fasr-vad-silero 0.5.2

Silero VAD model for fasr

fasr-vad-silero

Chinese documentation

Streaming Silero VAD for fasr. The model is loaded through torch.hub and wrapped as a fasr streaming VADModel that emits AudioChunk objects with segment_start, segment_mid, and segment_end states.

Install

pip install fasr-vad-silero

Registered Model

Registry name	Class	Best for
stream_silero	SileroStreamVAD	Lightweight streaming VAD

Streaming Usage

from fasr.config import registry

vad = registry.vad_models.get("stream_silero")(
    threshold=0.5,
    silence_duration_ms=400,
)

for input_chunk in audio_chunks:
    for speech_chunk in vad.push_chunk(input_chunk):
        print(speech_chunk.vad_state, speech_chunk.start_ms, speech_chunk.end_ms)

Quick choices:

Goal	Use	Result
Reduce noise triggers	threshold=0.65	Requires higher speech probability
Keep quiet speech	threshold=0.35	More sensitive, with more false-positive risk
End speech sooner	silence_duration_ms=200	Lower endpoint latency
Avoid chopping pauses	silence_duration_ms=700	More tolerant of short pauses

Confection Config

[vad_model]
@vad_models = "stream_silero"
threshold = 0.5
silence_duration_ms = 400
sample_rate = 16000
chunk_size_ms = 32

Parameters

Parameter	Type / range	Default	Higher value	Lower value	Change when
threshold	float, 0.0 to 1.0	0.5	More conservative; fewer noise starts	More sensitive; more weak speech	Noise triggers starts, or quiet speech is missed
silence_duration_ms	int >= 0	400	Longer pauses before ending speech	Faster endpoint	Speech is chopped, or endpoint is late
sample_rate	int	16000	Keep at 16 kHz for Silero	Keep at 16 kHz for Silero	Usually do not change
chunk_size_ms	int	32	Larger input chunks, fewer calls	Smaller chunks, lower latency	Realtime scheduling needs tuning

Tuning Guide

Symptom	Try first
Background noise starts speech	Raise threshold to 0.6 or 0.7
Quiet speech is missed	Lower threshold to 0.35 or 0.4
Speech ends too late	Lower silence_duration_ms to 200 or 300
Speech is split during pauses	Raise silence_duration_ms to 600 or 800

Dependencies

• fasr
• numpy >= 1.24
• torch >= 2.0.0
• Python 3.10-3.12