deepgeek/silero-vad
2
0
Fork 0
mirror of https://github.com/snakers4/silero-vad.git synced 2026-02-05 18:09:22 +08:00
Code Issues Packages Projects Releases Wiki Activity

Removes redundant hop_size_samples variable

Browse Source 
Remove redundant hop_size_samples variable
This commit is contained in:
Purfview
2025-10-23 05:23:18 +01:00
committed by GitHub
parent a14a23faa7
commit 81e8a48e25
1 changed files with 12 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
src/silero_vad/utils_vad.py
View File

@@ -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") 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 window_size_samples = 512 if sampling_rate == 16000 else 256
hop_size_samples = int(window_size_samples)
model.reset_states() model.reset_states()
min_speech_samples = sampling_rate * min_speech_duration_ms / 1000 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) audio_length_samples = len(audio)
speech_probs = [] 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] chunk = audio[current_start_sample: current_start_sample + window_size_samples]
if len(chunk) < window_size_samples: if len(chunk) < window_size_samples:
chunk = torch.nn.functional.pad(chunk, (0, int(window_size_samples - len(chunk)))) chunk = torch.nn.functional.pad(chunk, (0, int(window_size_samples - len(chunk))))
speech_prob = model(chunk, sampling_rate).item() speech_prob = model(chunk, sampling_rate).item()
speech_probs.append(speech_prob) speech_probs.append(speech_prob)
# calculate progress and send it to callback function # 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: if progress > audio_length_samples:
progress = audio_length_samples progress = audio_length_samples
progress_percent = (progress / audio_length_samples) * 100 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): for i, speech_prob in enumerate(speech_probs):
if (speech_prob >= threshold) and temp_end: if (speech_prob >= threshold) and temp_end:
if temp_end != 0: 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: if sil_dur > min_silence_samples_at_max_speech:
possible_ends.append((temp_end, sil_dur)) possible_ends.append((temp_end, sil_dur))
temp_end = 0 temp_end = 0
if next_start < prev_end: if next_start < prev_end:
next_start = hop_size_samples * i next_start = window_size_samples * i
if (speech_prob >= threshold) and not triggered: if (speech_prob >= threshold) and not triggered:
triggered = True triggered = True
current_speech['start'] = hop_size_samples * i current_speech['start'] = window_size_samples * i
continue 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 possible_ends:
if use_max_poss_sil_at_max_speech: 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 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) speeches.append(current_speech)
current_speech = {} current_speech = {}
next_start = prev_end + dur 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 #triggered = False
current_speech['start'] = next_start current_speech['start'] = next_start
else: else:
@@ -380,7 +379,7 @@ def get_speech_timestamps(audio: torch.Tensor,
prev_end = next_start = temp_end = 0 prev_end = next_start = temp_end = 0
possible_ends = [] possible_ends = []
else: else:
current_speech['end'] = hop_size_samples * i current_speech['end'] = window_size_samples * i
speeches.append(current_speech) speeches.append(current_speech)
current_speech = {} current_speech = {}
prev_end = next_start = temp_end = 0 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 (speech_prob < neg_threshold) and triggered:
if not temp_end: if not temp_end:
temp_end = hop_size_samples * i temp_end = window_size_samples * i
# if ((hop_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 # 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 continue
else: else:
current_speech['end'] = temp_end current_speech['end'] = temp_end
@@ -434,7 +433,7 @@ def get_speech_timestamps(audio: torch.Tensor,
speech_dict['end'] *= step speech_dict['end'] *= step
if visualize_probs: if visualize_probs:
make_visualization(speech_probs, hop_size_samples / sampling_rate) make_visualization(speech_probs, window_size_samples / sampling_rate)
return speeches return speeches