Silero VAD
Single Image Why our VAD is better than WebRTC
Silero VAD: pre-trained enterprise-grade Voice Activity and Number Detector. Enterprise-grade Speech Products made refreshingly simple (all see our STT).
Currently, there are hardly any high quality / modern / free / public voice activity detectors except for WebRTC Voice Activity Detector (link).
Also in enterprise it is crucial to be able to anonymize large-scale spoken corpora (i.e. remove personal data). Typically personal data is considered to be private / sensitive if it contains (i) a name (ii) some private ID. Name recognition is highly subjective and would depend on location, but Voice Activity and Number detections are quite general tasks.
Key advantages:
- Modern, portable;
- Small memory footprint (?);
- Trained on huge spoken corpora and noise / sound libraries;
- Slower than WebRTC, but sufficiently fast for IOT / edge / mobile applications;
- Superior metrics to WebRTC;
Typical use cases:
- Spoken corpora anonymization;
- Voice detection for IOT / edge / mobile use cases;
- Data cleaning and preparation, number and voice detection in general;
Key features / differences:
Getting Started
All of the provided models are listed in the models.yml file. Any meta-data and newer versions will be added there.
Currently we provide the following checkpoints:
| PyTorch | ONNX | Quantization | Languages | Colab | |
|---|---|---|---|---|---|
| VAD v1 (vad_v1) | ✔️ | ✔️ | ✔️ | ru, en, de, es |
|
PyTorch
import torch
import zipfile
import torchaudio
from glob import glob
device = torch.device('cpu') # gpu also works, but our models are fast enough for CPU
model, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models',
model='silero_stt',
language='en', # also available 'de', 'es'
device=device)
(read_batch, split_into_batches,
read_audio, prepare_model_input) = utils # see function signature for details
# download a single file, any format compatible with TorchAudio (soundfile backend)
torch.hub.download_url_to_file('https://opus-codec.org/static/examples/samples/speech_orig.wav',
dst ='speech_orig.wav', progress=True)
test_files = glob('speech_orig.wav')
batches = split_into_batches(test_files, batch_size=10)
input = prepare_model_input(read_batch(batches[0]),
device=device)
output = model(input)
for example in output:
print(decoder(example.cpu()))
ONNX
You can run our model everywhere, where you can import the ONNX model or run ONNX runtime.
import onnx
import torch
import onnxruntime
from omegaconf import OmegaConf
language = 'en' # also available 'de', 'es'
# load provided utils
_, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_stt', language=language)
(read_batch, split_into_batches,
read_audio, prepare_model_input) = utils
# see available models
torch.hub.download_url_to_file('https://raw.githubusercontent.com/snakers4/silero-models/master/models.yml', 'models.yml')
models = OmegaConf.load('models.yml')
available_languages = list(models.stt_models.keys())
assert language in available_languages
# load the actual ONNX model
torch.hub.download_url_to_file(models.stt_models.en.latest.onnx, 'model.onnx', progress=True)
onnx_model = onnx.load('model.onnx')
onnx.checker.check_model(onnx_model)
ort_session = onnxruntime.InferenceSession('model.onnx')
# download a single file, any format compatible with TorchAudio (soundfile backend)
torch.hub.download_url_to_file('https://opus-codec.org/static/examples/samples/speech_orig.wav', dst ='speech_orig.wav', progress=True)
test_files = ['speech_orig.wav']
batches = split_into_batches(test_files, batch_size=10)
input = prepare_model_input(read_batch(batches[0]))
# actual onnx inference and decoding
onnx_input = input.detach().cpu().numpy()
ort_inputs = {'input': onnx_input}
ort_outs = ort_session.run(None, ort_inputs)
decoded = decoder(torch.Tensor(ort_outs[0])[0])
print(decoded)
Metrics
Performance Metrics
Speed metrics here.
Quality Metrics
Quality metrics here.
Contact
Get in Touch
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Commercial Inquiries
Please see our wiki and tiers for relevant information and email us.