Analyze a song
em 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.
Os ganhos que times entregam.
Tempo até um produto ao vivo, horas economizadas por arquivo e dólares economizados. Mesma plataforma, aplicações muito diferentes.
Empresa de legal tech constrói uma plataforma de deposition white-label, 8 meses mais rápido.
Agência de pesquisa global lança uma plataforma de pesquisa qualitativa white-label.
Empresa de inteligência jurídica processa 5.100+ horas de chamadas de operadoras, 95% mais rápido.
Empresa de consultoria de saúde reduziu o processamento de sessão de 8 horas para 0,3.
Fabricante de e-commerce centraliza revisão de chamadas e reduz em 85%.
Empresa de recrutamento reduz o tempo de relatório de candidatos de 5 horas para 10 minutos.
Bring a song. Leave with it analyzed.
Uma sessão de trabalho, não um discurso de vendas. Sem obrigação.
You bring a real track
A song you’re reviewing, studying, writing about, or pitching. Whatever you’d normally listen through by hand.
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.
Você vê isso analisado, ao vivo
Your own track, transcribed and broken down on your criteria, with notes you can build a review or analysis on.
Song analysis for every kind of listener.
The same engine, pointed at whatever track you’re working through.
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.
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.
Songwriters & producers
Compare drafts and takes on structure, rhyme, and vocal delivery, so revisions target the section that is actually losing the listener.
Reaction & breakdown content
Turn a first listen into a scripted breakdown video or post, with themes, standout lines, and mood shifts already pulled out.
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.
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, painéis que você pode personalizar e marca branca 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 pontuação de chamadas e coaching on the same platform.
Desenvolvido com você, preciso desde o primeiro dia.
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.
- Projetamos o contexto, campos e pontuação around how you actually analyze songs, not a template.
- Your reference tracks and past breakdowns prime the base de conhecimento antes de go-live.
- Structured data on every song, queryable from Claude, ChatGPT, and Cursor through the servidor MCP.
Traga suas aplicações para Claude, ChatGPT e Cursor.
Sem terminal. Sem npm. Sem configuração. O servidor MCP do Speak AI oferece qualquer assistente 100+ ferramentas para pesquisar, analisar e agir sobre sua base de conhecimento em cerca de 60 segundos. É a mesma camada em que seus aplicativos rodam, conectada aos centenas de aplicativos em sua pilha através de uma camada de integrações e uma API completa de desenvolvedor.
Um único sistema de registro para tudo o que sua equipe diz.
Presencial e virtual, em um só lugar. Sem precisar costurar uma ferramenta de reunião, um gravador de voz e três outros aplicativos. Speak AI captura tudo em uma base de conhecimento pesquisável na qual seus aplicativos são construídos.
Times constroem com Speak AI.
Feedback real de equipes usando Speak AI para pesquisa, transcrição, reuniões e trabalho com clientes.
Perguntas que recebemos
Seu primeiro scorecard é executado em uma gravação real durante a consulta. O lançamento para o time leva dias, não meses, porque o construímos com você e o preparamos em suas gravações existentes.
Uso agrupado, não por assento, sem volumes mínimos. Pilotos são creditados integralmente. Escopo a precificação para seu fluxo de trabalho exato na chamada.
Speak AI suporta 100+ idiomas, incluindo conversas que mudam de idioma no meio da frase, e pode traduzir para dentro e para fora.
Sim. Implantações white-label são executadas em seu próprio domínio com seu logo, incluindo plataformas de clientes que agências revendem, além de aplicativos iOS e Android personalizados.
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 builds suportam BAAs, acordos de processamento de dados personalizados, SSO e opções de residência de dados. Compartilhamos documentação de segurança sob solicitação e escopo cada build conforme seus requisitos.
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.