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+ [![Mailing list : test](http://img.shields.io/badge/Email-gray.svg?style=for-the-badge&logo=gmail)](mailto:hello@silero.ai) [![Mailing list : test](http://img.shields.io/badge/Telegram-blue.svg?style=for-the-badge&logo=telegram)](https://t.me/joinchat/Bv9tjhpdXTI22OUgpOIIDg) [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-GNU%20AGPL%203.0-lightgrey.svg?style=for-the-badge)](https://github.com/snakers4/silero-models/blob/master/LICENSE) 
+ 
+ [![Open on Torch Hub](https://img.shields.io/badge/Torch-Hub-red?logo=pytorch&style=for-the-badge)](https://pytorch.org/hub/snakers4_silero-models_stt/) [![Open on TF Hub](https://img.shields.io/badge/TF-Hub-yellow?logo=tensorflow&style=for-the-badge)](https://tfhub.dev/silero/collections/silero-stt/1)
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples.ipynb)
+
+![header)](https://user-images.githubusercontent.com/12515440/89997349-b3523080-dc94-11ea-9906-ca2e8bc50535.png)
+
+- [Silero VAD](#silero-vad)
+  - [Getting Started](#getting-started)
+    - [PyTorch](#pytorch)
+    - [ONNX](#onnx)
+  - [Metrics](#metrics)
+    - [Performance Metrics](#performance-metrics)
+    - [Quality Metrics](#quality-metrics)
+  - [Contact](#contact)
+    - [Get in Touch](#get-in-touch)
+    - [Commercial Inquiries](#commercial-inquiries)
+
+
+# Silero VAD
+
+Silero VAD: pre-trained enterprise-grade Voice Activity and Number Detector.
+Enterprise-grade Speech Products made refreshingly simple (all see our [STT](https://github.com/snakers4/silero-models)).
+
+Currently, there are hardly any high quality / modern / free / public voice activity detectors except for WebRTC Voice Activity Detector ([link](https://github.com/wiseman/py-webrtcvad)).
+
+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;
+
+**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](https://github.com/snakers4/silero-models/blob/master/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) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:  | `ru`, `en`, `de`, `es` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples.ipynb) |
+
+
+### PyTorch
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples.ipynb)
+
+[![Open on Torch Hub](https://img.shields.io/badge/Torch-Hub-red?logo=pytorch&style=for-the-badge)](https://pytorch.org/hub/snakers4_silero-models_stt/)
+
+```python
+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
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples.ipynb)
+
+You can run our model everywhere, where you can import the ONNX model or run ONNX runtime.
+
+```python
+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
+
+Try our models, create an [issue](https://github.com/snakers4/silero-models/issues/new), join our [chat](https://t.me/joinchat/Bv9tjhpdXTI22OUgpOIIDg), [email](mailto:hello@silero.ai) us.
+
+### Commercial Inquiries
+
+Please see our [wiki](https://github.com/snakers4/silero-models/wiki) and [tiers](https://github.com/snakers4/silero-models/wiki/Licensing-and-Tiers) for relevant information and [email](mailto:hello@silero.ai) us.