Song analysis on Speak AI

Analyze a song
inn i words, tone, and feel.

Speak AI runs AI song analysis on every track: the lyrics transcribed, the tone and emotion in the vocal delivery, and the themes and structure pulled into notes you can actually write from. We build it with you.

★★★★★ 4.9 på G2 300,000+ teams Siden 2018
yourteam.speakai.co
00:13 / 07:08
JR
Jordan R. 00:42
Uploaded the acoustic demo. Let’s see what Speak pulls from the lyrics and the delivery.
JR
Jordan R. 01:22
Chorus repeats the loss-and-reunion theme three times, vocal energy peaks each time it lands.
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Bring a song. Leave with it analyzed.

En arbeidssesjon, ikke en salgspitch. Ingen forpliktelse.

Trinn 1

You bring a real track

A song you’re reviewing, studying, writing about, or pitching. Whatever you’d normally listen through by hand.

Trinn 2

We map what you’re looking for

The themes, structure, and mood cues that matter for your review, assignment, or pitch. Your words, your framework.

Trinn 3

Du ser det analysert, live

Your own track, transcribed and broken down on your criteria, with notes you can build a review or analysis on.

One engine, every listener

Song analysis for every kind of listener.

The same engine, pointed at whatever track you’re working through.

Utdanning

Song analysis for class

Break down an assigned track into lyrics, structure, and theme in minutes, with a transcript and notes a discussion or essay can build on.

Journalism

Reviews & features

Transcribe interviews and lyric breakdowns fast, then pull the exact lines and quotes a review or feature needs without replaying a track five times.

Songwriting

Songwriters & producers

Compare drafts and takes on structure, rhyme, and vocal delivery, so revisions target the section that is actually losing the listener.

Creators

Reaction & breakdown content

Turn a first listen into a scripted breakdown video or post, with themes, standout lines, and mood shifts already pulled out.

Therapy

Music therapy sessions

Analyze the songs a client brings in for tone, emotional arc, and recurring themes, building a record you can track across sessions.

Licensing

Sync & licensing review

Score a catalog on mood, energy, and theme at scale, so pitching the right track for a scene or campaign takes minutes.

A different approach to song analysis.

Song analysis is the process of breaking a track into its component parts, lyrics, melody, structure, and instrumentation, to understand its meaning, its mood, and how it lands on a listener. Marketers, researchers, educators, and music writers have used it for years to understand what a song communicates and why it works, and to make better decisions about the pieces they write about, teach, license, or pitch.

Why song analysis breaks down

For most people, analyzing a song by ear means the same slow loop: replay the track, pause to catch a line, rewind because you missed the bridge, and try to hold the whole shape of it in your head at once. Lyrics get copied from a fan site that got a word wrong. The emotional turn in the vocal, the moment the delivery gets quieter or the energy spikes, never makes it into the notes at all, because that part never had text to grab onto.

Reading the song, not just the words

Speak AI treats a song the way a trained ear would, at machine speed. The track is transcribed in your language, with 100+ supported, and then the recording itself is analyzed: the tone and energy in the vocal delivery, the mood shifts between verse and chorus, the moments where the performance pushes harder or pulls back. Sections, verse, chorus, bridge, outro, are identified and labeled, and recurring words, images, and themes are pulled into structured notes you can use directly.

Then the questions start. Ask across a track, an album, or an entire catalog with AI chat, using the same close-reading workflow a lyrics class or a music blog would do by hand, now running natively over the recording with ChatGPT, Claude, and Gemini built in.

What people ask about a song

  • “What is this song actually about, and where does the lyric say it most directly?”
  • “Which lines carry the emotional peak, and how does the vocal delivery change there?”
  • “What themes or images recur across the verses, and where do they first appear?”
  • “How does the melody and instrumentation support what the lyrics are saying?”
  • “Where does the mood shift between sections, and what causes the shift?”

From a replayed track to a finished breakdown

The result is a first draft of the analysis instead of a blank page. A class assignment starts with the structure and themes already pulled out. A review starts with the exact quote and timestamp instead of a half-remembered line. And because a single song rarely stands alone, instrumentbord du kan tilpasse og white-label track theme, tone, and structure across an artist’s catalog over time, so this album is measured against the last one instead of just described from memory. One legal intelligence firm put a very different kind of audio, 5,100+ hours of carrier calls, through the same underlying engine and saved 700K+ across a five-figure hour volume, which is the same scale of listening and tagging a song catalog runs on, just pointed at a different kind of recording.

And because a track is rarely the only thing you are working through, the same engine that reads a song’s tone and structure also scores calls and coaches conversations, connecting a lyric breakdown to samtalevurdering og coaching on the same platform.

Dine felt, automatisk ekstrahert
Primary themeLoss & reunion
Tone shiftVerse 2 to chorus
FølelserWistful
Emotional intensity8.4 / 10
Motif frequency across the album’s 12 tracks
Konstruert sammen med deg

Konstruert med deg, nøyaktig fra dag én.

A generic AI tool starts from zero. We shape the fields, tags, and prompts around how you actually analyze songs, whether that is a class assignment, a review, or a catalog pitch, then prime the workspace on tracks you already know so it is useful from the first file. You get structured notes back, not just a transcript.

  • Vi designer konteksten, feltene, og scoring around how you actually analyze songs, not a template.
  • Your reference tracks and past breakdowns prime the kunnskapsbase før go-live.
  • Structured data on every song, queryable from Claude, ChatGPT, and Cursor through the MCP server.
MCP, API & integrasjoner

Bring applikasjonene dine til Claude, ChatGPT og Cursor.

Ingen terminal. Ingen npm. Ingen konfigurering. Speak AI’s MCP-server gir enhver assistent 100+ verktøy for å søke i, analysere og handle på kunnskapsbasen din på cirka 60 sekunder. Det er det samme laget applikasjonene dine kjører på, koblet til hundrevis av apper i stacken din gjennom et integrasjonslag og et fullstendig API for utviklere.

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Hent samtaledata direkte inn i utviklingsmiljøet ditt.
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Ét system for alt som teamet ditt sier.

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Ekte tilbakemelding fra team som bruker Speak AI for forskning, transkribering, møter og klientarbeid.

"Vi gikk fra uker av kvalitativ analyse til en dag. Enkelt å bruke, enkelt å implementere, og støtten har vært utrolig."
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Connor H.
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François L.
Finansiell rådgiver
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Jeg pleide å bruke 45 minutter på å transkribere notater. Nå er det gjort på sekunder, og jeg skriver på minutter.
T
Ted H.
Eier, småbedrift
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“Enkelt å bruke for møter. Gjør det enkelt å ta notater og gjøre dem om til en ren, delbar rapport.”
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Naison S.
Prosjektleder
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Markus B.
Medisinsk direktør
★★★★★ Verifisert G2-vurdering

Spørsmål vi får

Ditt første resultatkort kjører på en ekte opptak under konsultasjonen. Teamutruling tar dager, ikke måneder, fordi vi bygger det med deg og primer det på dine eksisterende opptak.

Samlet bruk, ikke per-setning, uten volumminimum. Piloter kreditteres fullt ut. Vi tilpasser prising for ditt nøyaktige arbeidsflyt under samtalen.

Speak AI håndterer 100+ språk, inkludert samtaler som bytter språk midt i setningen, og kan oversette inn og ut.

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Song analysis breaks a track into its parts, lyrics, melody, structure, and instrumentation, to understand what it communicates and how. Speak AI automates the first pass: transcribing the words, reading the tone and energy in the vocal delivery, and surfacing the themes and structure so you can build from there instead of starting from a blank page.

Any song with a clear narrative or emotional arc works well: a verse-chorus structure with a shift in tone, a bridge that changes the story, or lyrics that build in intensity. Speak AI works the same way across genres, so the best song to analyze is usually the one you already have questions about.

That is a matter of taste, not something Speak AI scores for you. What the platform can do is transcribe and analyze any track, YungBlud’s catalog included, so you can compare themes, energy, and delivery across songs and form your own answer with the data in front of you.

ChatGPT can discuss lyrics you paste in, but it cannot listen to the audio itself. Speak AI transcribes the actual recording, reads tone, energy, and delivery from the audio, and then lets you ask ChatGPT, Claude, or Gemini questions across that transcript and analysis, so you get audio-aware answers instead of guesses from text alone.

Enterprise inkluderer BAA-støtte, egendefinerte databehandlingsavtaler, SSO og dataoppholdsalternativer. Vi deler sikkerhetsdokumentasjon på forespørsel og tilpasser hver build til dine krav.

From a first listen to a finished analysis.

Book a free consult, bring a real song, and watch it transcribed, broken down, and ready to write from before the meeting ends. Consults include early access to new features, an extended trial, and implementation credits.

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