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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Text task notebook template\n",
"## Loading the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"from fastapi import APIRouter\n",
"from datetime import datetime\n",
"from datasets import load_dataset\n",
"import librosa\n",
"from sklearn.metrics import accuracy_score\n",
"import random\n",
"import pandas as pd\n",
"import numpy as np\n",
"import sys\n",
"import json\n",
"sys.path.append('../tasks')\n",
"\n",
"from utils.evaluation import AudioEvaluationRequest\n",
"from utils.emissions import tracker, clean_emissions_data, get_space_info\n",
"\n",
"\n",
"# Define the label mapping\n",
"LABEL_MAPPING = {\n",
" \"chainsaw\": 0,\n",
" \"environment\": 1\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import login\n",
"login()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the datasets and splitting them"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"request = AudioEvaluationRequest()\n",
"\n",
"# Load and prepare the dataset\n",
"dataset = load_dataset(request.dataset_name)\n",
"\n",
"# Split dataset\n",
"train_test = dataset[\"train\"].train_test_split(test_size=request.test_size, seed=request.test_seed)\n",
"test_dataset = train_test[\"test\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = dataset[\"train\"]\n",
"test = dataset['test']\n",
"\n",
"train_df = pd.DataFrame(train)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"train_df[\"path\"] = train_df[\"audio\"].apply(lambda x: x['path'])\n",
"train_df[\"array\"] = train_df[\"audio\"].apply(lambda x: x['array'])\n",
"train_df[\"sampling_rate\"] = train_df[\"audio\"].apply(lambda x: x['sampling_rate'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Target sampling rate\n",
"target_sr = 12000\n",
"\n",
"# Function to resample the audio array\n",
"def resample_audio(array, orig_sr, target_sr):\n",
" array = np.array(array) # Ensure it's a numpy array\n",
" if orig_sr != target_sr:\n",
" array = librosa.resample(array, orig_sr=orig_sr, target_sr=target_sr)\n",
" return array\n",
"\n",
"# Apply resampling to each row\n",
"train_df[\"resampled_array\"] = train_df.apply(\n",
" lambda row: resample_audio(row[\"array\"], row[\"sampling_rate\"], target_sr), axis=1\n",
")\n",
"\n",
"# Update the sampling rate column to reflect the target rate\n",
"train_df[\"sampling_rate\"] = target_sr\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_df.sampling_rate.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Baseline"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Start tracking emissions\n",
"tracker.start()\n",
"tracker.start_task(\"inference\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#--------------------------------------------------------------------------------------------\n",
"# YOUR MODEL INFERENCE CODE HERE\n",
"# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
"#-------------------------------------------------------------------------------------------- \n",
"\n",
"# Make random predictions (placeholder for actual model inference)\n",
"true_labels = test_dataset[\"label\"]\n",
"\n",
"\n",
"predictions = [random.randint(0, 1) for _ in range(len(true_labels))]\n",
"\n",
"predictions\n",
"\n",
"#--------------------------------------------------------------------------------------------\n",
"# YOUR MODEL INFERENCE STOPS HERE\n",
"#-------------------------------------------------------------------------------------------- "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Stop tracking emissions\n",
"emissions_data = tracker.stop_task()\n",
"emissions_data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate accuracy\n",
"accuracy = accuracy_score(true_labels, predictions)\n",
"accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Prepare results dictionary\n",
"results = {\n",
" \"submission_timestamp\": datetime.now().isoformat(),\n",
" \"accuracy\": float(accuracy),\n",
" \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
" \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
" \"emissions_data\": clean_emissions_data(emissions_data),\n",
" \"dataset_config\": {\n",
" \"dataset_name\": request.dataset_name,\n",
" \"test_size\": request.test_size,\n",
" \"test_seed\": request.test_seed\n",
" }\n",
"}\n",
"\n",
"results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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