Audio Embeddings: Finding the Sound

Just as an image is a collection of pixels, audio is a collection of Frequencies over Time. To search audio, we don't look at the raw waveform; we look at the Spectral Signature.

In this lesson, we explore how to turn sound into searchable vectors for music recommendation, voice matching, and acoustic monitoring.

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1. The Anatomy of an Audio Vector

To represent sound in a vector database, models typically look at:

1. Timbre: The "Texture" of the sound (e.g., Violin vs. Trumpet).
2. Pitch: The fundamental frequencies.
3. Rhythm: The pattern of energy over time.

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2. Models for Audio Search

Several architectures have revolutionized audio vector search:

• CLAP (Contrastive Language-Audio Pretraining): Like CLIP for images, CLAP maps audio and text into the same space. You can search for "Rain falling on a tin roof" and find the exact audio file.
• Wav2Vec 2.0: Primarily for speech; it creates vectors that capture phonetic meaning.
• VGGish: A model trained to classify environmental sounds (Barking, Beeping, Breaking glass).

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3. Implementation: Audio to Vector (Python)

Using the transformers library to extract features from a sound file:

from transformers import AutoProcessor, ClapModel
import librosa
import torch

# Load CLAP Model
model = ClapModel.from_pretrained("laion/clap-htsat-fused")
processor = AutoProcessor.from_pretrained("laion/clap-htsat-fused")

# 1. Load Audio file
audio, sample_rate = librosa.load("bark.wav", sr=48000)

# 2. Preprocess and Generate Vector
inputs = processor(audios=audio, return_tensors="pt", sampling_rate=48000)
with torch.no_grad():
    audio_embeds = model.get_audio_features(**inputs)

# 3. audio_embeds is now your vector for the database!
print(audio_embeds.shape) # Likely a 512 or 768 dimensional vector

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4. Real-World Use Cases

• Copyright Detection: Finding songs that sound like other copyrighted songs.
• Voice Identity: Retrieving user profiles based on a short voice snippet.
• Anomaly Detection: In factory maintenance, searching a database of "Piston sounds" to find matches with "Failing hardware."

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5. Summary and Key Takeaways

1. Spectral Meaning: We embed the frequencies and patterns, not the raw wave.
2. CLAP and Text: Multimodal audio-text models are the current state-of-the-art for search.
3. Sample Rate Matters: Ensure your sample rate (e.g., 44.1kHz vs 48kHz) matches the model's training data for accurate vectors.
4. Windowing: For long files, you may need to "Chunk" the audio (10-second segments) and store each as a separate vector.

In the next lesson, we’ll tackle the most complex modality: Video and Multi-page Document Embeddings.

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Congratulations on completing Module 13 Lesson 2! You are now building auditory AI systems.