Speaker Embeddings

Extract 256-dimensional L2-normalized speaker vectors using WeSpeaker ResNet34-LM. These embeddings capture the unique vocal characteristics of a speaker and can be used for identification, verification, and voice search.

Architecture

WeSpeaker ResNet34-LM is a deep residual network trained for speaker representation learning.

Model size: ~6.6M parameters, ~25 MB on disk.

Mel Features

The model uses 80-dimensional mel-frequency features computed with a Hamming window. Log scaling uses a simple log(max(mel, 1e-10)) formula without additional normalization. Batch normalization is fused into the Conv2d layers at conversion time for inference efficiency.

CLI Usage

# Extract speaker embedding
.build/release/audio embed-speaker voice.wav

# JSON output (includes the 256-dim vector)
.build/release/audio embed-speaker voice.wav --json

# Choose inference engine
.build/release/audio embed-speaker voice.wav --engine coreml

Options

Use Cases

Speaker Verification

Compare two audio samples to determine if they are from the same speaker. Extract embeddings from both and compute the cosine similarity. A higher similarity score indicates a higher probability of the same speaker.

import SpeechVAD

let model = try await WeSpeaker.loadFromHub()
let embedding1 = try await model.embed(audioFile: "sample1.wav")
let embedding2 = try await model.embed(audioFile: "sample2.wav")

let similarity = cosineSimilarity(embedding1, embedding2)
print("Similarity: \(similarity)")  // > 0.7 typically same speaker

Speaker Identification

Match an unknown audio sample against a database of enrolled speaker embeddings. The enrolled speaker with the highest cosine similarity is the predicted identity.

Voice Search

Index a collection of audio recordings by speaker embedding, then query with a new audio sample to find all recordings from the same speaker.

Model Downloads

Swift API

import SpeechVAD

let model = try await WeSpeaker.loadFromHub()

// Extract embedding from file
let embedding = try await model.embed(audioFile: "voice.wav")
print("Embedding dimensions: \(embedding.count)")  // 256

// Extract embedding from audio samples
let samples: [Float] = loadAudio("voice.wav")
let embedding = try await model.embed(samples: samples, sampleRate: 16000)