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AI-GENERATED MUSIC VIDEO

Attention Is All We Need

Channel singles, track 1 · VoynichLabs · Sugar pop · 2:12

An AI-generated sugar-pop love song explaining Transformers and self-attention through a dating history of RNNs, LSTMs and attention diagrams.

Published on YouTube October 4, 2026

“Attention is all we need, now I realize.”

A love song to the Transformer, told as a dating history drawn as architecture diagrams. The singer's first love was the RNN, who faded with every word (the vanishing gradient); the rebound was the LSTM, all fancy gates and still stuck in sequential time; then self-attention looked at every word at once. Vertical, for Shorts, and every frame is drawn in code from the real vocal timing.

Timeline

click a scene to hear it
Verse 1Pre-chorusChorusInstrumentalVerse 2Outro
CODE
Drawn in code (Canvas2D) from the real word timing.

Visual rules

Every ex is an architecture
The RNN, the LSTM and the Transformer are drawn the way they really work: a chain, a column of gated cells, and a ring of tokens that all attend to each other.
The words are the tokens
Each sung word lands in a cell, a gate or an attention node exactly when it is sung, so the diagram is the lyric.
The loss falls with the story
A JSON readout at the top tracks the model, the training step, the current token and a loss that falls from about four to near zero by the last line.
No generated footage
Nothing here came from an image or video model. It is all Canvas2D, rendered frame by frame in headless Chrome.

Cast

The RNN
First love. Recurrent, one word at a time, and the feeling fades with every hop.
The LSTM
The rebound. Input, forget and output gates, long short-term memory, still sequential.
The Transformer
Self-attention, multi-head, positional encoding and residual streams. The one.

Scene book

19 scenes

Word timings from Whisper aligned to the lyrics, on a 120 BPM beat grid. Each scene runs for the lyric lines that share one diagram.

  1. 01 0:00–0:19 Verse 1 CODE

    First love: the RNN

    You were my first love, RNN, recurrent neural network / You promised me everything but you left me hanging / On vanishing gradients, one word at a time / Drowning in your hesitance

    RNN. A chain of hidden-state cells zig-zags down the screen, one sung word per cell. A pink heart is handed down the chain and shrinks at every hop while the gradient readout falls (0.72 to the power of the step): the vanishing gradient as a love that fades. On "drowning" the cells sink.

    cut
    Glitch cut on the downbeat.
  2. 02 0:19–0:27 Verse 1 CODE

    The LSTM with the fancy gates

    Then came LSTM with your fancy gates / Long, short-term memory, you said we'd relate

    LSTM. The words fill a column of memory cells, each with input, forget and output gate bars that slam open and shut on the beat. Cells not yet sung sit "waiting...".

    cut
    Glitch cut on the downbeat.
  3. 03 0:27–0:31 Verse 1 CODE

    Stuck in sequential time

    But you were still stuck in sequential time

    A clock that will not advance: the hands tick one step per word, a single token at a time, while a training progress bar crawls.

    cut
    Glitch cut on the downbeat.
  4. 04 0:31–0:35 Verse 1 CODE

    Training took forever

    Training took forever, you were never really mine

    An epoch counter climbs and climbs; the loss barely moves. "never really mine" breaks the heart in two.

    cut
    Glitch cut on the downbeat.
  5. 05 0:35–0:43 Pre-chorus CODE

    Then you showed me how to parallelize

    Then you showed me how to parallelize / With self-attention in your transformer eyes

    The sequential chain snaps apart and every word lights at once, side by side, in parallel lanes.

    cut
    Glitch cut on the downbeat.
  6. 06 0:43–0:52 Chorus CODE

    No more waiting

    No more waiting, no more lies / Attention is all we need, now I realize

    The line lays out as a ring of tokens. Every word draws attention arcs to every other word at once, arc brightness from an attention matrix, while the matrix fills in as a heatmap below.

    cut
    Glitch cut on the downbeat.
  7. 07 0:52–1:07 Instrumental CODE

    Forward pass (instrumental)

    (instrumental)

    The break: "the love of my life is a transformer" as a ring of tokens wired all-to-all, the arcs pulsing, the layer counter cycling through twelve layers.

    cut
    Glitch cut on the downbeat.
  8. 08 1:07–1:18 Verse 2 CODE

    The LSTM with the fancy gates

    My rebound, LSTM / With your input, output, and forget gates / Seemed you'd handle long texts better, or so you claimed (?)

    LSTM. The words fill a column of memory cells, each with input, forget and output gate bars that slam open and shut on the beat. Cells not yet sung sit "waiting...".

    cut
    Glitch cut on the downbeat.
  9. 09 1:18–1:22 Verse 2 CODE

    Stuck in sequential time

    But you still process slow, I was still chained

    A clock that will not advance: the hands tick one step per word, a single token at a time, while a training progress bar crawls.

    cut
    Glitch cut on the downbeat.
  10. 10 1:22–1:30 Verse 2 CODE

    Backprop, endless night

    I was stuck in backprop, endless night / Waiting for gradients to propagate right

    A night sky over a stack of twelve layers. A red heart (the gradient) travels back down the stack and shrinks by a power of ten at each layer.

    cut
    Glitch cut on the downbeat.
  11. 11 1:30–1:38 Pre-chorus CODE

    Multi-head attention

    Then your multi-head attention held me tight / And showed me all words can shine in the same light

    The attention ring again, now with eight heads, eight coloured dots all looking at once; on "the same light" every head weights every word equally.

    cut
    Glitch cut on the downbeat.
  12. 12 1:38–1:42 Chorus CODE

    No recurrence

    No more sequences, no more delay

    The line lays out as a ring of tokens. Every word draws attention arcs to every other word at once, arc brightness from an attention matrix, while the matrix fills in as a heatmap below.

    cut
    Glitch cut on the downbeat.
  13. 13 1:42–1:46 Chorus CODE

    Context doesn't fade

    Context windows don't fade away

    The line lays out as a ring of tokens. Every word draws attention arcs to every other word at once, arc brightness from an attention matrix, while the matrix fills in as a heatmap below.

    cut
    Glitch cut on the downbeat.
  14. 14 1:46–1:50 Chorus CODE

    Query meets key

    Query meets key in perfect array

    The line lays out as a ring of tokens. Every word draws attention arcs to every other word at once, arc brightness from an attention matrix, while the matrix fills in as a heatmap below.

    cut
    Glitch cut on the downbeat.
  15. 15 1:50–1:54 Chorus CODE

    Attention is all we need

    Attention is all we need, now I can say

    The line lays out as a ring of tokens. Every word draws attention arcs to every other word at once, arc brightness from an attention matrix, while the matrix fills in as a heatmap below.

    cut
    Glitch cut on the downbeat.
  16. 16 1:54–1:58 Outro CODE

    Positional encoding, residual streams

    Positional encoding, residual streams

    A scrolling sine and cosine grid (positional encoding) above residual-stream bars that loop around each block.

    cut
    Glitch cut on the downbeat.
  17. 17 1:58–2:02 Outro CODE

    Rebuilt

    You rebuilt my broken machine learning dreams

    Transformer blocks (multi-head attention, add and norm, feed forward) fall into place on the beat, one per beat, building the machine back up.

    cut
    Glitch cut on the downbeat.
  18. 18 2:02–2:05 Outro CODE

    No more RNNs or LSTMs

    No more RNNs or LSTMs

    RNN and LSTM in big letters, struck through in red; "attention" glows in neon underneath.

    cut
    Glitch cut on the downbeat.
  19. 19 2:05–2:11 Outro CODE

    Transformer hymns

    Just attention forever in transformer hymns

    The last line: the attention matrix becomes a stained-glass window, lit from behind, with "transformer hymns" in gold serif. Fade to black.

    cut
    Fade to black.

Notes

  1. The verse 2 line about handling long texts is still a best guess at the sung words.
  2. The old still-image Short of this song stays up for now.

How it's made

  1. Timing. The vocal is transcribed with Whisper and aligned word by word to the lyrics, on a 120 BPM beat grid.
  2. Diagrams. Each scene is a real architecture drawn in Canvas2D: the RNN chain and its vanishing gradient, LSTM gates, attention rings and heatmaps, backprop through twelve layers, positional encoding.
  3. Render. Headless Chrome renders every frame at 1080 by 1920, then ffmpeg adds the song. No generated clips, no stills.

Built outside Remotion, in the private music-videos repo (attention-video: engine, timing and render). · plan: pipeline plan

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