mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-04 17:39:22 +08:00
428 lines
9.3 KiB
Plaintext
428 lines
9.3 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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"source": [
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"# Jit 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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"outputs": [],
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"source": [
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"!pip install -q torchaudio\n",
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"!pip install -q ipython # For jupyter audio display"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:54.623434Z",
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"start_time": "2020-12-15T13:09:54.241855Z"
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}
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},
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"outputs": [],
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"source": [
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"# dependencies\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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"from IPython.display import Audio\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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"\n",
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"\n",
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"(get_speech_ts,\n",
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" save_audio,\n",
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" read_audio,\n",
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" state_generator,\n",
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" single_audio_stream,\n",
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" collect_speeches) = 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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"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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:56.879818Z",
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"start_time": "2020-12-15T13:09:56.864765Z"
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}
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},
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"outputs": [],
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"source": [
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"wav = read_audio('files/en.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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:58.876034Z",
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"start_time": "2020-12-15T13:09:57.139254Z"
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}
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},
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"outputs": [],
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"source": [
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"speech_timestamps = get_speech_ts(wav, model, num_steps=4) # get speech timestamps from full audio file"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:58.885802Z",
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"start_time": "2020-12-15T13:09:58.877327Z"
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}
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},
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"outputs": [],
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"source": [
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"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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:58.941063Z",
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"start_time": "2020-12-15T13:09:58.887006Z"
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}
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},
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"outputs": [],
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"source": [
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"save_audio('only_speech.wav', collect_speeches(speech_timestamps, wav), 16000) # merge all speech chunks to one audio\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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"source": [
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"## Single audio stream"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:59.199321Z",
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"start_time": "2020-12-15T13:09:59.196823Z"
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}
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},
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"outputs": [],
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"source": [
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"wav = 'files/en.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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"ExecuteTime": {
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"end_time": "2020-12-15T13:10:03.585644Z",
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"start_time": "2020-12-15T13:09:59.429757Z"
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}
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},
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"outputs": [],
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"source": [
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"for batch in single_audio_stream(model, wav):\n",
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" if batch:\n",
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" print(batch)"
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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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"source": [
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"## Multiple audio stream"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:10:03.590358Z",
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"start_time": "2020-12-15T13:10:03.587071Z"
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}
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},
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"outputs": [],
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"source": [
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"audios_for_stream = glob.glob('files/*.wav')\n",
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"len(audios_for_stream) # total 4 audios"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:10:15.762491Z",
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"start_time": "2020-12-15T13:10:03.591388Z"
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}
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},
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"outputs": [],
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"source": [
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"for batch in state_generator(model, audios_for_stream, audios_in_stream=2): # 2 audio stream\n",
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" if batch:\n",
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" print(batch)"
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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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"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": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -q ipython # For jupyter audio display\n",
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"!pip install -q onnxruntime"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:05.932256Z",
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"start_time": "2020-12-15T13:09:05.043659Z"
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}
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},
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"outputs": [],
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"source": [
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"# dependencies\n",
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"import glob\n",
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"import torch\n",
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"from IPython.display import Audio\n",
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"torch.set_num_threads(1)\n",
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"import onnxruntime\n",
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"\n",
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"from utils import (get_speech_ts, save_audio, read_audio, \n",
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" state_generator, single_audio_stream, collect_speeches)\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": {},
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:06.643812Z",
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"start_time": "2020-12-15T13:09:06.473386Z"
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}
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},
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"outputs": [],
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"source": [
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"model = init_onnx_model('files/model.onnx')\n",
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"wav = read_audio('files/en.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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:08.094414Z",
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"start_time": "2020-12-15T13:09:07.073253Z"
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}
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},
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"outputs": [],
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"source": [
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"speech_timestamps = get_speech_ts(wav, model, num_steps=4, run_function=validate_onnx) # get speech timestamps from full audio file"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:08.107584Z",
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"start_time": "2020-12-15T13:09:08.096550Z"
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}
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},
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"outputs": [],
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"source": [
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"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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:08.862421Z",
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"start_time": "2020-12-15T13:09:08.820014Z"
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}
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},
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"outputs": [],
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"source": [
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"save_audio('only_speech.wav', collect_speeches(speech_timestamps, wav), 16000) # merge all speech chunks to one audio\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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"source": [
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"## Single audio stream"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:09.606031Z",
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"start_time": "2020-12-15T13:09:09.504239Z"
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}
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},
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"outputs": [],
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"source": [
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"model = init_onnx_model('files/model.onnx')\n",
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"wav = 'files/en.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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:11.453171Z",
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"start_time": "2020-12-15T13:09:09.633435Z"
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}
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},
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"outputs": [],
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"source": [
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"for batch in single_audio_stream(model, wav, run_function=validate_onnx):\n",
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" if batch:\n",
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" print(batch)"
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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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"source": [
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"## Multiple audio stream"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:11.540423Z",
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"start_time": "2020-12-15T13:09:11.455706Z"
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}
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},
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"outputs": [],
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"source": [
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"model = init_onnx_model('files/model.onnx')"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:11.550815Z",
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"start_time": "2020-12-15T13:09:11.542954Z"
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}
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},
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"outputs": [],
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"source": [
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"audios_for_stream = glob.glob('files/*.wav')\n",
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"len(audios_for_stream) # total 4 audios"
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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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"ExecuteTime": {
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"end_time": "2020-12-15T13:09:19.565434Z",
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"start_time": "2020-12-15T13:09:11.552097Z"
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}
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},
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"outputs": [],
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"source": [
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"for batch in state_generator(model, audios_for_stream, audios_in_stream=2, run_function=validate_onnx): # 2 audio stream\n",
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" if batch:\n",
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" print(batch)"
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]
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}
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],
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"metadata": {
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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": "3.8.3"
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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": 4
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}
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