audio embeddings api

If you mean an API for creating embeddings from audio (so you can search, compare, cluster, or classify sounds), there are a few common options depending on your stack:

1) OpenAI

OpenAI does not currently provide a dedicated public “audio embeddings” endpoint in the same way as text embeddings.
Typical approach:

• transcribe audio first with Whisper / speech-to-text
• then embed the transcript with a text embeddings model

This works well for spoken audio, but not for non-speech sounds or music.

2) Self-hosted / open-source audio embedding models

Good if you need embeddings for:

• speech similarity
• speaker identification
• music/audio event search
• general audio classification

Popular models:

• CLAP (Contrastive Language-Audio Pretraining) — great for text-audio matching and audio retrieval
• wav2vec 2.0 / HuBERT — strong speech/audio representations
• VGGish — older but common for general audio features
• OpenL3 — general-purpose audio embeddings
• BEATs — strong audio understanding embeddings

You can wrap these in your own API using:

• FastAPI
• Flask
• TorchServe
• BentoML

3) Cloud vendor APIs

Some providers offer audio/speech features but not always direct embeddings:

• Google Cloud Speech-to-Text
• AWS Transcribe
• Azure Speech Usually used for transcription, then embeddings are built on the text.

4) Vector databases for storing embeddings

Once you have embeddings, store/search them with:

• Pinecone
• Weaviate
• Milvus
• FAISS
• Qdrant

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Typical architecture

1. Upload audio
2. Convert to waveform / standard sample rate
3. Run audio embedding model
4. Store vector in DB
5. Query with new audio or text

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Example: Python with CLAP

import torch
import laion_clap

model = laion_clap.CLAP_Module(enable_fusion=False)
model.load_ckpt()

audio_files = ["example.wav"]
embeddings = model.get_audio_embedding_from_filelist(audio_files)
print(embeddings.shape)

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If you want, I can help with one of these specifically:

• OpenAI-based workflow
• best open-source audio embedding model
• build a FastAPI audio embeddings service
• search by audio similarity
• search audio with text