Auto-format by https://ultralytics.com/actions
UltralyticsAssistant
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"authorship_tag": "ABX9TyNnTQLE9Hln//EdaNSeva+y",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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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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"colab_type": "text",
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"id": "view-in-github"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/Leedong414/yolov5/blob/master/Untitled1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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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": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/Leedong414/yolov5/blob/master/Untitled1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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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": "8pmMHhGEOE2B"
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "NBJOrKoMOGT5",
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"outputId": "f1b29278-57c4-4886-93a5-ecf01612288e"
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},
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"outputs": [
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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": "8pmMHhGEOE2B"
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"source": [
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "NBJOrKoMOGT5",
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"outputId": "f1b29278-57c4-4886-93a5-ecf01612288e"
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},
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Mounted at /content/drive\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
|
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"pip install gtts"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"collapsed": true,
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"id": "BeEnl52JOKNY",
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"outputId": "7b73f30b-f650-45ef-af5f-4b5f7c85eff3"
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},
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Collecting gtts\n",
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" Downloading gTTS-2.5.1-py3-none-any.whl (29 kB)\n",
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"Requirement already satisfied: requests<3,>=2.27 in /usr/local/lib/python3.10/dist-packages (from gtts) (2.31.0)\n",
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"Requirement already satisfied: click<8.2,>=7.1 in /usr/local/lib/python3.10/dist-packages (from gtts) (8.1.7)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (3.3.2)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (3.7)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (2.0.7)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (2024.2.2)\n",
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"Installing collected packages: gtts\n",
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"Successfully installed gtts-2.5.1\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"from gtts import gTTS\n",
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"\n",
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"def speak(text):\n",
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"\ttts = gTTS(text=text, lang='ko')\n",
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"\ttts.save('voice.mp3')\n",
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"\n",
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"speak(\"안녕하세요, 저는 IML이에요.\")"
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],
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"metadata": {
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"id": "VnDDOpZjOYGe"
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},
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"tts = gTTS(text=text, lang='ko')\n",
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"mp3_fp = BytesIO()\n",
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"tts.write_to_fp(mp3_fp)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 184
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},
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"id": "jLuBkQBZO5cv",
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"outputId": "3de9b61b-8168-4272-c409-5d5b6eca5c7d"
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},
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "error",
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"ename": "NameError",
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"evalue": "name 'text' is not defined",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-5-ace0f3904d56>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgTTS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ko'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmp3_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_to_fp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmp3_fp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'text' is not defined"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"%cd /content/drive/MyDrive/yolov5"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "BJw9zD_OUeYI",
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"outputId": "f8e4baf3-9809-4504-d5db-2f0a487910d6"
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},
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"execution_count": 16,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content/drive/MyDrive/yolov5\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"!python detect.py --weights /content/drive/MyDrive/yolov5/runs/train/Project_yolo5/weights/best.pt --conf 0.5 --source /content/drive/MyDrive/13.jpg\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "44W31K33V2nK",
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"outputId": "c38dd175-49ca-4622-b830-7603bd7a9b38"
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},
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"execution_count": 20,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
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"YOLOv5 🚀 2024-5-28 Python-3.10.12 torch-2.3.0+cu121 CPU\n",
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"\n",
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"Fusing layers... \n",
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"YOLOv5s summary: 157 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
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"Adding AutoShape... \n",
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"Detected Classes:\n",
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"Hardhat\n",
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"NO-Mask\n",
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"NO-Mask\n",
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"Hardhat\n",
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"Safety Vest\n",
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"sh: 1: mpg321: not found\n",
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"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/content/drive/MyDrive/yolov5/runs/train/Project_yolo5/weights/best.pt'], source=/content/drive/MyDrive/13.jpg, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.5, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
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"YOLOv5 🚀 2024-5-28 Python-3.10.12 torch-2.3.0+cu121 CPU\n",
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"\n",
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"Fusing layers... \n",
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"YOLOv5s summary: 157 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
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"image 1/1 /content/drive/MyDrive/13.jpg: 608x640 2 Hardhats, 2 NO-Masks, 1 Safety Vest, 674.3ms\n",
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"Speed: 9.7ms pre-process, 674.3ms inference, 1.1ms NMS per image at shape (1, 3, 640, 640)\n",
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"Results saved to \u001b[1mruns/detect/exp3\u001b[0m\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import pyaudio\n",
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"import wave\n",
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"import speech_recognition as sr\n",
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"\n",
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"def audio_save():\n",
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"\n",
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" FORMAT = pyaudio.paInt16 # 16비트 형식으로 설정\n",
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" CHANNELS = 1\n",
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" RATE = 44100\n",
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" CHUNK = 1024\n",
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" RECORD_SECONDS = 10\n",
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" OUTPUT_FILENAME = \"recorded_audio.wav\" # 저장할 파일 이름\n",
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"\n",
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"\n",
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" audio = pyaudio.PyAudio()\n",
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"\n",
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" stream = audio.open(format=FORMAT, channels=CHANNELS,\n",
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" rate=RATE, input=True,\n",
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" frames_per_buffer=CHUNK)\n",
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"\n",
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" print(\"녹음 시작...\")\n",
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"\n",
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" frames = []\n",
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" for _ in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n",
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" data = stream.read(CHUNK)\n",
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" frames.append(data)\n",
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"\n",
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" print(\"녹음 완료.\")\n",
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"\n",
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"\n",
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" stream.stop_stream()\n",
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" stream.close()\n",
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" audio.terminate()\n",
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"\n",
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" # 음성 파일로 저장\n",
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" with wave.open(OUTPUT_FILENAME, 'wb') as wf:\n",
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" wf.setnchannels(CHANNELS)\n",
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" wf.setsampwidth(audio.get_sample_size(FORMAT))\n",
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" wf.setframerate(RATE)\n",
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" wf.writeframes(b''.join(frames))\n",
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"\n",
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" return print(\"파일 저장 완료:\", OUTPUT_FILENAME)\n",
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"\n",
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"def tts_module():\n",
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"\n",
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" r = sr.Recognizer()\n",
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" kr_audio = sr.AudioFile('recorded_audio.wav')\n",
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"\n",
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" with kr_audio as source:\n",
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" audio = r.record(source)\n",
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"\n",
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" a=r.recognize_google(audio, language='ko-KR')\n",
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" print(r.recognize_google(audio, language='ko-KR'))\n",
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"\n",
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"\n",
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"\n",
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" return a\n",
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"\n",
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"if __name__ == '__main__':\n",
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" audio_save()\n",
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" sentiment_data=tts_module()\n",
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" print(\"a\",sentiment_data)"
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],
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"metadata": {
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"id": "-uVK5NOKaiF6"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"sentiment_data=='내 눈 앞에 뭐가 있어'"
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],
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"metadata": {
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"id": "P9WufmsAbG02"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import speech_recognition as sr\n",
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"import subprocess\n",
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"\n",
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"def tts_module(audio_file):\n",
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" r = sr.Recognizer()\n",
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"\n",
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" with sr.AudioFile(audio_file) as source:\n",
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" audio = r.record(source)\n",
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"\n",
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" try:\n",
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" recognized_text = r.recognize_google(audio, language='ko-KR')\n",
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" print(\"인식된 텍스트:\", recognized_text)\n",
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" return recognized_text\n",
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" except sr.UnknownValueError:\n",
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" print(\"Google Speech Recognition이 음성을 인식할 수 없습니다.\")\n",
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" return \"\"\n",
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" except sr.RequestError as e:\n",
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" print(f\"Google Speech Recognition 서비스에 요청할 수 없습니다; {e}\")\n",
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" return \"\"\n",
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"\n",
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"if __name__ == '__main__':\n",
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" audio_file = '/content/drive/MyDrive/recorded_audio (1).wav' # 이미 녹음된 음성 파일 경로\n",
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" recognized_text = tts_module(audio_file)\n",
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"\n",
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" if recognized_text == \"내 앞에 뭐 있어\":\n",
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" print(\"명령어를 인식했습니다. detect.py를 실행합니다.\")\n",
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" subprocess.run([\"python\", \"/content/drive/MyDrive/yolov5/detect.py\", \"--weights\", \"/content/drive/MyDrive/yolov5/runs/train/Project_yolo5/weights/best.pt\", \"--conf\", \"0.5\", \"--source\",\"/content/drive/MyDrive/33.jpg\"\n",
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"])\n",
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"\n",
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" else:\n",
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" print(\"인식된 명령어가 없습니다.\")"
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],
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"metadata": {
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"colab": {
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||||
"base_uri": "https://localhost:8080/"
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},
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"id": "KXSMitA_xiJE",
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"outputId": "e667394c-2649-446c-c41e-22b915a7bec1"
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},
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"execution_count": 101,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"인식된 텍스트: 내 앞에 뭐 있어\n",
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"명령어를 인식했습니다. detect.py를 실행합니다.\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "K1iWPfmVx1Wp"
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},
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"execution_count": null,
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"outputs": []
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mounted at /content/drive\n"
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]
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}
|
||||
]
|
||||
],
|
||||
"source": [
|
||||
"from google.colab import drive\n",
|
||||
"\n",
|
||||
"drive.mount(\"/content/drive\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"collapsed": true,
|
||||
"id": "BeEnl52JOKNY",
|
||||
"outputId": "7b73f30b-f650-45ef-af5f-4b5f7c85eff3"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Collecting gtts\n",
|
||||
" Downloading gTTS-2.5.1-py3-none-any.whl (29 kB)\n",
|
||||
"Requirement already satisfied: requests<3,>=2.27 in /usr/local/lib/python3.10/dist-packages (from gtts) (2.31.0)\n",
|
||||
"Requirement already satisfied: click<8.2,>=7.1 in /usr/local/lib/python3.10/dist-packages (from gtts) (8.1.7)\n",
|
||||
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (3.3.2)\n",
|
||||
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (3.7)\n",
|
||||
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (2.0.7)\n",
|
||||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (2024.2.2)\n",
|
||||
"Installing collected packages: gtts\n",
|
||||
"Successfully installed gtts-2.5.1\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pip install gtts"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"id": "VnDDOpZjOYGe"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from gtts import gTTS\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def speak(text):\n",
|
||||
" tts = gTTS(text=text, lang=\"ko\")\n",
|
||||
" tts.save(\"voice.mp3\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"speak(\"안녕하세요, 저는 IML이에요.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 184
|
||||
},
|
||||
"id": "jLuBkQBZO5cv",
|
||||
"outputId": "3de9b61b-8168-4272-c409-5d5b6eca5c7d"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'text' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-5-ace0f3904d56>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgTTS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ko'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmp3_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_to_fp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmp3_fp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mNameError\u001b[0m: name 'text' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tts = gTTS(text=text, lang=\"ko\")\n",
|
||||
"mp3_fp = BytesIO()\n",
|
||||
"tts.write_to_fp(mp3_fp)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "BJw9zD_OUeYI",
|
||||
"outputId": "f8e4baf3-9809-4504-d5db-2f0a487910d6"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/content/drive/MyDrive/yolov5\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%cd /content/drive/MyDrive/yolov5"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "44W31K33V2nK",
|
||||
"outputId": "c38dd175-49ca-4622-b830-7603bd7a9b38"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
|
||||
"YOLOv5 🚀 2024-5-28 Python-3.10.12 torch-2.3.0+cu121 CPU\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv5s summary: 157 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
|
||||
"Adding AutoShape... \n",
|
||||
"Detected Classes:\n",
|
||||
"Hardhat\n",
|
||||
"NO-Mask\n",
|
||||
"NO-Mask\n",
|
||||
"Hardhat\n",
|
||||
"Safety Vest\n",
|
||||
"sh: 1: mpg321: not found\n",
|
||||
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/content/drive/MyDrive/yolov5/runs/train/Project_yolo5/weights/best.pt'], source=/content/drive/MyDrive/13.jpg, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.5, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
|
||||
"YOLOv5 🚀 2024-5-28 Python-3.10.12 torch-2.3.0+cu121 CPU\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv5s summary: 157 layers, 7037095 parameters, 0 gradients, 15.8 GFLOPs\n",
|
||||
"image 1/1 /content/drive/MyDrive/13.jpg: 608x640 2 Hardhats, 2 NO-Masks, 1 Safety Vest, 674.3ms\n",
|
||||
"Speed: 9.7ms pre-process, 674.3ms inference, 1.1ms NMS per image at shape (1, 3, 640, 640)\n",
|
||||
"Results saved to \u001b[1mruns/detect/exp3\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!python detect.py --weights /content/drive/MyDrive/yolov5/runs/train/Project_yolo5/weights/best.pt --conf 0.5 --source /content/drive/MyDrive/13.jpg"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "-uVK5NOKaiF6"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import wave\n",
|
||||
"\n",
|
||||
"import pyaudio\n",
|
||||
"import speech_recognition as sr\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def audio_save():\n",
|
||||
" FORMAT = pyaudio.paInt16 # 16비트 형식으로 설정\n",
|
||||
" CHANNELS = 1\n",
|
||||
" RATE = 44100\n",
|
||||
" CHUNK = 1024\n",
|
||||
" RECORD_SECONDS = 10\n",
|
||||
" OUTPUT_FILENAME = \"recorded_audio.wav\" # 저장할 파일 이름\n",
|
||||
"\n",
|
||||
" audio = pyaudio.PyAudio()\n",
|
||||
"\n",
|
||||
" stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)\n",
|
||||
"\n",
|
||||
" print(\"녹음 시작...\")\n",
|
||||
"\n",
|
||||
" frames = []\n",
|
||||
" for _ in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n",
|
||||
" data = stream.read(CHUNK)\n",
|
||||
" frames.append(data)\n",
|
||||
"\n",
|
||||
" print(\"녹음 완료.\")\n",
|
||||
"\n",
|
||||
" stream.stop_stream()\n",
|
||||
" stream.close()\n",
|
||||
" audio.terminate()\n",
|
||||
"\n",
|
||||
" # 음성 파일로 저장\n",
|
||||
" with wave.open(OUTPUT_FILENAME, \"wb\") as wf:\n",
|
||||
" wf.setnchannels(CHANNELS)\n",
|
||||
" wf.setsampwidth(audio.get_sample_size(FORMAT))\n",
|
||||
" wf.setframerate(RATE)\n",
|
||||
" wf.writeframes(b\"\".join(frames))\n",
|
||||
"\n",
|
||||
" return print(\"파일 저장 완료:\", OUTPUT_FILENAME)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def tts_module():\n",
|
||||
" r = sr.Recognizer()\n",
|
||||
" kr_audio = sr.AudioFile(\"recorded_audio.wav\")\n",
|
||||
"\n",
|
||||
" with kr_audio as source:\n",
|
||||
" audio = r.record(source)\n",
|
||||
"\n",
|
||||
" a = r.recognize_google(audio, language=\"ko-KR\")\n",
|
||||
" print(r.recognize_google(audio, language=\"ko-KR\"))\n",
|
||||
"\n",
|
||||
" return a\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"if __name__ == \"__main__\":\n",
|
||||
" audio_save()\n",
|
||||
" sentiment_data = tts_module()\n",
|
||||
" print(\"a\", sentiment_data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "P9WufmsAbG02"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentiment_data == \"내 눈 앞에 뭐가 있어\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 101,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "KXSMitA_xiJE",
|
||||
"outputId": "e667394c-2649-446c-c41e-22b915a7bec1"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"인식된 텍스트: 내 앞에 뭐 있어\n",
|
||||
"명령어를 인식했습니다. detect.py를 실행합니다.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import subprocess\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def tts_module(audio_file):\n",
|
||||
" r = sr.Recognizer()\n",
|
||||
"\n",
|
||||
" with sr.AudioFile(audio_file) as source:\n",
|
||||
" audio = r.record(source)\n",
|
||||
"\n",
|
||||
" try:\n",
|
||||
" recognized_text = r.recognize_google(audio, language=\"ko-KR\")\n",
|
||||
" print(\"인식된 텍스트:\", recognized_text)\n",
|
||||
" return recognized_text\n",
|
||||
" except sr.UnknownValueError:\n",
|
||||
" print(\"Google Speech Recognition이 음성을 인식할 수 없습니다.\")\n",
|
||||
" return \"\"\n",
|
||||
" except sr.RequestError as e:\n",
|
||||
" print(f\"Google Speech Recognition 서비스에 요청할 수 없습니다; {e}\")\n",
|
||||
" return \"\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"if __name__ == \"__main__\":\n",
|
||||
" audio_file = \"/content/drive/MyDrive/recorded_audio (1).wav\" # 이미 녹음된 음성 파일 경로\n",
|
||||
" recognized_text = tts_module(audio_file)\n",
|
||||
"\n",
|
||||
" if recognized_text == \"내 앞에 뭐 있어\":\n",
|
||||
" print(\"명령어를 인식했습니다. detect.py를 실행합니다.\")\n",
|
||||
" subprocess.run(\n",
|
||||
" [\n",
|
||||
" \"python\",\n",
|
||||
" \"/content/drive/MyDrive/yolov5/detect.py\",\n",
|
||||
" \"--weights\",\n",
|
||||
" \"/content/drive/MyDrive/yolov5/runs/train/Project_yolo5/weights/best.pt\",\n",
|
||||
" \"--conf\",\n",
|
||||
" \"0.5\",\n",
|
||||
" \"--source\",\n",
|
||||
" \"/content/drive/MyDrive/33.jpg\",\n",
|
||||
" ]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" else:\n",
|
||||
" print(\"인식된 명령어가 없습니다.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "K1iWPfmVx1Wp"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"authorship_tag": "ABX9TyNnTQLE9Hln//EdaNSeva+y",
|
||||
"include_colab_link": true,
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Loading…
Reference in New Issue