hearing.vad

Voice-activity detection and utterance segmentation — re-exported from scribed.

The VAD logic (energy/neural detectors + utterance segmentation) is now owned by scribed.vad so it can be shared across every scribed consumer. hearing keeps this module as the stable import path; the names below are scribed’s.

See the hearing-live-pipeline skill for the rationale and the latency budget.

Functions

segment_utterances(frames, *, sample_rate[, ...])

Group an async stream of audio blocks into utterances.

Classes

VAD(*args, **kwargs)	Decides whether a block of audio contains speech.
EnergyVAD([threshold])	RMS-energy threshold VAD — the dependency-free default.
SileroVAD([speech_prob_threshold])	Neural VAD via silero-vad (optional, lazy).

class hearing.vad.EnergyVAD(threshold=0.01)

Bases: object

RMS-energy threshold VAD — the dependency-free default.

A block counts as speech when its RMS energy exceeds threshold (in the same units as the float audio, roughly [-1, 1]). Simple and deterministic; for noisy real-world audio prefer SileroVAD.

is_speech(block, sample_rate)

True if the block’s RMS energy is above threshold.

Return type:

bool

class hearing.vad.SileroVAD(speech_prob_threshold=0.5)

Bases: object

Neural VAD via silero-vad (optional, lazy). Accurate on noisy audio.

Requires pip install silero-vad (or torch.hub). Raises an informative error if unavailable; EnergyVAD needs no dependencies.

is_speech(block, sample_rate)

True if silero’s speech probability for the block exceeds the threshold.

Return type:

bool

class hearing.vad.VAD(*args, **kwargs)

Bases: Protocol

Decides whether a block of audio contains speech.

is_speech(block, sample_rate)

True if block (mono float32) contains speech.

Return type:

bool

async hearing.vad.segment_utterances(frames, *, sample_rate, vad=None, silence_ms=700, min_speech_ms=200, max_utterance_ms=30000)

Group an async stream of audio blocks into utterances.

Yields (utterance_samples, start_ms) once per finalized utterance: speech accumulates into a buffer; after silence_ms of trailing silence (or max_utterance_ms of continuous speech) the buffer is emitted with its start offset (integer ms from the beginning of the stream). Utterances with less than min_speech_ms of actual speech are dropped as noise. sample_rate is the rate of the incoming blocks (used only for the ms arithmetic).

Return type:

AsyncIterator[Tuple[ndarray, int]]