mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-04 17:39:22 +08:00
440 lines
10 KiB
Plaintext
440 lines
10 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": "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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"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": "pSifus5IilRp"
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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_vad',\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_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"
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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('en_example.wav', sampling_rate=SAMPLING_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=SAMPLING_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=SAMPLING_RATE) \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'en_example.wav', sampling_rate=SAMPLING_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('en_example.wav', sampling_rate=SAMPLING_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], SAMPLING_RATE).item()\n",
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" speech_probs.append(speech_prob)\n",
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"vad_iterator.reset_states() # reset model states after each audio\n",
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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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"colab": {
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"name": "silero-vad.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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