{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":46716,"databundleVersionId":5075280,"sourceType":"competition"}],"dockerImageVersionId":30407,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"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\nimport numpy as np # linear algebra\nimport 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\nimport os\nfor 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install librosa","metadata":{"execution":{"iopub.status.busy":"2023-02-25T19:36:59.491885Z","iopub.execute_input":"2023-02-25T19:36:59.492797Z","iopub.status.idle":"2023-02-25T19:37:27.626936Z","shell.execute_reply.started":"2023-02-25T19:36:59.492747Z","shell.execute_reply":"2023-02-25T19:37:27.624898Z"},"_kg_hide-output":true,"trusted":true},"outputs":[{"name":"stdout","text":"Requirement already satisfied: librosa in /opt/conda/lib/python3.7/site-packages (0.10.0)\nRequirement already satisfied: msgpack>=1.0 in /opt/conda/lib/python3.7/site-packages (from librosa) (1.0.4)\nRequirement already satisfied: scipy>=1.2.0 in /opt/conda/lib/python3.7/site-packages (from librosa) (1.7.3)\nRequirement already satisfied: audioread>=2.1.9 in /opt/conda/lib/python3.7/site-packages (from librosa) (3.0.0)\nRequirement already satisfied: joblib>=0.14 in /opt/conda/lib/python3.7/site-packages (from librosa) (1.2.0)\nRequirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.7/site-packages (from librosa) (5.1.1)\nRequirement already satisfied: scikit-learn>=0.20.0 in /opt/conda/lib/python3.7/site-packages (from librosa) (1.0.2)\nRequirement already satisfied: soxr>=0.3.2 in /opt/conda/lib/python3.7/site-packages (from librosa) (0.3.3)\nRequirement already satisfied: pooch>=1.0 in /opt/conda/lib/python3.7/site-packages (from librosa) (1.6.0)\nRequirement already satisfied: lazy-loader>=0.1 in /opt/conda/lib/python3.7/site-packages (from librosa) (0.1)\nCollecting soundfile>=0.12.1\n  Downloading soundfile-0.12.1-py2.py3-none-manylinux_2_31_x86_64.whl (1.2 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m0m\n\u001b[?25hRequirement already satisfied: numpy>=1.20.3 in /opt/conda/lib/python3.7/site-packages (from librosa) (1.21.6)\nRequirement already satisfied: numba>=0.51.0 in /opt/conda/lib/python3.7/site-packages (from librosa) (0.56.4)\nRequirement already satisfied: typing-extensions>=4.1.1 in /opt/conda/lib/python3.7/site-packages (from librosa) (4.4.0)\nRequirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /opt/conda/lib/python3.7/site-packages (from numba>=0.51.0->librosa) (0.39.1)\nRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from numba>=0.51.0->librosa) (4.11.4)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from numba>=0.51.0->librosa) (59.8.0)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from pooch>=1.0->librosa) (23.0)\nRequirement already satisfied: appdirs>=1.3.0 in /opt/conda/lib/python3.7/site-packages (from pooch>=1.0->librosa) (1.4.4)\nRequirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from pooch>=1.0->librosa) (2.28.2)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn>=0.20.0->librosa) (3.1.0)\nRequirement already satisfied: cffi>=1.0 in /opt/conda/lib/python3.7/site-packages (from soundfile>=0.12.1->librosa) (1.15.1)\nRequirement already satisfied: pycparser in /opt/conda/lib/python3.7/site-packages (from cffi>=1.0->soundfile>=0.12.1->librosa) (2.21)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2022.12.7)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (1.26.14)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2.1.1)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.4)\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->numba>=0.51.0->librosa) (3.11.0)\nInstalling collected packages: soundfile\n  Attempting uninstall: soundfile\n    Found existing installation: soundfile 0.11.0\n    Uninstalling soundfile-0.11.0:\n      Successfully uninstalled soundfile-0.11.0\n\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.\nwfdb 4.1.0 requires SoundFile<0.12.0,>=0.10.0, but you have soundfile 0.12.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed soundfile-0.12.1\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2023-03-09T17:09:27.573276Z","iopub.execute_input":"2023-03-09T17:09:27.573624Z","iopub.status.idle":"2023-03-09T17:09:27.603842Z","shell.execute_reply.started":"2023-03-09T17:09:27.573591Z","shell.execute_reply":"2023-03-09T17:09:27.602714Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"!pip install pydub","metadata":{"execution":{"iopub.status.busy":"2023-03-08T19:35:32.865997Z","iopub.execute_input":"2023-03-08T19:35:32.867007Z","iopub.status.idle":"2023-03-08T19:35:43.956821Z","shell.execute_reply.started":"2023-03-08T19:35:32.866937Z","shell.execute_reply":"2023-03-08T19:35:43.955629Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Requirement already satisfied: pydub in /opt/conda/lib/python3.7/site-packages (0.25.1)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"import pandas as pd\nimport IPython.display as ipd\nimport librosa\nimport librosa.display\nimport math\nfrom pydub import AudioSegment\nimport numpy as np\nfrom tqdm import tqdm\nimport os","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:18:16.501803Z","iopub.execute_input":"2023-03-09T20:18:16.502188Z","iopub.status.idle":"2023-03-09T20:18:16.573277Z","shell.execute_reply.started":"2023-03-09T20:18:16.502154Z","shell.execute_reply":"2023-03-09T20:18:16.572282Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"filename = '/kaggle/input/ml-olympiad-dialectrecognition/batch_1/6k_SHTV_67_2.wav'","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:20:00.056743Z","iopub.execute_input":"2023-03-09T20:20:00.057305Z","iopub.status.idle":"2023-03-09T20:20:00.063079Z","shell.execute_reply.started":"2023-03-09T20:20:00.057268Z","shell.execute_reply":"2023-03-09T20:20:00.061875Z"},"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"train_data = pd.read_csv('/kaggle/input/ml-olympiad-dialectrecognition/train.csv')","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:19:30.576569Z","iopub.execute_input":"2023-03-09T20:19:30.57728Z","iopub.status.idle":"2023-03-09T20:19:31.743941Z","shell.execute_reply.started":"2023-03-09T20:19:30.577242Z","shell.execute_reply":"2023-03-09T20:19:31.742896Z"},"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"new_audio = AudioSegment.from_wav(filename)\nstarts = train_data['SegmentStart']\nends = train_data['SegmentEnd']\n\nnew_audio = new_audio[math.floor(starts[4])*1000:math.ceil(ends[4])*1000]","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:20:03.341149Z","iopub.execute_input":"2023-03-09T20:20:03.341739Z","iopub.status.idle":"2023-03-09T20:20:03.730231Z","shell.execute_reply.started":"2023-03-09T20:20:03.341699Z","shell.execute_reply":"2023-03-09T20:20:03.7292Z"},"trusted":true},"outputs":[],"execution_count":9},{"cell_type":"code","source":"new_audio.export('new_audio.wav', format='wav')","metadata":{"execution":{"iopub.status.busy":"2023-03-09T17:21:24.258782Z","iopub.execute_input":"2023-03-09T17:21:24.259182Z","iopub.status.idle":"2023-03-09T17:21:24.269051Z","shell.execute_reply.started":"2023-03-09T17:21:24.259146Z","shell.execute_reply":"2023-03-09T17:21:24.267794Z"},"trusted":true},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"<_io.BufferedRandom name='new_audio.wav'>"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"print(math.floor(starts[4])*1000)","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:23:54.091885Z","iopub.execute_input":"2023-03-09T20:23:54.09247Z","iopub.status.idle":"2023-03-09T20:23:54.098991Z","shell.execute_reply.started":"2023-03-09T20:23:54.092433Z","shell.execute_reply":"2023-03-09T20:23:54.097709Z"},"trusted":true},"outputs":[{"name":"stdout","text":"175000\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"audio_dataset_path=\"/kaggle/input/ml-olympiad-dialectrecognition/\"\nmetadata = pd.read_csv(\"/kaggle/input/ml-olympiad-dialectrecognition/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:18:27.897365Z","iopub.execute_input":"2023-03-09T20:18:27.898029Z","iopub.status.idle":"2023-03-09T20:18:29.809859Z","shell.execute_reply.started":"2023-03-09T20:18:27.89799Z","shell.execute_reply":"2023-03-09T20:18:29.80881Z"},"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"for index_name,row in tqdm(metadata.iterrows()):\n    value = f'new_audio is ({index_name}).wav'\n    print(value)\n    break","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:37:17.752475Z","iopub.execute_input":"2023-03-09T20:37:17.752841Z","iopub.status.idle":"2023-03-09T20:37:17.824232Z","shell.execute_reply.started":"2023-03-09T20:37:17.752808Z","shell.execute_reply":"2023-03-09T20:37:17.823183Z"},"trusted":true},"outputs":[{"name":"stderr","text":"0it [00:00, ?it/s]","output_type":"stream"},{"name":"stdout","text":"new_audio is (0).wav\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}],"execution_count":29},{"cell_type":"code","source":"def features_extractor(file):\n    audio, sample_rate = librosa.load(file_name)\n    mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)\n    mfccs_scaled_features = np.mean(mfccs_features.T, axis=0)\n    \n    return mfccs_scaled_features","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:26:32.957515Z","iopub.execute_input":"2023-03-09T20:26:32.958219Z","iopub.status.idle":"2023-03-09T20:26:32.963844Z","shell.execute_reply.started":"2023-03-09T20:26:32.95818Z","shell.execute_reply":"2023-03-09T20:26:32.962786Z"},"trusted":true},"outputs":[],"execution_count":22},{"cell_type":"code","source":"extracted_features=[]\n\nfor index_name,row in tqdm(metadata.iterrows()):\n    file_name = os.path.join(os.path.abspath(audio_dataset_path), str(row[\"FileName\"]))\n    \n    starts = (math.floor(row['SegmentStart'])*1000)\n    ends = (math.ceil(row['SegmentEnd'])*1000)\n    \n    new_audio = AudioSegment.from_wav(file_name)\n    \n    new_audio = new_audio[starts:ends]\n    value = f'new_audio is ({index_name}).wav'\n    new_audio.export(value, format='wav')\n    \n    final_class_labels=row[\"SpeakerDialect\"]\n    data=features_extractor(value)\n    extracted_features.append([data, final_class_labels])","metadata":{"execution":{"iopub.status.busy":"2023-03-09T20:38:11.668132Z","iopub.execute_input":"2023-03-09T20:38:11.669049Z"},"trusted":true},"outputs":[{"name":"stderr","text":"6075it [2:23:56,  1.47s/it]","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(14, 5))\ndata,sample_rate=librosa.load('new_audio.wav')\nlibrosa.display.waveshow(data,sr=sample_rate)\nipd.Audio(data, rate=sample_rate)","metadata":{"execution":{"iopub.status.busy":"2025-01-24T21:08:02.793051Z","iopub.execute_input":"2025-01-24T21:08:02.793402Z","iopub.status.idle":"2025-01-24T21:08:02.866729Z","shell.execute_reply.started":"2025-01-24T21:08:02.793365Z","shell.execute_reply":"2025-01-24T21:08:02.865483Z"},"trusted":true},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_27/2004605630.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'new_audio.wav'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaveshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mipd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"],"ename":"NameError","evalue":"name 'plt' is not defined","output_type":"error"}],"execution_count":1},{"cell_type":"code","source":"mfccs = librosa.feature.mfcc(y=data, sr=sample_rate, n_mfcc=40)\nprint(mfccs.shape)","metadata":{"execution":{"iopub.status.busy":"2023-03-09T17:24:32.518295Z","iopub.execute_input":"2023-03-09T17:24:32.518873Z","iopub.status.idle":"2023-03-09T17:24:32.549388Z","shell.execute_reply.started":"2023-03-09T17:24:32.518827Z","shell.execute_reply":"2023-03-09T17:24:32.548129Z"},"trusted":true},"outputs":[{"name":"stdout","text":"(40, 130)\n","output_type":"stream"}],"execution_count":15},{"cell_type":"code","source":"import librosa\naudio_file_path = \"/kaggle/input/ml-olympiad-dialectrecognition/batch_2/6k_v_SBA3_2453_1.wav\"\nlibrosa_file, librosa_sample = librosa.load(audio_file_path)","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:13:47.147823Z","iopub.execute_input":"2023-03-01T10:13:47.148492Z","iopub.status.idle":"2023-03-01T10:14:02.316164Z","shell.execute_reply.started":"2023-03-01T10:13:47.148453Z","shell.execute_reply":"2023-03-01T10:14:02.314494Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.figure(figsize=(12, 4))\nplt.plot(librosa_file)","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:15:17.078378Z","iopub.execute_input":"2023-03-01T10:15:17.07909Z","iopub.status.idle":"2023-03-01T10:15:20.246408Z","shell.execute_reply.started":"2023-03-01T10:15:17.079046Z","shell.execute_reply":"2023-03-01T10:15:20.245426Z"},"trusted":true},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7f8665af2490>]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x400 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"###Lets read with scipy\nfrom scipy.io import wavfile as wav\nwave_sample, wave_audio = wav.read(audio_file_path)","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:15:31.767991Z","iopub.execute_input":"2023-03-01T10:15:31.768405Z","iopub.status.idle":"2023-03-01T10:15:31.780955Z","shell.execute_reply.started":"2023-03-01T10:15:31.76837Z","shell.execute_reply":"2023-03-01T10:15:31.779245Z"},"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"wave_audio","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:15:34.617649Z","iopub.execute_input":"2023-03-01T10:15:34.618052Z","iopub.status.idle":"2023-03-01T10:15:34.627548Z","shell.execute_reply.started":"2023-03-01T10:15:34.618013Z","shell.execute_reply":"2023-03-01T10:15:34.626107Z"},"trusted":true},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"array([-107, -150, -160, ...,  -66,  -77,  -70], dtype=int16)"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"plt.figure(figsize=(12, 4))\nplt.plot(wave_audio)","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:15:39.268342Z","iopub.execute_input":"2023-03-01T10:15:39.268949Z","iopub.status.idle":"2023-03-01T10:15:41.654183Z","shell.execute_reply.started":"2023-03-01T10:15:39.268884Z","shell.execute_reply":"2023-03-01T10:15:41.652711Z"},"trusted":true},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7f866625e590>]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x400 with 1 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\n"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"mfccs = librosa.feature.mfcc(y=librosa_file, sr=librosa_sample, n_mfcc=40)\nprint(mfccs.shape)","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:15:46.15334Z","iopub.execute_input":"2023-03-01T10:15:46.153881Z","iopub.status.idle":"2023-03-01T10:15:48.811647Z","shell.execute_reply.started":"2023-03-01T10:15:46.153838Z","shell.execute_reply":"2023-03-01T10:15:48.809853Z"},"trusted":true},"outputs":[{"name":"stdout","text":"(40, 26133)\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"mfccs","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:15:52.687413Z","iopub.execute_input":"2023-03-01T10:15:52.688101Z","iopub.status.idle":"2023-03-01T10:15:52.696413Z","shell.execute_reply.started":"2023-03-01T10:15:52.688061Z","shell.execute_reply":"2023-03-01T10:15:52.694918Z"},"trusted":true},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"array([[-4.60539093e+02, -4.25065002e+02, -4.03836761e+02, ...,\n        -4.55816620e+02, -4.55113586e+02, -4.74821381e+02],\n       [ 8.29618225e+01,  1.02948395e+02,  1.15255432e+02, ...,\n         8.53087463e+01,  8.55737000e+01,  7.01318054e+01],\n       [ 3.17386284e+01,  2.51931725e+01,  1.67581730e+01, ...,\n         2.81013145e+01,  3.15087948e+01,  3.34135590e+01],\n       ...,\n       [-3.65258265e+00, -9.39516902e-01,  3.46241713e-01, ...,\n        -1.09010041e+00, -1.54096186e+00,  1.35015249e+00],\n       [-2.84294605e+00, -1.09440041e+00,  2.07893515e+00, ...,\n         1.87818503e+00, -8.87959123e-01,  1.39782894e+00],\n       [ 1.15713429e+00,  2.32141399e+00,  4.64566851e+00, ...,\n         3.65232801e+00,  1.59804678e+00, -4.00499105e-02]], dtype=float32)"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"import pandas as pd\naudio_dataset_path=\"/kaggle/input/ml-olympiad-dialectrecognition/\"\nmetadata = pd.read_csv(\"/kaggle/input/ml-olympiad-dialectrecognition/train.csv\")\nmetadata.head(5)","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:15:58.23812Z","iopub.execute_input":"2023-03-01T10:15:58.23856Z","iopub.status.idle":"2023-03-01T10:16:00.238921Z","shell.execute_reply.started":"2023-03-01T10:15:58.238521Z","shell.execute_reply":"2023-03-01T10:16:00.237438Z"},"trusted":true},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"                       FileName                     ShowName  FullFileLength  \\\n0    batch_4/6k_v_SBA_330_3.wav             وجه بن فهرة - 06          327.10   \n1  batch_2/6k_v_SBA3_2922_2.wav           مطبخ ضحى - 01 - 22          600.70   \n2    batch_4/6k_v_SBA_412_2.wav  حكايات بابا فرحان - 06 - 11          615.55   \n3  batch_2/6k_v_SBA3_2681_0.wav           مطبخ ضحى - 01 - 44          637.11   \n4      batch_1/6k_SHTV_67_2.wav                    خزنة - 22          600.43   \n\n                              SegmentID  SegmentLength  SegmentStart  \\\n0    6k_v_SBA_330_3-seg_147_480-148_410       0.930000    147.480000   \n1    6k_v_SBA3_2922_2-seg_39_800-42_180       2.380000     39.800000   \n2    6k_v_SBA_412_2-seg_254_382-255_695       1.312519    254.382091   \n3  6k_v_SBA3_2681_0-seg_168_410-169_130       0.720000    168.410000   \n4      6k_SHTV_67_2-seg_175_750-177_190       1.440000    175.750000   \n\n   SegmentEnd               SpeakerAge SpeakerGender SpeakerDialect  \\\n0   148.41000            Adult -- بالغ          Male        Khaliji   \n1    42.18000            Adult -- بالغ        Female          Najdi   \n2   255.69461  Elderly -- كبير في السن          Male         Hijazi   \n3   169.13000            Adult -- بالغ        Female          Najdi   \n4   177.19000                  Unknown          Male        Khaliji   \n\n         Speaker      Environment                 GroundTruthText  \\\n0  Speaker6متحدث  Music -- موسيقى                       يعني شلون   \n1  Speaker1متحدث   Noisy -- ضوضاء  أحسن شيء هذه الأشياء تدقونه دق   \n2  Speaker4متحدث   Noisy -- ضوضاء                   إيش جاب لجاب    \n3  Speaker1متحدث   Noisy -- ضوضاء                    حنبدأ الحين،   \n4  Speaker6متحدث    Clean -- نظيف                تستعمل المخدرات؟   \n\n                    ProcessedText  \n0                       يعني شلون  \n1  احسن شيء هذه الاشياء تدقونه دق  \n2                    ايش جاب لجاب  \n3                     حنبدا الحين  \n4                 تستعمل المخدرات  ","text/html":"<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>FileName</th>\n      <th>ShowName</th>\n      <th>FullFileLength</th>\n      <th>SegmentID</th>\n      <th>SegmentLength</th>\n      <th>SegmentStart</th>\n      <th>SegmentEnd</th>\n      <th>SpeakerAge</th>\n      <th>SpeakerGender</th>\n      <th>SpeakerDialect</th>\n      <th>Speaker</th>\n      <th>Environment</th>\n      <th>GroundTruthText</th>\n      <th>ProcessedText</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>batch_4/6k_v_SBA_330_3.wav</td>\n      <td>وجه بن فهرة - 06</td>\n      <td>327.10</td>\n      <td>6k_v_SBA_330_3-seg_147_480-148_410</td>\n      <td>0.930000</td>\n      <td>147.480000</td>\n      <td>148.41000</td>\n      <td>Adult -- بالغ</td>\n      <td>Male</td>\n      <td>Khaliji</td>\n      <td>Speaker6متحدث</td>\n      <td>Music -- موسيقى</td>\n      <td>يعني شلون</td>\n      <td>يعني شلون</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>batch_2/6k_v_SBA3_2922_2.wav</td>\n      <td>مطبخ ضحى - 01 - 22</td>\n      <td>600.70</td>\n      <td>6k_v_SBA3_2922_2-seg_39_800-42_180</td>\n      <td>2.380000</td>\n      <td>39.800000</td>\n      <td>42.18000</td>\n      <td>Adult -- بالغ</td>\n      <td>Female</td>\n      <td>Najdi</td>\n      <td>Speaker1متحدث</td>\n      <td>Noisy -- ضوضاء</td>\n      <td>أحسن شيء هذه الأشياء تدقونه دق</td>\n      <td>احسن شيء هذه الاشياء تدقونه دق</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>batch_4/6k_v_SBA_412_2.wav</td>\n      <td>حكايات بابا فرحان - 06 - 11</td>\n      <td>615.55</td>\n      <td>6k_v_SBA_412_2-seg_254_382-255_695</td>\n      <td>1.312519</td>\n      <td>254.382091</td>\n      <td>255.69461</td>\n      <td>Elderly -- كبير في السن</td>\n      <td>Male</td>\n      <td>Hijazi</td>\n      <td>Speaker4متحدث</td>\n      <td>Noisy -- ضوضاء</td>\n      <td>إيش جاب لجاب</td>\n      <td>ايش جاب لجاب</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>batch_2/6k_v_SBA3_2681_0.wav</td>\n      <td>مطبخ ضحى - 01 - 44</td>\n      <td>637.11</td>\n      <td>6k_v_SBA3_2681_0-seg_168_410-169_130</td>\n      <td>0.720000</td>\n      <td>168.410000</td>\n      <td>169.13000</td>\n      <td>Adult -- بالغ</td>\n      <td>Female</td>\n      <td>Najdi</td>\n      <td>Speaker1متحدث</td>\n      <td>Noisy -- ضوضاء</td>\n      <td>حنبدأ الحين،</td>\n      <td>حنبدا الحين</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>batch_1/6k_SHTV_67_2.wav</td>\n      <td>خزنة - 22</td>\n      <td>600.43</td>\n      <td>6k_SHTV_67_2-seg_175_750-177_190</td>\n      <td>1.440000</td>\n      <td>175.750000</td>\n      <td>177.19000</td>\n      <td>Unknown</td>\n      <td>Male</td>\n      <td>Khaliji</td>\n      <td>Speaker6متحدث</td>\n      <td>Clean -- نظيف</td>\n      <td>تستعمل المخدرات؟</td>\n      <td>تستعمل المخدرات</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"!pip install llvmlite==0.31.0","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:20:43.006351Z","iopub.execute_input":"2023-03-01T10:20:43.006912Z","iopub.status.idle":"2023-03-01T10:20:54.853323Z","shell.execute_reply.started":"2023-03-01T10:20:43.00685Z","shell.execute_reply":"2023-03-01T10:20:54.851791Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Requirement already satisfied: llvmlite==0.31.0 in /opt/conda/lib/python3.7/site-packages (0.31.0)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"!pip install resampy","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:21:39.569266Z","iopub.execute_input":"2023-03-01T10:21:39.569711Z","iopub.status.idle":"2023-03-01T10:21:54.931172Z","shell.execute_reply.started":"2023-03-01T10:21:39.569668Z","shell.execute_reply":"2023-03-01T10:21:54.929307Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Collecting resampy\n  Downloading resampy-0.4.2-py3-none-any.whl (3.1 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from resampy) (1.21.6)\nRequirement already satisfied: numba>=0.53 in /opt/conda/lib/python3.7/site-packages (from resampy) (0.56.4)\nCollecting llvmlite<0.40,>=0.39.0dev0\n  Downloading llvmlite-0.39.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.6 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.6/34.6 MB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from numba>=0.53->resampy) (59.8.0)\nRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from numba>=0.53->resampy) (4.11.4)\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->numba>=0.53->resampy) (3.11.0)\nRequirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->numba>=0.53->resampy) (4.4.0)\nInstalling collected packages: llvmlite, resampy\n  Attempting uninstall: llvmlite\n    Found existing installation: llvmlite 0.31.0\n    Uninstalling llvmlite-0.31.0:\n      Successfully uninstalled llvmlite-0.31.0\n\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.\nlibrosa 0.10.0 requires soundfile>=0.12.1, but you have soundfile 0.11.0 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed llvmlite-0.39.1 resampy-0.4.2\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":18},{"cell_type":"code","source":"def features_extractor(file):\n    audio, sample_rate = librosa.load(file_name)\n    mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)\n    mfccs_scaled_features = np.mean(mfccs_features.T, axis=0)\n    \n    return mfccs_scaled_features","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:27:30.527924Z","iopub.execute_input":"2023-03-01T10:27:30.528403Z","iopub.status.idle":"2023-03-01T10:27:30.535146Z","shell.execute_reply.started":"2023-03-01T10:27:30.528335Z","shell.execute_reply":"2023-03-01T10:27:30.5341Z"},"trusted":true},"outputs":[],"execution_count":23},{"cell_type":"code","source":"extracted_features=[]\nfor index_name,row in tqdm(metadata.iterrows()):\n    file_name = os.path.join(os.path.abspath(audio_dataset_path), str(row[\"FileName\"]))\n    final_class_labels=row[\"SpeakerDialect\"]\n    data=features_extractor(file_name)\n    extracted_features.append([data, final_class_labels])","metadata":{"execution":{"iopub.status.busy":"2023-03-01T10:27:34.368301Z","iopub.execute_input":"2023-03-01T10:27:34.369734Z"},"trusted":true},"outputs":[{"name":"stderr","text":"1881it [55:40,  1.81s/it]","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}