Releases: snakers4/silero-vad
Releases · snakers4/silero-vad
# New V4 VAD Released
New V4 VAD Released
- Improved quality
- Improved perfomance
- Both 8k and 16k sampling rates are now supported by the ONNX model
- Batching is now supported by the ONNX model
- Added audio_forward method for one-line processing of a single or multiple audio without postprocessing
- Hotfix applied - wrong model was uploaded
- Minor hotfix re. PyTorch version
New V3 ONNX VAD Released
We finally were able to port a model to ONNX:
- Compact model (~100k params);
- Both PyTorch and ONNX models are not quantized;
- Same quality model as the latest best PyTorch release;
- Only 16kHz available now (ONNX has some issues with if-statements and / or tracing vs scripting) with cryptic errors;
- In our tests, on short audios (chunks) ONNX is 2-3x faster than PyTorch (this is mitigated with larger batches or long audios);
- Audio examples and non-core models moved out of the repo to save space;
New V3 Silero VAD is Already Here
Main changes
- One VAD to rule them all! New model includes the functionality of the previous ones with improved quality and speed!
- Flexible sampling rate,
8000 Hz
and16000 Hz
are supported; - Flexible chunk size, minimum chunk size is just 30 milliseconds!
- 100k parameters;
- GPU and batching are supported;
- Radically simplified examples;
Migration
Please see the new examples.
New get_speech_timestamps
is a simplified and unified version of the old deprecated get_speech_ts
or get_speech_ts_adaptive
methods.
speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=16000)
New VADIterator
class serves as an example for streaming tasks instead of old deprecated VADiterator
and VADiteratorAdaptive
.
vad_iterator = VADIterator(model)
window_size_samples = 1536
for i in range(0, len(wav), window_size_samples):
speech_dict = vad_iterator(wav[i: i+ window_size_samples], return_seconds=True)
if speech_dict:
print(speech_dict, end=' ')
vad_iterator.reset_states()
V2 Legacy Release for History
This is a technical tag, so that users, who do now want to use newer models, could just checkout this tag.