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add neg_threshold parameter explicitly

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adamnsandle
2024-08-20 08:53:15 +00:00
parent 36854a90db
commit 4109b107c1
1 changed files with 15 additions and 9 deletions
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24
src/silero_vad/utils_vad.py
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@@ -53,10 +53,10 @@ class OnnxWrapper():
x, sr = self._validate_input(x, sr)
num_samples = 512 if sr == 16000 else 256
if x.shape[-1] != num_samples:
raise ValueError(f"Provided number of samples is {x.shape[-1]} (Supported values: 256 for 8000 sample rate, 512 for 16000)")
batch_size = x.shape[0]
context_size = 64 if sr == 16000 else 32
@@ -133,7 +133,7 @@ class Validator():
def read_audio(path: str,
sampling_rate: int = 16000):
list_backends = torchaudio.list_audio_backends()
assert len(list_backends) > 0, 'The list of available backends is empty, please install backend manually. \
\n Recommendations: \n \tSox (UNIX OS) \n \tSoundfile (Windows OS, UNIX OS) \n \tffmpeg (Windows OS, UNIX OS)'
@@ -195,6 +195,7 @@ def get_speech_timestamps(audio: torch.Tensor,
return_seconds: bool = False,
visualize_probs: bool = False,
progress_tracking_callback: Callable[[float], None] = None,
neg_threshold: float = None,
window_size_samples: int = 512,):
"""
@@ -237,6 +238,9 @@ def get_speech_timestamps(audio: torch.Tensor,
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 !!!
@@ -298,15 +302,17 @@ def get_speech_timestamps(audio: torch.Tensor,
triggered = False
speeches = []
current_speech = {}
neg_threshold = threshold - 0.15
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
if neg_threshold is None:
neg_threshold = threshold - 0.15
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
for i, speech_prob in enumerate(speech_probs):
if (speech_prob >= threshold) and temp_end:
temp_end = 0
if next_start < prev_end:
next_start = window_size_samples * i
next_start = window_size_samples * i
if (speech_prob >= threshold) and not triggered:
triggered = True
@@ -318,7 +324,7 @@ def get_speech_timestamps(audio: torch.Tensor,
current_speech['end'] = prev_end
speeches.append(current_speech)
current_speech = {}
if next_start < prev_end: # previously reached silence (< neg_thres) and is still not speech (< thres)
if next_start < prev_end: # previously reached silence (< neg_thres) and is still not speech (< thres)
triggered = False
else:
current_speech['start'] = next_start
@@ -334,7 +340,7 @@ def get_speech_timestamps(audio: torch.Tensor,
if (speech_prob < neg_threshold) and triggered:
if not temp_end:
temp_end = window_size_samples * i
if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech : # condition to avoid cutting in very short silence
if ((window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech: # condition to avoid cutting in very short silence
prev_end = temp_end
if (window_size_samples * i) - temp_end < min_silence_samples:
continue