VocalID: A Lightweight Voice Authentication Toolkit

VocalID is a practical and lightweight voice authentication library built around ECAPA-TDNN speaker embeddings and a simple classification layer. It lets you train your own voice model, evaluate its performance, and verify identities from recorded or live audio to use voice as a biometric to unlock apps, applications and for privacy. The goal is to make voice verification simple to run, easy to extend, and stable across devices.

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Features

• ECAPA-TDNN speaker embeddings (speechbrain/spkrec-ecapa-voxceleb)
• Easy training workflow for positive (owner) and negative samples
• Evaluation with accuracy and a full classification report
• File-based verification and optional live microphone verification
• Clean CLI for training, testing and verification
• Modular, readable codebase
• Simple model storage using pickle
• Test suite included
• Simple FastAPI server for verification

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How It Works

1. Audio Processing Audio is loaded or recorded, resampled to the target rate, converted to mono and padded to a minimum length.

2. Embedding Extraction We extract fixed-dimensional embeddings using ECAPA-TDNN. These embeddings capture speaker-specific characteristics.

3. Training A logistic regression classifier is trained on positive and negative embeddings.

4. Verification When verifying a sample:

1. Extract the embedding
2. Run it through the model
3. Get a probability score
4. Compare with the threshold from config.py

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Package Structure

VocalID/
│
├── voice_verifier/
│   ├── trainer.py        # Training, evaluation, saving, loading
│   ├── verifier.py       # Verification from file or tensor
│   ├── embeddings.py     # ECAPA-TDNN embedding extractor
│   ├── audio_utils.py    # Audio loading / microphone recording
│   ├── config.py         # Threshold, sample rate, model config
│   ├── model_store.py    # Pickle storage helpers
│   ├── cli.py            # CLI interface
│
├── tests/                # Pytest suite
├── examples/             # Example scripts
├── requirements.txt
├── api/app.py            # Optional API server example
└── README.md

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Installation

For windows users

pip install vocalid

Or install from source:

git clone https://github.com/Khubaib8281/VocalID.git
cd VocalID
pip install -e .

For Linux Users

Also install;

apt-get install -y libportaudio2

Since, soundevice relies on libportaudio2

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Dataset Layout

Your dataset should be organized as:

dataset/
│
├── my_voice/            # Positive samples (your voice)
│   sample1.wav
│   sample2.wav
│   ...
│
└── other_voices/        # Negative samples (others)
    voice1.wav
    voice2.wav
    ...

Each sample should ideally be 4–6 seconds with varied tone, distance, and background conditions.

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Example Usage (Python)

Train

from vocalid.trainer import VoiceTrainer
import glob

pos_files = glob.glob("dataset/my_voice/*.wav")
neg_files = glob.glob("dataset/other_voices/*.wav")

trainer = VoiceTrainer()
trainer.train(pos_files, neg_files, save_path="my_voice_model.pkl")

Evaluate

trainer.load("my_voice_model.pkl")

test_pos = glob.glob("dataset/my_voice_test/*.wav")
test_neg = glob.glob("dataset/other_voices_test/*.wav")

metrics = trainer.evaluate(test_pos, test_neg)
print("Accuracy:", metrics["accuracy"])
print(metrics["report"])

Verify a file

from vocalid.verifier import VoiceVerifier

verifier = VoiceVerifier("my_voice_model.pkl")
ok, score = verifier.verify_file("verify_samples/unknown.wav")

print(ok, score)

Verify live audio

ok, score = verifier.verify_live(audio_tensor)
print(ok, score)

Live recording only works on systems with a real microphone. It will not run in cloud notebooks.

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CLI Usage

Train:

vocalid train --positive my_voice --negative others --output model.pkl

Evaluate:

vocalid evaluate --model model.pkl --positive my_voice --negative others

Verify a file:

vocalid verify sample.wav --model model.pkl

Live verification:

vocalid live --model model.pkl --seconds 4

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Use Cases

• Personal voice-unlock systems for apps, applications and systems
• Lightweight speaker verification
• Research in speaker embeddings
• Prototyping identity checks
• Classroom or research demonstrations
• Testing spoofing and adversarial audio

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Why This Matters

VocalID helps developers learn how practical speaker verification works without dealing with heavy frameworks. The library focuses on transparency, modularity and simplicity:

• Clear separation of embedding extraction and classification
• Easy to swap in a different classifier
• Works on CPU
• No special hardware needed for training

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Contributing

Pull requests are welcome. To run tests:

pytest -v

Feel free to open issues for bugs, improvement ideas, or feature requests.

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Author

Muhammad Khubaib Ahmad

AI/ML Research Engineer | Speech and Language Intelligence

• Portfolio:
• Email khubaib.ahmad@inference-lab.org
• GitHub
• LinkedIn
• Kaggle
• HuggingFace

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License

MIT License