Notebook
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bc566fe1-26f3-4e4f-9c62-ef5e480e0055",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from openai import OpenAI"
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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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"id": "b1f11cc6-4daa-4563-8dc0-fcb763b3c44f",
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"metadata": {},
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"outputs": [],
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"source": [
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"client = OpenAI()\n",
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"client.api_key = os.getenv('OPENAI_API_KEY')"
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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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"id": "87169d33-5a39-4270-91d7-50353b888a80",
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"metadata": {},
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"outputs": [],
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"source": [
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"def transcrire_audio(fichier_audio):\n",
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" reponse = client.audio.transcriptions.create(\n",
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" model=\"whisper-1\",\n",
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" file=open(fichier_audio, \"rb\"),\n",
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" language=\"fr\"\n",
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" )\n",
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" return reponse.text"
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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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"id": "89eb827d-abb9-4b46-92be-f26c02e3ba4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def resumer_texte(texte):\n",
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" completion = client.chat.completions.create(\n",
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" model=\"gpt-4\",\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": \"Tu es un assistant personnel compétent, utile et pertinent.\"},\n",
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" {\"role\": \"user\", \"content\": \"\"\"\n",
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"Tu dois résumer un texte qui est une transcription d'un enregistrement audio d'une intervention faite à l'occasion d'une conférence.\n",
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"\n",
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"Tu dois d'abord proposer un court paragraphe de résumé du contexte global de l'intervention du conférencier.\n",
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"Puis, tu dois proposer une synthèse de l'intervention sous forme d'une liste à puces.\n",
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"La liste à puces doit être organisée sur au maximum 2 niveaux hiérarchiques.\n",
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"Les entrées de la liste à puces doivent être très synthétiques, exprimées en quelques mots.\n",
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"Il est très important que le résumé n'omette pas d'informations importantes.\n",
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"Tu dois bien mettre en relief les données chiffrées mentionnés durant le discours. \n",
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"\n",
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"Le résultat doit être au format Markdown. La réponse doit contenir uniquement le code Markdown et rien d'autre.\n",
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"Voici le modèle de réponse au format Markdown :\n",
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"\n",
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"# Un titre pour le résumé\n",
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"## Contexte\n",
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"Court paragraphe qui résume le contexte de l'intervention.\n",
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"## Synthèse\n",
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"- Premier point important\n",
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" - Premier détail sur le premier point important.\n",
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" - Second détail sur le premier point important.\n",
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" - Etc. Etc.\n",
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"- Second point important\n",
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" - Premier détail sur le second point important.\n",
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" - Etc. Etc.\n",
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"- Etc. Etc.\n",
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" -Etc. Etc.\n",
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" - Etc. Etc.\n",
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"\n",
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"Voici le texte que tu dois résumer :\n",
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"\n",
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" \"\"\" + texte}\n",
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" ]\n",
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" )\n",
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" return completion.choices[0].message.content"
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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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"id": "279849d5-56f8-4b59-906e-14992ac10c4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"texte_transcrit = transcrire_audio('../transcribe_data/jardin_du_carrousel_2.m4a')"
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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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"id": "382c92b9-1c28-4906-beff-1d9405b765ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"texte_transcrit"
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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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"id": "3c5df824-f3cc-4a51-b1ad-c52695fa475f",
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"metadata": {},
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"outputs": [],
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"source": [
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"texte_transcrit_2 = transcrire_audio('../transcribe_data/jardin_du_carrousel_1.m4a')"
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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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"id": "8db68ab5-7351-4872-b5e7-0fe02c9760cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"texte_transcrit_2"
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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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"id": "3272e3b0-e758-4d0b-b4bd-01633d2aab20",
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"metadata": {},
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"outputs": [],
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"source": [
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"resume_texte_2 = resumer_texte(texte_transcrit_2)"
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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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"id": "0287311f-9c68-4443-bed4-c7fcf2de2344",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(resume_texte_2)"
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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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"id": "c4a18f17-1bf6-48e1-a2d2-c850c2d8c266",
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"metadata": {},
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"outputs": [],
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"source": [
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"client.api_key"
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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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"id": "af604560-1798-4817-80c4-3be63ea7d8f6",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "TRANSCRIBE_ENV",
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"language": "python",
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"name": "transcribe_env"
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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": "3.9.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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