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Merge pull request #707 from Purfview/patch-1

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Fixes few typos
This commit is contained in:
Alexander Veysov
2025-10-23 06:35:58 +03:00
committed by GitHub
parent a66c890188 a30b5843c1
commit a14a23faa7
1 changed files with 3 additions and 3 deletions
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6
src/silero_vad/utils_vad.py
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@@ -248,7 +248,7 @@ def get_speech_timestamps(audio: torch.Tensor,
max_speech_duration_s: int (default - inf) max_speech_duration_s: int (default - inf)
Maximum duration of speech chunks in seconds 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. 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 aggressive cutting.
Otherwise, they will be split aggressively just before max_speech_duration_s. Otherwise, they will be split aggressively just before max_speech_duration_s.
min_silence_duration_ms: int (default - 100 milliseconds) min_silence_duration_ms: int (default - 100 milliseconds)
@@ -328,7 +328,7 @@ def get_speech_timestamps(audio: torch.Tensor,
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)
# caculate progress and seng it to callback function # calculate progress and send it to callback function
progress = current_start_sample + hop_size_samples progress = current_start_sample + hop_size_samples
if progress > audio_length_samples: if progress > audio_length_samples:
progress = audio_length_samples progress = audio_length_samples
@@ -366,7 +366,7 @@ def get_speech_timestamps(audio: torch.Tensor,
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
else: else:
prev_end, dur = possible_ends[-1] # use the last possible silence segement prev_end, dur = possible_ends[-1] # use the last possible silence segment
current_speech['end'] = prev_end current_speech['end'] = prev_end
speeches.append(current_speech) speeches.append(current_speech)
current_speech = {} current_speech = {}