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Merge pull request #708 from Purfview/patch-2

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Removes redundant hop_size_samples variable
This commit is contained in:
Alexander Veysov
2025-10-23 15:58:00 +03:00
committed by GitHub
parent a14a23faa7 81e8a48e25
commit df22fcaec8
1 changed files with 12 additions and 13 deletions
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25
src/silero_vad/utils_vad.py
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@@ -310,7 +310,6 @@ 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)
model.reset_states()
min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
@@ -322,14 +321,14 @@ def get_speech_timestamps(audio: torch.Tensor,
audio_length_samples = len(audio)
speech_probs = []
for current_start_sample in range(0, audio_length_samples, hop_size_samples):
for current_start_sample in range(0, audio_length_samples, window_size_samples):
chunk = audio[current_start_sample: current_start_sample + window_size_samples]
if len(chunk) < window_size_samples:
chunk = torch.nn.functional.pad(chunk, (0, int(window_size_samples - len(chunk))))
speech_prob = model(chunk, sampling_rate).item()
speech_probs.append(speech_prob)
# calculate progress and send it to callback function
progress = current_start_sample + hop_size_samples
progress = current_start_sample + window_size_samples
if progress > audio_length_samples:
progress = audio_length_samples
progress_percent = (progress / audio_length_samples) * 100
@@ -349,19 +348,19 @@ def get_speech_timestamps(audio: torch.Tensor,
for i, speech_prob in enumerate(speech_probs):
if (speech_prob >= threshold) and temp_end:
if temp_end != 0:
sil_dur = (hop_size_samples * i) - temp_end
sil_dur = (window_size_samples * i) - temp_end
if sil_dur > min_silence_samples_at_max_speech:
possible_ends.append((temp_end, sil_dur))
temp_end = 0
if next_start < prev_end:
next_start = hop_size_samples * i
next_start = window_size_samples * i
if (speech_prob >= threshold) and not triggered:
triggered = True
current_speech['start'] = hop_size_samples * i
current_speech['start'] = window_size_samples * i
continue
if triggered and (hop_size_samples * i) - current_speech['start'] > max_speech_samples:
if triggered and (window_size_samples * i) - current_speech['start'] > max_speech_samples:
if possible_ends:
if use_max_poss_sil_at_max_speech:
prev_end, dur = max(possible_ends, key=lambda x: x[1]) # use the longest possible silence segment in the current speech chunk
@@ -371,7 +370,7 @@ def get_speech_timestamps(audio: torch.Tensor,
speeches.append(current_speech)
current_speech = {}
next_start = prev_end + dur
if next_start < prev_end + hop_size_samples * i: # previously reached silence (< neg_thres) and is still not speech (< thres)
if next_start < prev_end + window_size_samples * i: # previously reached silence (< neg_thres) and is still not speech (< thres)
#triggered = False
current_speech['start'] = next_start
else:
@@ -380,7 +379,7 @@ def get_speech_timestamps(audio: torch.Tensor,
prev_end = next_start = temp_end = 0
possible_ends = []
else:
current_speech['end'] = hop_size_samples * i
current_speech['end'] = window_size_samples * i
speeches.append(current_speech)
current_speech = {}
prev_end = next_start = temp_end = 0
@@ -390,10 +389,10 @@ def get_speech_timestamps(audio: torch.Tensor,
if (speech_prob < neg_threshold) and triggered:
if not temp_end:
temp_end = hop_size_samples * i
# if ((hop_size_samples * i) - temp_end) > min_silence_samples_at_max_speech: # condition to avoid cutting in very short silence
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
# prev_end = temp_end
if (hop_size_samples * i) - temp_end < min_silence_samples:
if (window_size_samples * i) - temp_end < min_silence_samples:
continue
else:
current_speech['end'] = temp_end
@@ -434,7 +433,7 @@ def get_speech_timestamps(audio: torch.Tensor,
speech_dict['end'] *= step
if visualize_probs:
make_visualization(speech_probs, hop_size_samples / sampling_rate)
make_visualization(speech_probs, window_size_samples / sampling_rate)
return speeches