02-deep-audio-classification-audio-detection.ipynb

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**1. Prepare Data**"],"metadata":{"id":"eXlZKKhR45Er"}},{"cell_type":"markdown","source":["## **1.1 Mount Google Drive**"],"metadata":{"id":"s3tiagSz_-qL"}},{"cell_type":"code","source":["# Mount google drive for files and folder\n","from google.colab import drive\n","drive.mount(\"/content/Drive/\", force_remount=True)"],"metadata":{"id":"JXlXFoTq5AfF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702989094201,"user_tz":-330,"elapsed":23134,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"7a3574f5-b799-4391-a951-35bca353cbcc"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/Drive/\n"]}]},{"cell_type":"markdown","source":["## **1.2 Basic Imports**"],"metadata":{"id":"ts1VKg64AB3T"}},{"cell_type":"code","source":["# Basic python imports\n","import os\n","import shutil\n","!pip install tabulate --quiet\n","from tabulate import tabulate\n","\n","# Ignore warnings\n","import warnings\n","warnings.filterwarnings(\"ignore\")"],"metadata":{"id":"doR8Yyow6_xO","executionInfo":{"status":"ok","timestamp":1702989104903,"user_tz":-330,"elapsed":10708,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["## **1.3 Extract the Audio Data**"],"metadata":{"id":"hPHgbRfZAFrb"}},{"cell_type":"code","source":["# Path to dataset zip file\n","dataset_path = os.path.normpath(\"/content/Drive/MyDrive/Deep-Audio-Classification/archive.zip\")"],"metadata":{"id":"dEEu_eIL7K3U","executionInfo":{"status":"ok","timestamp":1702989104903,"user_tz":-330,"elapsed":6,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["# Check if data folder already exists\n","if \"data\" not in os.listdir(\"/content/\"):\n","    # Create folder to store the data\n","    os.makedirs(\"/content/data/\")\n","\n","    # Extract the file to the data folder\n","    shutil.unpack_archive(dataset_path, \"/content/data/\")"],"metadata":{"id":"Kv3Ufnsy7eTN","executionInfo":{"status":"ok","timestamp":1702989122173,"user_tz":-330,"elapsed":17275,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["## **1.4 View the Audio Composition**"],"metadata":{"id":"1OQ6xjZKAKJW"}},{"cell_type":"code","source":["# Base path to dataset\n","dataset_path = os.path.normpath(\"/content/data/\")\n","\n","# Table headers\n","table_headers = [\"Folder\", \"No. of Audio Files\"]\n","table_data = []\n","\n","# Traverse over the folders\n","for folder in os.listdir(dataset_path):\n","    # Update the list with folder and audio count\n","    table_data.append([os.path.join(dataset_path, folder), len(os.listdir(os.path.join(dataset_path, folder)))])\n","\n","# Print the table\n","print(tabulate(table_data, table_headers, tablefmt=\"grid\"))"],"metadata":{"id":"eptm6kgJ-2E0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702989122173,"user_tz":-330,"elapsed":20,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"c0adbd70-7425-433e-9c0f-160adf9c1f91"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["+---------------------------------------------+----------------------+\n","| Folder                                      |   No. of Audio Files |\n","+=============================================+======================+\n","| /content/data/Parsed_Not_Capuchinbird_Clips |                  593 |\n","+---------------------------------------------+----------------------+\n","| /content/data/Forest Recordings             |                  100 |\n","+---------------------------------------------+----------------------+\n","| /content/data/Parsed_Capuchinbird_Clips     |                  217 |\n","+---------------------------------------------+----------------------+\n"]}]},{"cell_type":"markdown","source":["# **2. Install and Import Dependencies**"],"metadata":{"id":"7izR7yqwBuV5"}},{"cell_type":"markdown","source":["## **2.1 Install Dependencies**"],"metadata":{"id":"0t0jMIKgBxrI"}},{"cell_type":"code","source":["# Install packages\n","!pip uninstall tensorflow --quiet --yes\n","!pip uninstall tensorflow-io --quiet --yes\n","\n","!pip install pip install tensorflow==2.10.0 tensorflow-io==0.27.0 --quiet"],"metadata":{"id":"-RKbb_dIFntM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702989194366,"user_tz":-330,"elapsed":72211,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"8f1cbe91-65f0-4956-b840-070219c578b9"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[33mWARNING: Skipping tensorflow-io as it is not installed.\u001b[0m\u001b[33m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m578.0/578.0 MB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m25.0/25.0 MB\u001b[0m \u001b[31m60.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m83.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m69.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.9/5.9 MB\u001b[0m \u001b[31m82.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m438.7/438.7 kB\u001b[0m \u001b[31m47.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m96.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m102.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m781.3/781.3 kB\u001b[0m \u001b[31m57.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","pandas-gbq 0.19.2 requires google-auth-oauthlib>=0.7.0, but you have google-auth-oauthlib 0.4.6 which is incompatible.\n","tensorflow-datasets 4.9.3 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n","tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0m"]}]},{"cell_type":"markdown","source":["## **2.2 Load Dependencies**"],"metadata":{"id":"8L00Tr8MC9G4"}},{"cell_type":"code","source":["# Import requried packages\n","import matplotlib.pyplot as plt\n","import tensorflow as tf\n","import tensorflow_io as tfio"],"metadata":{"id":"vLgPd3_eCz1s","executionInfo":{"status":"ok","timestamp":1702989197255,"user_tz":-330,"elapsed":2898,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":["# **3. Load Model**"],"metadata":{"id":"YzcW2rrJ2qMk"}},{"cell_type":"markdown","source":["## **3.1 Load Pretrained Model**"],"metadata":{"id":"-CcR9eKH2uY7"}},{"cell_type":"code","source":["# Load the model\n","model = tf.keras.models.load_model(\"/content/Drive/MyDrive/Deep-Audio-Classification/model_05-0.90.h5\")\n","\n","# View model summary\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6QP3PG7p2sTj","executionInfo":{"status":"ok","timestamp":1702989255758,"user_tz":-330,"elapsed":58508,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"975802c6-b75a-4edd-fc53-761bb3984312"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 1739, 255, 32)     320       \n","                                                                 \n"," batch_normalization (BatchN  (None, 1739, 255, 32)    128       \n"," ormalization)                                                   \n","                                                                 \n"," spatial_dropout2d (SpatialD  (None, 1739, 255, 32)    0         \n"," ropout2D)                                                       \n","                                                                 \n"," max_pooling2d (MaxPooling2D  (None, 869, 127, 32)     0         \n"," )                                                               \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 867, 125, 64)      18496     \n","                                                                 \n"," batch_normalization_1 (Batc  (None, 867, 125, 64)     256       \n"," hNormalization)                                                 \n","                                                                 \n"," spatial_dropout2d_1 (Spatia  (None, 867, 125, 64)     0         \n"," lDropout2D)                                                     \n","                                                                 \n"," max_pooling2d_1 (MaxPooling  (None, 433, 62, 64)      0         \n"," 2D)                                                             \n","                                                                 \n"," conv2d_2 (Conv2D)           (None, 431, 60, 128)      73856     \n","                                                                 \n"," batch_normalization_2 (Batc  (None, 431, 60, 128)     512       \n"," hNormalization)                                                 \n","                                                                 \n"," spatial_dropout2d_2 (Spatia  (None, 431, 60, 128)     0         \n"," lDropout2D)                                                     \n","                                                                 \n"," max_pooling2d_2 (MaxPooling  (None, 215, 30, 128)     0         \n"," 2D)                                                             \n","                                                                 \n"," flatten (Flatten)           (None, 825600)            0         \n","                                                                 \n"," dense (Dense)               (None, 256)               211353856 \n","                                                                 \n"," batch_normalization_3 (Batc  (None, 256)              1024      \n"," hNormalization)                                                 \n","                                                                 \n"," dropout (Dropout)           (None, 256)               0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 257       \n","                                                                 \n","=================================================================\n","Total params: 211,448,705\n","Trainable params: 211,447,745\n","Non-trainable params: 960\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["# **4. Build Forest Parsing Functions**"],"metadata":{"id":"EjSK80hs5tvx"}},{"cell_type":"markdown","source":["## **4.1 Load Up MP3s**"],"metadata":{"id":"LJFwiU4X5yUp"}},{"cell_type":"code","source":["# Function to load the MP3 audio files\n","def load_mp3_16k_mono(filename):\n","    # Load the audio file\n","    res = tfio.audio.AudioIOTensor(filename)\n","\n","    # Convert to tensor and combine channels\n","    tensor =  tf.math.reduce_sum(res.to_tensor(), axis=1) / 2\n","\n","    # Extract sample rate and cast\n","    sample_rate = tf.cast(res.rate, dtype=tf.int64)\n","\n","    # Resample to 16k hz\n","    wav = tfio.audio.resample(tensor, rate_in=sample_rate, rate_out=16000)\n","\n","    # Return the wav\n","    return wav"],"metadata":{"id":"q3dPPFl33POk","executionInfo":{"status":"ok","timestamp":1702989256434,"user_tz":-330,"elapsed":696,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# Load a sample file using the function\n","wav = load_mp3_16k_mono(\"/content/data/Forest Recordings/recording_00.mp3\")\n","\n","# View the data\n","wav"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wBgTHZno6j3C","executionInfo":{"status":"ok","timestamp":1702989257313,"user_tz":-330,"elapsed":884,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"94205a65-f4f6-4732-80ef-55abb378070c"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(2880666,), dtype=float32, numpy=\n","array([ 8.1433272e-12, -5.7019250e-12, -5.3486417e-12, ...,\n","       -1.1291276e-02, -1.4230422e-02, -3.0555837e-03], dtype=float32)>"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["## **4.2 Slice the Sample Audio**"],"metadata":{"id":"E2jn1RRm8WIM"}},{"cell_type":"code","source":["# Slice the audio file into multiple segments\n","audio_slices = tf.keras.utils.timeseries_dataset_from_array(wav, wav, sequence_length=56000, sequence_stride=56000, batch_size=1)\n","\n","# Extract the sample from from the slices\n","sample, idx = audio_slices.as_numpy_iterator().next()"],"metadata":{"id":"7LJHDj1x7G6Q","executionInfo":{"status":"ok","timestamp":1702989257871,"user_tz":-330,"elapsed":562,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["# View the shape of audio and number of audio slices\n","sample.shape, len(audio_slices)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fi8uq-eG7fRk","executionInfo":{"status":"ok","timestamp":1702989257871,"user_tz":-330,"elapsed":10,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"e50c9aef-89db-477e-865d-170537e9116b"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((1, 56000), 51)"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["## **4.3 Build Function to Convert Clips into Windowed Spectrograms**"],"metadata":{"id":"9eqQjpDc8Zm8"}},{"cell_type":"code","source":["# Function to preprocess and get the spectrogram\n","def preprocess_mp3(sample, idx):\n","    # Get the sample\n","    sample = sample[0]\n","\n","    # Add the zero padding\n","    zero_padding = tf.zeros([56000] - tf.shape(sample), dtype=tf.float32)\n","\n","    # Get the padded wave\n","    wav = tf.concat([zero_padding, sample], 0)\n","\n","    # Get the spectrogram\n","    spectrogram = tf.expand_dims(tf.abs(tf.signal.stft(wav, frame_length=320, frame_step=32)), axis=2)\n","\n","    # Return the spectrogram\n","    return spectrogram"],"metadata":{"id":"K6wYftL_76zz","executionInfo":{"status":"ok","timestamp":1702989257871,"user_tz":-330,"elapsed":8,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":["## **4.4 Convert Longer Clips into Windows**"],"metadata":{"id":"AUZptFJb9ZZD"}},{"cell_type":"code","source":["# Slice the audio file into multiple segments\n","audio_slices = tf.keras.utils.timeseries_dataset_from_array(wav, wav, sequence_length=56000, sequence_stride=56000, batch_size=1)\n","\n","# Map the audio slices to the function\n","audio_slices = audio_slices.map(preprocess_mp3)\n","\n","# Batch the slices\n","audio_slices = audio_slices.batch(64)"],"metadata":{"id":"8ej801Va9SqV","executionInfo":{"status":"ok","timestamp":1702989257872,"user_tz":-330,"elapsed":9,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":["## **5. Prediction on Sample**"],"metadata":{"id":"VwCergsr-CCI"}},{"cell_type":"markdown","source":["## **5.1 Make Predictions on Sample**"],"metadata":{"id":"5DTrJQy5-F-4"}},{"cell_type":"code","source":["# Imports\n","import numpy as np\n","\n","# Get the predictions\n","pred_labels = model.predict(audio_slices)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iZKNxFP29piH","executionInfo":{"status":"ok","timestamp":1702989278967,"user_tz":-330,"elapsed":21103,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"429864c4-5397-4076-ebe2-c6a44cdce28c"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 21s 21s/step\n"]}]},{"cell_type":"code","source":["# Round the predictions\n","pred_labels = np.round(pred_labels.flatten())\n","\n","# # Round the predictions with increased confidence\n","# pred_labels = np.where(pred_labels.flatten() > 0.8, 1, 0)"],"metadata":{"id":"OAdRLwD5CKQy","executionInfo":{"status":"ok","timestamp":1702989278968,"user_tz":-330,"elapsed":34,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["# View the predicted labels\n","len(pred_labels), pred_labels"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3rsAYLCZ-UdJ","executionInfo":{"status":"ok","timestamp":1702989278970,"user_tz":-330,"elapsed":35,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"550d8c94-3317-4d9d-ae2c-8806f2662e5a"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(51,\n"," array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n","        0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n","        0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       dtype=float32))"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["# Get the sum to count the number of times the bird sound was found\n","np.sum(pred_labels)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sNFibucy-XPp","executionInfo":{"status":"ok","timestamp":1702989278970,"user_tz":-330,"elapsed":33,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"550d5daa-f105-4c7e-bfe3-7153307bbca9"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["5.0"]},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","source":["## **5.2 Group Consecutive Detections**"],"metadata":{"id":"yIDzAwOO_vj-"}},{"cell_type":"code","source":["# Imports\n","from itertools import groupby\n","\n","# Apply the function to predictions\n","pred_labels = [key for key, group in groupby(pred_labels)]\n","\n","# View the grouped results\n","pred_labels"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XCDLF-SU_z-z","executionInfo":{"status":"ok","timestamp":1702989278971,"user_tz":-330,"elapsed":33,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"7156e143-739a-4333-9006-f33b6588e7da"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0]"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["# Get the sum to get the final number of calls\n","np.sum(pred_labels)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UMfPtXxvACcz","executionInfo":{"status":"ok","timestamp":1702989278971,"user_tz":-330,"elapsed":31,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"312d9458-a127-4407-c67e-82549a554c44"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["5.0"]},"metadata":{},"execution_count":20}]},{"cell_type":"markdown","source":["# **6. Prediction on All Forest Recordings**"],"metadata":{"id":"amvQu-K9Cgny"}},{"cell_type":"markdown","source":["## **6.1 Get the Number of Calls in Each Recording**"],"metadata":{"id":"JQizreCCDk-y"}},{"cell_type":"code","source":["# Import\n","from tqdm import tqdm_notebook\n","\n","# List to store the results\n","results = []\n","\n","# Traverse over the folder for files\n","for file in tqdm_notebook(os.listdir(\"/content/data/Forest Recordings\")):\n","    # Get the path to file\n","    file_path = os.path.join(\"/content/data/Forest Recordings\", file)\n","\n","    # Get the wave for the file\n","    wav = load_mp3_16k_mono(file_path)\n","\n","    # Get the audio slices\n","    audio_slices = tf.keras.utils.timeseries_dataset_from_array(\n","        wav, wav,\n","        sequence_length=56000,\n","        sequence_stride=56000,\n","        batch_size=1\n","    ).map(preprocess_mp3).batch(64)\n","\n","    # Get the predictions\n","    pred_labels = model.predict(audio_slices)\n","\n","    # Round the predictions\n","    pred_labels = np.round(pred_labels.flatten())\n","\n","    # Group consecutive calls\n","    pred_labels = [key for key, group in groupby(pred_labels)]\n","\n","    # Get the sum to get the final number of calls\n","    total_calls = np.sum(pred_labels)\n","\n","    # Add data to file\n","    results.append((file, total_calls))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["1bcdd7626e8e4a89b7d90f6775143b90","a00212b5e33641528a08cfe267a614ed","983255945efb4efdb9575b2736d07807","3315fcdd65f14f55a32976ea4f384c4e","1740ec8c269349c392a923c65e04246d","98caae2802f04e339f54e92717455723","5077c24cb5ee4322808813f7a1c197a2","df20d6ffc430481e99185265272eb575","f151b08d16f6459e8ef4c1923d47f806","e665f03948804cf6969e4f7caaddcaf4","e86eb6bde0c4420798dbd4320d8c1c29"]},"id":"zNNSicXSCkde","executionInfo":{"status":"ok","timestamp":1702991272241,"user_tz":-330,"elapsed":1993300,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"d5d980fb-0c8a-457b-8a27-eb386f0a823b"},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1bcdd7626e8e4a89b7d90f6775143b90"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 20s 20s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 20s 20s/step\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:5 out of the last 5 calls to <function pfor.<locals>.f at 0x7893d832e440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 19s 19s/step\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:6 out of the last 6 calls to <function pfor.<locals>.f at 0x7893d832e4d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 21s 21s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 22s 22s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 20s 20s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 18s 18s/step\n","1/1 [==============================] - 20s 20s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 19s 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18s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 18s 18s/step\n","1/1 [==============================] - 18s 18s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 20s 20s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 20s 20s/step\n","1/1 [==============================] - 18s 18s/step\n","1/1 [==============================] - 19s 19s/step\n","1/1 [==============================] - 18s 18s/step\n","1/1 [==============================] - 19s 19s/step\n"]}]},{"cell_type":"markdown","source":["## **6.3 Conver the List to Pandas Dataframe**"],"metadata":{"id":"OX2bitvUD3CL"}},{"cell_type":"code","source":["# Imports\n","import pandas as pd\n","\n","# Create pandas dataframe\n","result_df = pd.DataFrame(results, columns=[\"recording\", \"capuchin_calls\"])"],"metadata":{"id":"Q7ots0UwD6Rq","executionInfo":{"status":"ok","timestamp":1702991404489,"user_tz":-330,"elapsed":5,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}}},"execution_count":24,"outputs":[]},{"cell_type":"code","source":["# View the dataframe\n","result_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"KaNbIvRQMnO8","executionInfo":{"status":"ok","timestamp":1702991412981,"user_tz":-330,"elapsed":10,"user":{"displayName":"Rohit Ingole","userId":"15140123141073107924"}},"outputId":"f066eaad-8bdd-4ff4-d9aa-203f03ba036d"},"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           recording  capuchin_calls\n","0   recording_17.mp3             3.0\n","1   recording_55.mp3             0.0\n","2   recording_74.mp3             3.0\n","3   recording_66.mp3             0.0\n","4   recording_79.mp3             0.0\n","..               ...             ...\n","95  recording_61.mp3             2.0\n","96  recording_50.mp3             0.0\n","97  recording_38.mp3             1.0\n","98  recording_58.mp3             0.0\n","99  recording_95.mp3             4.0\n","\n","[100 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-d79feeb9-8be5-425f-879a-2e42a9d9e76a\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>recording</th>\n","      <th>capuchin_calls</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>recording_17.mp3</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      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recording_02.mp3             0.0\n","3   recording_03.mp3             0.0\n","4   recording_04.mp3             4.0\n","..               ...             ...\n","95  recording_95.mp3             4.0\n","96  recording_96.mp3             1.0\n","97  recording_97.mp3             3.0\n","98  recording_98.mp3            20.0\n","99  recording_99.mp3             5.0\n","\n","[100 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-0e8cf3e7-0da4-406e-89d9-406d2bba7f2b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>recording</th>\n","      <th>capuchin_calls</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>recording_00.mp3</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>recording_01.mp3</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>recording_02.mp3</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>recording_03.mp3</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>recording_04.mp3</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>95</th>\n","      <td>recording_95.mp3</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>96</th>\n","      <td>recording_96.mp3</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>recording_97.mp3</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>98</th>\n","      <td>recording_98.mp3</td>\n","      <td>20.0</td>\n","    </tr>\n","    <tr>\n","      <th>99</th>\n","      <td>recording_99.mp3</td>\n","      <td>5.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>100 rows × 2 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e8cf3e7-0da4-406e-89d9-406d2bba7f2b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      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