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get rid of hop_size_ratio

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adamnsandle
2025-08-26 06:53:53 +00:00
parent 34dea51680
commit 11631356a2
1 changed files with 8 additions and 7 deletions
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15
src/silero_vad/utils_vad.py
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@@ -202,7 +202,6 @@ def get_speech_timestamps(audio: torch.Tensor,
progress_tracking_callback: Callable[[float], None] = None,
neg_threshold: float = None,
window_size_samples: int = 512,
hop_size_ratio: float = 1,
min_silence_at_max_speech: float = 98,
use_max_poss_sil_at_max_speech: bool = True):
@@ -252,12 +251,14 @@ def get_speech_timestamps(audio: torch.Tensor,
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.
min_silence_at_max_speech: float (default - 98ms)
Minimum silence duration in ms which is used to avoid abrupt cuts when max_speech_duration_s is reached
use_max_poss_sil_at_max_speech: bool (default - True)
Whether to use the maximum possible silence at max_speech_duration_s or not. If not, the last silence is used.
window_size_samples: int (default - 512 samples)
!!! DEPRECATED, DOES NOTHING !!!
hop_size_ratio: float (default - 1), number of samples by which the window is shifted, 1 means hop_size_samples = window_size_samples
min_silence_at_max_speech: float (default - 25ms), minimum silence duration in ms which is used to avoid abrupt cuts when max_speech_duration_s is reached
use_max_poss_sil_at_max_speech: bool (default - True), whether to use the maximum possible silence at max_speech_duration_s or not. If not, the last silence is used.
Returns
----------
@@ -288,7 +289,7 @@ def get_speech_timestamps(audio: torch.Tensor,
raise ValueError("Currently silero VAD models support 8000 and 16000 (or multiply of 16000) sample rates")
window_size_samples = 512 if sampling_rate == 16000 else 256
hop_size_samples = int(window_size_samples * hop_size_ratio)
hop_size_samples = int(window_size_samples)
model.reset_states()
min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
@@ -326,7 +327,7 @@ def get_speech_timestamps(audio: torch.Tensor,
temp_end = 0 # to save potential segment end (and tolerate some silence)
prev_end = next_start = 0 # to save potential segment limits in case of maximum segment size reached
possible_ends = []
for i, speech_prob in enumerate(speech_probs):
if (speech_prob >= threshold) and temp_end:
if temp_end != 0: