silero-vad

What it is and what it does

Silero VAD is a pre-trained neural network model that identifies when speech is present in audio. It takes audio input and returns timestamps marking where voice activity occurs, useful for filtering silence, segmenting conversations, or triggering downstream processing only when speech is detected.

The package wraps PyTorch models (or ONNX alternatives) and handles audio loading via torchaudio, supporting 8000 Hz and 16000 Hz sampling rates. It's designed for production use: the model is around two megabytes, processes audio chunks in under 1ms on CPU, and was trained on diverse multilingual data. You can run it on CPU, GPU, or via ONNX runtime on various architectures.

Use it for:

• Filter silence from voice recordings or call center logs before transcription or analysis.
• Trigger voice bot responses only when speech is detected, reducing false activations.
• Segment long audio files into speech regions for downstream processing (transcription, speaker diarization).
• Implement voice interfaces on edge devices or mobile by detecting when a user starts speaking.
• Clean training datasets by identifying and extracting only speech-containing portions.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Silero VAD detects speech activity in audio files and streams, identifying when voice is present and returning timestamps of speech segments.

Yes. Active maintenance, no known vulnerabilities, permissive MIT license, low install friction, and production-grade accuracy make it a solid choice for any speech detection task. The main gotcha is ensuring an audio backend (FFmpeg, sox, or soundfile) is available on your deployment target—verify that before committing to it in a containerized or embedded environment.

Install

silero-vad on PyPI

pip install silero-vad

uv add silero-vad

poetry add silero-vad

Installing silero-vad

Before you install

Low friction: pure Python wheel with no compiled dependencies beyond torch and torchaudio. Active maintenance with recent commits and strong repository engagement.

License in practice

MIT license (permissive) means you can use, modify, and distribute freely with minimal restrictions. No telemetry, vendor lock-in, or expiration built in.

Quickstart

pip install silero-vad

from silero_vad import load_silero_vad, read_audio, get_speech_timestamps
model = load_silero_vad()
wav = read_audio('audio.wav')
speech_timestamps = get_speech_timestamps(wav, model, return_seconds=True)

Requires torch>=1.12.0 and torchaudio>=0.12.0; also needs an audio backend (FFmpeg, sox, or soundfile) installed on the system for audio I/O.

Verify before relying

• Whether the package's claimed support for over 6000 languages is validated independently or represents training data scope only.
• Exact CPU instruction set requirements (AVX, AVX2, AVX-512, AMX) and fallback behavior on older hardware.
• Whether audio backend (FFmpeg, sox, or soundfile) installation is automatic or manual for each deployment target.

Package facts

Evidence: silero_vad-6.2.1-py3-none-any.whl

Tags

More Scientific/Engineering packages