mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-04 17:39:22 +08:00
663 lines
15 KiB
Plaintext
663 lines
15 KiB
Plaintext
{
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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": "sVNOuHQQjsrp"
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},
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"source": [
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"# PyTorch Examples"
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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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"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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"heading_collapsed": true,
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"id": "62A6F_072Fwq"
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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": "5w5AkskZ2Fwr"
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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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"SAMPLE_RATE = 16000\n",
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"\n",
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"import glob\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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"\n",
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"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
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" model='silero_vad',\n",
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" force_reload=True)\n",
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"\n",
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"(get_speech_timestamps,\n",
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" save_audio,\n",
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" read_audio,\n",
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" VADIterator,\n",
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" collect_chunks) = utils\n",
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"\n",
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"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
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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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"id": "fXbbaUO3jsrw"
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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": "markdown",
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"metadata": {
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"id": "RAfJPb_a-Auj"
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},
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"source": [
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"**Speech timestapms from 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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"id": "aI_eydBPjsrx"
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},
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"outputs": [],
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"source": [
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"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
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"# get speech timestamps from full audio file\n",
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"speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=SAMPLE_RATE)\n",
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"pprint(speech_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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"id": "OuEobLchjsry"
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},
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"outputs": [],
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"source": [
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"# merge all speech chunks to one audio\n",
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"save_audio('only_speech.wav',\n",
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" collect_chunks(speech_timestamps, wav), sampling_rate=16000) \n",
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"Audio('only_speech.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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"id": "iDKQbVr8jsry"
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},
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"source": [
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"### Stream imitation example"
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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": "q-lql_2Wjsry"
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},
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"outputs": [],
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"source": [
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"## using VADIterator class\n",
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"\n",
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"vad_iterator = VADIterator(model)\n",
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"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
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"\n",
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"window_size_samples = 1536 # number of samples in a single audio chunk\n",
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"for i in range(0, len(wav), window_size_samples):\n",
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" speech_dict = vad_iterator(wav[i: i+ window_size_samples], return_seconds=True)\n",
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" if speech_dict:\n",
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" print(speech_dict, end=' ')\n",
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"vad_iterator.reset_states() # reset model states after each 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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"id": "BX3UgwwB2Fwv"
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},
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"outputs": [],
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"source": [
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"## just probabilities\n",
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"\n",
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"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
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"speech_probs = []\n",
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"window_size_samples = 1536\n",
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"for i in range(0, len(wav), window_size_samples):\n",
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" speech_prob = model(wav[i: i+ window_size_samples], SAMPLE_RATE).item()\n",
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" speech_probs.append(speech_prob)\n",
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"\n",
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"pprint(speech_probs[:100])"
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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 soundfile\n",
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"\n",
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"import glob\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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"\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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"\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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"\n",
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"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
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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(f'{files_dir}/en_num.wav')\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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"sample_rate = 16000\n",
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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'] * sample_rate / 1000)\n",
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" timestamp['end'] = int(timestamp['end'] * sample_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), sample_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), sample_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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},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Install and Import Dependencies\n",
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"\n",
|
|
"# this assumes that you have a relevant version of PyTorch installed\n",
|
|
"!pip install -q torchaudio soundfile\n",
|
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"\n",
|
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"import glob\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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"\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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"\n",
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"(get_language,\n",
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" read_audio) = utils\n",
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"\n",
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"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
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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(f'{files_dir}/en.wav')\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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"cell_type": "markdown",
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"metadata": {
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"id": "57avIBd6jsrz"
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},
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"source": [
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"# ONNX Example"
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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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"id": "hEhnfORV2Fw0"
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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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"id": "Cy7y-NAyALSe"
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},
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"source": [
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"**TO BE DONE**"
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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": "7QMvUvpg2Fw4"
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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": "tBPDkpHr2Fw4"
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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": {
|
|
"cellView": "form",
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|
"hidden": true,
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"id": "PdjGd56R2Fw5"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@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 soundfile onnxruntime\n",
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"\n",
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"import glob\n",
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"import torch\n",
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"import onnxruntime\n",
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"from pprint import pprint\n",
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"\n",
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"from IPython.display import Audio\n",
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"\n",
|
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"_, 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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"\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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"\n",
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"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
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"\n",
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"def init_onnx_model(model_path: str):\n",
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" return onnxruntime.InferenceSession(model_path)\n",
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"\n",
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"def validate_onnx(model, inputs):\n",
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" with torch.no_grad():\n",
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" ort_inputs = {'input': inputs.cpu().numpy()}\n",
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" outs = model.run(None, ort_inputs)\n",
|
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" outs = [torch.Tensor(x) for x in outs]\n",
|
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" return outs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
|
|
"heading_collapsed": true,
|
|
"hidden": true,
|
|
"id": "I9QWSFZh2Fw5"
|
|
},
|
|
"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": {
|
|
"hidden": true,
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|
"id": "_r6QZiwu2Fw5"
|
|
},
|
|
"outputs": [],
|
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"source": [
|
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"model = init_onnx_model(f'{files_dir}/number_detector.onnx')\n",
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"wav = read_audio(f'{files_dir}/en_num.wav')\n",
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"\n",
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"# get number timestamps from full audio file\n",
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"number_timestamps = get_number_ts(wav, model, run_function=validate_onnx)\n",
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"pprint(number_timestamps)"
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
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|
"metadata": {
|
|
"hidden": true,
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|
"id": "FN4aDwLV2Fw5"
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|
},
|
|
"outputs": [],
|
|
"source": [
|
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"sample_rate = 16000\n",
|
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"# convert ms in timestamps to samples\n",
|
|
"for timestamp in number_timestamps:\n",
|
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" timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
|
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" timestamp['end'] = int(timestamp['end'] * sample_rate / 1000)"
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"hidden": true,
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|
"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",
|
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"Audio('only_numbers.wav')"
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"hidden": true,
|
|
"id": "yUxOcOFG2Fw6"
|
|
},
|
|
"outputs": [],
|
|
"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), 16000) \n",
|
|
"Audio('no_numbers.wav')"
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]
|
|
},
|
|
{
|
|
"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": {
|
|
"cellView": "form",
|
|
"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) = utils\n",
|
|
"\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(f'{files_dir}/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",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"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
|
|
}
|