wav’ });<br/>    audioChunks = []; // Reset for next session<br/>    processAudioSegments(audioBlob, transcriptList); // Function to process and match audio to transcripts<br/>};<br/><br/>function processAudioSegments(completeAudioBlob, transcripts) {<br/>    // Placeholder to load and process the complete audio blob and match slices with transcripts<br/>    const audioUrl = URL.createObjectURL(completeAudioBlob);<br/>    // Create a new audio element to work with the recorded blob<br/>    const audioElement = new Audio(audioUrl);<br/>    audioElement.onloadedmetadata = () =&gt; {<br/>        // Once metadata is loaded, we can determine the duration and start cutting<br/>        transcripts.forEach(transcriptObj =&gt; {<br/>            // Calculate the start and end time in seconds<br/>            const startTimeSeconds = calculateSecondsFromTimestamp(transcriptObj.start);<br/>            const endTimeSeconds = calculateSecondsFromTimestamp(transcriptObj.end);<br/>            // Calculate the start and end offsets as a fraction of the total duration<br/>            const startOffset = audioElement.duration * (startTimeSeconds / audioElement.duration);<br/>            const endOffset = audioElement.duration * (endTimeSeconds / audioElement.duration);<br/>            // We’ll need an audio processing library or custom Web Audio API code to extract segments here<br/>            extractAudioSegment(completeAudioBlob, startOffset, endOffset, (segmentBlob) =&gt; {<br/>                // Do something with segmentBlob (like saving or sending to server)<br/>                // Associate this segmentBlob with the corresponding transcript in transcripts list<br/>                transcriptObj.audio = segmentBlob;<br/>            });<br/>        });<br/>    };<br/>}</code></pre><br/><br/>The placeholder <code>extractAudioSegment</code> is a theoretical function you would have to implement that extracts a segment from <code>completeAudioBlob</code> from <code>startOffset</code> to <code>endOffset</code>. Actual implementation to slice the blob based on the offsets would be quite complex and would likely require a server-side processing solution due to limitations in the browser environment. JavaScript’s Web Audio API could be utilized for such operations, but slicing audio accurately and efficiently is outside of the scope for typical client-side scripting.<br/><br/>Always keep in mind that timestamping within JavaScript does not guarantee millisecond precision synchronization with real-time audio or visual events due to the nature of web browser event processing and execution.<br/><br/>This implies you will likely need a robust solution using either a combination of client and server-side programming or a specialized library/service which can handle timed text track (WebVTT, for instance) and audio processing to sync speech recognition with recorded audio.
END OF PREVIOUS DISCUSSION
user: how do i change how <code></code> shows code? like i want it to show functions in different color etc, like ide