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[sunnnnnnnny]

❓ Questions and Help

If i want to vad and keep little silence with chunk_wav's begining and ending, how do it?

[snakers4]

silero-vad/src/silero_vad/utils_vad.py

Lines 187 to 251 in e531cd3

	def get_speech_timestamps(audio: torch.Tensor,
	model,
	threshold: float = 0.5,
	sampling_rate: int = 16000,
	min_speech_duration_ms: int = 250,
	max_speech_duration_s: float = float('inf'),
	min_silence_duration_ms: int = 100,
	speech_pad_ms: int = 30,
	return_seconds: bool = False,
	visualize_probs: bool = False,
	progress_tracking_callback: Callable[[float], None] = None,
	neg_threshold: float = None,
	window_size_samples: int = 512,):
	"""
	This method is used for splitting long audios into speech chunks using silero VAD
	Parameters
	----------
	audio: torch.Tensor, one dimensional
	One dimensional float torch.Tensor, other types are casted to torch if possible
	model: preloaded .jit/.onnx silero VAD model
	threshold: float (default - 0.5)
	Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH.
	It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
	sampling_rate: int (default - 16000)
	Currently silero VAD models support 8000 and 16000 (or multiply of 16000) sample rates
	min_speech_duration_ms: int (default - 250 milliseconds)
	Final speech chunks shorter min_speech_duration_ms are thrown out
	max_speech_duration_s: int (default - inf)
	Maximum duration of speech chunks in seconds
	Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100ms (if any), to prevent agressive cutting.
	Otherwise, they will be split aggressively just before max_speech_duration_s.
	min_silence_duration_ms: int (default - 100 milliseconds)
	In the end of each speech chunk wait for min_silence_duration_ms before separating it
	speech_pad_ms: int (default - 30 milliseconds)
	Final speech chunks are padded by speech_pad_ms each side
	return_seconds: bool (default - False)
	whether return timestamps in seconds (default - samples)
	visualize_probs: bool (default - False)
	whether draw prob hist or not
	progress_tracking_callback: Callable[[float], None] (default - None)
	callback function taking progress in percents as an argument
	neg_threshold: float (default = threshold - 0.15)
	Negative threshold (noise or exit threshold). If model's current state is SPEECH, values BELOW this value are considered as NON-SPEECH.
	window_size_samples: int (default - 512 samples)
	!!! DEPRECATED, DOES NOTHING !!!
	Returns
	----------
	speeches: list of dicts
	list containing ends and beginnings of speech chunks (samples or seconds based on return_seconds)
	"""

try fiddling with min_silence_duration_ms and speech_pad_ms.