training-of-customized-urbansound8k-multi-labeled.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/owaiskhan9654/training-of-customized-urbansound8k-multi-labeled?scriptVersionId=179603063\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"22e03dc4","metadata":{"papermill":{"duration":0.011741,"end_time":"2024-05-24T20:38:20.474742","exception":false,"start_time":"2024-05-24T20:38:20.463001","status":"completed"},"tags":[]},"source":["# **<span style=\"color:#F32321;\">Connect on Linkedin if you have any doubts**</span> - [Contact](https://www.linkedin.com/in/owaiskhan9654/)"]},{"cell_type":"markdown","id":"a76654ed","metadata":{"papermill":{"duration":0.010026,"end_time":"2024-05-24T20:38:20.49532","exception":false,"start_time":"2024-05-24T20:38:20.485294","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Training of Customized UrbanSound8K Multi Labeled Audio Data.<br><br> This Notebook is Highly Motivated by Jackson Goode's work <a href=\"https://jacksongoo.de/about/\">Link</a>.</div>"]},{"cell_type":"markdown","id":"30bbfb48","metadata":{"papermill":{"duration":0.01007,"end_time":"2024-05-24T20:38:20.515727","exception":false,"start_time":"2024-05-24T20:38:20.505657","status":"completed"},"tags":[]},"source":["<p style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\"><a href=\"https://urbansounddataset.weebly.com/urbansound8k.html\">UrbanSound8K</a> contains over 8000 sound files separated by categories of sounds typically found in an urban setting. Instead of exclusive categorical labels in its original state, the dataset has been recreated with the purpose of exploring how a successful architecture might perform on multi-label samples rather than simply uni-label, multi-class sounds. Thus, this notebook investigates how techniques in classifying an environmental noise data might generalize to a <b>Multi-Label scenario with a new Audio data </b>composed of overlaid sound pairs.\n","</p>\n","\n","<h2 style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\">Overview of dataset and fabrication</h2>\n","<p style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\">The original dataset, UrbanSound8K, contains 8732 .wav sounds sourced from Freesound.org across 10 classes of urban sounds:</p>\n","<ol style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">\n","<li>Air Conditioner</li>\n","<li>Car Horn</li>\n","<li>Children Playing</li>\n","<li>Dog Bark</li>\n","<li>Drilling</li>\n","<li>Engine Idling</li>\n","<li>Gun Shot</li>\n","<li>Jackhammer</li>\n","<li>Siren</li>\n","<li>Street Music</li>\n","</ol>\n","<p style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\">All are separated into 10 folds (each containing an equal distribution selection of the classes). These sounds are all less than 4s but vary in length, recording device, sample rate and perceptual loudness. Many of the sounds were generated as slices from longer sounds - meaning that many sounds share the same sound file source.</p>\n","<p style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\">For creating a new dataset, composition of multiple tracks of audio from UrbanSound8K. Using the library pydub he created a separate script to use a randomized list of each fold’s files and overplayed each file with a randomized gain reduction between -6 and 0. This enabled a more authentic mixture of sound in a live context and would also contribute to the robustness of the model and its consequent difficulty during training (10 classes to 45 conceptual classes (or 10 choose 2)).</p>"]},{"cell_type":"markdown","id":"3f4f455b","metadata":{"papermill":{"duration":0.010094,"end_time":"2024-05-24T20:38:20.53611","exception":false,"start_time":"2024-05-24T20:38:20.526016","status":"completed"},"tags":[]},"source":["\n"," <h2 style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\"> This Notebook is Rebuild version by <a href=\"https://www.linkedin.com/in/owaiskhan9654/\">Owais Ahmad</a> for training of Multi-Labeled audio data processed from orignal audios taken from UrbanSound8K.</h2>\n","<ol style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">\n","\n","<br>\n","<br>\n","\n","> \n","> <p style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\">I have integrated W&B for visualizations and logging artifacts here in below link!</p>\n","> <p style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\"> <a href=\"https://wandb.ai/owaiskhan9515/Training%20of%20Customized%20UrbanSound8K%20Multi%20Labeled%20Audio%20Data\">Training of Customized UrbanSound8K Multi Labeled Audio Data</a></p>\n","\n","\n","> - <p style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\">To get the API key, create an account in the <a href=\"https://wandb.ai/site\">website.</a></p>\n","> - <p style=\"line-height: 1.7;color: #666;font-weight: 600;margin-bottom: 20px;letter-spacing: 0.4px;\">Use secrets to use API Keys more securely </p>"]},{"cell_type":"code","execution_count":1,"id":"afb28db1","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:38:20.55834Z","iopub.status.busy":"2024-05-24T20:38:20.557934Z","iopub.status.idle":"2024-05-24T20:38:21.597484Z","shell.execute_reply":"2024-05-24T20:38:21.596476Z"},"papermill":{"duration":1.053632,"end_time":"2024-05-24T20:38:21.600006","exception":false,"start_time":"2024-05-24T20:38:20.546374","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Fri May 24 20:38:21 2024       \r\n","+---------------------------------------------------------------------------------------+\r\n","| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |\r\n","|-----------------------------------------+----------------------+----------------------+\r\n","| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n","| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\r\n","|                                         |                      |               MIG M. |\r\n","|=========================================+======================+======================|\r\n","|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\r\n","| N/A   41C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\r\n","|                                         |                      |                  N/A |\r\n","+-----------------------------------------+----------------------+----------------------+\r\n","|   1  Tesla T4                       Off | 00000000:00:05.0 Off |                    0 |\r\n","| N/A   41C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\r\n","|                                         |                      |                  N/A |\r\n","+-----------------------------------------+----------------------+----------------------+\r\n","                                                                                         \r\n","+---------------------------------------------------------------------------------------+\r\n","| Processes:                                                                            |\r\n","|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\r\n","|        ID   ID                                                             Usage      |\r\n","|=======================================================================================|\r\n","|  No running processes found                                                           |\r\n","+---------------------------------------------------------------------------------------+\r\n"]}],"source":["!nvidia-smi # 2 GPUs available in Kaggle "]},{"cell_type":"code","execution_count":2,"id":"373d1c2b","metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2024-05-24T20:38:21.624281Z","iopub.status.busy":"2024-05-24T20:38:21.62398Z","iopub.status.idle":"2024-05-24T20:38:37.498356Z","shell.execute_reply":"2024-05-24T20:38:37.496893Z"},"papermill":{"duration":15.888909,"end_time":"2024-05-24T20:38:37.501024","exception":false,"start_time":"2024-05-24T20:38:21.612115","status":"completed"},"tags":[]},"outputs":[],"source":["%%capture\n","!pip install wandb --upgrade"]},{"cell_type":"code","execution_count":3,"id":"5f8de015","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:38:37.525622Z","iopub.status.busy":"2024-05-24T20:38:37.525233Z","iopub.status.idle":"2024-05-24T20:38:44.986675Z","shell.execute_reply":"2024-05-24T20:38:44.985528Z"},"papermill":{"duration":7.476055,"end_time":"2024-05-24T20:38:44.988854","exception":false,"start_time":"2024-05-24T20:38:37.512799","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<Figure size 864x288 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import os\n","import sys \n","import time\n","import random\n","import glob\n","import csv\n","from ast import literal_eval\n","\n","from tqdm import tqdm\n","from tqdm.notebook import tqdm_notebook\n","\n","\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","import librosa\n","import librosa.display\n","plt.figure(figsize=(12, 4))\n","\n","import wandb\n","# from wandb.keras import WandbCallback\n","from wandb.integration.keras import WandbMetricsLogger\n","from kaggle_secrets import UserSecretsClient\n","\n","from pydub import AudioSegment, effects\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import MultiLabelBinarizer\n","from sklearn.metrics import accuracy_score, hamming_loss, multilabel_confusion_matrix, classification_report\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras.callbacks import ModelCheckpoint\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\", category=np.VisibleDeprecationWarning) "]},{"cell_type":"code","execution_count":4,"id":"6591a31c","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:38:45.012309Z","iopub.status.busy":"2024-05-24T20:38:45.011729Z","iopub.status.idle":"2024-05-24T20:39:02.658613Z","shell.execute_reply":"2024-05-24T20:39:02.657555Z"},"papermill":{"duration":17.661156,"end_time":"2024-05-24T20:39:02.660902","exception":false,"start_time":"2024-05-24T20:38:44.999746","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mowaiskhan9515\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n","\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.17.0\n","\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20240524_203846-cflgqje9\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33m16.Training of Customized UrbanSound8K-Run-16\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/owaiskhan9515/Training%20of%20Customized%20UrbanSound8K%20Multi%20Labeled%20Audio%20Data\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/owaiskhan9515/Training%20of%20Customized%20UrbanSound8K%20Multi%20Labeled%20Audio%20Data/runs/cflgqje9\u001b[0m\n"]},{"data":{"text/html":["<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/owaiskhan9515/Training%20of%20Customized%20UrbanSound8K%20Multi%20Labeled%20Audio%20Data/runs/cflgqje9?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"],"text/plain":["<wandb.sdk.wandb_run.Run at 0x7e115c8bc090>"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["try:\n","    user_secrets = UserSecretsClient()\n","    secret_value_0 = user_secrets.get_secret(\"wandb_api\")\n","    wandb.login(key=secret_value_0)\n","    anony=None\n","except:\n","    anony = \"must\"\n","    print('If you want to use your W&B account, go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as wandb_api. \\nGet your W&B access token from here: https://wandb.ai/authorize')\n","    \n","wandb.init(project=\"Training of Customized UrbanSound8K Multi Labeled Audio Data\",name=f\"16.Training of Customized UrbanSound8K-Run-16\")\n"]},{"cell_type":"code","execution_count":5,"id":"5c7391a6","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:39:02.685384Z","iopub.status.busy":"2024-05-24T20:39:02.685073Z","iopub.status.idle":"2024-05-24T20:39:13.159497Z","shell.execute_reply":"2024-05-24T20:39:13.158379Z"},"papermill":{"duration":10.489365,"end_time":"2024-05-24T20:39:13.162031","exception":false,"start_time":"2024-05-24T20:39:02.672666","status":"completed"},"tags":[]},"outputs":[],"source":["!mkdir /kaggle/working/multi/\n","!mkdir /kaggle/working/multi/fold1\n","\n","!mkdir /kaggle/working/multi/fold2\n","\n","!mkdir /kaggle/working/multi/fold3\n","\n","!mkdir /kaggle/working/multi/fold4\n","\n","!mkdir /kaggle/working/multi/fold5\n","\n","!mkdir /kaggle/working/multi/fold6\n","\n","!mkdir /kaggle/working/multi/fold7\n","\n","!mkdir /kaggle/working/multi/fold8\n","\n","!mkdir /kaggle/working/multi/fold9\n","\n","!mkdir /kaggle/working/multi/fold10"]},{"cell_type":"code","execution_count":6,"id":"f662afca","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:39:13.187733Z","iopub.status.busy":"2024-05-24T20:39:13.187377Z","iopub.status.idle":"2024-05-24T20:39:14.182588Z","shell.execute_reply":"2024-05-24T20:39:14.181105Z"},"papermill":{"duration":1.011201,"end_time":"2024-05-24T20:39:14.185902","exception":false,"start_time":"2024-05-24T20:39:13.174701","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[0m\u001b[01;34mfold1\u001b[0m/  \u001b[01;34mfold10\u001b[0m/  \u001b[01;34mfold2\u001b[0m/  \u001b[01;34mfold3\u001b[0m/  \u001b[01;34mfold4\u001b[0m/  \u001b[01;34mfold5\u001b[0m/  \u001b[01;34mfold6\u001b[0m/  \u001b[01;34mfold7\u001b[0m/  \u001b[01;34mfold8\u001b[0m/  \u001b[01;34mfold9\u001b[0m/\r\n"]}],"source":["ls /kaggle/working/multi"]},{"cell_type":"markdown","id":"bdbd6258","metadata":{"papermill":{"duration":0.01161,"end_time":"2024-05-24T20:39:14.213552","exception":false,"start_time":"2024-05-24T20:39:14.201942","status":"completed"},"tags":[]},"source":["\n","# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Data generation with pydub (2 samples per file)</div>"]},{"cell_type":"code","execution_count":7,"id":"81fae208","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:39:14.238445Z","iopub.status.busy":"2024-05-24T20:39:14.238076Z","iopub.status.idle":"2024-05-24T20:53:50.510782Z","shell.execute_reply":"2024-05-24T20:53:50.509582Z"},"papermill":{"duration":876.287837,"end_time":"2024-05-24T20:53:50.512921","exception":false,"start_time":"2024-05-24T20:39:14.225084","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5350601832394639ba4367356a5f406e","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/10 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Reading On fold: fold1. Numbers of Audio files: 873\n","Unable to read file... 88466-7-0-0.wav\n","Reading On fold: fold2. Numbers of Audio files: 888\n","Reading On fold: fold3. Numbers of Audio files: 925\n","Reading On fold: fold4. Numbers of Audio files: 990\n","Reading On fold: fold5. Numbers of Audio files: 936\n","Unable to read file... 19007-4-0-0.wav\n","Reading On fold: fold6. Numbers of Audio files: 823\n","Reading On fold: fold7. Numbers of Audio files: 838\n","Reading On fold: fold8. Numbers of Audio files: 806\n","Unable to read file... 36429-2-0-6.wav\n","Unable to read file... 36429-2-0-15.wav\n","Unable to read file... 36429-2-0-23.wav\n","Unable to read file... 36429-2-0-7.wav\n","Unable to read file... 36429-2-0-13.wav\n","Unable to read file... 36429-2-0-14.wav\n","Unable to read file... 36429-2-0-18.wav\n","Reading On fold: fold9. Numbers of Audio files: 816\n","Reading On fold: fold10. Numbers of Audio files: 837\n","CPU times: user 12min 37s, sys: 13.2 s, total: 12min 50s\n","Wall time: 14min 36s\n"]}],"source":["%%time\n","\n","source_dir = '../input/urbansound8k/'\n","dest_dir = '/kaggle/working/multi/' # new folds files are stored in multi folder next to original audio folder\n","folds = np.array(['fold1','fold2','fold3','fold4',\n","                  'fold5','fold6','fold7','fold8',\n","                  'fold9','fold10'])\n","\n","multi_num = 2 # number of overlayed files\n","files = [0] * multi_num\n","labels = [0] * multi_num\n","samples = [0] * multi_num\n","sounds = [0] * multi_num\n","names = [0] * multi_num\n","label_dict = {}\n","exception = False\n","\n","for fold in tqdm_notebook(folds):\n","    fold_files = os.listdir(source_dir+fold)\n","    fold_files = [f for f in fold_files if f.endswith('.wav')]\n","    random.shuffle(fold_files)\n","    print(f'Reading On fold: {fold}. Numbers of Audio files:',len(fold_files))\n","\n","    while len(fold_files) > 1:\n","        rand_gain = [random.randint(-6,0) for _ in range(multi_num)]\n","        p_ratio = [0] * multi_num\n","        for j in range(multi_num):\n","            try:\n","                samples[j] = fold_files[j]\n","                sounds[j] = AudioSegment.from_file(f'{source_dir}{fold}/{samples[j]}')\n","            except:\n","                fold_files.remove(fold_files[j]) # remove file from list unable to load - prevents unused files when 1st loads and 2nd fails\n","                exception = True \n","                break\n","\n","        if exception is True:\n","            print('Unable to read file...',samples[j])\n","            exception = False # reset\n","            continue # back to iter of last successful load\n","        else:\n","            del fold_files[0:multi_num] # pop loaded files off top\n","        \n","        for j in range(multi_num): # go through again to mix\n","            labels[j] = samples[j].split('-')[1] # get label\n","            sounds[j] = effects.normalize(sounds[j]) # normalize\n","            sounds[j] = sounds[j] + rand_gain[j] # add random gain reduction\n","            p_ratio[j] = pow(10, rand_gain[j]/10) # convert to power\n","            names[j] = f'-{labels[j]}({round(p_ratio[j],3)})'\n","\n","        combined = []\n","        combined.append(sounds[0].overlay(sounds[1], times=20)) # overlay sound (with repeat if base sound is longer)\n","        \n","\n","\n","        name_info = ''.join(names)\n","        fn = f'{dest_dir}{fold}/comb{name_info}.wav' # create unique filename with label\n","        combined[-1].export(fn, format=\"wav\")\n","\n","        full_label = []\n","        for j in range(multi_num):\n","            full_label.append((labels[j], p_ratio[j]))\n","        label_dict[fn] = full_label # use fn as key\n","\n","df = pd.DataFrame.from_dict(label_dict, orient=\"index\",columns=[0, 1])\n","df.to_csv(f\"{dest_dir}multi-labels.csv\")"]},{"cell_type":"code","execution_count":8,"id":"47ad17b4","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:53:50.541486Z","iopub.status.busy":"2024-05-24T20:53:50.541175Z","iopub.status.idle":"2024-05-24T20:53:50.547747Z","shell.execute_reply":"2024-05-24T20:53:50.546889Z"},"papermill":{"duration":0.023394,"end_time":"2024-05-24T20:53:50.549899","exception":false,"start_time":"2024-05-24T20:53:50.526505","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["4141"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["len(label_dict.keys())"]},{"cell_type":"markdown","id":"76a16b37","metadata":{"papermill":{"duration":0.013739,"end_time":"2024-05-24T20:53:50.577467","exception":false,"start_time":"2024-05-24T20:53:50.563728","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Defining Multi Label Dictionary from the autio path as keys </div>"]},{"cell_type":"code","execution_count":9,"id":"2459474f","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:53:50.606203Z","iopub.status.busy":"2024-05-24T20:53:50.605919Z","iopub.status.idle":"2024-05-24T20:53:50.629891Z","shell.execute_reply":"2024-05-24T20:53:50.629186Z"},"papermill":{"duration":0.040298,"end_time":"2024-05-24T20:53:50.631909","exception":false,"start_time":"2024-05-24T20:53:50.591611","status":"completed"},"tags":[]},"outputs":[],"source":["dest_dur = '/kaggle/working/multi'\n","\n","\n","# Load label csv as dict\n","reader = csv.reader(open(f'{dest_dur}/multi-labels.csv',encoding='utf-8-sig'), delimiter=',')\n","multi_dict = {}\n","for column in reader: # convert to dict\n","    key = column[0]\n","    if key in multi_dict:\n","        pass\n","    multi_dict[key] = column[1:]"]},{"cell_type":"markdown","id":"8da3f41e","metadata":{"papermill":{"duration":0.013104,"end_time":"2024-05-24T20:53:50.658389","exception":false,"start_time":"2024-05-24T20:53:50.645285","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\"> Extract features method for multi-category</div>"]},{"cell_type":"code","execution_count":10,"id":"678db357","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:53:50.686693Z","iopub.status.busy":"2024-05-24T20:53:50.686014Z","iopub.status.idle":"2024-05-24T20:53:50.700784Z","shell.execute_reply":"2024-05-24T20:53:50.699886Z"},"papermill":{"duration":0.0311,"end_time":"2024-05-24T20:53:50.702765","exception":false,"start_time":"2024-05-24T20:53:50.671665","status":"completed"},"tags":[]},"outputs":[],"source":["def extract_features_audio(parent_dir,sub_dir, file_ext=\"*.wav\", bands=60, frames=41):\n","    def _windows(data, window_size):\n","        start = 0\n","        while start < len(data):\n","            yield int(start), int(start + window_size)\n","            start += (window_size // 2)\n","            \n","    window_size = 512 * (frames - 1) # size of sliding window for taking multiple samples of each sound, could be extended\n","    features, labels = [], []\n","    for fn in tqdm_notebook(glob.glob(os.path.join(parent_dir, sub_dir, file_ext))):\n","        segment_log_specgrams, segment_labels = [], []\n","        sound_clip, xsr = librosa.load(fn)\n","        try:\n","            label = multi_dict[fn] # use fn as key (see mult_label.csv)\n","            label = [int(literal_eval(point)[0]) for point in label] # add labels (multi categories) ex. [1,2]\n","        except KeyError: \n","            print('Unseen sample',fn)\n","            label = None\n","        \n","        for (start,end) in _windows(sound_clip,window_size):\n","            if(len(sound_clip[start:end]) == window_size):\n","                signal = sound_clip[start:end]\n","                melspec = librosa.feature.melspectrogram(y=signal, n_mels=bands) # extracting melspec with 60 bands\n","                logspec = librosa.amplitude_to_db(melspec)  # converting to log scale\n","                logspec = logspec.T.flatten()[:, np.newaxis].T\n","                segment_log_specgrams.append(logspec)\n","                segment_labels.append(label)\n","            \n","        segment_log_specgrams = np.asarray(segment_log_specgrams).reshape(\n","            len(segment_log_specgrams),bands,frames,1) # reshape to (60, 41, 1)\n","        segment_features = np.concatenate((segment_log_specgrams, np.zeros(\n","            np.shape(segment_log_specgrams))), axis=3) # add one layer to axis -> (60, 40, 2)\n","        for i in range(len(segment_features)): \n","            segment_features[i, :, :, 1] = librosa.feature.delta(\n","                segment_features[i, :, :, 0]) # extract deltas into new layer\n","        \n","        if len(segment_features) > 0: # check for empty segments \n","            features.append(segment_features)\n","            labels.append(segment_labels)\n","\n","    return features, labels"]},{"cell_type":"markdown","id":"7dd130a6","metadata":{"papermill":{"duration":0.013525,"end_time":"2024-05-24T20:53:50.729733","exception":false,"start_time":"2024-05-24T20:53:50.716208","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Print spectrogram features</div>"]},{"cell_type":"code","execution_count":11,"id":"378fb02e","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:53:50.757827Z","iopub.status.busy":"2024-05-24T20:53:50.757562Z","iopub.status.idle":"2024-05-24T20:53:52.6407Z","shell.execute_reply":"2024-05-24T20:53:52.639651Z"},"papermill":{"duration":1.900214,"end_time":"2024-05-24T20:53:52.643444","exception":false,"start_time":"2024-05-24T20:53:50.74323","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1296x648 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["class_look_up_dict = {\"air_conditioner\": 0,\"car_horn\": 1,\"children_playing\": 2,\"dog_bark\": 3,\"drilling\": 4,\"engine_idling\": 5,\n","    \"gun_shot\": 6,\"jackhammer\": 7,\"siren\":8,\"street_music\": 9}\n","\n","class_look_up = list(class_look_up_dict.keys())\n","\n","audio = glob.glob(f'{dest_dir}{fold}/*.wav')[random.randint(0, 300)] #Random Audio spectrogram file selection for visualizations\n","file = audio.split('/')[-1][:-4]\n","\n","\n","y, sr = librosa.load(audio)\n","librosa.feature.melspectrogram(y=y, sr=sr)\n","\n","D = np.abs(librosa.stft(y))**2\n","S = librosa.feature.melspectrogram(S=D)\n","S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=60)\n","\n","plt.figure(figsize=(18, 9))\n","librosa.display.specshow(librosa.power_to_db(S, ref=np.max), y_axis='mel', x_axis='time')\n","plt.colorbar(format='%+3.0f dB')\n","plt.title( f\"Mel spectrogram for {class_look_up[int(file.split('-')[1][0])]} {file.split('-')[1][1:]} and {class_look_up[int(file.split('-')[2][0])]} {file.split('-')[2][1:8]}\" )\n","plt.tight_layout()\n","plt.savefig(f'{file}.png')\n","plt.show()"]},{"cell_type":"markdown","id":"5516dfb1","metadata":{"papermill":{"duration":0.014715,"end_time":"2024-05-24T20:53:52.673836","exception":false,"start_time":"2024-05-24T20:53:52.659121","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Extract features into file (per fold and full)</div>"]},{"cell_type":"code","execution_count":12,"id":"fa751ec0","metadata":{"execution":{"iopub.execute_input":"2024-05-24T20:53:52.705485Z","iopub.status.busy":"2024-05-24T20:53:52.704934Z","iopub.status.idle":"2024-05-24T21:11:23.272335Z","shell.execute_reply":"2024-05-24T21:11:23.271039Z"},"papermill":{"duration":1050.585793,"end_time":"2024-05-24T21:11:23.27507","exception":false,"start_time":"2024-05-24T20:53:52.689277","status":"completed"},"tags":["outputPrepend"]},"outputs":[{"name":"stdout","output_type":"stream","text":["Processing files in: fold1\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"32da4927579e473fbae179f269cd24cf","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/413 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold2\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"60d66c7b37444f54bf4885c248522980","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/423 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold3\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0b3703b14d704b47a0d9c722d09f8591","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/437 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold4\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"79ed64c281644fcebd2df86039716bc4","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/464 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold5\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fa87511a08cb4df8bb1eea1ac4a0f793","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/448 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold6\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"299d5fa63ead4a75a406fb06302f65cb","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/391 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold7\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2d51a1fc2d8a4f9fa8dc8c41afc798c1","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/393 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold8\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f749c0d12568492fa46cf9e0c5b17301","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/383 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold9\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9178fb9c7b0a4505b4b632851d8bd968","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/393 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Processing files in: fold10\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ea33a060d33d41cd9ce1a511d9b29d12","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/396 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"}],"source":["!mkdir /kaggle/working/multi-processed #making Directory\n","parent_dir = '/kaggle/working/multi'\n","feat_dir = \"/kaggle/working/multi-processed\"\n","folds = sub_dirs = np.array(['fold1','fold2','fold3','fold4',\n","                  'fold5','fold6','fold7','fold8',\n","                  'fold9','fold10'],dtype=object,)\n","\n","for fold in folds:\n","    print(f'Processing files in: {fold}')\n","    features, labels = extract_features_audio(parent_dir, fold) # Pre-process and extract feature from the data (per fold)\n","    np.savez(f'{feat_dir}/{fold}', features=features, labels=labels)"]},{"cell_type":"markdown","id":"2bb63754","metadata":{"papermill":{"duration":0.016836,"end_time":"2024-05-24T21:11:23.309723","exception":false,"start_time":"2024-05-24T21:11:23.292887","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Create single file on full dataset through appending for quick access</div>"]},{"cell_type":"code","execution_count":13,"id":"e34dbe81","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:11:23.346317Z","iopub.status.busy":"2024-05-24T21:11:23.345361Z","iopub.status.idle":"2024-05-24T21:11:28.25611Z","shell.execute_reply":"2024-05-24T21:11:28.254982Z"},"papermill":{"duration":4.93207,"end_time":"2024-05-24T21:11:28.258566","exception":false,"start_time":"2024-05-24T21:11:23.326496","status":"completed"},"tags":[]},"outputs":[],"source":["!mkdir /kaggle/working/multi-processed/full_features\n","feat_dir = \"/kaggle/working/multi-processed\"\n","folds = sub_dirs = np.array(['fold1','fold2','fold3','fold4',\n","                  'fold5','fold6','fold7','fold8',\n","                  'fold9','fold10'],dtype=object,)\n","full_features = []\n","full_labels = []\n","\n","for fold in folds:\n","    data = np.load(f'{feat_dir}/{fold}.npz', allow_pickle=True)\n","    features = data[\"features\"]\n","    labels = data[\"labels\"]\n","    full_features.append(features)\n","    full_labels.append(labels)\n","    \n","np.savez(f'{feat_dir}/full_features', features=full_features, labels=full_labels) # combined and saved"]},{"cell_type":"code","execution_count":14,"id":"09baaab3","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:11:28.295014Z","iopub.status.busy":"2024-05-24T21:11:28.294705Z","iopub.status.idle":"2024-05-24T21:11:29.303032Z","shell.execute_reply":"2024-05-24T21:11:29.301957Z"},"papermill":{"duration":1.029336,"end_time":"2024-05-24T21:11:29.305404","exception":false,"start_time":"2024-05-24T21:11:28.276068","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["fold1.npz   fold2.npz  fold4.npz  fold6.npz  fold8.npz\tfull_features\r\n","fold10.npz  fold3.npz  fold5.npz  fold7.npz  fold9.npz\tfull_features.npz\r\n"]}],"source":["!ls '{feat_dir}' "]},{"cell_type":"markdown","id":"68c0fb1b","metadata":{"papermill":{"duration":0.017254,"end_time":"2024-05-24T21:11:29.340933","exception":false,"start_time":"2024-05-24T21:11:29.323679","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Define architecture of CNN with Keras</div>"]},{"cell_type":"code","execution_count":15,"id":"13c13520","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:11:29.377658Z","iopub.status.busy":"2024-05-24T21:11:29.377297Z","iopub.status.idle":"2024-05-24T21:11:29.391624Z","shell.execute_reply":"2024-05-24T21:11:29.390844Z"},"papermill":{"duration":0.03507,"end_time":"2024-05-24T21:11:29.393773","exception":false,"start_time":"2024-05-24T21:11:29.358703","status":"completed"},"tags":[]},"outputs":[],"source":["def Multi_Label_Audio_model():\n","    num_filters = [32, 32, 64, 128] \n","    pool_size = (2, 2) \n","    kernel_size = (3, 3) \n","    input_shape = (60, 41, 2) # two layers for melspec and deltas of melspec\n","    num_classes = 10 # categories of sound\n","    keras.backend.clear_session()\n","    \n","    model = keras.models.Sequential()\n","    model.add(keras.layers.Conv2D(32, kernel_size,\n","                padding=\"same\", input_shape=input_shape))\n","    model.add(keras.layers.BatchNormalization())\n","    model.add(keras.layers.Activation(\"relu\"))\n","    model.add(keras.layers.MaxPooling2D(pool_size=pool_size))\n","\n","    model.add(keras.layers.Conv2D(32, kernel_size,\n","                                  padding=\"same\"))\n","    model.add(keras.layers.BatchNormalization())\n","    model.add(keras.layers.Activation(\"relu\"))\n","    model.add(keras.layers.MaxPooling2D(pool_size=pool_size))\n","    \n","    model.add(keras.layers.Conv2D(64, kernel_size,\n","                                  padding=\"same\"))\n","    model.add(keras.layers.BatchNormalization())\n","    model.add(keras.layers.Activation(\"relu\")) \n","    model.add(keras.layers.MaxPooling2D(pool_size=pool_size))\n","    \n","    model.add(keras.layers.Conv2D(128, kernel_size,\n","                                  padding=\"same\"))\n","    model.add(keras.layers.BatchNormalization())\n","    model.add(keras.layers.Activation(\"relu\"))  \n","\n","    model.add(keras.layers.GlobalMaxPooling2D())\n","    model.add(keras.layers.Dense(128, activation=\"relu\"))\n","    model.add(keras.layers.Dense(num_classes, activation='sigmoid')) # sigmoid provides unconstrained probability distrib vs softmax\n","\n","    model.compile(optimizer=keras.optimizers.Adam(1e-4), \n","        loss='binary_crossentropy', # one-hot encoding for binary arrays\n","        metrics=[\"categorical_accuracy\"]) # provides better estimates of accuracy for one-hot labels\n","    return model"]},{"cell_type":"markdown","id":"3ddf3b48","metadata":{"papermill":{"duration":0.017013,"end_time":"2024-05-24T21:11:29.428174","exception":false,"start_time":"2024-05-24T21:11:29.411161","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Split data into training and test sets</div>"]},{"cell_type":"code","execution_count":16,"id":"ea9f071a","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:11:29.464334Z","iopub.status.busy":"2024-05-24T21:11:29.464066Z","iopub.status.idle":"2024-05-24T21:11:32.396374Z","shell.execute_reply":"2024-05-24T21:11:32.395282Z"},"papermill":{"duration":2.953439,"end_time":"2024-05-24T21:11:32.39914","exception":false,"start_time":"2024-05-24T21:11:29.445701","status":"completed"},"tags":[]},"outputs":[],"source":["!mkdir /kaggle/working/models_24_05_2024\n","\n","cp_path = \"/kaggle/working/models_24_05_2024/checkpoint.hdf5\"\n","checkpoint = ModelCheckpoint(cp_path, monitor='val_loss', verbose=15, save_best_only=True,mode='auto', save_freq='epoch')\n","load_dir = \"/kaggle/working/multi-processed\"\n","\n","data = np.load(\"{0}/{1}.npz\".format(load_dir,'full_features'),\n","                allow_pickle=True)\n","\n","features = np.concatenate(data[\"features\"], axis = 0)\n","labels = np.concatenate(data[\"labels\"], axis = 0)\n","\n","x_train, x_test, y_train, y_test = train_test_split(features, labels, test_size=.5)\n","\n","x_train = np.concatenate(x_train, axis = 0).astype(np.float32)\n","y_train = np.concatenate(y_train, axis = 0).astype(np.float32)"]},{"cell_type":"markdown","id":"eb6f111a","metadata":{"papermill":{"duration":0.018752,"end_time":"2024-05-24T21:11:32.436105","exception":false,"start_time":"2024-05-24T21:11:32.417353","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Encode labels as one-hot binary arrays</div>"]},{"cell_type":"code","execution_count":17,"id":"8022b175","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:11:32.473221Z","iopub.status.busy":"2024-05-24T21:11:32.472882Z","iopub.status.idle":"2024-05-24T21:11:32.7764Z","shell.execute_reply":"2024-05-24T21:11:32.775604Z"},"papermill":{"duration":0.324958,"end_time":"2024-05-24T21:11:32.778708","exception":false,"start_time":"2024-05-24T21:11:32.45375","status":"completed"},"tags":[]},"outputs":[],"source":["mlb = MultiLabelBinarizer()\n","mlb.fit([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]) # fit mlb to the category range\n","\n","y_tr_array = np.array(y_train, dtype=object,)\n","y_tr_hot = mlb.transform(y_tr_array) # this can be done because multi one-hot arrays are not needed (as they are when comparing test to predict)\n","\n","y_te_hot = []\n","for y in y_test: # for each window sample of one sound file in test set\n","    y_te_array = np.array(y , dtype=object,)\n","    y_hot = mlb.transform(y_te_array) # encoding with transform\n","    y_te_hot.append(y_hot)\n","y_te_hot = np.array(y_te_hot, dtype=object,)"]},{"cell_type":"markdown","id":"e7961732","metadata":{"papermill":{"duration":0.017838,"end_time":"2024-05-24T21:11:32.814196","exception":false,"start_time":"2024-05-24T21:11:32.796358","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Train model and present performance</div>"]},{"cell_type":"code","execution_count":18,"id":"8d9aa973","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:11:32.850505Z","iopub.status.busy":"2024-05-24T21:11:32.850212Z","iopub.status.idle":"2024-05-24T21:11:32.873436Z","shell.execute_reply":"2024-05-24T21:11:32.872578Z"},"papermill":{"duration":0.043674,"end_time":"2024-05-24T21:11:32.875431","exception":false,"start_time":"2024-05-24T21:11:32.831757","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/owaiskhan9515/Training%20of%20Customized%20UrbanSound8K%20Multi%20Labeled%20Audio%20Data/runs/cflgqje9?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"],"text/plain":["<wandb.sdk.wandb_run.Run at 0x7e115c8bc090>"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["wandb.init()"]},{"cell_type":"code","execution_count":19,"id":"5839e8ff","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:11:32.912194Z","iopub.status.busy":"2024-05-24T21:11:32.911925Z","iopub.status.idle":"2024-05-24T21:18:07.490082Z","shell.execute_reply":"2024-05-24T21:18:07.489228Z"},"papermill":{"duration":394.603266,"end_time":"2024-05-24T21:18:07.496523","exception":false,"start_time":"2024-05-24T21:11:32.893257","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/100\n","802/802 [==============================] - 14s 5ms/step - loss: 0.4282 - categorical_accuracy: 0.2560\n","Epoch 2/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.3716 - categorical_accuracy: 0.3139\n","Epoch 3/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.3480 - categorical_accuracy: 0.3427\n","Epoch 4/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.3274 - categorical_accuracy: 0.3690\n","Epoch 5/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.3087 - categorical_accuracy: 0.3891\n","Epoch 6/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.2926 - categorical_accuracy: 0.4089\n","Epoch 7/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.2765 - categorical_accuracy: 0.4150\n","Epoch 8/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.2613 - categorical_accuracy: 0.4289\n","Epoch 9/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.2481 - categorical_accuracy: 0.4476\n","Epoch 10/100\n","802/802 [==============================] - 8s 11ms/step - loss: 0.2337 - categorical_accuracy: 0.4516 - val_loss: 0.3736 - val_categorical_accuracy: 0.3470\n","\n","Epoch 00010: val_loss improved from inf to 0.37365, saving model to /kaggle/working/models_24_05_2024/checkpoint.hdf5\n","Epoch 11/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.2211 - categorical_accuracy: 0.4587\n","Epoch 12/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.2099 - categorical_accuracy: 0.4702\n","Epoch 13/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1966 - categorical_accuracy: 0.4782\n","Epoch 14/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1859 - categorical_accuracy: 0.4824\n","Epoch 15/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1751 - categorical_accuracy: 0.4900\n","Epoch 16/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1654 - categorical_accuracy: 0.4926\n","Epoch 17/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1541 - categorical_accuracy: 0.4999\n","Epoch 18/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1441 - categorical_accuracy: 0.5036\n","Epoch 19/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1356 - categorical_accuracy: 0.5133\n","Epoch 20/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1256 - categorical_accuracy: 0.5055\n","Epoch 21/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1185 - categorical_accuracy: 0.5126\n","Epoch 22/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1100 - categorical_accuracy: 0.5128\n","Epoch 23/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.1028 - categorical_accuracy: 0.5176\n","Epoch 24/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0959 - categorical_accuracy: 0.5208\n","Epoch 25/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0905 - categorical_accuracy: 0.5208\n","Epoch 26/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0836 - categorical_accuracy: 0.5173\n","Epoch 27/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0779 - categorical_accuracy: 0.5232\n","Epoch 28/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0723 - categorical_accuracy: 0.5303\n","Epoch 29/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0684 - categorical_accuracy: 0.5259\n","Epoch 30/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0630 - categorical_accuracy: 0.5259\n","Epoch 31/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0577 - categorical_accuracy: 0.5252\n","Epoch 32/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0554 - categorical_accuracy: 0.5302\n","Epoch 33/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0509 - categorical_accuracy: 0.5293\n","Epoch 34/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0490 - categorical_accuracy: 0.5311\n","Epoch 35/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0460 - categorical_accuracy: 0.5257\n","Epoch 36/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0415 - categorical_accuracy: 0.5285\n","Epoch 37/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0403 - categorical_accuracy: 0.5277\n","Epoch 38/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0390 - categorical_accuracy: 0.5361\n","Epoch 39/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0365 - categorical_accuracy: 0.5299\n","Epoch 40/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0330 - categorical_accuracy: 0.5424\n","Epoch 41/100\n","802/802 [==============================] - 4s 4ms/step - loss: 0.0324 - categorical_accuracy: 0.5322\n","Epoch 42/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0316 - categorical_accuracy: 0.5303\n","Epoch 43/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0279 - categorical_accuracy: 0.5320\n","Epoch 44/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0285 - categorical_accuracy: 0.5263\n","Epoch 45/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0265 - categorical_accuracy: 0.5350\n","Epoch 46/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0253 - categorical_accuracy: 0.5319\n","Epoch 47/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0245 - categorical_accuracy: 0.5333\n","Epoch 48/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0240 - categorical_accuracy: 0.5317\n","Epoch 49/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0244 - categorical_accuracy: 0.5359\n","Epoch 50/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0195 - categorical_accuracy: 0.5315\n","Epoch 51/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0215 - categorical_accuracy: 0.5359\n","Epoch 52/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0224 - categorical_accuracy: 0.5374\n","Epoch 53/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0178 - categorical_accuracy: 0.5385\n","Epoch 54/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0206 - categorical_accuracy: 0.5352\n","Epoch 55/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0198 - categorical_accuracy: 0.5329\n","Epoch 56/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0160 - categorical_accuracy: 0.5390\n","Epoch 57/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0185 - categorical_accuracy: 0.5378\n","Epoch 58/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0164 - categorical_accuracy: 0.5367\n","Epoch 59/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0169 - categorical_accuracy: 0.5350\n","Epoch 60/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0170 - categorical_accuracy: 0.5367\n","Epoch 61/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0176 - categorical_accuracy: 0.5370\n","Epoch 62/100\n","802/802 [==============================] - 4s 4ms/step - loss: 0.0163 - categorical_accuracy: 0.5370\n","Epoch 63/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0144 - categorical_accuracy: 0.5374\n","Epoch 64/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0153 - categorical_accuracy: 0.5364\n","Epoch 65/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0142 - categorical_accuracy: 0.5403\n","Epoch 66/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0142 - categorical_accuracy: 0.5345\n","Epoch 67/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0165 - categorical_accuracy: 0.5378\n","Epoch 68/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0140 - categorical_accuracy: 0.5427\n","Epoch 69/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0131 - categorical_accuracy: 0.5452\n","Epoch 70/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0133 - categorical_accuracy: 0.5382\n","Epoch 71/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0128 - categorical_accuracy: 0.5387\n","Epoch 72/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0131 - categorical_accuracy: 0.5342\n","Epoch 73/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0116 - categorical_accuracy: 0.5367\n","Epoch 74/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0133 - categorical_accuracy: 0.5330\n","Epoch 75/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0163 - categorical_accuracy: 0.5397\n","Epoch 76/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0110 - categorical_accuracy: 0.5329\n","Epoch 77/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0127 - categorical_accuracy: 0.5406\n","Epoch 78/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0116 - categorical_accuracy: 0.5382\n","Epoch 79/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0126 - categorical_accuracy: 0.5372\n","Epoch 80/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0104 - categorical_accuracy: 0.5386\n","Epoch 81/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0104 - categorical_accuracy: 0.5384\n","Epoch 82/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0132 - categorical_accuracy: 0.5397\n","Epoch 83/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0112 - categorical_accuracy: 0.5435\n","Epoch 84/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0101 - categorical_accuracy: 0.5350\n","Epoch 85/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0149 - categorical_accuracy: 0.5338\n","Epoch 86/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0109 - categorical_accuracy: 0.5413\n","Epoch 87/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0090 - categorical_accuracy: 0.5420\n","Epoch 88/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0102 - categorical_accuracy: 0.5409\n","Epoch 89/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0120 - categorical_accuracy: 0.5378\n","Epoch 90/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0092 - categorical_accuracy: 0.5411\n","Epoch 91/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0109 - categorical_accuracy: 0.5275\n","Epoch 92/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0099 - categorical_accuracy: 0.5405\n","Epoch 93/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0091 - categorical_accuracy: 0.5445\n","Epoch 94/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0105 - categorical_accuracy: 0.5403\n","Epoch 95/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0118 - categorical_accuracy: 0.5415\n","Epoch 96/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0080 - categorical_accuracy: 0.5357\n","Epoch 97/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0110 - categorical_accuracy: 0.5390\n","Epoch 98/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0101 - categorical_accuracy: 0.5346\n","Epoch 99/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0092 - categorical_accuracy: 0.5410\n","Epoch 100/100\n","802/802 [==============================] - 4s 5ms/step - loss: 0.0092 - categorical_accuracy: 0.5327\n"]}],"source":["# strategy = tf.distribute.MirroredStrategy([\"GPU:0\", \"GPU:1\"]) #Setting Up the strategy for 2 GPUS utilizations\n","# with strategy.scope():\n","model = Multi_Label_Audio_model()\n","\n","\n","history = model.fit(x_train, y_tr_hot, epochs = 100,validation_data=zip(x_test, y_te_hot),validation_freq=[0, 10, 20, 30, 40, 50,60, 70, 80, 90], batch_size = 16, verbose = 1,callbacks=[checkpoint,WandbMetricsLogger(log_freq=15)])\n","\n","model.save('/kaggle/working/models_24_05_2024/models-multi-%s' % time.time()) "]},{"cell_type":"code","execution_count":20,"id":"7d127eda","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:18:08.689395Z","iopub.status.busy":"2024-05-24T21:18:08.688478Z","iopub.status.idle":"2024-05-24T21:18:09.181372Z","shell.execute_reply":"2024-05-24T21:18:09.18047Z"},"papermill":{"duration":1.06289,"end_time":"2024-05-24T21:18:09.183625","exception":false,"start_time":"2024-05-24T21:18:08.120735","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 864x432 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["with plt.style.context('bmh'):\n","    plt.figure(figsize=(12, 6))\n","    plt.plot(history.history['categorical_accuracy'] ,'magenta')\n","    plt.title('Train Categorical Accuracy')\n","    plt.ylabel('Categorical Accuracy')\n","    plt.xlabel('Epoch')\n","    plt.tight_layout()\n","\n","    plt.show()\n","\n","    plt.figure(figsize=(12, 6))\n","    plt.plot(history.history['loss'] ,'magenta')\n","    plt.title('Train Model Loss')\n","    plt.ylabel('Loss')\n","    plt.xlabel('Epoch')\n","    plt.tight_layout()\n","\n","    # plt.savefig('loss.png')\n","    plt.show()"]},{"cell_type":"markdown","id":"729f4bcf","metadata":{"papermill":{"duration":0.567116,"end_time":"2024-05-24T21:18:10.32628","exception":false,"start_time":"2024-05-24T21:18:09.759164","status":"completed"},"tags":[]},"source":["\n","\n","# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Load pre-trained model for testing</div>"]},{"cell_type":"code","execution_count":21,"id":"1c2c2806","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:18:11.521201Z","iopub.status.busy":"2024-05-24T21:18:11.520811Z","iopub.status.idle":"2024-05-24T21:18:11.528183Z","shell.execute_reply":"2024-05-24T21:18:11.527233Z"},"papermill":{"duration":0.587046,"end_time":"2024-05-24T21:18:11.530201","exception":false,"start_time":"2024-05-24T21:18:10.943155","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['models_24_05_2024/checkpoint.hdf5',\n"," 'models_24_05_2024/models-multi-1716585484.3489418']"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["glob.glob('models_24_05_2024/*')"]},{"cell_type":"code","execution_count":22,"id":"cae0387f","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:18:12.721926Z","iopub.status.busy":"2024-05-24T21:18:12.721271Z","iopub.status.idle":"2024-05-24T21:18:12.925946Z","shell.execute_reply":"2024-05-24T21:18:12.925099Z"},"papermill":{"duration":0.831942,"end_time":"2024-05-24T21:18:12.928323","exception":false,"start_time":"2024-05-24T21:18:12.096381","status":"completed"},"tags":[]},"outputs":[],"source":["model = tf.keras.models.load_model(glob.glob('models_24_05_2024/*')[0])"]},{"cell_type":"markdown","id":"41fe7e6d","metadata":{"papermill":{"duration":0.567437,"end_time":"2024-05-24T21:18:14.06067","exception":false,"start_time":"2024-05-24T21:18:13.493233","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Test model performance with performance metrics</div>"]},{"cell_type":"code","execution_count":23,"id":"c4cb72e3","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:18:15.248407Z","iopub.status.busy":"2024-05-24T21:18:15.248033Z","iopub.status.idle":"2024-05-24T21:19:26.301119Z","shell.execute_reply":"2024-05-24T21:19:26.300082Z"},"papermill":{"duration":72.253981,"end_time":"2024-05-24T21:19:26.88136","exception":false,"start_time":"2024-05-24T21:18:14.627379","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Evaluating on test set...\n","Average Accuracy: 0.1717019133937563\n","Average Hamming: 0.16017119838872104\n","\n","[[[1392  143]\n","  [ 236  215]]\n","\n"," [[1800   20]\n","  [  61  105]]\n","\n"," [[1435  137]\n","  [ 275  139]]\n","\n"," [[1306  253]\n","  [ 137  290]]\n","\n"," [[1459   92]\n","  [ 188  247]]\n","\n"," [[1315  242]\n","  [ 122  307]]\n","\n"," [[1833    8]\n","  [  95   50]]\n","\n"," [[1114  446]\n","  [  61  365]]\n","\n"," [[1359  223]\n","  [ 105  299]]\n","\n"," [[1422  121]\n","  [ 216  227]]]\n","\n","              precision    recall  f1-score   support\n","\n","           0       0.60      0.48      0.53       451\n","           1       0.84      0.63      0.72       166\n","           2       0.50      0.34      0.40       414\n","           3       0.53      0.68      0.60       427\n","           4       0.73      0.57      0.64       435\n","           5       0.56      0.72      0.63       429\n","           6       0.86      0.34      0.49       145\n","           7       0.45      0.86      0.59       426\n","           8       0.57      0.74      0.65       404\n","           9       0.65      0.51      0.57       443\n","\n","   micro avg       0.57      0.60      0.59      3740\n","   macro avg       0.63      0.59      0.58      3740\n","weighted avg       0.60      0.60      0.58      3740\n"," samples avg       0.67      0.62      0.60      3740\n","\n"]}],"source":["y_true, y_pred = [], []\n","\n","print('Evaluating on test set...')\n","for x, y in zip(x_test, y_te_hot):\n","    # average predictions over segments of a sound clip\n","    pred = model.predict(x)\n","    avg_p = np.unique(np.argmax(pred, axis=1)) # avg predictions of each array\n","    hot_p = mlb.transform([avg_p]) # convert to one-hot encoding - no idea why it wont pass without brackets \n","\n","    # print(f'Predict: {hot_p}')\n","    y_pred.append(hot_p[0]) \n","    \n","    # print(f'True: {y[0]}') \n","    y_true.append(y[0]) # pick single label for a sound clip (they're all identical)\n","\n","hamming = hamming_loss(y_true, y_pred)\n","accuracy = accuracy_score(y_true, y_pred)\n","     \n","print(f'Average Accuracy: {accuracy}')\n","print(f'Average Hamming: {hamming}\\n')\n","print(f'{multilabel_confusion_matrix(y_true, y_pred)}\\n')\n","print(classification_report(y_true, y_pred))"]},{"cell_type":"code","execution_count":24,"id":"00049939","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:19:28.027485Z","iopub.status.busy":"2024-05-24T21:19:28.026613Z","iopub.status.idle":"2024-05-24T21:19:30.681784Z","shell.execute_reply":"2024-05-24T21:19:30.681002Z"},"papermill":{"duration":3.235098,"end_time":"2024-05-24T21:19:30.68427","exception":false,"start_time":"2024-05-24T21:19:27.449172","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n","\u001b[34m\u001b[1mwandb\u001b[0m: \n","\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n","\u001b[34m\u001b[1mwandb\u001b[0m:               batch/batch_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███\n","\u001b[34m\u001b[1mwandb\u001b[0m:   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summary:\n","\u001b[34m\u001b[1mwandb\u001b[0m:               batch/batch_step 80985\n","\u001b[34m\u001b[1mwandb\u001b[0m:     batch/categorical_accuracy 0.53204\n","\u001b[34m\u001b[1mwandb\u001b[0m:            batch/learning_rate 0.0001\n","\u001b[34m\u001b[1mwandb\u001b[0m:                     batch/loss 0.00916\n","\u001b[34m\u001b[1mwandb\u001b[0m:     epoch/categorical_accuracy 0.53269\n","\u001b[34m\u001b[1mwandb\u001b[0m:                    epoch/epoch 99\n","\u001b[34m\u001b[1mwandb\u001b[0m:            epoch/learning_rate 0.0001\n","\u001b[34m\u001b[1mwandb\u001b[0m:                     epoch/loss 0.00918\n","\u001b[34m\u001b[1mwandb\u001b[0m: epoch/val_categorical_accuracy 0.347\n","\u001b[34m\u001b[1mwandb\u001b[0m:                 epoch/val_loss 0.37365\n","\u001b[34m\u001b[1mwandb\u001b[0m: \n","\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33m16.Training of Customized UrbanSound8K-Run-16\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/owaiskhan9515/Training%20of%20Customized%20UrbanSound8K%20Multi%20Labeled%20Audio%20Data/runs/cflgqje9\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at: \u001b[34m\u001b[4mhttps://wandb.ai/owaiskhan9515/Training%20of%20Customized%20UrbanSound8K%20Multi%20Labeled%20Audio%20Data\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n","\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240524_203846-cflgqje9/logs\u001b[0m\n"]}],"source":["wandb.finish()"]},{"cell_type":"markdown","id":"67a59a1d","metadata":{"papermill":{"duration":0.56996,"end_time":"2024-05-24T21:19:31.87363","exception":false,"start_time":"2024-05-24T21:19:31.30367","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:75%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">Predicting on new data(FOLD 10)</div>"]},{"cell_type":"code","execution_count":25,"id":"f3b978f6","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:19:33.015806Z","iopub.status.busy":"2024-05-24T21:19:33.015403Z","iopub.status.idle":"2024-05-24T21:23:26.938056Z","shell.execute_reply":"2024-05-24T21:23:26.936993Z"},"papermill":{"duration":235.182875,"end_time":"2024-05-24T21:23:27.628093","exception":false,"start_time":"2024-05-24T21:19:32.445218","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Predicting!\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fb5d8c62e7c747e993c50082b50d3dd4","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/837 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Unseen sample /kaggle/input/urbansound8k/fold10/136558-9-1-21.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/174289-6-3-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/165640-4-0-1.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/207213-2-0-52.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/99192-4-0-43.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/117889-9-0-12.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/162134-7-1-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/188813-7-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/162134-7-8-2.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/172519-9-0-49.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/141240-5-1-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/93567-8-0-12.wav\n","Unseen sample 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/kaggle/input/urbansound8k/fold10/159742-8-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/99192-4-0-12.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/138017-9-1-16.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/39847-5-0-6.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/88121-8-2-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/162134-7-8-7.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/196086-2-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/162134-7-8-3.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/20571-3-1-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/103438-5-0-1.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/100795-3-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/197554-2-0-28.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/138017-9-1-6.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/74364-8-1-23.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/73524-0-0-114.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/188813-7-3-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/34050-7-2-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/189982-0-0-27.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/14468-3-5-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/159742-8-0-6.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/103438-5-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/202334-9-0-137.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/189982-0-0-17.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/102857-5-0-19.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/146244-4-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/73524-0-0-113.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/164377-9-1-92.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/100648-1-3-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/39847-5-0-2.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/93567-8-0-1.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/199261-3-0-1.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/180127-4-0-4.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/26255-3-6-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/99192-4-0-28.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/162148-3-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/167464-0-0-16.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/115241-9-0-6.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/73524-0-0-8.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/162134-7-10-3.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/81791-3-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/54086-1-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/102857-5-0-13.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/196127-3-0-1.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/106014-5-0-4.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/115241-9-0-14.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/59513-3-0-3.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/167464-0-0-12.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/187110-2-0-30.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/167464-0-0-7.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/102857-5-0-11.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/155280-2-0-13.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/189985-0-0-3.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/119067-0-0-1.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/83502-0-0-7.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/115418-9-0-20.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/99192-4-0-55.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/73524-0-0-92.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/189982-0-0-35.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/157207-6-10-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/136558-9-1-39.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/155241-9-0-50.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/73524-0-0-101.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/162134-7-5-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/129750-2-0-34.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/115241-9-0-2.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/39847-5-0-12.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/93567-8-0-13.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/167464-0-0-24.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/188813-7-12-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/93567-8-0-14.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/164377-9-1-3.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/162134-7-7-1.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/59513-3-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/164377-9-1-43.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/39847-5-0-5.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/187863-4-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/167750-4-2-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/26173-8-0-0.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/99192-4-0-18.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/99192-4-0-15.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/180127-4-0-5.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/178826-2-0-14.wav\n","Unseen sample /kaggle/input/urbansound8k/fold10/159709-6-0-0.wav\n","\n","Predictions\n","File: 136558-9-1-21.wav -> Prediction category: [0 3 8 9]\n","air_conditioner\n","dog_bark\n","siren\n","street_music\n","File: 174289-6-3-0.wav -> Prediction category: [3 6]\n","dog_bark\n","gun_shot\n","File: 165640-4-0-1.wav -> Prediction category: [8]\n","siren\n","File: 207213-2-0-52.wav -> Prediction category: [2 3]\n","children_playing\n","dog_bark\n","File: 99192-4-0-43.wav -> Prediction category: [3 4]\n","dog_bark\n","drilling\n","File: 117889-9-0-12.wav -> Prediction category: [3 9]\n","dog_bark\n","street_music\n","File: 162134-7-1-0.wav -> Prediction category: [2 7]\n","children_playing\n","jackhammer\n","File: 188813-7-0-0.wav -> Prediction category: [4 7]\n","drilling\n","jackhammer\n","File: 162134-7-8-2.wav -> Prediction category: [2 3 9]\n","children_playing\n","dog_bark\n","street_music\n","File: 172519-9-0-49.wav -> Prediction category: [3]\n","dog_bark\n"]}],"source":["parent_dir = '/kaggle/input/urbansound8k/'\n","sub_dir = 'fold10' # directory for unseen samples\n","test_files = [fn for fn in os.listdir(parent_dir+sub_dir) if fn.endswith('.wav')][0:10]\n","\n","print('\\nPredicting!')\n","features, labels = extract_features_audio(parent_dir, sub_dir)\n","\n","predictions = []\n","for x in features:\n","    pred = model.predict(x)\n","    avg_p = np.unique(np.argmax(pred, axis=1))\n","    predictions.append(avg_p)\n","\n","print('\\nPredictions')\n","for fn, pred in zip(test_files, predictions):\n","    print(f'File: {fn} -> Prediction category: {pred}')\n","    [print(class_look_up[i]) for i in pred]"]},{"cell_type":"code","execution_count":26,"id":"87dbbff4","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:23:28.887031Z","iopub.status.busy":"2024-05-24T21:23:28.88613Z","iopub.status.idle":"2024-05-24T21:23:31.807085Z","shell.execute_reply":"2024-05-24T21:23:31.805613Z"},"papermill":{"duration":3.555492,"end_time":"2024-05-24T21:23:31.80977","exception":false,"start_time":"2024-05-24T21:23:28.254278","status":"completed"},"tags":[]},"outputs":[],"source":["%%capture\n","\n","!curl -O http://www.fsnradionews.com/FSNNews/FSNHeadlines.mp3 -A \"Mozilla/4.0\"\n","!ffmpeg -i FSNHeadlines.mp3 FSNHeadlines.wav "]},{"cell_type":"code","execution_count":27,"id":"d4f9fe84","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:23:33.134808Z","iopub.status.busy":"2024-05-24T21:23:33.13434Z","iopub.status.idle":"2024-05-24T21:23:34.212107Z","shell.execute_reply":"2024-05-24T21:23:34.210902Z"},"papermill":{"duration":1.771184,"end_time":"2024-05-24T21:23:34.21484","exception":false,"start_time":"2024-05-24T21:23:32.443656","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":[" FSNHeadlines.mp3    'comb-8(0.501)-2(0.501).png'   \u001b[0m\u001b[01;34mmulti-processed\u001b[0m/\r\n"," FSNHeadlines.wav     \u001b[01;34mmodels_24_05_2024\u001b[0m/            \u001b[01;34mwandb\u001b[0m/\r\n"," __notebook__.ipynb   \u001b[01;34mmulti\u001b[0m/\r\n"]}],"source":["ls"]},{"cell_type":"code","execution_count":28,"id":"158fa40a","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:23:35.482398Z","iopub.status.busy":"2024-05-24T21:23:35.481413Z","iopub.status.idle":"2024-05-24T21:23:35.495242Z","shell.execute_reply":"2024-05-24T21:23:35.494249Z"},"papermill":{"duration":0.643664,"end_time":"2024-05-24T21:23:35.49728","exception":false,"start_time":"2024-05-24T21:23:34.853616","status":"completed"},"tags":[]},"outputs":[],"source":["def extract_features_single_audio(audio_path, file_ext=\"*.wav\", bands=60, frames=41):\n","    def _windows(data, window_size):\n","        start = 0\n","        while start < len(data):\n","            yield int(start), int(start + window_size)\n","            start += (window_size // 2)\n","        \n","    feature, segment_log_specgrams = [], []    \n","    window_size = 512 * (frames - 1) # size of sliding window for taking multiple samples of each sound, could be extended\n","    sound_clip, xsr = librosa.load(audio_path)\n","    for (start,end) in _windows(sound_clip,window_size):\n","        if(len(sound_clip[start:end]) == window_size):\n","            signal = sound_clip[start:end]\n","            melspec = librosa.feature.melspectrogram(y=signal, n_mels=bands) # extracting melspec with 60 bands\n","            logspec = librosa.amplitude_to_db(melspec)  # converting to log scale\n","            logspec = logspec.T.flatten()[:, np.newaxis].T\n","            segment_log_specgrams.append(logspec)\n","    segment_log_specgrams = np.asarray(segment_log_specgrams).reshape(\n","    len(segment_log_specgrams),bands,frames,1) # reshape to (60, 41, 1)\n","    segment_features = np.concatenate((segment_log_specgrams, np.zeros(\n","    np.shape(segment_log_specgrams))), axis=3) # add one layer to axis -> (60, 40, 2)\n","    for i in range(len(segment_features)): \n","        segment_features[i, :, :, 1] = librosa.feature.delta(\n","            segment_features[i, :, :, 0]) # extract deltas into new layer\n","\n","    if len(segment_features) > 0: # check for empty segments \n","        feature.append(segment_features)\n","            \n","    return feature"]},{"cell_type":"code","execution_count":29,"id":"89b6e914","metadata":{"execution":{"iopub.execute_input":"2024-05-24T21:23:36.81737Z","iopub.status.busy":"2024-05-24T21:23:36.816641Z","iopub.status.idle":"2024-05-24T21:23:38.533027Z","shell.execute_reply":"2024-05-24T21:23:38.531731Z"},"papermill":{"duration":2.348291,"end_time":"2024-05-24T21:23:38.535255","exception":false,"start_time":"2024-05-24T21:23:36.186964","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["children_playing\n","dog_bark\n","gun_shot\n","street_music\n"]}],"source":["feature = extract_features_single_audio(audio_path = \"FSNHeadlines.wav\")\n","pred = model.predict(feature)\n","pred = np.unique(np.argmax(pred, axis=1))\n","p = [print(class_look_up[i]) for i in pred]"]},{"cell_type":"markdown","id":"f9d6ef9f","metadata":{"papermill":{"duration":0.63042,"end_time":"2024-05-24T21:23:39.81757","exception":false,"start_time":"2024-05-24T21:23:39.18715","status":"completed"},"tags":[]},"source":["# <div style=\"padding:20px;color:#f0f2f0;margin:0;font-size:80%;text-align:center;display:fill;border-radius:5px;background-color:#1a0a36;overflow:hidden\">References</div>\n","\n","\n","<ol style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">\n","    \n","<li>\n","<p style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">Abdoli, S., Cardinal, P., &amp; Koerich, A. L. (2019). End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network. ArXiv:1904.08990 [Cs, Stat]. <a href=\"https://arxiv.org/abs/1904.08990\">http://arxiv.org/abs/1904.08990</a></p>\n","</li>\n","<li>\n","<p style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">Mushtaq, Z., &amp; Su, S.-F. (2020). Environmental sound classification using a regularized deep convolutional neural network with data augmentation. Applied Acoustics, 167, 107389. <a href=\"https://doi.org/10.1016/j.apacoust.2020.107389\">https://doi.org/10.1016/j.apacoust.2020.107389</a></p>\n","</li>\n","<li>\n","<p style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">Piczak, K. J. (2020). Karolpiczak/ESC-50 [Python]. <a href=\"https://github.com/karolpiczak/ESC-50\">https://github.com/karolpiczak/ESC-50</a> (Original work published 2015)</p>\n","</li>\n","<li>\n","<p style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">Piczak, K. J. (2015a). Environmental sound classification with convolutional neural networks. 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), 1–6. <a href=\"https://doi.org/10.1109/MLSP.2015.7324337\">https://doi.org/10.1109/MLSP.2015.7324337</a></p>\n","</li>\n","<li>\n","<p style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">Piczak, K. J. (2015b). ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd ACM International Conference on Multimedia, 1015–1018. <a href=\"https://doi.org/10.1145/2733373.2806390\">https://doi.org/10.1145/2733373.2806390</a></p>\n","</li>\n","<li>\n","<p style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">Saeed, A. (n.d.). Urban Sound Classification, Part 2. Retrieved 18 September 2020, from <a href=\"https://aqibsaeed.github.io/2016-09-24-urban-sound-classification-part-2/\">http://aqibsaeed.github.io/2016-09-24-urban-sound-classification-part-2/</a></p>\n","</li>\n","<li>\n","<p>Aaqib. (2020). Aqibsaeed/Urban-Sound-Classification [Jupyter Notebook]. <a href=\"https://github.com/aqibsaeed/Urban-Sound-Classification\">https://github.com/aqibsaeed/Urban-Sound-Classification</a> (Original work published 2016)</p>\n","</li>\n","<li>\n","<p style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">Su, Y., Zhang, K., Wang, J., &amp; Madani, K. (2019). Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion. Sensors, 19(7), 1733. <a href=\"https://doi.org/10.3390/s19071733\">https://doi.org/10.3390/s19071733</a></p>\n","</li>\n","<li>\n","<p style=\"display: block;list-style-type: decimal;font-weight: 500; margin-block-start: 1em;margin-block-end: 1em;margin-inline-start: 0px; margin-inline-end: 0px;padding-inline-start: 40px;color: #666\">UrbanSound8K. (n.d.). Urban Sound Datasets. Retrieved 23 August 2020, from <a href=\"https://urbansounddataset.weebly.com/urbansound8k.html\">https://urbansounddataset.weebly.com/urbansound8k.html</a></p>\n","</li>\n","</ol>"]}],"metadata":{"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"datasetId":500970,"sourceId":928025,"sourceType":"datasetVersion"}],"dockerImageVersionId":30302,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 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