Skip to main content

A CLI + Web tool for speaker enrollment and identification using SpeechBrain.

Project description

08/08: a lot of improvements to the speaker-detector-client which resulted in a few changes to this backend.

23/07/2025 - Lara Whybrow, Creator - it has a few bugs that need fixing, but I ma determining if it is data related or software related. Feel free to clone from Github and help with bug fixes.

speaker-detector 🎙️

A lightweight CLI tool for speaker enrollment and voice identification, powered by SpeechBrain.

🔧 Features

  • ✅ Enroll speakers from .wav audio
  • 🕵️ Identify speakers from audio samples
  • 🧠 ECAPA-TDNN embedding-based matching
  • 🎛️ Simple, fast command-line interface
  • 📁 Clean file storage in ~/.speaker-detector/
  • 🔊 Optional --verbose mode for debugging

Web UI note: The web client uses a guided-only enrollment flow (multiple short recordings). Quick enroll with a single clip has been removed to ensure model accuracy.

📦 Installation

pip install speaker-detector


When installing packages with a stale requirement file you might need to use:  pip install --break-system-packages soundfile to install on WSL Ubuntu environment.

Run this version with -m module flag if you are having issues with running server.py:
python3 -m speaker_detector.server

🚀 Example Usage

🎙️ Enroll a speaker:

speaker-detector record --enroll Lara

🕵️ Identify a speaker:

speaker-detector record --test

📋 List enrolled speakers:

speaker-detector list

🗂️ Project Structure

~/.speaker-detector/enrollments/ Saved .pt voice embeddings ~/.speaker-detector/recordings/ CLI-recorded .wav audio files

🧹 Clean vs Verbose Mode By default, warnings from speechbrain, torch, etc. are hidden for a clean CLI experience. To enable full logs & deprecation warnings:

speaker-detector --verbose identify samples/test_sample.wav

🛠 Requirements Python 3.8+ torch speechbrain numpy soundfile onnxruntime

Step Command When / Purpose Output
1. Export ECAPA Model to ONNX speaker-detector export-model --pt models/embedding_model.ckpt --out ecapa_model.onnx Run once unless model changes ecapa_model.onnx
2. Enroll Speaker speaker-detector enroll <speaker_id> <audio_path>
Example:
speaker-detector enroll Lara samples/lara1.wav
Run per new speaker Individual .pt files (e.g., Lara.pt)
3. Combine Embeddings speaker-detector combine --folder data/embeddings/ --out data/enrolled_speakers.pt After enrolling speakers enrolled_speakers.pt
4. Export Speakers to JSON speaker-detector export-speaker-json --pt data/enrolled_speakers.pt --out public/speakers.json For frontend use speakers.json
5. Identify Speaker speaker-detector identify samples/test_sample.wav Identify speaker from audio Console output: name + score
6. List Enrolled Speakers speaker-detector list-speakers Show all enrolled speakers Console output: list of IDs
Verbose Mode (optional) Add --verbose to any command:
speaker-detector --verbose identify samples/test_sample.wav
Show warnings, detailed logs Developer debug info

NB: When pushing to Github, do not include any .identifier files.

You can manually clean up stale embeddings that don’t match any existing speaker folder with a quick script:

Run inside your project root

cd storage/embeddings for f in *.pt; do speaker="${f%.pt}" if [ ! -d "../speakers/$speaker" ]; then echo "Deleting stale embedding: $f" rm "$f" fi done

HTTP API: Online & Detection State

This backend exposes simple endpoints to let a client know when the server is reachable and when live detection is ready to be polled.

Online (one-shot SSE)

  • Path: GET /api/online
  • Headers:
    • Content-Type: text/event-stream
    • Cache-Control: no-cache
    • Connection: keep-alive
    • Access-Control-Allow-Origin: http://localhost:5173 (override with env CLIENT_ORIGIN)
  • Behavior: immediately emits a single event and closes the stream.

Example event:

event: online
data: 1

This removes the need for heartbeat polling: as soon as the client connects, it can mark the backend as reachable.

Detection State (SSE)

  • Path: GET /api/detection-state
  • Emits an immediate state and then re-emits on changes; includes keep-alives.
  • Event name: detection
  • Data: running | stopped

Example stream excerpts:

event: detection
data: stopped

: keep-alive

event: detection
data: running

Clients can start polling /api/active-speaker only when the state is running, and pause when stopped.

Active Speaker (readiness semantics)

  • Path: GET /api/active-speaker
  • Responses:
    • When listening mode is OFF: 200 { "status": "disabled", "speaker": null, "confidence": null, "is_speaking": false }
    • When mode is ON but engine not yet ready (e.g., mic unavailable or loop not running): 200 { "status": "pending", ... }
    • When running and healthy: 200 with the usual payload including speaker, confidence, is_speaking, status: "listening", and optional suggested.

These semantics avoid red 503s in DevTools while still making state transitions explicit for the client.

Quick Examples

Curl (SSE streams)

# One-shot online event
curl -N -H 'Accept: text/event-stream' http://127.0.0.1:9000/api/online

# Detection state stream (emits running|stopped)
curl -N -H 'Accept: text/event-stream' http://127.0.0.1:9000/api/detection-state

Browser client (minimal)

// Reachability: mark backend online as soon as server is up
const online = new EventSource('http://127.0.0.1:9000/api/online');
online.addEventListener('online', () => {
  console.log('Backend online');
  online.close(); // one-shot
});

// Detection state: start/stop polling active speaker
let pollTimer = null;
function startPolling() {
  if (pollTimer) return;
  pollTimer = setInterval(async () => {
    try {
      const r = await fetch('http://127.0.0.1:9000/api/active-speaker');
      const j = await r.json();
      if (j.status === 'disabled' || j.status === 'pending') return; // wait
      console.log('Active:', j);
    } catch (e) {
      console.warn('poll failed', e);
    }
  }, 500);
}
function stopPolling() { clearInterval(pollTimer); pollTimer = null; }

const detect = new EventSource('http://127.0.0.1:9000/api/detection-state');
detect.addEventListener('detection', (ev) => {
  const state = (ev.data || '').trim();
  if (state === 'running') startPolling(); else stopPolling();
});

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

speaker_detector-0.2.4.tar.gz (83.1 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

speaker_detector-0.2.4-py3-none-any.whl (83.1 MB view details)

Uploaded Python 3

File details

Details for the file speaker_detector-0.2.4.tar.gz.

File metadata

  • Download URL: speaker_detector-0.2.4.tar.gz
  • Upload date:
  • Size: 83.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.3

File hashes

Hashes for speaker_detector-0.2.4.tar.gz
Algorithm Hash digest
SHA256 1a24d98f7ff5812e6cdd8ae9ba8c21616c754b73ca460f7b430ca9d9b8bb0a21
MD5 dec5419c151ac72229c8298f3bde78df
BLAKE2b-256 e269ba2f3f662605ada1a4c4aa78d442264d56db608b1085ee63d4c117e25d2c

See more details on using hashes here.

File details

Details for the file speaker_detector-0.2.4-py3-none-any.whl.

File metadata

File hashes

Hashes for speaker_detector-0.2.4-py3-none-any.whl
Algorithm Hash digest
SHA256 810670d99ec185d5fb8e9b5247f92bb2dbd62954fdc4c733e470a89b4d1ce9de
MD5 18126e98ee5a24fc53f5f1b313c81331
BLAKE2b-256 d791dd30a199cb9fbe28f216d455883903541450f4a8f8129ec8cdc5d2ed1754

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page