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silero-vad/silero-vad.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "sVNOuHQQjsrp"
},
"source": [
"# PyTorch Examples"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FpMplOCA2Fwp"
},
"source": [
"## VAD"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "62A6F_072Fwq"
},
"source": [
"### Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "5w5AkskZ2Fwr"
},
"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",
"SAMPLE_RATE = 16000\n",
"\n",
"import glob\n",
"import torch\n",
"torch.set_num_threads(1)\n",
"\n",
"from IPython.display import Audio\n",
"from pprint import pprint\n",
"\n",
"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_vad',\n",
" force_reload=True)\n",
"\n",
"(get_speech_timestamps,\n",
" save_audio,\n",
" read_audio,\n",
" VADIterator,\n",
" collect_chunks) = utils\n",
"\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fXbbaUO3jsrw"
},
"source": [
"### Full Audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RJRBksv39xf5"
},
"outputs": [],
"source": [
"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tEKb0YF_9y-i"
},
"outputs": [],
"source": [
"wav"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RAfJPb_a-Auj"
},
"source": [
"**Speech timestapms from full audio**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aI_eydBPjsrx"
},
"outputs": [],
"source": [
"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
"# get speech timestamps from full audio file\n",
"speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=SAMPLE_RATE)\n",
"pprint(speech_timestamps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OuEobLchjsry"
},
"outputs": [],
"source": [
"# merge all speech chunks to one audio\n",
"save_audio('only_speech.wav',\n",
" collect_chunks(speech_timestamps, wav), sampling_rate=16000) \n",
"Audio('only_speech.wav')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iDKQbVr8jsry"
},
"source": [
"### Stream imitation example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "q-lql_2Wjsry"
},
"outputs": [],
"source": [
"## using VADIterator class\n",
"\n",
"vad_iterator = VADIterator(model)\n",
"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
"\n",
"window_size_samples = 1536 # number of samples in a single audio chunk\n",
"for i in range(0, len(wav), window_size_samples):\n",
" speech_dict = vad_iterator(wav[i: i+ window_size_samples], return_seconds=True)\n",
" if speech_dict:\n",
" print(speech_dict, end=' ')\n",
"vad_iterator.reset_states() # reset model states after each audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BX3UgwwB2Fwv"
},
"outputs": [],
"source": [
"## just probabilities\n",
"\n",
"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
"speech_probs = []\n",
"window_size_samples = 1536\n",
"for i in range(0, len(wav), window_size_samples):\n",
" speech_prob = model(wav[i: i+ window_size_samples], SAMPLE_RATE).item()\n",
" speech_probs.append(speech_prob)\n",
"\n",
"pprint(speech_probs[:100])"
]
},
{
"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 soundfile\n",
"\n",
"import glob\n",
"import torch\n",
"torch.set_num_threads(1)\n",
"\n",
"from IPython.display import Audio\n",
"from pprint import pprint\n",
"\n",
"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_number_detector',\n",
" force_reload=True)\n",
"\n",
"(get_number_ts,\n",
" save_audio,\n",
" read_audio,\n",
" collect_chunks,\n",
" drop_chunks,\n",
" _) = utils\n",
"\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
]
},
{
"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(f'{files_dir}/en_num.wav')\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": [
"sample_rate = 16000\n",
"# convert ms in timestamps to samples\n",
"for timestamp in number_timestamps:\n",
" timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
" timestamp['end'] = int(timestamp['end'] * sample_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), sample_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), sample_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 soundfile\n",
"\n",
"import glob\n",
"import torch\n",
"torch.set_num_threads(1)\n",
"\n",
"from IPython.display import Audio\n",
"from pprint import pprint\n",
"\n",
"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_lang_detector',\n",
" force_reload=True)\n",
"\n",
"(get_language,\n",
" read_audio,\n",
" _) = utils\n",
"\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
]
},
{
"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(f'{files_dir}/en.wav')\n",
"lang = get_language(wav, model)\n",
"print(lang)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "57avIBd6jsrz"
},
"source": [
"# ONNX Example"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hEhnfORV2Fw0"
},
"source": [
"## VAD"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cy7y-NAyALSe"
},
"source": [
"**TO BE DONE**"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "7QMvUvpg2Fw4"
},
"source": [
"## Number detector"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "tBPDkpHr2Fw4"
},
"source": [
"### Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "PdjGd56R2Fw5"
},
"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 soundfile onnxruntime\n",
"\n",
"import glob\n",
"import torch\n",
"import onnxruntime\n",
"from pprint import pprint\n",
"\n",
"from IPython.display import Audio\n",
"\n",
"_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_number_detector',\n",
" force_reload=True)\n",
"\n",
"(get_number_ts,\n",
" save_audio,\n",
" read_audio,\n",
" collect_chunks,\n",
" drop_chunks,\n",
" donwload_onnx_model) = utils\n",
"\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
"donwload_onnx_model('number_detector')\n",
"\n",
"def init_onnx_model(model_path: str):\n",
" return onnxruntime.InferenceSession(model_path)\n",
"\n",
"def validate_onnx(model, inputs):\n",
" with torch.no_grad():\n",
" ort_inputs = {'input': inputs.cpu().numpy()}\n",
" outs = model.run(None, ort_inputs)\n",
" outs = [torch.Tensor(x) for x in outs]\n",
" return outs"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "I9QWSFZh2Fw5"
},
"source": [
"### Full Audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "_r6QZiwu2Fw5"
},
"outputs": [],
"source": [
"model = init_onnx_model('number_detector.onnx')\n",
"wav = read_audio(f'{files_dir}/en_num.wav')\n",
"\n",
"# get number timestamps from full audio file\n",
"number_timestamps = get_number_ts(wav, model, run_function=validate_onnx)\n",
"pprint(number_timestamps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "FN4aDwLV2Fw5"
},
"outputs": [],
"source": [
"sample_rate = 16000\n",
"# convert ms in timestamps to samples\n",
"for timestamp in number_timestamps:\n",
" timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
" timestamp['end'] = int(timestamp['end'] * sample_rate / 1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "JnvS6WTK2Fw5"
},
"outputs": [],
"source": [
"# merge all number chunks to one audio\n",
"save_audio('only_numbers.wav',\n",
" collect_chunks(number_timestamps, wav), 16000) \n",
"Audio('only_numbers.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "yUxOcOFG2Fw6"
},
"outputs": [],
"source": [
"# drop all number chunks from audio\n",
"save_audio('no_numbers.wav',\n",
" drop_chunks(number_timestamps, wav), 16000) \n",
"Audio('no_numbers.wav')"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "SR8Bgcd52Fw6"
},
"source": [
"## Language detector"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "PBnXPtKo2Fw6"
},
"source": [
"### Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "iNkDWJ3H2Fw6"
},
"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 soundfile onnxruntime\n",
"\n",
"import glob\n",
"import torch\n",
"import onnxruntime\n",
"from pprint import pprint\n",
"\n",
"from IPython.display import Audio\n",
"\n",
"_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_lang_detector',\n",
" force_reload=True)\n",
"\n",
"(get_language,\n",
" read_audio,\n",
" donwload_onnx_model) = utils\n",
"\n",
"donwload_onnx_model('number_detector')\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
"\n",
"def init_onnx_model(model_path: str):\n",
" return onnxruntime.InferenceSession(model_path)\n",
"\n",
"def validate_onnx(model, inputs):\n",
" with torch.no_grad():\n",
" ort_inputs = {'input': inputs.cpu().numpy()}\n",
" outs = model.run(None, ort_inputs)\n",
" outs = [torch.Tensor(x) for x in outs]\n",
" return outs"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true,
"id": "G8N8oP4q2Fw6"
},
"source": [
"### Full Audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "WHXnh9IV2Fw6"
},
"outputs": [],
"source": [
"model = init_onnx_model('number_detector.onnx')\n",
"wav = read_audio(f'{files_dir}/en.wav')\n",
"\n",
"lang = get_language(wav, model, run_function=validate_onnx)\n",
"print(lang)"
]
}
],
"metadata": {
"colab": {
"name": "silero-vad.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
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"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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