mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-04 17:39:22 +08:00
Merge branch 'master' into master
This commit is contained in:
Dimitrii Voronin
GitHub
@@ -10,8 +10,6 @@
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**Silero VAD** - pre-trained enterprise-grade [Voice Activity Detector](https://en.wikipedia.org/wiki/Voice_activity_detection) (also see our [STT models](https://github.com/snakers4/silero-models)).
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This repository also includes Number Detector and Language classifier [models](https://github.com/snakers4/silero-vad/wiki/Other-Models)
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<br/>
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<p align="center">
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@@ -79,7 +77,6 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-
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- [Examples and Dependencies](https://github.com/snakers4/silero-vad/wiki/Examples-and-Dependencies#dependencies)
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- [Quality Metrics](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics)
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- [Performance Metrics](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics)
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- [Number Detector and Language classifier models](https://github.com/snakers4/silero-vad/wiki/Other-Models)
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- [Versions and Available Models](https://github.com/snakers4/silero-vad/wiki/Version-history-and-Available-Models)
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- [Further reading](https://github.com/snakers4/silero-models#further-reading)
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- [FAQ](https://github.com/snakers4/silero-vad/wiki/FAQ)
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Binary file not shown.
@@ -58,6 +58,7 @@ def silero_number_detector(onnx=False, force_onnx_cpu=False):
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Returns a model with a set of utils
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Please see https://github.com/snakers4/silero-vad for usage examples
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"""
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raise NotImplementedError('This model has been deprecated and is not supported anymore.')
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if onnx:
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url = 'https://models.silero.ai/vad_models/number_detector.onnx'
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else:
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@@ -77,6 +78,7 @@ def silero_lang_detector(onnx=False, force_onnx_cpu=False):
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Returns a model with a set of utils
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Please see https://github.com/snakers4/silero-vad for usage examples
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"""
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raise NotImplementedError('This model has been deprecated and is not supported anymore.')
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if onnx:
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url = 'https://models.silero.ai/vad_models/number_detector.onnx'
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else:
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@@ -93,6 +95,7 @@ def silero_lang_detector_95(onnx=False, force_onnx_cpu=False):
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Returns a model with a set of utils
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Please see https://github.com/snakers4/silero-vad for usage examples
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"""
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raise NotImplementedError('This model has been deprecated and is not supported anymore.')
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if onnx:
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url = 'https://models.silero.ai/vad_models/lang_classifier_95.onnx'
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else:
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241
silero-vad.ipynb
241
silero-vad.ipynb
@@ -1,14 +1,5 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "FpMplOCA2Fwp"
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},
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"source": [
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"#VAD"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -170,238 +161,6 @@
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"\n",
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"print(speech_probs[:10]) # first 10 chunks predicts"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"id": "36jY0niD2Fww"
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},
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"source": [
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"# Number detector"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"hidden": true,
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"id": "scd1DlS42Fwx"
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},
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"source": [
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"## Install Dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true,
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"id": "Kq5gQuYq2Fwx"
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},
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"outputs": [],
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"source": [
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"#@title Install and Import Dependencies\n",
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"\n",
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"# this assumes that you have a relevant version of PyTorch installed\n",
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"!pip install -q torchaudio\n",
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"\n",
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"SAMPLING_RATE = 16000\n",
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"\n",
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"import torch\n",
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"torch.set_num_threads(1)\n",
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"\n",
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"from IPython.display import Audio\n",
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"from pprint import pprint\n",
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"# download example\n",
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"torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en_num.wav', 'en_number_example.wav')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "dPwCFHmFycUF"
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},
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"outputs": [],
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"source": [
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"USE_ONNX = False # change this to True if you want to test onnx model\n",
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"if USE_ONNX:\n",
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" !pip install -q onnxruntime\n",
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" \n",
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"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
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" model='silero_number_detector',\n",
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" force_reload=True,\n",
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" onnx=USE_ONNX)\n",
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"\n",
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"(get_number_ts,\n",
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" save_audio,\n",
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" read_audio,\n",
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" collect_chunks,\n",
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" drop_chunks) = utils\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"hidden": true,
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"id": "qhPa30ij2Fwy"
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},
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"source": [
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"## Full audio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true,
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"id": "EXpau6xq2Fwy"
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},
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"outputs": [],
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"source": [
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"wav = read_audio('en_number_example.wav', sampling_rate=SAMPLING_RATE)\n",
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"# get number timestamps from full audio file\n",
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"number_timestamps = get_number_ts(wav, model)\n",
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"pprint(number_timestamps)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true,
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"id": "u-KfXRhZ2Fwy"
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},
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"outputs": [],
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"source": [
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"# convert ms in timestamps to samples\n",
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"for timestamp in number_timestamps:\n",
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" timestamp['start'] = int(timestamp['start'] * SAMPLING_RATE / 1000)\n",
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" timestamp['end'] = int(timestamp['end'] * SAMPLING_RATE / 1000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true,
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"id": "iwYEC4aZ2Fwy"
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},
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"outputs": [],
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"source": [
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"# merge all number chunks to one audio\n",
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"save_audio('only_numbers.wav',\n",
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" collect_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
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"Audio('only_numbers.wav')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true,
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"id": "fHaYejX12Fwy"
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},
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"outputs": [],
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"source": [
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"# drop all number chunks from audio\n",
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"save_audio('no_numbers.wav',\n",
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" drop_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
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"Audio('no_numbers.wav')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"id": "PnKtJKbq2Fwz"
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},
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"source": [
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"# Language detector"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"hidden": true,
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"id": "F5cAmMbP2Fwz"
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},
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"source": [
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"## Install Dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true,
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"id": "Zu9D0t6n2Fwz"
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},
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"outputs": [],
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"source": [
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"#@title Install and Import Dependencies\n",
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"\n",
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"# this assumes that you have a relevant version of PyTorch installed\n",
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"!pip install -q torchaudio\n",
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"\n",
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"SAMPLING_RATE = 16000\n",
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"\n",
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"import torch\n",
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"torch.set_num_threads(1)\n",
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"\n",
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"from IPython.display import Audio\n",
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"from pprint import pprint\n",
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"# download example\n",
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"torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en_example.wav')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "JfRKDZiRztFe"
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},
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"outputs": [],
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"source": [
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"USE_ONNX = False # change this to True if you want to test onnx model\n",
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"if USE_ONNX:\n",
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" !pip install -q onnxruntime\n",
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" \n",
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"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
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" model='silero_lang_detector',\n",
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" force_reload=True,\n",
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" onnx=USE_ONNX)\n",
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"\n",
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"get_language, read_audio = utils"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true,
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"hidden": true,
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"id": "iC696eMX2Fwz"
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},
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"source": [
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"## Full audio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"hidden": true,
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"id": "c8UYnYBF2Fw0"
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},
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"outputs": [],
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"source": [
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"wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
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"lang = get_language(wav, model)\n",
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"print(lang)"
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]
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}
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],
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"metadata": {
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@@ -13,11 +13,11 @@ class OnnxWrapper():
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import numpy as np
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global np
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import onnxruntime
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opts = onnxruntime.SessionOptions()
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opts.inter_op_num_threads = 1
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opts.intra_op_num_threads = 1
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if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
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self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts)
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else:
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@@ -291,7 +291,7 @@ def get_speech_timestamps(audio: torch.Tensor,
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triggered = True
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current_speech['start'] = window_size_samples * i
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continue
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if triggered and (window_size_samples * i) - current_speech['start'] > max_speech_samples:
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if prev_end:
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current_speech['end'] = prev_end
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@@ -309,7 +309,6 @@ def get_speech_timestamps(audio: torch.Tensor,
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prev_end = next_start = temp_end = 0
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triggered = False
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continue
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if (speech_prob < neg_threshold) and triggered:
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if not temp_end:
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