Final_Model_Chirp.ipynb

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    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
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      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "sourceId": 19596,
          "databundleVersionId": 1292430,
          "sourceType": "competition"
        },
        {
          "sourceId": 1262046,
          "sourceType": "datasetVersion",
          "datasetId": 726424
        },
        {
          "sourceId": 1264575,
          "sourceType": "datasetVersion",
          "datasetId": 725893
        }
      ],
      "dockerImageVersionId": 30626,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": false
    },
    "colab": {
      "name": "Final Model Chirp",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/kuldeepthakare7/Chirp-Birdsong-recognition/blob/main/Final_Model_Chirp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'birdsong-recognition:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-competitions-data%2Fkaggle-v2%2F19596%2F1292430%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240204%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240204T192531Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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,birdcall-check:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F726424%2F1262046%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240204%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240204T192531Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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,birdcall-resnet50-init-weights:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F725893%2F1264575%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240204%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240204T192531Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "id": "hw4XYkJNlUEm"
      },
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n"
      ],
      "metadata": {
        "execution": {
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    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/kaggle/input/birdsong-recognition/train.csv\")"
      ],
      "metadata": {
        "execution": {
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    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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      "cell_type": "code",
      "source": [
        "df.size"
      ],
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        "df.shape"
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      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
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      "source": [
        "df.info()"
      ],
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        "new_df  = df.isnull().sum()\n",
        "new_df.sort_values(ascending=False)"
      ],
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        "df['rating'].unique()"
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        "df['playback_used'].value_counts()"
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    {
      "cell_type": "code",
      "source": [
        "le  = LabelEncoder()\n",
        "df['playback_used'] = le.fit_transform(df['playback_used'])\n",
        "df['playback_used']\n"
      ],
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      "execution_count": null,
      "outputs": []
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        "df['recordist'].nunique()"
      ],
      "metadata": {
        "execution": {
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['author']!=df['recordist']]"
      ],
      "metadata": {
        "execution": {
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      "outputs": []
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    {
      "cell_type": "code",
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        "df.drop('recordist',axis=1,inplace=True)"
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      "metadata": {
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        "df['length'].isnull().sum()"
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        "df['length'].unique()"
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    },
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      "cell_type": "code",
      "source": [
        "df['min_value'] = df['length'].str.extract(r'(\\d+)')\n",
        "df['min_value'] = pd.to_numeric(df['min_value'],errors = 'coerce')\n",
        "df['max_value'] = df['length'].str.extract(r'-(\\d+)')\n",
        "df['max_value'] = pd.to_numeric(df['max_value'],errors = 'coerce')\n",
        "df.head()"
      ],
      "metadata": {
        "execution": {
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        "df['min_value'].unique()"
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        "df['max_value'].unique()"
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        "df['time'].isnull().sum()"
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        "df['time'].unique()"
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        "df['time'].nunique()"
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      "cell_type": "code",
      "source": [
        "df['license'].isnull().sum()"
      ],
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    {
      "cell_type": "code",
      "source": [
        "df['license'].unique()\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:00.297565Z",
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "df['license'] = le.fit_transform(df['license'])\n",
        "df['license']"
      ],
      "metadata": {
        "execution": {
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:04.056936Z",
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(15,5)})\n",
        "sns.countplot(x = 'rating',hue='playback_used',data=df,palette='hls')\n",
        "plt.title('Rating vs Playback used')\n",
        "plt.xlabel('Rating AND Playback used')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:05.755016Z",
          "iopub.execute_input": "2024-01-11T09:52:05.755483Z",
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(15,5)})\n",
        "sns.countplot(x = 'rating',hue='pitch',data=df,palette='Set2')\n",
        "plt.title('Rating vs Pitch')\n",
        "plt.xlabel('Rating AND Pitch')\n",
        "plt.ylabel('count')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:07.716741Z",
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numbrs = df['pitch'].value_counts()\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.pie(numbrs,labels=None,autopct='%1.1f%%')\n",
        "plt.title('Pitch Distribution')\n",
        "plt.legend(numbrs.index,title='Categories',loc='upper right',bbox_to_anchor=(1,0,0.5,1))\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:09.637686Z",
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import librosa\n",
        "import librosa.display\n",
        "import soundfile as sf\n",
        "import IPython.display as ipd"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:11.484683Z",
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a1 = \"/kaggle/input/birdsong-recognition/train_audio/ameavo/XC178760.mp3\"\n",
        "a2 = \"/kaggle/input/birdsong-recognition/train_audio/ameavo/XC178761.mp3\"\n",
        "b1 = \"/kaggle/input/birdsong-recognition/train_audio/calgul/XC161035.mp3\"\n",
        "b2 = \"/kaggle/input/birdsong-recognition/train_audio/calgul/XC161036.mp3\"\n",
        "c1 = \"/kaggle/input/birdsong-recognition/train_audio/fiscro/XC130581.mp3\"\n",
        "c2 = \"/kaggle/input/birdsong-recognition/train_audio/fiscro/XC130579.mp3\"\n",
        "d1 = \"/kaggle/input/birdsong-recognition/train_audio/magwar/XC111149.mp3\"\n",
        "d2 = \"/kaggle/input/birdsong-recognition/train_audio/magwar/XC134515.mp3\"\n",
        "e1 = \"/kaggle/input/birdsong-recognition/train_audio/swahaw/XC325718.mp3\"\n",
        "e2 = \"/kaggle/input/birdsong-recognition/train_audio/swahaw/XC267710.mp3\""
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:13.169968Z",
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y1,sr1 = librosa.load(a1)\n",
        "y2,sr2 = librosa.load(a2)\n",
        "y3,sr3 = librosa.load(b1)\n",
        "y4,sr4 = librosa.load(b2)\n",
        "y5,sr5 = librosa.load(c1)\n",
        "y6,sr6 = librosa.load(c2)\n",
        "y7,sr7 = librosa.load(d1)\n",
        "y8,sr8 = librosa.load(d2)\n",
        "y9,sr9 = librosa.load(e1)\n",
        "y10,sr10 = librosa.load(e2)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:15.269045Z",
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      },
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipd.Audio(y1,rate = sr1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:34.83827Z",
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      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "ipd.Audio(y2,rate = sr2)"
      ],
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        "execution": {
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    },
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      "cell_type": "code",
      "source": [
        "ipd.Audio(y3,rate = sr3)"
      ],
      "metadata": {
        "execution": {
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      "outputs": []
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      "cell_type": "code",
      "source": [
        "ipd.Audio(y4,rate = sr4)"
      ],
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      "cell_type": "code",
      "source": [
        "ipd.Audio(y5,rate = sr5)"
      ],
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "ipd.Audio(y7,rate = sr7)"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "ipd.Audio(y8,rate = sr8)"
      ],
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        "execution": {
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      "source": [
        "ipd.Audio(y9,rate = sr9)"
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    {
      "cell_type": "code",
      "source": [
        "ipd.Audio(y10,rate = sr10)"
      ],
      "metadata": {
        "execution": {
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        "id": "boYP2rq0lUE7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y1)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:55.43317Z",
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y2)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:52:57.723491Z",
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        "id": "lij0gT8llUE8"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y3)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:00.507512Z",
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        "id": "1qADv0_2lUE8"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y4)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:03.571081Z",
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        "id": "L1MFhak3lUE8"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y5)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:05.923019Z",
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y6)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:08.520682Z",
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        "id": "d1sKQnA7lUE8"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y7)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:11.219559Z",
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        "id": "4N2IABCdlUE8"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y8)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:14.059907Z",
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        "id": "V5vldwZFlUE8"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y9)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:16.510303Z",
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        "id": "39CMgmf0lUE8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y10)\n",
        "plt.ylabel(\"Amplitude\")\n",
        "plt.xlabel(\"Time\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:19.046738Z",
          "iopub.execute_input": "2024-01-11T09:53:19.048138Z",
          "iopub.status.idle": "2024-01-11T09:53:19.969826Z",
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        "id": "Ym9h_Y1glUE8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y1, hop_length=256)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr1, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:21.511786Z",
          "iopub.execute_input": "2024-01-11T09:53:21.512336Z",
          "iopub.status.idle": "2024-01-11T09:53:24.165137Z",
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        "trusted": true,
        "id": "tY-jQwsHlUE8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y2, hop_length=256)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr2, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:26.000482Z",
          "iopub.execute_input": "2024-01-11T09:53:26.002203Z",
          "iopub.status.idle": "2024-01-11T09:53:28.896104Z",
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        "trusted": true,
        "id": "UFUehWK6lUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y3, hop_length=256)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr3, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:30.896115Z",
          "iopub.execute_input": "2024-01-11T09:53:30.897379Z",
          "iopub.status.idle": "2024-01-11T09:53:33.982374Z",
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        "trusted": true,
        "id": "FXBE4nQilUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y4, hop_length=256)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr4, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:35.915212Z",
          "iopub.execute_input": "2024-01-11T09:53:35.915654Z",
          "iopub.status.idle": "2024-01-11T09:53:40.599211Z",
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        "trusted": true,
        "id": "ojofaIMflUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y5, hop_length=256)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr5, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:42.454053Z",
          "iopub.execute_input": "2024-01-11T09:53:42.454472Z",
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        "trusted": true,
        "id": "JoL633kJlUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y6, hop_length=256)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr6, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:46.403174Z",
          "iopub.execute_input": "2024-01-11T09:53:46.403593Z",
          "iopub.status.idle": "2024-01-11T09:53:48.385641Z",
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        "trusted": true,
        "id": "JpoiemKFlUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y7, hop_length=512)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr7, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:50.512702Z",
          "iopub.execute_input": "2024-01-11T09:53:50.513139Z",
          "iopub.status.idle": "2024-01-11T09:53:52.605689Z",
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        "trusted": true,
        "id": "ULiIngK3lUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y8, hop_length=512)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr8, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:53:56.009546Z",
          "iopub.execute_input": "2024-01-11T09:53:56.010069Z",
          "iopub.status.idle": "2024-01-11T09:53:58.731626Z",
          "shell.execute_reply.started": "2024-01-11T09:53:56.010031Z",
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        },
        "trusted": true,
        "id": "1MX-4N4YlUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y9, hop_length=512)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr9, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:00.588499Z",
          "iopub.execute_input": "2024-01-11T09:54:00.588968Z",
          "iopub.status.idle": "2024-01-11T09:54:03.822713Z",
          "shell.execute_reply.started": "2024-01-11T09:54:00.588932Z",
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        },
        "trusted": true,
        "id": "PwIcKtOPlUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "D = librosa.amplitude_to_db(np.abs(librosa.stft(y10, hop_length=512)), ref=np.max)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(D, sr=sr10, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('Spectrogram of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:06.194384Z",
          "iopub.execute_input": "2024-01-11T09:54:06.19525Z",
          "iopub.status.idle": "2024-01-11T09:54:09.296737Z",
          "shell.execute_reply.started": "2024-01-11T09:54:06.195203Z",
          "shell.execute_reply": "2024-01-11T09:54:09.295862Z"
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        "trusted": true,
        "id": "oNmf1VamlUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y1, sr=sr1, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:11.285401Z",
          "iopub.execute_input": "2024-01-11T09:54:11.287021Z",
          "iopub.status.idle": "2024-01-11T09:54:15.539612Z",
          "shell.execute_reply.started": "2024-01-11T09:54:11.286968Z",
          "shell.execute_reply": "2024-01-11T09:54:15.538138Z"
        },
        "trusted": true,
        "id": "ytBXxkkFlUE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y2, sr=sr2, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:17.759Z",
          "iopub.execute_input": "2024-01-11T09:54:17.75944Z",
          "iopub.status.idle": "2024-01-11T09:54:20.565306Z",
          "shell.execute_reply.started": "2024-01-11T09:54:17.759404Z",
          "shell.execute_reply": "2024-01-11T09:54:20.563842Z"
        },
        "trusted": true,
        "id": "eHTiTZI4lUE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y3, sr=sr3, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:22.497817Z",
          "iopub.execute_input": "2024-01-11T09:54:22.498297Z",
          "iopub.status.idle": "2024-01-11T09:54:25.835176Z",
          "shell.execute_reply.started": "2024-01-11T09:54:22.498263Z",
          "shell.execute_reply": "2024-01-11T09:54:25.833725Z"
        },
        "trusted": true,
        "id": "oaBmGiNKlUE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y4, sr=sr4, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:27.641966Z",
          "iopub.execute_input": "2024-01-11T09:54:27.642481Z",
          "iopub.status.idle": "2024-01-11T09:54:32.48927Z",
          "shell.execute_reply.started": "2024-01-11T09:54:27.642442Z",
          "shell.execute_reply": "2024-01-11T09:54:32.488046Z"
        },
        "trusted": true,
        "id": "QjtSnFqclUE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y5, sr=sr5, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:34.305186Z",
          "iopub.execute_input": "2024-01-11T09:54:34.306063Z",
          "iopub.status.idle": "2024-01-11T09:54:36.156434Z",
          "shell.execute_reply.started": "2024-01-11T09:54:34.306014Z",
          "shell.execute_reply": "2024-01-11T09:54:36.155275Z"
        },
        "trusted": true,
        "id": "BZ0pGEvelUE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y6, sr=sr6, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:38.382929Z",
          "iopub.execute_input": "2024-01-11T09:54:38.383366Z",
          "iopub.status.idle": "2024-01-11T09:54:40.400223Z",
          "shell.execute_reply.started": "2024-01-11T09:54:38.383333Z",
          "shell.execute_reply": "2024-01-11T09:54:40.399287Z"
        },
        "trusted": true,
        "id": "4Re-elLRlUE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y7, sr=sr7, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:42.095673Z",
          "iopub.execute_input": "2024-01-11T09:54:42.097189Z",
          "iopub.status.idle": "2024-01-11T09:54:46.124966Z",
          "shell.execute_reply.started": "2024-01-11T09:54:42.097142Z",
          "shell.execute_reply": "2024-01-11T09:54:46.124021Z"
        },
        "trusted": true,
        "id": "yRN7Q_-wlUE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y8, sr=sr8, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:47.797927Z",
          "iopub.execute_input": "2024-01-11T09:54:47.798424Z",
          "iopub.status.idle": "2024-01-11T09:54:52.429273Z",
          "shell.execute_reply.started": "2024-01-11T09:54:47.798387Z",
          "shell.execute_reply": "2024-01-11T09:54:52.428107Z"
        },
        "trusted": true,
        "id": "GDZCuZVAlUE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y9, sr=sr9, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:54:54.313688Z",
          "iopub.execute_input": "2024-01-11T09:54:54.314233Z",
          "iopub.status.idle": "2024-01-11T09:54:59.912947Z",
          "shell.execute_reply.started": "2024-01-11T09:54:54.314195Z",
          "shell.execute_reply": "2024-01-11T09:54:59.911555Z"
        },
        "trusted": true,
        "id": "GuxTyb7IlUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mel_signal = librosa.feature.melspectrogram(y=y10, sr=sr10, n_fft=1024, hop_length=64)\n",
        "mel_spect = librosa.power_to_db(np.abs(mel_signal), ref=np.max)\n",
        "plt.figure(figsize=(7, 6))\n",
        "librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time');\n",
        "plt.title('Mel Spectrogram');\n",
        "plt.colorbar(format='%+2.0f dB');"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:02.094151Z",
          "iopub.execute_input": "2024-01-11T09:55:02.094646Z",
          "iopub.status.idle": "2024-01-11T09:55:07.717418Z",
          "shell.execute_reply.started": "2024-01-11T09:55:02.094608Z",
          "shell.execute_reply": "2024-01-11T09:55:07.715681Z"
        },
        "trusted": true,
        "id": "NRWqEgeqlUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y1,sr=sr1)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr1, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:09.860093Z",
          "iopub.execute_input": "2024-01-11T09:55:09.861711Z",
          "iopub.status.idle": "2024-01-11T09:55:10.620811Z",
          "shell.execute_reply.started": "2024-01-11T09:55:09.861655Z",
          "shell.execute_reply": "2024-01-11T09:55:10.619597Z"
        },
        "trusted": true,
        "id": "RZwcemHvlUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y2,sr=sr2)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr2, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:12.186907Z",
          "iopub.execute_input": "2024-01-11T09:55:12.188314Z",
          "iopub.status.idle": "2024-01-11T09:55:12.945441Z",
          "shell.execute_reply.started": "2024-01-11T09:55:12.18827Z",
          "shell.execute_reply": "2024-01-11T09:55:12.944214Z"
        },
        "trusted": true,
        "id": "9r1_eqTMlUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y3,sr=sr3)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr3, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:14.527487Z",
          "iopub.execute_input": "2024-01-11T09:55:14.52795Z",
          "iopub.status.idle": "2024-01-11T09:55:15.305585Z",
          "shell.execute_reply.started": "2024-01-11T09:55:14.527914Z",
          "shell.execute_reply": "2024-01-11T09:55:15.304158Z"
        },
        "trusted": true,
        "id": "aHGkVN3ElUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y4,sr=sr4)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr4, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:16.876165Z",
          "iopub.execute_input": "2024-01-11T09:55:16.876626Z",
          "iopub.status.idle": "2024-01-11T09:55:17.750229Z",
          "shell.execute_reply.started": "2024-01-11T09:55:16.876593Z",
          "shell.execute_reply": "2024-01-11T09:55:17.74889Z"
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        "trusted": true,
        "id": "3ctruQ3ElUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y5,sr=sr5)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr5, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:19.448196Z",
          "iopub.execute_input": "2024-01-11T09:55:19.448741Z",
          "iopub.status.idle": "2024-01-11T09:55:20.123265Z",
          "shell.execute_reply.started": "2024-01-11T09:55:19.448705Z",
          "shell.execute_reply": "2024-01-11T09:55:20.12185Z"
        },
        "trusted": true,
        "id": "ZPQA_ssslUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y6,sr=sr6)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr6, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:21.651316Z",
          "iopub.execute_input": "2024-01-11T09:55:21.651828Z",
          "iopub.status.idle": "2024-01-11T09:55:22.335581Z",
          "shell.execute_reply.started": "2024-01-11T09:55:21.651775Z",
          "shell.execute_reply": "2024-01-11T09:55:22.334363Z"
        },
        "trusted": true,
        "id": "JmpLHjdilUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y7,sr=sr7)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr7, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:24.272349Z",
          "iopub.execute_input": "2024-01-11T09:55:24.272836Z",
          "iopub.status.idle": "2024-01-11T09:55:25.060556Z",
          "shell.execute_reply.started": "2024-01-11T09:55:24.272783Z",
          "shell.execute_reply": "2024-01-11T09:55:25.059387Z"
        },
        "trusted": true,
        "id": "-0n3mwVblUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y8,sr=sr8)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr8, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:26.687169Z",
          "iopub.execute_input": "2024-01-11T09:55:26.687924Z",
          "iopub.status.idle": "2024-01-11T09:55:27.527863Z",
          "shell.execute_reply.started": "2024-01-11T09:55:26.687888Z",
          "shell.execute_reply": "2024-01-11T09:55:27.526582Z"
        },
        "trusted": true,
        "id": "hZ4HhLiZlUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y9,sr=sr9)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr9, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:29.264027Z",
          "iopub.execute_input": "2024-01-11T09:55:29.264439Z",
          "iopub.status.idle": "2024-01-11T09:55:30.11792Z",
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          "shell.execute_reply": "2024-01-11T09:55:30.117049Z"
        },
        "trusted": true,
        "id": "tlOusiQslUFA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mfcc = librosa.feature.mfcc(y=y10,sr=sr10)\n",
        "plt.figure(figsize=(8, 4))\n",
        "librosa.display.specshow(mfcc, sr=sr10, x_axis='time', y_axis='log')\n",
        "plt.colorbar(format='%+2.0f dB')\n",
        "plt.title('MFCC of the Audio Signal')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T09:55:31.865697Z",
          "iopub.execute_input": "2024-01-11T09:55:31.866515Z",
          "iopub.status.idle": "2024-01-11T09:55:32.730579Z",
          "shell.execute_reply.started": "2024-01-11T09:55:31.866472Z",
          "shell.execute_reply": "2024-01-11T09:55:32.7293Z"
        },
        "trusted": true,
        "id": "eXHSNUE3lUFA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# By doing the feature extraction we see that mel-spectrogram and mfcc are both very good\n",
        "# for using in the model. I prefer to use mel-spectrogram."
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-09T17:44:26.711875Z",
          "iopub.execute_input": "2024-01-09T17:44:26.71329Z",
          "iopub.status.idle": "2024-01-09T17:44:26.71822Z",
          "shell.execute_reply.started": "2024-01-09T17:44:26.713224Z",
          "shell.execute_reply": "2024-01-09T17:44:26.717Z"
        },
        "trusted": true,
        "id": "Gp9aLul8lUFA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import audioread\n",
        "import logging\n",
        "import os\n",
        "import random\n",
        "import time\n",
        "import warnings\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as data\n",
        "from contextlib import contextmanager\n",
        "from pathlib import Path\n",
        "from typing import Optional\n",
        "from fastprogress import progress_bar\n",
        "from sklearn.metrics import f1_score\n",
        "from torchvision import models\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T10:12:42.127891Z",
          "iopub.execute_input": "2024-01-11T10:12:42.128403Z",
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        },
        "trusted": true,
        "id": "12Oqu2PglUFA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def set_seed( seed: int=42):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backendscudnn.benchmark\n",
        "\n",
        "def get_logger(out_file=None):\n",
        "    logger = logging.getLogger()\n",
        "    formatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(message)s\")\n",
        "    logger.handlers = []\n",
        "    logger.setLevel(logging.INFO)\n",
        "    handler = logging.StreamHandler()\n",
        "    handler.setFormatter(formatter)\n",
        "    handler.seLevel(logging.INFO)\n",
        "    logger.addHandler(handler)\n",
        "\n",
        "    if out_file is not None:\n",
        "        fh = logging.FileHandler(out_file)\n",
        "        fh.setFormatter(formatter)\n",
        "        fh.setLevel(logging.INFO)\n",
        "        logger.addHandler(fh)\n",
        "    logger.info(\"logger set up\")\n",
        "    return logger\n",
        "\n",
        "@contextmanager\n",
        "def timer(name: str, logger: Optional[logging.Logger] = None):\n",
        "    t0 = time.time()\n",
        "    msg = f\"[{name}] start\"\n",
        "    if logger is None:\n",
        "        print(msg)\n",
        "    else:\n",
        "        logger.info(msg)\n",
        "    yield\n",
        "\n",
        "    msg = f\"[{name}] done in {time.time() - t0:.2f} s\"\n",
        "    if logger is None:\n",
        "        print(msg)\n",
        "    else:\n",
        "        logger.info(msg)\n",
        ""
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T10:29:40.566297Z",
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        },
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        "id": "Iep4QDjrlUFA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test = pd.read_csv(\"/kaggle/input/birdcall-check/test.csv\")\n",
        "test_audio = \"/kaggle/input/birdcall-check/test_audio\"\n",
        "test.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T10:58:10.073823Z",
          "iopub.execute_input": "2024-01-11T10:58:10.075164Z",
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ResNet(nn.Module):\n",
        "    def __init__(self, base_model_name: str, pretrained=False,num_classes=264):\n",
        "        super().__init__()\n",
        "        base_model = models.__getattribute__(base_model_name)(pretrained=pretrained)\n",
        "        layers.append(nn.AdaptiveMaxPool2d(1))\n",
        "        seld.encoder = nn.sequential(*layers)\n",
        "\n",
        "        in_features = base_model.fc.in_features\n",
        "        self.classifier = nn.Sequential(\n",
        "             nn.Linear(in_fetaures,1024, nn.ReLU(),nn.Dropout(p=0.2)),\n",
        "             nn.Linear(1024,1024), nn.ReLU(), nn.Dropout(p=02.),\n",
        "             nn.Linear(1024, num_classes))\n",
        "    def forward(self, x):\n",
        "        batch_size = x.size(0)\n",
        "        x = self.encoder(x).view(batch_size, -1)\n",
        "        x = self.classifier(x)\n",
        "        multiclass_proba = F.softmax(x, dim=1)\n",
        "        multilabel_proba = F.sigmoid(x)\n",
        "        return {\n",
        "            \"logits\": x,\n",
        "            \"multiclass_proba\": multiclass_proba,\n",
        "            \"multilabel_proba\": multilabel_proba,\n",
        "        }"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T10:58:13.833365Z",
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        },
        "trusted": true,
        "id": "ITgDlX_YlUFA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_config = {\n",
        "    \"base_model_name\": \"resnet50\",\n",
        "    \"pretrained\": False,\n",
        "    \"num_classes\": 264,\n",
        "}\n",
        "melspectrogram_parameters = {\n",
        "    \"n_mels\": 128,\n",
        "    \"fmin\": 20,\n",
        "    \"fmax\":16000,\n",
        "}\n",
        "weights_path = \"/kaggle/input/birdcall-resnet50-init-weights/best.pth\""
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T11:05:02.549578Z",
          "iopub.execute_input": "2024-01-11T11:05:02.550064Z",
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        },
        "trusted": true,
        "id": "7WgYXuWklUFA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/kaggle/input/birdsong-recognition/train.csv\")\n",
        "unique = df['ebird_code'].unique()\n",
        "le = LabelEncoder()\n",
        "encoded_labels = le.fit_transform(unique)\n",
        "BIRD_CODE = dict(zip(unique, encoded_labels))\n",
        "INV_BIRD_CODE = {v :k for k , v in BIRD_CODE.items()}\n",
        "\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T11:13:33.606513Z",
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        },
        "trusted": true,
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def mono_to_color(X: np.ndarray,mean=None,std=None,norm_max=None,norm_min=None,eps=1e-6):\n",
        "    X = np.stack([X,X,X],axis=-1)\n",
        "\n",
        "    mean = mean or X.mean()\n",
        "    X = X-mean\n",
        "    std = std or X.std()\n",
        "    Xstd = X/(std+eps)\n",
        "    _min, _max = Xstd.min(), Xstd.max()\n",
        "    norm_max = norm_max or max\n",
        "    norm_min = norm_min or min\n",
        "\n",
        "    if((_max-_min) > eps):\n",
        "        V = Xstd\n",
        "        V[V< norm_min] = norm_min\n",
        "        V[V> norm_max] = norm_max\n",
        "        V = 255*(V-norm_min)/(norm_max-norm_min)\n",
        "        V = V.astype(np.uint8)\n",
        "    else:\n",
        "        V = np.zeros_like(Xstd, dtype=np.uitn8)\n",
        "    return V\n",
        "\n",
        "class TestDataset(data.Daateset):\n",
        "    def __init__(self, df:pd.DataFrame, clip: np.ndarray,img_size=224,melspectrogram_parameters = {}):\n",
        "        self.df = df\n",
        "        self.clip = clip\n",
        "        self.img_size = img_size\n",
        "        self.melspectrogram_parameters = melspectrogram_parameters\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "    def __getitem__(self, idx:int):\n",
        "        SR = 32000\n",
        "        sample = self.df.loc[idx,:]\n",
        "        site = sample.site\n",
        "        row_id = sample.row_id\n",
        "\n",
        "        if site==\"site_3\":\n",
        "            y = self.clip.astype(np.float32)\n",
        "            len_y = len(y)\n",
        ""
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-01-11T11:12:34.860468Z",
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  ]
}