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vendor/ruvector/examples/vibecast-7sense/crates/sevensense-embedding/README.md
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# sevensense-embedding
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[](https://crates.io/crates/sevensense-embedding)
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[](https://docs.rs/sevensense-embedding)
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[](../../LICENSE)
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> Neural embedding generation using Perch 2.0 for bioacoustic analysis.
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**sevensense-embedding** transforms audio segments into rich 1536-dimensional embedding vectors using Google's Perch 2.0 model via ONNX Runtime. These embeddings capture the acoustic essence of bird vocalizations, enabling similarity search, clustering, and species identification.
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## Features
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- **Perch 2.0 Integration**: State-of-the-art bird audio embeddings
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- **ONNX Runtime**: Cross-platform GPU/CPU inference
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- **1536-Dimensional Vectors**: Rich semantic representation
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- **Batch Processing**: Efficient multi-segment inference
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- **Product Quantization (PQ)**: 4x memory reduction for storage
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- **L2 Normalization**: Optimized for cosine similarity search
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## Use Cases
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| Use Case | Description | Key Functions |
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|----------|-------------|---------------|
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| Single Inference | Embed one audio segment | `embed()` |
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| Batch Processing | Embed multiple segments efficiently | `embed_batch()` |
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| Streaming | Real-time embedding generation | `EmbeddingStream::new()` |
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| Quantization | Compress embeddings for storage | `quantize_pq()` |
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| Validation | Verify embedding quality | `validate()` |
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## Installation
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Add to your `Cargo.toml`:
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```toml
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[dependencies]
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sevensense-embedding = "0.1"
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```
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### ONNX Model Setup
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The Perch 2.0 ONNX model is automatically downloaded on first use. For manual setup:
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```bash
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# Download model manually
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curl -L https://example.com/perch-2.0.onnx -o models/perch-2.0.onnx
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```
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## Quick Start
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```rust
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use sevensense_embedding::{EmbeddingPipeline, EmbeddingConfig};
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use sevensense_audio::AudioLoader;
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Initialize the embedding pipeline
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let config = EmbeddingConfig::default();
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let pipeline = EmbeddingPipeline::new(config).await?;
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// Load audio and generate embedding
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let audio = AudioLoader::load("birdsong.wav").await?;
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let embedding = pipeline.embed(&audio).await?;
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println!("Embedding dimension: {}", embedding.len()); // 1536
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println!("L2 norm: {:.4}", embedding.iter().map(|x| x*x).sum::<f32>().sqrt());
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Ok(())
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}
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```
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---
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<details>
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<summary><b>Tutorial: Basic Embedding Generation</b></summary>
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### Single Audio Embedding
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```rust
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use sevensense_embedding::{EmbeddingPipeline, EmbeddingConfig};
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Create pipeline with default config
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let pipeline = EmbeddingPipeline::new(EmbeddingConfig::default()).await?;
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// Embed from mel spectrogram
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let mel = compute_mel_spectrogram(&audio)?;
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let embedding = pipeline.embed_mel(&mel).await?;
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// Embedding properties
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assert_eq!(embedding.len(), 1536);
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// L2 normalized by default
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let norm: f32 = embedding.iter().map(|x| x*x).sum::<f32>().sqrt();
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assert!((norm - 1.0).abs() < 1e-5);
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Ok(())
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}
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```
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### From Raw Audio
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```rust
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use sevensense_embedding::EmbeddingPipeline;
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use sevensense_audio::AudioLoader;
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let audio = AudioLoader::load("recording.wav").await?;
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let pipeline = EmbeddingPipeline::new(Default::default()).await?;
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// Pipeline handles mel spectrogram computation internally
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let embedding = pipeline.embed_audio(&audio).await?;
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```
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</details>
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<details>
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<summary><b>Tutorial: Batch Processing</b></summary>
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### Efficient Batch Embedding
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```rust
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use sevensense_embedding::{EmbeddingPipeline, BatchConfig};
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let pipeline = EmbeddingPipeline::new(Default::default()).await?;
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// Configure batching
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let batch_config = BatchConfig {
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batch_size: 32, // Process 32 segments at once
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max_concurrent: 4, // 4 concurrent batches
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prefetch: true, // Prefetch next batch
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};
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// Embed multiple segments
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let segments = load_segments("recordings/")?;
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let embeddings = pipeline.embed_batch(&segments, batch_config).await?;
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println!("Generated {} embeddings", embeddings.len());
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```
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### Progress Tracking
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```rust
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use sevensense_embedding::EmbeddingPipeline;
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let pipeline = EmbeddingPipeline::new(Default::default()).await?;
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let embeddings = pipeline.embed_batch_with_progress(&segments, |progress| {
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println!("Progress: {}/{} ({:.1}%)",
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progress.completed,
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progress.total,
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progress.percentage());
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}).await?;
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```
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### Parallel Processing
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```rust
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use sevensense_embedding::EmbeddingPipeline;
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use futures::stream::{self, StreamExt};
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let pipeline = Arc::new(EmbeddingPipeline::new(Default::default()).await?);
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let embeddings: Vec<_> = stream::iter(segments)
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.map(|seg| {
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let pipeline = Arc::clone(&pipeline);
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async move { pipeline.embed(&seg).await }
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})
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.buffer_unordered(8) // 8 concurrent embeddings
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.collect()
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.await;
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```
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</details>
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<details>
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<summary><b>Tutorial: Embedding Quantization</b></summary>
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### Product Quantization (PQ)
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Product Quantization reduces embedding size by 4x while maintaining search quality.
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```rust
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use sevensense_embedding::{EmbeddingPipeline, ProductQuantizer};
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let pipeline = EmbeddingPipeline::new(Default::default()).await?;
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// Generate embeddings
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let embeddings: Vec<Vec<f32>> = generate_embeddings(&segments).await?;
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// Train PQ codebook on embeddings
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let pq = ProductQuantizer::train(&embeddings, 96, 256)?; // 96 subvectors, 256 centroids
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// Quantize embeddings
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let quantized: Vec<Vec<u8>> = embeddings.iter()
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.map(|e| pq.encode(e))
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.collect();
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// Memory reduction
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let original_size = embeddings.len() * 1536 * 4; // f32 = 4 bytes
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let quantized_size = quantized.len() * 96; // u8 per subvector
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println!("Compression ratio: {:.1}x", original_size as f32 / quantized_size as f32);
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// Output: Compression ratio: 64.0x
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```
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### Asymmetric Distance Computation
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```rust
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use sevensense_embedding::ProductQuantizer;
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// Query embedding (full precision)
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let query = pipeline.embed(&query_audio).await?;
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// Compute distances to quantized vectors
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let distances: Vec<f32> = quantized.iter()
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.map(|q| pq.asymmetric_distance(&query, q))
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.collect();
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// Find nearest neighbors
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let mut indexed: Vec<_> = distances.iter().enumerate().collect();
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indexed.sort_by(|a, b| a.1.partial_cmp(b.1).unwrap());
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let top_10: Vec<_> = indexed.iter().take(10).collect();
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```
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</details>
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<details>
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<summary><b>Tutorial: Model Configuration</b></summary>
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### Custom ONNX Configuration
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```rust
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use sevensense_embedding::{EmbeddingConfig, ExecutionProvider};
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let config = EmbeddingConfig {
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model_path: "models/perch-2.0.onnx".into(),
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execution_provider: ExecutionProvider::CUDA, // GPU acceleration
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num_threads: 4, // CPU threads (if CPU)
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normalize: true, // L2 normalize output
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warmup: true, // Warmup inference
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};
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let pipeline = EmbeddingPipeline::new(config).await?;
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```
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### Execution Providers
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```rust
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use sevensense_embedding::ExecutionProvider;
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// CPU (default)
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let cpu_config = EmbeddingConfig {
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execution_provider: ExecutionProvider::CPU,
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..Default::default()
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};
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// CUDA (NVIDIA GPU)
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let cuda_config = EmbeddingConfig {
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execution_provider: ExecutionProvider::CUDA,
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..Default::default()
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};
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// CoreML (Apple Silicon)
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let coreml_config = EmbeddingConfig {
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execution_provider: ExecutionProvider::CoreML,
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..Default::default()
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};
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```
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### Memory Optimization
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```rust
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use sevensense_embedding::{EmbeddingConfig, MemoryConfig};
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let config = EmbeddingConfig {
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memory: MemoryConfig {
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arena_extend_strategy: ArenaExtendStrategy::NextPowerOfTwo,
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initial_chunk_size: 1024 * 1024, // 1MB
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max_chunk_size: 16 * 1024 * 1024, // 16MB
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},
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..Default::default()
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};
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```
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</details>
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<details>
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<summary><b>Tutorial: Embedding Validation</b></summary>
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### Quality Checks
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```rust
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use sevensense_embedding::{EmbeddingValidator, ValidationResult};
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let validator = EmbeddingValidator::new();
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let embedding = pipeline.embed(&audio).await?;
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let result = validator.validate(&embedding)?;
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match result {
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ValidationResult::Valid => println!("Embedding is valid"),
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ValidationResult::Invalid(reasons) => {
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for reason in reasons {
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eprintln!("Invalid: {}", reason);
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}
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}
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}
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```
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### Validation Criteria
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```rust
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use sevensense_embedding::{ValidationCriteria, EmbeddingValidator};
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let criteria = ValidationCriteria {
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expected_dim: 1536,
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max_nan_ratio: 0.0, // No NaN values allowed
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max_inf_ratio: 0.0, // No Inf values allowed
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min_variance: 1e-6, // Minimum variance threshold
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norm_range: (0.99, 1.01), // Expected L2 norm range
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};
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let validator = EmbeddingValidator::with_criteria(criteria);
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```
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### Batch Validation
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```rust
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let results = validator.validate_batch(&embeddings);
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let valid_count = results.iter().filter(|r| r.is_valid()).count();
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let invalid_count = results.len() - valid_count;
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println!("{} valid, {} invalid embeddings", valid_count, invalid_count);
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```
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</details>
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---
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## Configuration
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### EmbeddingConfig Parameters
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `model_path` | Auto-download | Path to ONNX model |
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| `execution_provider` | CPU | CUDA, CoreML, or CPU |
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| `num_threads` | 4 | CPU inference threads |
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| `normalize` | true | L2 normalize embeddings |
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| `warmup` | true | Run warmup inference |
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### Model Specifications
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| Property | Value |
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|----------|-------|
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| Input | Mel spectrogram [batch, 128, 312] |
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| Output | Embedding vector [batch, 1536] |
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| Model Size | ~25 MB |
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| Inference Time | ~15ms (CPU) / ~3ms (GPU) |
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## Performance
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| Operation | CPU (i7-12700) | GPU (RTX 3080) |
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|-----------|----------------|----------------|
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| Single Inference | 15ms | 3ms |
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| Batch (32) | 120ms | 20ms |
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| Throughput | 260/s | 1600/s |
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## Links
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- **Homepage**: [ruv.io](https://ruv.io)
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- **Repository**: [github.com/ruvnet/ruvector](https://github.com/ruvnet/ruvector)
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- **Crates.io**: [crates.io/crates/sevensense-embedding](https://crates.io/crates/sevensense-embedding)
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- **Documentation**: [docs.rs/sevensense-embedding](https://docs.rs/sevensense-embedding)
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## License
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MIT License - see [LICENSE](../../LICENSE) for details.
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---
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*Part of the [7sense Bioacoustic Intelligence Platform](https://ruv.io) by rUv*
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