AudioVector — AI audio embeddings
Create Audio Embeddings & AI Audio Vectors
AudioVector turns your tracks into 512-dimensional Microsoft CLAP audio embeddings — dense AI audio vectors for semantic similarity search, catalog matching, and recommendation pipelines.
Buy credits, share MP3, WAV, or FLAC files, and receive embedding JSON within one business day — ready for Pinecone, Qdrant, pgvector, Weaviate, Chroma, and Milvus.
Uploaded tracks are deleted after processing in line with our AudioVector Terms.
How it works
Three steps to create audio embeddings for your catalog.
1
Buy credits
One credit creates one AI audio vector. Choose a pack sized for your music library or sound archive.
2
Share your tracks
Upload or grant access to audio files — MP3, WAV, FLAC, AIFF, M4A, AAC — and we generate CLAP embeddings for each track.
3
Receive embedding JSON
Within one business day, 512-dimensional audio embedding JSON arrives by email — cosine-similarity ready vectors for your database.
Example output
Sample AI audio vector output (512 dimensions).
Each track returns a JSON object with the track name and a 512-dimensional CLAP embedding — a fixed-length audio vector encoding timbre, rhythm, and acoustic texture for similarity search.
All tracks together
[
{
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What AudioVector does
Create audio embeddings with AI.
Create audio embeddings with AI.
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512-Dim CLAP Audio Embeddings
Every track is encoded with Microsoft's CLAP (Contrastive Language-Audio Pretraining) model — a leading audio embedding model that maps sound into 512-dimensional AI audio vectors for semantic search and zero-shot classification.
What is an audio vector? →Vectorize Your Music Catalog
Batch-create audio embeddings from MP3, WAV, FLAC, AIFF, M4A, and AAC files — one credit per track. Ideal for labels, libraries, and developers building audio similarity search.
How audio embeddings are generated →Email Delivery
Embedding JSON files are delivered to the email address on your order. Each file contains the track name and full vector array — ready to import into your pipeline.
Building similarity search with vectors →One Business Day Turnaround
Processing typically completes within one business day from when we receive your tracks. Larger batches may take longer — we'll confirm timing when you submit.
AudioVector Terms →Audio Similarity Search Ready
Embedding JSON plugs into Pinecone, Qdrant, Postgres pgvector, Weaviate, Chroma, and Milvus. Build "sounds like this" search, duplicate detection, and music recommendation on top of your vectors.
Vector databases for audio search →Secure Handling
Your source audio is used only to generate embeddings and is deleted after processing, in accordance with our AudioVector Terms. We do not retain or redistribute your tracks.
What is audio similarity search? →
Pricing
Buy credits to create audio embeddings.
One credit. One track. One AI audio vector. €0.05 per embedding.
Starter
100 Credits
€5
- 100 AI audio vectors
- 512-dim CLAP audio embeddings
- Email delivery
- ~1 business day turnaround
Studio
200 Credits
€10
- 200 AI audio vectors
- 512-dim CLAP audio embeddings
- Email delivery
- ~1 business day turnaround
Catalog
500 Credits
€25
- 500 AI audio vectors
- 512-dim CLAP audio embeddings
- Email delivery
- ~1 business day turnaround
Need more than 500 tracks? Contact us for custom volume pricing.
By purchasing credits you agree to our AudioVector Terms & Conditions.
Turnaround
1 business day
typical delivery
AI Model
Microsoft CLAP
512 Dimensions
Audio Formats
MP3 320 kbps, WAV
FLAC, AIFF, M4A, AAC
Delivery
JSON to your
email inbox
Retention
Tracks deleted
after processing
Common Questions
Frequently Asked Questions
What is AudioVector?
AudioVector is a service to create audio embeddings and AI audio vectors from your music or sound catalog. We use Microsoft's CLAP model to generate 512-dimensional vectors and deliver them as JSON — ready for Pinecone, Qdrant, pgvector, and other vector databases.
How do I create audio embeddings for my tracks?
Purchase credits, share your audio files (MP3, WAV, FLAC, and more), and we vectorize each track with CLAP. You receive 512-dimensional embedding JSON by email within one business day — no GPU setup or ML pipeline required.
What are AI audio vectors?
An AI audio vector is a fixed-length numerical embedding that encodes the semantic content of a sound — timbre, rhythm, energy, and texture. CLAP maps each track to a 512-dimensional vector so you can compare tracks with cosine similarity and power audio similarity search.
How do credits work?
One credit vectorizes one track. Purchase a credit pack, share your audio files with us, and we process each file into a separate embedding JSON. Unused credits remain on your account for future batches.
How long does processing take?
Processing typically completes within one business day from when we receive your tracks. Business days are Monday through Friday, excluding UK public holidays. Larger catalogs may require additional time — we'll confirm when you submit.
How do I receive my embeddings?
Embedding JSON files are delivered to the email address associated with your purchase. Each file contains the track name and the full 512-dimensional vector array.
What happens to my audio files?
Your source tracks are used solely to generate embeddings and are deleted after processing, in line with our AudioVector Terms & Conditions. We do not retain, sell, or redistribute your audio.
What audio formats do you accept?
We accept MP3 (including 320 kbps), WAV, FLAC, AIFF, M4A, AAC, and other common audio formats. Share your files after purchase — one credit vectorizes one track.
Which vector databases work with AudioVector's JSON output?
Any vector database that accepts 512-dimensional float arrays: Pinecone, Qdrant, Postgres pgvector, Weaviate, Chroma, and Milvus. Use embeddings for music library similarity search, duplicate detection, and semantic audio retrieval.
What is CLAP and why use it for audio embeddings?
CLAP (Contrastive Language-Audio Pretraining) is a Microsoft audio embedding model trained to align audio and text in a shared vector space. It produces high-quality embeddings for similarity search, zero-shot audio classification, and multimodal retrieval — the same family of models used in modern music intelligence pipelines.