{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_i4QyVZ72fmP",
"outputId": "d5317952-a58c-45c9-cfd9-15a0630b55a9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.9/94.9 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.2/11.2 MB\u001b[0m \u001b[31m116.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.3/62.3 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate openai gradio httpx==0.27.2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "XagYm-VV25T3"
},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import torch\n",
"import requests\n",
"import gradio as gr\n",
"import re\n",
"from openai import OpenAI\n",
"\n",
"from IPython.display import Markdown, display, update_display\n",
"from google.colab import drive, userdata\n",
"from huggingface_hub import login\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n"
]
},
{
"cell_type": "code",
"source": [
"# Model Constants\n",
"\n",
"AUDIO_MODEL = \"whisper-1\"\n",
"LLAMA = \"meta-llama/Meta-Llama-3.1-8B-Instruct\""
],
"metadata": {
"id": "XlBqO09ZKAmZ"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "0zFe47qg4lTe"
},
"outputs": [],
"source": [
"# Authentication\n",
"\n",
"hf_token = userdata.get(\"HF_TOKEN\")\n",
"openai_api_key = userdata.get('OPENAI_API_KEY')\n",
"if not hf_token or not openai_api_key:\n",
" raise ValueError(\"Missing HF_TOKEN or OPENAI_API_KEY. Set them as environment variables.\")\n",
"\n",
"login(hf_token, add_to_git_credential=True)\n",
"openai = OpenAI(api_key=openai_api_key)\n"
]
},
{
"cell_type": "code",
"source": [
"# Tokenizer Setup\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(LLAMA)\n",
"tokenizer.pad_token = tokenizer.eos_token"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
"4e6af82ad8df48ffb5462e0e141d116a",
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]
},
"id": "u2dg5OEkKkP8",
"outputId": "cad5fde9-97fe-4a48-f155-f18cc80197fc"
},
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/55.4k [00:00, ?B/s]"
],
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}
},
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer.json: 0%| | 0.00/9.09M [00:00, ?B/s]"
],
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/296 [00:00, ?B/s]"
],
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"version_major": 2,
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]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "vi7JAWRs60CU"
},
"outputs": [],
"source": [
"# Model Quantization for Performance Optimization\n",
"\n",
"quant_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_quant_type=\"nf4\"\n",
")"
]
},
{
"cell_type": "code",
"source": [
"# Load Model Efficiently\n",
"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map=\"auto\", quantization_config=quant_config)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 305,
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},
"id": "dl6VCcBmK2Oi",
"outputId": "ab4e510b-47b8-44fd-9680-f0df33b45275"
},
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/855 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
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"# Function to trancribe audio file\n",
"\n",
"def transcribe_audio(audio_file):\n",
" with open(audio_file, \"rb\") as file:\n",
" try:\n",
" transcription = openai.audio.transcriptions.create(\n",
" model=\"whisper-1\", file=file, response_format=\"text\"\n",
" )\n",
" return transcription if transcription else \"Transcription failed.\"\n",
" except Exception as e:\n",
" return f\"Error during transcription: {e}\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "IjFKUbkEUBh_"
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"#Generate Meeting Minutes\n",
"\n",
"def generate_meeting_minutes(transcription):\n",
" system_message = (\n",
" \"You are an AI assistant that generates structured meeting minutes from transcripts.\"\n",
" \" Provide a summary with attendees, location, date; discussion points; key takeaways; \"\n",
" \"and action items (with owners) in markdown format.\"\n",
" )\n",
"\n",
" user_prompt = f\"Transcript:\\n{transcription}\\n\\nGenerate structured minutes in markdown.\"\n",
"\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
"\n",
" inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\").to(\"cuda\", non_blocking=True)\n",
"\n",
" max_tokens = min(2000, len(transcription.split()) * 2) # Adjust max tokens based on input size\n",
"\n",
" with torch.no_grad():\n",
" outputs = model.generate(\n",
" inputs,\n",
" max_new_tokens=max_tokens,\n",
" pad_token_id=tokenizer.eos_token_id\n",
" )\n",
"\n",
" minutes = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()\n",
"\n",
" # Post-processing: Remvoed redundent trancription text\n",
" if transcription[:50] in minutes:\n",
" minutes = minutes.replace(transcription, \"\").strip()\n",
"\n",
" return minutes or \"Minutes generation failed\""
]
},
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"cell_type": "code",
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"metadata": {
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"def process_audio(file_path):\n",
" transcription = transcribe_audio(file_path)\n",
" if \"Error\" in transcription:\n",
" return transcription, \"Meeting minutes generation failed due to transcription error.\"\n",
" minutes = generate_meeting_minutes(transcription)\n",
" return transcription, minutes"
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},
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"output_type": "stream",
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"text": [
"Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
"\n",
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
"* Running on public URL: https://3f14d8d74d8fcb3d64.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
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""
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"text": [
"The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
]
},
{
"output_type": "stream",
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"text": [
"Keyboard interruption in main thread... closing server.\n",
"Killing tunnel 127.0.0.1:7860 <> https://3f14d8d74d8fcb3d64.gradio.live\n"
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},
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"# Gradio interface\n",
"with gr.Blocks() as iface:\n",
" gr.Markdown(\"# AI Meeting Minutes Generator\")\n",
" gr.Markdown(\"Upload an audio file, and the AI will transcribe it and generate structured meeting minutes.\")\n",
"\n",
" audio_input = gr.Audio(type=\"filepath\", label=\"Upload Audio File\")\n",
" submit_button = gr.Button(\"Generate Transcript and Meeting Minutes\")\n",
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" with gr.Row():\n",
" with gr.Column():\n",
" gr.Markdown(\"### Transcription\")\n",
" transcription_box = gr.Textbox(label=\"Transcription\", lines=10)\n",
"\n",
" with gr.Column():\n",
" gr.Markdown(\"### Meeting Minutes\")\n",
" minutes_box = gr.Markdown(label=\"Meeting Minutes\")\n",
"\n",
" submit_button.click(\n",
" process_audio,\n",
" inputs=audio_input,\n",
" outputs=[transcription_box, minutes_box]\n",
" )\n",
"\n",
"iface.launch(debug=True)"
]
}
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