deepgeek/silero-vad
2
0
Fork 0
mirror of https://github.com/snakers4/silero-vad.git synced 2026-02-04 17:39:22 +08:00
Code Issues Packages Projects Releases Wiki Activity

git pushMerge branch 'master' of github.com:snakers4/silero-vad

Browse Source
This commit is contained in:
adamnsandle
2020-12-15 14:18:59 +00:00
parent c87243b21a 5df4f0793c
commit 7cc9b3af52
1 changed files with 19 additions and 83 deletions
Download Patch File Download Diff File Expand all files Collapse all files

102
README.md
View File

@@ -1,6 +1,6 @@
[![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-MIT-lightgrey.svg?style=for-the-badge)](https://github.com/snakers4/silero-models/blob/master/LICENSE)
[![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-MIT-lightgrey.svg?style=for-the-badge)](https://github.com/snakers4/silero-vad/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-vad/)
[![Open on Torch Hub](https://img.shields.io/badge/Torch-Hub-red?logo=pytorch&style=for-the-badge)](https://pytorch.org/hub/snakers4_silero-vad/) (coming soon)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-vad/blob/master/silero-vad.ipynb)
@@ -16,24 +16,23 @@
- [Contact](#contact)
- [Get in Touch](#get-in-touch)
- [Commercial Inquiries](#commercial-inquiries)
- [History](#history)
# Silero VAD
`Single Image Why our VAD is better than WebRTC`
Silero VAD: pre-trained enterprise-grade Voice Activity and Number Detector.
Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier.
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.
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 locale and business case, but Voice Activity and Number detections are quite general tasks.
**Key advantages:**
**Key advantages / features:**
- Modern, portable;
- Small memory footprint (?);
- 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;
@@ -44,53 +43,28 @@ Also in enterprise it is crucial to be able to anonymize large-scale spoken corp
- 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.
The models are small enough to be included directly into this repository. Newer models will supersede older models directly.
Currently we provide the following checkpoints:
Currently we provide the following models:
| | 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-vad/blob/master/silero-vad.ipynb) |
| | Released |PyTorch | ONNX | VAD | Number Detector | Language Classifier | Languages | Colab |
|----|------------|-------------------|--------------------|---------------------| --------------------|---------------------|-------------------------|-------|
| v1 | 2020-12-15 |: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-vad/blob/master/silero-vad.ipynb) |
Version history:
- v1, 2020-12-15, initial release, no Number Detector or Language Classifier heads yet;
### PyTorch
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snakers4/silero-vad/blob/master/silero-vad.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-vad/)
[![Open on Torch Hub](https://img.shields.io/badge/Torch-Hub-red?logo=pytorch&style=for-the-badge)](https://pytorch.org/hub/snakers4_silero-vad/) (coming soon)
```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()))
TBD
```
### ONNX
@@ -100,42 +74,7 @@ for example in output:
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)
TBD
```
## Metrics
@@ -152,11 +91,8 @@ Quality metrics here.
### 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.
Try our models, create an [issue](https://github.com/snakers4/silero-vad/issues/new), start a [discussion](https://github.com/snakers4/silero-vad/discussions/new), join our telegram [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.
# History
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 directly.