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Merge pull request #330 from snakers4/adamnsandle

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del deprecated models examples
This commit is contained in:
Dimitrii Voronin
2023-04-27 15:05:45 +03:00
committed by GitHub
parent 51fbbcb32e 874c66ccbc
commit c3c67cdcb8
1 changed files with 0 additions and 241 deletions
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silero-vad.ipynb
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@@ -1,14 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "FpMplOCA2Fwp"
},
"source": [
"#VAD"
]
},
{
"cell_type": "markdown",
"metadata": {
@@ -170,238 +161,6 @@
"\n",
"print(speech_probs[:10]) # first 10 chunks predicts"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "36jY0niD2Fww"
},
"source": [
"# Number detector"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "scd1DlS42Fwx"
},
"source": [
"## Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "Kq5gQuYq2Fwx"
},
"outputs": [],
"source": [
"#@title Install and Import Dependencies\n",
"\n",
"# this assumes that you have a relevant version of PyTorch installed\n",
"!pip install -q torchaudio\n",
"\n",
"SAMPLING_RATE = 16000\n",
"\n",
"import torch\n",
"torch.set_num_threads(1)\n",
"\n",
"from IPython.display import Audio\n",
"from pprint import pprint\n",
"# download example\n",
"torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en_num.wav', 'en_number_example.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dPwCFHmFycUF"
},
"outputs": [],
"source": [
"USE_ONNX = False # change this to True if you want to test onnx model\n",
"if USE_ONNX:\n",
" !pip install -q onnxruntime\n",
" \n",
"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_number_detector',\n",
" force_reload=True,\n",
" onnx=USE_ONNX)\n",
"\n",
"(get_number_ts,\n",
" save_audio,\n",
" read_audio,\n",
" collect_chunks,\n",
" drop_chunks) = utils\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "qhPa30ij2Fwy"
},
"source": [
"## Full audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "EXpau6xq2Fwy"
},
"outputs": [],
"source": [
"wav = read_audio('en_number_example.wav', sampling_rate=SAMPLING_RATE)\n",
"# get number timestamps from full audio file\n",
"number_timestamps = get_number_ts(wav, model)\n",
"pprint(number_timestamps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "u-KfXRhZ2Fwy"
},
"outputs": [],
"source": [
"# convert ms in timestamps to samples\n",
"for timestamp in number_timestamps:\n",
" timestamp['start'] = int(timestamp['start'] * SAMPLING_RATE / 1000)\n",
" timestamp['end'] = int(timestamp['end'] * SAMPLING_RATE / 1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "iwYEC4aZ2Fwy"
},
"outputs": [],
"source": [
"# merge all number chunks to one audio\n",
"save_audio('only_numbers.wav',\n",
" collect_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
"Audio('only_numbers.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "fHaYejX12Fwy"
},
"outputs": [],
"source": [
"# drop all number chunks from audio\n",
"save_audio('no_numbers.wav',\n",
" drop_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
"Audio('no_numbers.wav')"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "PnKtJKbq2Fwz"
},
"source": [
"# Language detector"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "F5cAmMbP2Fwz"
},
"source": [
"## Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "Zu9D0t6n2Fwz"
},
"outputs": [],
"source": [
"#@title Install and Import Dependencies\n",
"\n",
"# this assumes that you have a relevant version of PyTorch installed\n",
"!pip install -q torchaudio\n",
"\n",
"SAMPLING_RATE = 16000\n",
"\n",
"import torch\n",
"torch.set_num_threads(1)\n",
"\n",
"from IPython.display import Audio\n",
"from pprint import pprint\n",
"# download example\n",
"torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en_example.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JfRKDZiRztFe"
},
"outputs": [],
"source": [
"USE_ONNX = False # change this to True if you want to test onnx model\n",
"if USE_ONNX:\n",
" !pip install -q onnxruntime\n",
" \n",
"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_lang_detector',\n",
" force_reload=True,\n",
" onnx=USE_ONNX)\n",
"\n",
"get_language, read_audio = utils"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "iC696eMX2Fwz"
},
"source": [
"## Full audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "c8UYnYBF2Fw0"
},
"outputs": [],
"source": [
"wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
"lang = get_language(wav, model)\n",
"print(lang)"
]
}
],
"metadata": {