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

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;

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 and 16000 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()

This is a technical tag, so that users, who do now want to use newer models, could just checkout this tag.