FinalProject_CNN_242255_242873_290432.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["gEX2T4LJJZCV","YvS753rrz-70","pdjx9JQTzvWj","6D4qDYV1JfmH","Sf1Rd6rJJkrt","Jla947rH7dN-","zIHBiWQy9BLU","4KNDFp2eMAEs","bXWJoFy70QAi"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"TyqSIGTz_Ksu"},"source":["# Members of Group 3 (P102)\n","\n","*   Àlex Montoya - 242873\n","*   Miles Churchland - 290432\n","*   Daniel Marín - 242255"]},{"cell_type":"markdown","metadata":{"id":"4_M981WJ_NXT"},"source":["# Environment Setup\n"]},{"cell_type":"markdown","metadata":{"id":"OsdH2i3U-_lC"},"source":["### Imports"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VXLTa8ko-9uD"},"outputs":[],"source":["import os\n","import librosa\n","import numpy as np\n","import pandas as pd\n","from google.colab import drive\n","from tqdm import tqdm\n","import shutil\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch.nn.functional as F\n","from torch.utils.data import Dataset, DataLoader\n","from sklearn.model_selection import train_test_split\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.metrics import confusion_matrix, accuracy_score\n","from torchsummary import summary"]},{"cell_type":"markdown","metadata":{"id":"bcZsV0Fw-9eL"},"source":["### Mounting GoogleDrive folder"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6QCrWoIw-1-b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717150685319,"user_tz":-120,"elapsed":84971,"user":{"displayName":"MILES CHURCHLAND","userId":"06343733599797419764"}},"outputId":"b941f685-e4e5-4a69-a9bf-7ee1d3154063"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","/content/drive/Shareddrives/DeepLearning/FinalProject\n","/content/drive/Shareddrives/DeepLearning/FinalProject\n","Data\t\t\t\t\t FinalProject_242255_SORT\t\t\tPrepscripts\n","drum_cnn.pth\t\t\t\t FinalProject_CNN_242255_242873_290432.ipynb\tResults\n","FinalProject_242255_nnnnnn_nnnnnn.ipynb  FinalProject_Report_242255_242873_290432.gdoc\n"]}],"source":["#Mount Google drive\n","drive.mount('/content/drive')\n","#cd to the directory in drive you will use\n","%cd /content/drive/Shareddrives/DeepLearning/FinalProject/\n","#Print path and content of the current directory\n","!pwd\n","!ls"]},{"cell_type":"markdown","metadata":{"id":"vDhRPLMQ_TOq"},"source":["### File Paths"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YH_i0h7j_Q_i"},"outputs":[],"source":["data_dir = '/content/drive/Shareddrives/DeepLearning/FinalProject/Data/Dataset1'\n","results_path = '/content/drive/Shareddrives/DeepLearning/FinalProject/Results/'\n","base_dir = '/content/drive/Shareddrives/DeepLearning/FinalProject/'"]},{"cell_type":"markdown","metadata":{"id":"aLJfsHjp_U2q"},"source":["### Seed"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p9msFOVl_WyK"},"outputs":[],"source":["# Set seeds for reproducibility\n","seed = 42\n","torch.manual_seed(seed)\n","torch.cuda.manual_seed(seed)\n","np.random.seed(seed)"]},{"cell_type":"code","source":["#!pip install torch torchvision torchaudio librosa numpy matplotlib"],"metadata":{"id":"D4idSsU0MHsV"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#  Data Processing Functions"],"metadata":{"id":"gEX2T4LJJZCV"}},{"cell_type":"code","source":["# Define the root directory and categories\n","data_dir = '/content/drive/Shareddrives/DeepLearning/FinalProject/Data/Dataset1'\n","categories = ['kick', 'hat', 'snare', 'cymbal', 'tom', 'clap']\n","fixed_width = 128  # Fixed width for the mel spectrograms\n","\n","def generate_mel_spectrogram(y, sr, n_mels=128):\n","    S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels)\n","    S_DB = librosa.power_to_db(S, ref=np.max)\n","    return S_DB\n","\n","def pad_spectrogram(spectrogram, max_len=fixed_width):\n","    if spectrogram.shape[1] < max_len:\n","        pad_width = max_len - spectrogram.shape[1]\n","        padded_spectrogram = np.pad(spectrogram, ((0, 0), (0, pad_width)), mode='constant')\n","    else:\n","        padded_spectrogram = spectrogram[:, :max_len]\n","    return padded_spectrogram\n","\n","def process_audio_files(data_dir, categories):\n","    audio_data_list = []\n","    for category in categories:\n","        category_path = os.path.join(data_dir, category)\n","\n","        for file_name in tqdm(os.listdir(category_path), desc=f\"Processing {category}\"):\n","            file_path = os.path.join(category_path, file_name)\n","            if os.path.isfile(file_path):\n","                try:\n","                    # Load audio file\n","                    y, sr = librosa.load(file_path, sr=None)\n","                    audio_duration = librosa.get_duration(y=y, sr=sr)\n","\n","                    # Generate Mel-spectrogram\n","                    S_DB = generate_mel_spectrogram(y, sr)\n","\n","                    # Pad the mel spectrogram to fixed width\n","                    S_DB = pad_spectrogram(S_DB, max_len=fixed_width)\n","\n","                    # Append data to list\n","                    audio_data_list.append({\n","                        'label': category,\n","                        'file_name': file_name,\n","                        'sampling_rate': sr,\n","                        'duration': audio_duration,\n","                        'audio_data': y,\n","                        'mel_spectrogram': S_DB\n","                    })\n","                    print(f\"Processed {file_name} in category {category}\")\n","                except Exception as e:\n","                    print(f\"Could not process {file_name}: {e}\")\n","    return audio_data_list\n","\n","# Call the function\n","audio_data_list = process_audio_files(data_dir, categories)\n","\n","# Convert the data to a DataFrame for easy manipulation (optional)\n","audio_df = pd.DataFrame(audio_data_list)\n"],"metadata":{"id":"QORwCfl-LmTy","executionInfo":{"status":"ok","timestamp":1717073701423,"user_tz":-120,"elapsed":287691,"user":{"displayName":"ÀLEX MONTOYA PÉREZ","userId":"15889589265134071867"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b66b77c3-e36f-4ef0-96d2-d875659b32dd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Processing kick:   0%|          | 1/1301 [00:34<12:36:04, 34.90s/it]"]},{"output_type":"stream","name":"stdout","text":["Processed 171505.wav in category kick\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing kick:   0%|          | 2/1301 [00:35<5:19:45, 14.77s/it] "]},{"output_type":"stream","name":"stdout","text":["Processed 171829.wav in category 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signal of length=999\n","  warnings.warn(\n","Processing hat:  78%|███████▊  | 470/606 [00:24<00:01, 75.06it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed 75841.wav in category hat\n","Processed 76637.wav in category hat\n","Processed 76636.wav in category hat\n","Processed 76638.wav in category hat\n","Processed 78224.wav in category hat\n","Processed 78219.wav in category hat\n","Processed 78223.wav in category hat\n","Processed 78215.wav in category hat\n","Processed 78811.wav in category hat\n","Processed 78812.wav in category hat\n","Processed 811.wav in category hat\n","Processed 801.wav in category hat\n","Processed 80695.wav in category hat\n","Processed 80696.wav in category hat\n","Processed 813.wav in category hat\n","Processed 812.wav in category hat\n"]},{"output_type":"stream","name":"stderr","text":["Processing hat:  80%|████████  | 486/606 [00:24<00:01, 73.32it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed 85527.wav in category 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hat\n","Processed odh_hat_groove.wav in category hat\n","Processed odh_hat_lies.wav in category hat\n","Processed odh_hat_chances.wav in category hat\n","Processed odh_hat_ahara.wav in category hat\n","Processed odh_hat_cagefree2.wav in category hat\n","Processed odh_hat_cagefree3.wav in category hat\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing hat:  83%|████████▎ | 501/606 [00:24<00:01, 57.05it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed odh_hat_hevsnap.wav in category hat\n","Processed odh_hat_bonus.wav in category hat\n","Processed odh_hat_lofty2.wav in category hat\n","Processed odh_hat_lofty.wav in category hat\n","Processed odh_hat_ranger2.wav in category hat\n","Processed odh_hat_pleasure2.wav in category hat\n","Processed odh_hat_zenith.wav in category hat\n","Processed odh_hat_meta2.wav in category hat\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing hat:  84%|████████▍ | 508/606 [00:24<00:01, 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Hat clean hard.wav in category hat\n","Processed Classic 909 Open Hat.wav in category hat\n","Processed Hat Clsd Nov.wav in category hat\n","Processed Hat Alien pipe banger.wav in category hat\n","Processed Hat alien.wav in category hat\n","Processed Hard Open Hat.wav in category hat\n","Processed 808 Open Hat.wav in category hat\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing hat:  87%|████████▋ | 529/606 [00:25<00:01, 42.21it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Clsd ht 808 pack 5.wav in category hat\n","Processed Classic 909 Open Hat Verbr.wav in category hat\n","Processed Extra Lo-fi Open Hat.wav in category hat\n","Processed Hat 4 bit.wav in category hat\n","Processed Classic Low Open Hat.wav in category hat\n","Processed Classic Low Hat.wav in category hat\n","Processed Tin Hat.wav in category hat\n","Processed Wild Hat.wav in category hat\n"]},{"output_type":"stream","name":"stderr","text":["Processing hat:  89%|████████▉ | 539/606 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in category hat\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing hat:  91%|█████████▏| 554/606 [00:25<00:01, 43.56it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Hat Tekno Waoka.wav in category hat\n","Processed Lo-fi 909 Open Hat.wav in category hat\n","Processed Light Hat .wav in category hat\n","Processed Hat open Nova.wav in category hat\n","Processed Hat tech wachaw.wav in category hat\n","Processed Hat Tech wabba.wav in category hat\n","Processed Hat noise ding.wav in category hat\n","Processed Hat tech hah bong.wav in category hat\n","Processed Low 909 Open Hat.wav in category hat\n","Processed Tin Open Hat.wav in category hat\n"]},{"output_type":"stream","name":"stderr","text":["Processing hat:  92%|█████████▏| 559/606 [00:26<00:01, 41.87it/s]/usr/local/lib/python3.10/dist-packages/librosa/core/spectrum.py:266: UserWarning: n_fft=2048 is too large for input signal of length=1252\n","  warnings.warn(\n","Processing hat:  93%|█████████▎| 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21.72it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Processed 208344.wav in category snare\n","Processed 208347.wav in category snare\n","Processed 208355.wav in category snare\n","Processed 208350.wav in category snare\n","Processed 208361.wav in category snare\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:   0%|          | 1/474 [00:00<05:01,  1.57it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_machine_crash14.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:   0%|          | 2/474 [00:01<04:54,  1.60it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_machine_crash15.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:   1%|          | 3/474 [00:01<04:51,  1.62it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_machine_crash13.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:   1%|          | 4/474 [00:02<04:56,  1.59it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_vinyl_crash01.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:   1%|          | 5/474 [00:03<04:55,  1.59it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_vinyl_crash02.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:   2%|▏         | 10/474 [00:11<08:39,  1.12s/it]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_vinyl_crash03.wav in category cymbal\n","Processed cw_vinyl_crash04.wav in category cymbal\n","Processed cw_vinyl_crash05.wav in category cymbal\n","Processed cw_vinyl_crash06.wav in category cymbal\n","Processed cw_vinyl_crash07.wav in category cymbal\n","Processed cw_vinyl_crash08.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:   3%|▎         | 15/474 [00:11<03:40,  2.09it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_vinyl_crash09.wav in category cymbal\n","Processed cw_vinyl_crash10.wav in category cymbal\n","Processed cw_vinyl_crash11.wav in category cymbal\n","Processed cw_vinyl_crash12.wav in category cymbal\n","Processed cw_vinyl_crash13.wav in category cymbal\n","Processed cw_vinyl_crash14.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:   4%|▍         | 20/474 [00:12<01:52,  4.03it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_vinyl_crash15.wav in category cymbal\n","Processed cw_machine_crash01.wav in category cymbal\n","Processed cw_machine_crash03.wav in category cymbal\n","Processed cw_vinyl_crash17.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:   5%|▍         | 23/474 [00:12<01:20,  5.63it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_machine_crash04.wav in category cymbal\n","Processed cw_machine_crash05.wav in category cymbal\n","Processed cw_machine_crash02.wav in category cymbal\n","Processed cw_machine_crash07.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:   6%|▌         | 28/474 [00:12<00:52,  8.42it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cw_machine_crash08.wav in category cymbal\n","Processed cw_machine_crash09.wav in category cymbal\n","Processed cw_machine_crash06.wav in category cymbal\n","Processed cw_machine_crash11.wav in category cymbal\n","Processed cw_machine_crash10.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:   7%|▋         | 31/474 [00:12<00:41, 10.78it/s]<ipython-input-5-fa700c051ff6>:29: UserWarning: PySoundFile failed. Trying audioread instead.\n","  y, sr = librosa.load(file_path, sr=None)\n","/usr/local/lib/python3.10/dist-packages/librosa/core/audio.py:184: FutureWarning: librosa.core.audio.__audioread_load\n","\tDeprecated as of librosa version 0.10.0.\n","\tIt will be removed in librosa version 1.0.\n","  y, sr_native = __audioread_load(path, offset, duration, dtype)\n"]},{"output_type":"stream","name":"stdout","text":["Processed cw_machine_crash12.wav in category cymbal\n","Processed cw_vinyl_crash16.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:   8%|▊         | 36/474 [00:13<00:38, 11.50it/s]"]},{"output_type":"stream","name":"stdout","text":["Could not process info.txt: \n","Processed Cymbal 808 1.wav in category cymbal\n","Processed Crash 707.wav in category cymbal\n","Processed Cymbal 606X.wav in category cymbal\n","Processed Crash 909 2.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:   8%|▊    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11.27it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed nice-cymbal-mallet-role.wav in category cymbal\n","Processed reverse-echo-cymbal.wav in category cymbal\n","Processed clear-bell-pling.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  11%|█         | 50/474 [00:14<00:39, 10.69it/s]/usr/local/lib/python3.10/dist-packages/librosa/core/spectrum.py:266: UserWarning: n_fft=2048 is too large for input signal of length=1858\n","  warnings.warn(\n","Processing cymbal:  11%|█         | 53/474 [00:14<00:29, 14.15it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed cymbal-mallet-buildup.wav in category cymbal\n","Processed 121097.wav in category cymbal\n","Processed 121396.wav in category cymbal\n","Processed 121411.wav in category cymbal\n","Processed 121402.wav in category cymbal\n","Processed 121410.wav in category cymbal\n","Processed 121397.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  13%|█▎        | 61/474 [00:14<00:19, 21.18it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed 121400.wav in category cymbal\n","Processed 121409.wav in category cymbal\n","Processed 124101.wav in category cymbal\n","Processed 125594.wav in category cymbal\n","Processed 145341.wav in category cymbal\n","Processed 145343.wav in category cymbal\n","Processed 145342.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  14%|█▍        | 66/474 [00:14<00:15, 25.59it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed 145340.wav in category cymbal\n","Processed 150468.wav in category cymbal\n","Processed 150802.wav in category cymbal\n","Processed 15312.wav in category cymbal\n","Processed 15577.wav in category cymbal\n","Processed 15585.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  16%|█▌   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UserWarning: n_fft=2048 is too large for input signal of length=1537\n","  warnings.warn(\n","Processing cymbal:  19%|█▉        | 92/474 [00:15<00:10, 35.08it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed 209873.wav in category cymbal\n","Processed 209870.wav in category cymbal\n","Processed 22754.wav in category cymbal\n","Processed 234696.wav in category cymbal\n","Processed 264531.wav in category cymbal\n","Processed 264539.wav in category cymbal\n","Processed 28723.wav in category cymbal\n","Processed 28719.wav in category cymbal\n","Processed 28726.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  20%|██        | 97/474 [00:15<00:09, 38.46it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed 28717.wav in category cymbal\n","Processed 334069.wav in category cymbal\n","Processed 346721.wav in category cymbal\n","Processed 414478.wav in category cymbal\n","Processed 439791.wav in category cymbal\n","Processed 45190.wav in category cymbal\n","Processed 455634.wav in category cymbal\n","Processed 57129.wav in category cymbal\n","Processed 57130.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  23%|██▎       | 107/474 [00:15<00:09, 40.60it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed 61531.wav in category cymbal\n","Processed 61461.wav in category cymbal\n","Processed 61530.wav in category cymbal\n","Processed 66890.wav in category cymbal\n","Processed 67208.wav in category cymbal\n","Processed 803.wav in category cymbal\n","Processed 99275.wav in category cymbal\n","Processed descending-crashes.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  24%|██▍       | 116/474 [00:16<00:10, 33.50it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed sillycym.wav in category cymbal\n","Processed softcym.wav in category cymbal\n","Processed opencym.wav in category cymbal\n","Processed thin-splash.wav in category cymbal\n","Processed big_china_cym.wav in category cymbal\n","Processed darkrevcym.wav in category cymbal\n","Processed manyback.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  25%|██▌       | 120/474 [00:16<00:11, 29.90it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed curiouscym1.wav in category cymbal\n","Processed grungecymbal.wav in category cymbal\n","Processed piatti orch cymbal.wav in category cymbal\n","Processed backhit-crash-glissdown.wav in category cymbal\n","Processed curiouscym2.wav in category cymbal\n","Processed revflangcym.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  26%|██▌       | 124/474 [00:16<00:12, 27.38it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed crash-glissdown.wav in category cymbal\n","Processed Cymbal 808 Tone5 e.wav in category cymbal\n","Processed Cymbal 808 Tone11.wav in category cymbal\n","Processed Cymbal 808 Tone1 e.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  27%|██▋       | 127/474 [00:16<00:14, 23.69it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal 808 Tone11 e.wav in category cymbal\n","Processed Cymbal 808 Tone5 k.wav in category cymbal\n","Processed Cymbal 808 Tone5.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  27%|██▋       | 130/474 [00:16<00:17, 19.67it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal 808 Tone5 h.wav in category cymbal\n","Processed Cymbal 808 Tone8.wav in category cymbal\n","Processed Cymbal 808 Tone8 h.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  28%|██▊       | 133/474 [00:17<00:20, 16.79it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal 808 Tone8 e.wav in category cymbal\n","Processed Cymbal 808 Tone1 h.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  29%|██▉       | 137/474 [00:17<00:25, 13.29it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal 808 Tone11 k.wav in category cymbal\n","Processed Cymbal 808 Tone8 k.wav in category cymbal\n","Processed Cymbal 808 Tone11 h.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  30%|██▉       | 141/474 [00:17<00:22, 14.88it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal 808 Tone1 k.wav in category cymbal\n","Processed Cymbal 808 Tone1.wav in category cymbal\n","Processed Cymbal - Ride_10.wav in category cymbal\n","Processed Cymbal - Crash_5.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  31%|███       | 146/474 [00:18<00:18, 17.58it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_6.wav in category cymbal\n","Processed Cymbal - Screech.wav in category cymbal\n","Processed Cymbal - Crash_3.wav in category cymbal\n","Processed Cymbal - Muted.wav in category cymbal\n","Processed Cymbal - Ride_7.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  32%|███▏      | 150/474 [00:18<00:19, 16.75it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_3.wav in category cymbal\n","Processed Cymbal - Screech_2.wav in category cymbal\n","Processed Cymbal - Ride_8.wav in category cymbal\n","Processed Cymbal - Ride_9.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  32%|███▏      | 153/474 [00:18<00:17, 18.47it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Crash_4.wav in category cymbal\n","Processed Cymbal - Crash_2.wav in category cymbal\n","Processed Cymbal - Crash.wav in category cymbal\n","Processed Cymbal - Ride_4.wav in category cymbal\n","Processed Cymbal - Ride_2.wav in category cymbal\n","Processed Cymbal - Ride.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  34%|███▍      | 160/474 [00:18<00:13, 23.49it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_5.wav in category cymbal\n","Processed Cymbal - Crash_13.wav in category cymbal\n","Processed Cymbal - Muted_3.wav in category cymbal\n","Processed Cymbal - Ride_23.wav in category cymbal\n","Processed Cymbal - Other.wav in category cymbal\n","Processed Cymbal - Crash_9.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  35%|███▌      | 166/474 [00:18<00:12, 24.80it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_12.wav in category cymbal\n","Processed Cymbal - Ride_24.wav in category cymbal\n","Processed Cymbal - Crash_8.wav in category cymbal\n","Processed Cymbal - Crash_14.wav in category cymbal\n","Processed Cymbal - Ride_20.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  36%|███▌      | 169/474 [00:19<00:12, 23.57it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_21.wav in category cymbal\n","Processed Cymbal - Ride_13.wav in category cymbal\n","Processed Cymbal - other_2.wav in category cymbal\n","Processed Cymbal - Crash_17.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  37%|███▋      | 175/474 [00:19<00:14, 20.32it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_27.wav in category cymbal\n","Processed Cymbal - Crash_18.wav in category cymbal\n","Processed Cymbal - Crash_21.wav in category cymbal\n","Processed Cymbal - Ride_14.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  38%|███▊      | 178/474 [00:19<00:14, 19.93it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Crash_20.wav in category cymbal\n","Processed Cymbal - Muted_2.wav in category cymbal\n","Processed Cymbal - Crash_6.wav in category cymbal\n","Processed Cymbal - Ride_22.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  38%|███▊      | 181/474 [00:19<00:15, 18.64it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_19.wav in category cymbal\n","Processed Cymbal - Crash_22.wav in category cymbal\n","Processed Cymbal - Crash_15.wav in category cymbal\n","Processed Cymbal - Ride_25.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  39%|███▉      | 186/474 [00:19<00:15, 18.26it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Crash_16.wav in category cymbal\n","Processed Cymbal - Ride_11.wav in category cymbal\n","Processed Cymbal - Ride_28.wav in category cymbal\n","Processed Cymbal - Ride_26.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  40%|████      | 190/474 [00:20<00:16, 17.35it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_17.wav in category cymbal\n","Processed Cymbal - Ride_15.wav in category cymbal\n","Processed Cymbal - Crash_11.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  41%|████      | 192/474 [00:20<00:16, 16.81it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Ride_18.wav in category cymbal\n","Processed Cymbal - Crash_7.wav in category cymbal\n","Processed Cymbal - Other_3.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  41%|████▏     | 196/474 [00:20<00:17, 15.75it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Crash_19.wav in category cymbal\n","Processed Cymbal - Crash_10.wav in category cymbal\n","Processed Cymbal - Ride_29.wav in category cymbal\n","Processed Cymbal - Ride_16.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  42%|████▏     | 199/474 [00:20<00:15, 17.25it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal - Crash_12.wav in category cymbal\n","Processed Cymbal - Crash_23.wav in category cymbal\n","Processed ZDOT - CYMBAL (EARLY).wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  42%|████▏     | 201/474 [00:21<00:20, 13.33it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed ZDOT - CYMBAL (CRASH).wav in category cymbal\n","Processed ZDOT - CYMBAL (CHINA).wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  43%|████▎     | 205/474 [00:21<00:22, 12.00it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed ZDOT - CYMBAL (TACTIC).wav in category cymbal\n","Processed ZDOT - CYMBAL (CAPTURE).wav in category cymbal\n","Processed ZDOT - CYMBAL (RIDE).wav in category cymbal\n","Processed Cymatics - San Holo Free Crash 2.wav in category cymbal\n","Processed Cymatics - San Holo Free Crash 3.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  45%|████▍     | 211/474 [00:21<00:19, 13.27it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymatics - San Holo Free Crash 1.wav in category cymbal\n","Processed Cymatics - Odyssey Crash 2.wav in category cymbal\n","Processed Cymatics - Odyssey Crash 4.wav in category cymbal\n","Processed Cymatics - Odyssey Crash 3.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  45%|████▍     | 213/474 [00:22<00:21, 12.20it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymatics - Odyssey Crash 1.wav in category cymbal\n","Processed Crash China Edge TW.wav in category cymbal\n","Processed Crash Chop Yolo.wav in category cymbal\n","Processed Crash Terrified.wav in category cymbal\n","Processed Crash Mansion.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  46%|████▌     | 219/474 [00:22<00:15, 16.83it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymbal 808 Full.wav in category cymbal\n","Processed Crash DMX.wav in category cymbal\n","Processed Crash Roomy.wav in category cymbal\n","Processed Crash Acoustified High.wav in category cymbal\n","Processed Crash Kindified 1.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  47%|████▋     | 225/474 [00:22<00:12, 19.20it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Crash Stacked.wav in category cymbal\n","Processed Cymbal 808 XXXX.wav in category cymbal\n","Processed Crash BamVin.wav in category cymbal\n","Processed Crash China Stick Hit Cop Style.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  48%|████▊     | 228/474 [00:22<00:11, 21.22it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Crash Ironman.wav in category cymbal\n","Processed Cymbal Dax.wav in category cymbal\n","Processed Crash DMX Real 1.wav in category cymbal\n","Processed Crash Lofi.wav in category cymbal\n","Processed Crash Kindified 2.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  49%|████▉     | 233/474 [00:23<00:31,  7.68it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Crash China Edge ML.wav in category cymbal\n","Processed Crash Light.wav in category cymbal\n","Processed Crash 505 ASR.wav in category cymbal\n","Processed Crash Layered.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  51%|█████     | 240/474 [00:24<00:17, 13.41it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Crash DMX Real 2.wav in category cymbal\n","Processed Cymbal 808 VA90.wav in category cymbal\n","Processed Cymbal DM Vintage.wav in category cymbal\n","Processed Cymbal Crash Reverse Gnirob.wav in category cymbal\n","Processed Crash Build.wav in category cymbal\n","Processed Crash Acoustified Low.wav in category cymbal\n","Processed Clap noise crash.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  53%|█████▎    | 249/474 [00:24<00:09, 23.19it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Classic Low Crash.wav in category cymbal\n","Processed 808 Crash.wav in category cymbal\n","Processed Big Crash.wav in category cymbal\n","Processed Crash Mid Old cap.wav in category cymbal\n","Processed Crash Live.wav in category cymbal\n","Processed Crash jingle jangle.wav in category cymbal\n","Processed Crash Old Cap.wav in category cymbal\n","Processed Crash scare.wav in category cymbal\n","Processed Crash Silk.wav in category cymbal\n","Processed Crash sketch.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  54%|█████▍    | 258/474 [00:24<00:06, 31.19it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Crash Storm.wav in category cymbal\n","Processed Crash weel zombie.wav in category cymbal\n","Processed VWB1 Crash 046.wav in category cymbal\n","Processed VWB1 Crash 042.wav in category cymbal\n","Processed VWB1 Crash 033.wav in category cymbal\n","Processed VWB1 Crash 027.wav in category cymbal\n","Processed VWB1 Crash 018.wav in category cymbal\n","Processed VWB1 Crash 039.wav in category cymbal\n","Processed VWB1 Crash 036.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  56%|█████▌    | 266/474 [00:24<00:06, 32.53it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 015.wav in category cymbal\n","Processed VWB1 Crash 024.wav in category cymbal\n","Processed VWB1 Crash 030.wav in category cymbal\n","Processed VWB1 Crash 021.wav in category cymbal\n","Processed VWB1 Crash 044.wav in category cymbal\n","Processed VWB1 Crash 041.wav in category cymbal\n","Processed VWB1 Crash 035.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  57%|█████▋    | 270/474 [00:24<00:06, 30.08it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 045.wav in category cymbal\n","Processed VWB1 Crash 016.wav in category cymbal\n","Processed VWB1 Crash 029.wav in category cymbal\n","Processed VWB1 Crash 038.wav in category cymbal\n","Processed VWB1 Crash 023.wav in category cymbal\n","Processed VWB1 Crash 020.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  59%|█████▊    | 278/474 [00:25<00:07, 25.98it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 043.wav in category cymbal\n","Processed VWB1 Crash 032.wav in category cymbal\n","Processed VWB1 Crash 010.wav in category cymbal\n","Processed VWB1 Crash 011.wav in category cymbal\n","Processed VWB1 Crash 014.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  59%|█████▉    | 281/474 [00:25<00:08, 23.93it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 012.wav in category cymbal\n","Processed VWB1 Crash 019.wav in category cymbal\n","Processed VWB1 Crash 022.wav in category cymbal\n","Processed VWB1 Crash 040.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  60%|█████▉    | 284/474 [00:25<00:08, 21.44it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 025.wav in category cymbal\n","Processed VWB1 Crash 031.wav in category cymbal\n","Processed VWB1 Crash 037.wav in category cymbal\n","Processed VWB1 Crash 003.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  61%|██████    | 287/474 [00:25<00:09, 19.10it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 013.wav in category cymbal\n","Processed VWB1 Crash 050.wav in category cymbal\n","Processed VWB1 Crash 004.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  62%|██████▏   | 292/474 [00:26<00:11, 15.59it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 006.wav in category cymbal\n","Processed VWB1 Crash 026.wav in category cymbal\n","Processed VWB1 Crash 028.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  62%|██████▏   | 294/474 [00:26<00:12, 14.51it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 034.wav in category cymbal\n","Processed VWB1 Crash 005.wav in category cymbal\n","Processed VWB1 Crash 001.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  63%|██████▎   | 298/474 [00:26<00:14, 12.24it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 007.wav in category cymbal\n","Processed VWB1 Crash 017.wav in category cymbal\n","Processed VWB1 Crash 049.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  63%|██████▎   | 300/474 [00:27<00:15, 11.20it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 009.wav in category cymbal\n","Processed VWB1 Crash 002.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  64%|██████▎   | 302/474 [00:27<00:17,  9.66it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 048.wav in category cymbal\n","Processed VWB1 Crash 008.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  65%|██████▍   | 308/474 [00:27<00:11, 14.33it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Crash 047.wav in category cymbal\n","Processed VEH1 Crash - 10.wav in category cymbal\n","Processed VEC2 Cymbals Crash 09.wav in category cymbal\n","Processed VEC2 Cymbals Crash 02.wav in category cymbal\n","Processed VEC2 Cymbals Crash 06.wav in category cymbal\n","Processed VEC2 Cymbals Crash 04.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  66%|██████▋   | 315/474 [00:27<00:07, 21.27it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEC2 Cymbals Crash 03.wav in category cymbal\n","Processed VEC2 Cymbals Crash 05.wav in category cymbal\n","Processed VEC2 Cymbals Crash 14.wav in category cymbal\n","Processed VEC2 Cymbals Crash 12.wav in category cymbal\n","Processed VEC2 Cymbals Crash 11.wav in category cymbal\n","Processed VEC2 Cymbals Crash 07.wav in category cymbal\n","Processed VEC2 Cymbals Crash 08.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  67%|██████▋   | 318/474 [00:27<00:06, 22.33it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEC2 Cymbals Crash 10.wav in category cymbal\n","Processed VEC2 Cymbals Crash 15.wav in category cymbal\n","Processed VEH1 Crash - 09.wav in category cymbal\n","Processed VEC2 Cymbals Crash 13.wav in category cymbal\n","Processed VEH1 Crash - 06.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  68%|██████▊   | 324/474 [00:28<00:06, 22.68it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEH1 Crash - 29.wav in category cymbal\n","Processed VEH1 Crash - 07.wav in category cymbal\n","Processed VEH1 Crash - 24.wav in category cymbal\n","Processed VEH1 Crash - 30.wav in category cymbal\n","Processed VEH1 Crash - 26.wav in category cymbal\n","Processed VEH1 Crash - 01.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  70%|██████▉   | 330/474 [00:28<00:05, 24.78it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEH1 Crash - 20.wav in category cymbal\n","Processed VEH1 Crash - 25.wav in category cymbal\n","Processed VEH1 Crash - 32.wav in category cymbal\n","Processed VEH1 Crash - 33.wav in category cymbal\n","Processed VEH1 Crash - 41.wav in category cymbal\n","Processed VEH1 Crash - 18.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  71%|███████   | 337/474 [00:28<00:05, 26.96it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEH1 Crash - 38.wav in category cymbal\n","Processed VEH1 Crash - 42.wav in category cymbal\n","Processed VEH1 Crash - 11.wav in category cymbal\n","Processed VEH1 Crash - 12.wav in category cymbal\n","Processed VEH1 Crash - 39.wav in category cymbal\n","Processed VEH1 Crash - 34.wav in category cymbal\n","Processed VEH1 Crash - 40.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  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28.23it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEH1 Crash - 37.wav in category cymbal\n","Processed VEH1 Crash - 47.wav in category cymbal\n","Processed VEH1 Crash - 48.wav in category cymbal\n","Processed VEH1 Crash - 16.wav in category cymbal\n","Processed VEH1 Crash - 02.wav in category cymbal\n","Processed VEH1 Crash - 13.wav in category cymbal\n","Processed VEH1 Crash - 22.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  76%|███████▋  | 362/474 [00:29<00:03, 30.91it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEH1 Crash - 46.wav in category cymbal\n","Processed VEH1 Crash - 05.wav in category cymbal\n","Processed VEH1 Crash - 45.wav in category cymbal\n","Processed VEH1 Crash - 08.wav in category cymbal\n","Processed VEH1 Crash - 43.wav in category cymbal\n","Processed VEH1 Crash - 44.wav in category cymbal\n","Processed VEH1 Crash - 27.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  78%|███████▊  | 370/474 [00:29<00:03, 28.62it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEH1 Crash - 04.wav in category cymbal\n","Processed VEH1 Crash - 03.wav in category cymbal\n","Processed VEH1 Crash - 31.wav in category cymbal\n","Processed Crash-Cymbal-1.wav in category cymbal\n","Processed Crash-Cymbal-2.wav in category cymbal\n","Processed Crash-Cymbal-3.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  79%|███████▊  | 373/474 [00:30<00:04, 25.17it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Crash-Cymbal-5.wav in category cymbal\n","Processed Crash-Cymbal-7.wav in category cymbal\n","Processed Ensoniq-SQ-1-Crash-Cymbal.wav in category cymbal\n","Processed Kawai-K11-Bob-Crash-Cymbal.wav in category cymbal\n","Processed Korg-M3R-Crash-Cymbal.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  80%|███████▉  | 379/474 [00:30<00:04, 20.34it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Korg-N1R-Crash-Cymbal.wav in category cymbal\n","Processed Korg-N1R-Crash-Cymbal-2.wav in category cymbal\n","Processed Korg-TR-Rack-Standard-Kit-Crash-Cymbal.wav in category cymbal\n","Processed M-Audio-Venom-Crash-Cymbal.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  81%|████████  | 382/474 [00:30<00:04, 20.77it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed M-Audio-Venom-Splash-Cymbal.wav in category cymbal\n","Processed Roland-SC-88-Crash-Cymbal.wav in category cymbal\n","Processed Roland-R-8-Crash-Cymbal.wav in category cymbal\n","Processed first_half_CC13a14.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  81%|████████  | 385/474 [00:30<00:05, 17.75it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC13a28.wav in category cymbal\n","Processed first_half_CC13a25.wav in category cymbal\n","Processed first_half_CC13a12.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  82%|████████▏ | 389/474 [00:31<00:05, 15.79it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC13a10.wav in category cymbal\n","Processed first_half_CC13a20.wav in category cymbal\n","Processed first_half_CC13a19.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  82%|████████▏ | 391/474 [00:31<00:05, 14.50it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC13a23.wav in category cymbal\n","Processed first_half_CC13a22.wav in category cymbal\n","Processed first_half_CC13a13.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  83%|████████▎ | 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cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  87%|████████▋ | 413/474 [00:33<00:05, 12.07it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC13a11.wav in category cymbal\n","Processed first_half_CC13a27.wav in category cymbal\n","Processed first_half_CC15a16.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  88%|████████▊ | 415/474 [00:33<00:05, 11.45it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC15a10.wav in category cymbal\n","Processed first_half_CC15a23.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  88%|████████▊ | 417/474 [00:33<00:05, 10.47it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC15a15.wav in category cymbal\n","Processed first_half_CC15a21.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  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9.80it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC15a27.wav in category cymbal\n","Processed first_half_CC15a08.wav in category cymbal\n","Processed first_half_CC15a07.wav in category cymbal"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  90%|█████████ | 428/474 [00:34<00:04, 10.01it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","Processed first_half_CC15a02.wav in category cymbal\n","Processed first_half_CC15a19.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  91%|█████████ | 430/474 [00:34<00:04, 10.22it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC15a25.wav in category cymbal\n","Processed first_half_CC15a29.wav in category cymbal\n","Processed first_half_CC15a12.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  92%|█████████▏| 434/474 [00:35<00:04,  9.41it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC15a24.wav in category cymbal\n","Processed first_half_CC15a28.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  92%|█████████▏| 437/474 [00:35<00:03,  9.61it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC15a30.wav in category cymbal\n","Processed first_half_CC15a13.wav in category cymbal\n","Processed first_half_CC15a22.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  93%|█████████▎| 439/474 [00:35<00:03,  9.91it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC15a14.wav in category cymbal\n","Processed first_half_CC15a26.wav in category cymbal\n","Processed first_half_CC15a18.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  93%|█████████▎| 443/474 [00:36<00:02, 10.38it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC15a17.wav in category cymbal\n","Processed first_half_CC15a01.wav in category cymbal\n","Processed first_half_CC16a16.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  94%|█████████▍| 445/474 [00:36<00:02, 11.15it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC16a03.wav in category cymbal\n","Processed first_half_CC16a32.wav in category cymbal\n","Processed first_half_CC16a08.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal:  95%|█████████▍| 449/474 [00:36<00:02, 12.29it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC16a12.wav in category cymbal\n","Processed first_half_CC16a17.wav in category cymbal\n","Processed first_half_CC16a14.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  95%|█████████▌| 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cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal:  99%|█████████▉| 469/474 [00:38<00:00,  8.82it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC16a30.wav in category cymbal\n","Processed first_half_CC16a22.wav in category cymbal\n","Processed first_half_CC16a18.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing cymbal: 100%|█████████▉| 473/474 [00:39<00:00, 10.24it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC16a10.wav in category cymbal\n","Processed first_half_CC16a07.wav in category cymbal\n","Processed first_half_CC16a05.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing cymbal: 100%|██████████| 474/474 [00:39<00:00, 11.94it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Processed first_half_CC16a02.wav in category cymbal\n"]},{"output_type":"stream","name":"stderr","text":["Processing tom:   0%|        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tom\n"]},{"output_type":"stream","name":"stderr","text":["Processing tom:   2%|▏         | 11/464 [00:07<04:22,  1.72it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HiTom 808 1.wav in category tom\n","Processed MidTom 909 2.wav in category tom\n","Processed MidTom 909 5.wav in category tom\n","Processed MidTom 909 4.wav in category tom\n","Processed HiTom 909 5.wav in category tom\n","Processed LowTom 909 4.wav in category tom\n","Processed LowTom 606X 1.wav in category tom\n","Processed MidTom 808 2.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["Processing tom:   4%|▍         | 20/464 [00:08<01:26,  5.13it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed MidHiTom 909.wav in category tom\n","Processed LowTom 707X.wav in category tom\n","Processed HiTom 606X 1.wav in category tom\n","Processed LowTom 909 1.wav in category tom\n","Processed MidTom 707X.wav in category tom\n","Processed LowTom 909 5.wav in category tom\n","Processed MidTom 909 3.wav in category tom\n","Processed LowTom 909 6.wav in category tom\n","Processed LowTom 909 3.wav in category tom\n","Processed HiTom 909X.wav in category tom\n","Processed MidTom 909 1.wav in category tom\n","Processed HiTom 707.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["Processing tom:   7%|▋         | 33/464 [00:08<00:33, 12.99it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HiTom 707X.wav in category tom\n","Processed HiTom 909 4.wav in category tom\n","Processed LowTom 707.wav in category tom\n","Processed LowTom 808 2.wav in category tom\n","Processed HiTom 606X 2.wav in category tom\n","Processed MidTom 707.wav in category tom\n","Processed LowTom 808X.wav in category tom\n","Processed LowTom 909 2.wav in category tom\n","Processed HiTom 808 2.wav in category tom\n","Processed LowTom 606 2.wav in category tom\n","Processed MidTom 909X.wav in category tom\n","Processed HiTom 606 2.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["Processing tom:   9%|▉         | 44/464 [00:08<00:19, 21.80it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HiTom 808X.wav in category tom\n","Processed LowTom 909X.wav in category tom\n","Processed HiTom 909 1.wav in category tom\n","Processed LowTom 606 1.wav in category tom\n","Processed HiTom 909 6.wav in category tom\n","Processed HiTom 606 1.wav in category tom\n","Processed LowTom 606X 2.wav in category tom\n","Processed 100062.wav in category tom\n","Processed 100063.wav in category tom\n","Processed 100201.wav in category tom\n","Processed 100066.wav in category tom\n","Processed 100065.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing tom:  11%|█         | 50/464 [00:08<00:15, 26.86it/s]/usr/local/lib/python3.10/dist-packages/librosa/core/spectrum.py:266: UserWarning: n_fft=2048 is too large for input signal of length=1487\n","  warnings.warn(\n","Processing tom:  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Fatso 5.wav in category tom\n","Processed Tom DMX Hi 1.wav in category tom\n","Processed Tom Pluto Mop 2.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["Processing tom:  90%|████████▉ | 417/464 [00:18<00:01, 29.29it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Tom DMX Fatso 2.wav in category tom\n","Processed Tom Electronic SP 2.wav in category tom\n","Processed Tom Rack Stick Center 2.wav in category tom\n","Processed Tom 80s Space Craft 2.wav in category tom\n","Processed Tom 909 Tran 4.wav in category tom\n","Processed Tom 909 Tran 3.wav in category tom\n","Processed Tom 909 Hi K.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing tom:  91%|█████████ | 421/464 [00:18<00:01, 30.13it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Tom Slam Dam 1.wav in category tom\n","Processed Tom DMX Fatso 4.wav in category tom\n","Processed Tom Jupiter Mop 3.wav in category tom\n","Processed PML_Organica_2_Tom_003_Bb.wav in category tom\n","Processed PML_Organica_2_Tom_001_F.wav in category tom\n","Processed PML_Organica_2_Tom_010_D.wav in category tom\n","Processed PML_Organica_2_Tom_004_C.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["Processing tom:  92%|█████████▏| 429/464 [00:18<00:01, 30.92it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed PML_Organica_2_Tom_002_G.wav in category tom\n","Processed PML_Organica_2_Tom_008_Ab.wav in category tom\n","Processed PML_Organica_2_Tom_007_D.wav in category tom\n","Processed PML_Organica_2_Tom_009_G.wav in category tom\n","Processed PML_Organica_2_Tom_006_B.wav in category tom\n","Processed PML_Organica_2_Tom_005_Eb.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["Processing tom:  94%|█████████▍| 436/464 [00:18<00:01, 27.38it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VEH1 Tom - 20.wav in category tom\n","Processed VEH1 Tom - 26.wav in 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VEH1 Tom - 22.wav in category tom\n","Processed VEH1 Tom - 16.wav in category tom\n","Processed VEH1 Tom - 24.wav in category tom\n","Processed VEH1 Tom - 14.wav in category tom\n","Processed VEH1 Tom - 02.wav in category tom\n"]},{"output_type":"stream","name":"stderr","text":["\n","Processing clap:   0%|          | 1/475 [00:00<04:34,  1.73it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HM_Clap5.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:   0%|          | 2/475 [00:01<04:30,  1.75it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HM_Clap18.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:   1%|          | 3/475 [00:01<04:13,  1.86it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HM_Clap17.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:   1%|          | 4/475 [00:02<03:56,  1.99it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HM_Clap14.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:   1%|          | 5/475 [00:02<03:52,  2.02it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HM_Clap15.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:   3%|▎         | 13/475 [00:07<03:40,  2.09it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HM_Clap10.wav in category clap\n","Processed HM_Clap13.wav in category clap\n","Processed HM_Clap24.wav in category clap\n","Processed HM_Clap9.wav in category clap\n","Processed HM_Clap11.wav in category clap\n","Processed HM_Clap30.wav in category clap\n","Processed HM_Clap12.wav in category clap\n","Processed HM_Clap32.wav in category clap\n","Processed HM_Clap8.wav in category clap\n","Processed HM_Clap16.wav in category clap\n","Processed HM_Clap4.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:   5%|▍         | 22/475 [00:07<01:26,  5.24it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HM_Clap29.wav in category clap\n","Processed HM_Clap26.wav in category clap\n","Processed HM_Clap31.wav in category clap\n","Processed HM_Clap27.wav in category clap\n","Processed HM_Clap7.wav in category clap\n","Processed HM_Clap3.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:   7%|▋         | 31/475 [00:08<00:42, 10.45it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed HM_Clap6.wav in category clap\n","Processed HM_Clap28.wav in category clap\n","Processed HM_Clap1.wav in category clap\n","Processed HM_Clap2.wav in category clap\n","Processed HM_Clap21.wav in category clap\n","Processed HM_Clap20.wav in category clap\n","Processed HM_Clap25.wav in category clap\n","Processed HM_Clap19.wav in category clap\n","Processed HM_Clap23.wav in category clap\n","Processed HM_Clap22.wav in category clap\n","Processed Clap 707X.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:   9%|▉         | 43/475 [00:08<00:21, 20.01it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Clap 808 1 V1.wav in category clap\n","Processed Clap 707.wav in category clap\n","Processed Clap 808 2.wav in category clap\n","Processed Clap 808 1 V2.wav in category clap\n","Processed Vc-clap.wav in category clap\n","Processed 707-clap.wav in category clap\n","Processed HandClap.wav in category clap\n","Processed Strg-clap.wav in category clap\n","Processed muted-clap.wav in category clap\n","Processed Wsh-clap5.wav in category clap\n","Processed Thk-clap1.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  11%|█▏        | 54/475 [00:08<00:14, 29.94it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed thin-wispy-clap.wav in category 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clap\n","Processed Crk-clap.wav in category clap\n","Processed Vrb-clap2.wav in category clap\n","Processed CP_set3.wav in category clap\n","Processed Thk-clap3.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  16%|█▌        | 77/475 [00:09<00:09, 42.03it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Nc-clap.wav in category clap\n","Processed divine-clap-ring.wav in category clap\n","Processed cleap-real-1-verb2cleap-real.wav in category clap\n","Processed Tw-clap.wav in category clap\n","Processed cleap-real-5cleap-real.wav in category clap\n","Processed Clap2.wav in category clap\n","Processed Hardclap.wav in category clap\n","Processed cleap-real-4-verb2cleap-real.wav in category clap\n","Processed cleap-real-1-verb3cleap-real.wav in category clap\n","Processed Phn-clap.wav in category clap\n","Processed sharp-hard-clap.wav in category clap\n","Processed cleap-real-2-verb3cleap-real.wav in category clap\n","Processed s14-clap.wav in category clap"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  19%|█▉        | 90/475 [00:09<00:07, 50.52it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","Processed CP.WAV in category clap\n","Processed cleap-real-4-verb1cleap-real.wav in category clap\n","Processed squeezle-clap.wav in category clap\n","Processed cleap-real-1cleap-real.wav in category clap\n","Processed Mpl-clap.wav in category clap\n","Processed CP_set2.wav in category clap\n","Processed Sm-clap.wav in category clap\n","Processed Snp-clap.wav in category clap\n","Processed SDS5-Crack.WAV in category clap\n","Processed Lad-clap.wav in category clap\n","Processed Ug-clap.wav in category clap\n","Processed pong-verb-clap.wav in category clap\n","Processed Mxr-clap.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  21%|██▏       | 102/475 [00:09<00:09, 39.26it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed CP_set4.wav in category clap\n","Processed Bg-clap.wav in category clap\n","Processed Vrb-clap1.wav in category clap\n","Processed Thk-clap2.wav in category clap\n","Processed Wsh-clap3.wav in category clap\n","Processed cleap-real-2-verb2cleap-real.wav in category clap\n","Processed cleap-real-3-verb1cleap-real.wav in category clap\n","Processed clap10.wav in category clap\n","Processed Sml-clap2.wav in category clap\n","Processed CP_set5.wav in category clap\n","Processed clap.wav in category clap\n","Processed CP_set1.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  24%|██▍       | 114/475 [00:10<00:07, 46.35it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed CR8KCLAP.WAV in category clap\n","Processed Pil-clap.wav in category clap\n","Processed Hnd-clap2.wav in category clap\n","Processed Osc-clap.wav in category clap\n","Processed 105337.wav in category clap\n","Processed 125591.wav in category clap\n","Processed 167791.wav in category clap\n","Processed 203196.wav in category clap\n","Processed 203209.wav in category clap\n","Processed 203210.wav in category clap\n","Processed 203211.wav in category clap\n","Processed 21183.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  25%|██▌       | 120/475 [00:10<00:07, 48.91it/s]/usr/local/lib/python3.10/dist-packages/librosa/core/spectrum.py:266: UserWarning: n_fft=2048 is too large for input signal of length=1786\n","  warnings.warn(\n","Processing clap:  27%|██▋       | 128/475 [00:10<00:06, 56.40it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed 21181.wav in category clap\n","Processed 219170.wav in category clap\n","Processed 23329.wav in category clap\n","Processed 320277.wav in category clap\n","Processed 320271.wav in category clap\n","Processed 320272.wav in category clap\n","Processed 320273.wav in category clap\n","Processed 320276.wav in category clap\n","Processed 320274.wav in 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category clap\n","Processed rz1-clap.wav in category clap\n","Processed electro-cricket-clap4.wav in category clap\n","Processed thickclap.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  35%|███▍      | 165/475 [00:10<00:05, 61.38it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed clapverby.wav in category clap\n","Processed compressed-clap.wav in category clap\n","Processed squirt-hat.wav in category clap\n","Processed c2.wav in category clap\n","Processed verbyclapesque.wav in category clap\n","Processed brightclap.wav in category clap\n","Processed wide-clap.wav in category clap\n","Processed clap808.wav in category clap\n","Processed verby-percussion-pop.wav in category clap\n","Processed small909clap.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  38%|███▊      | 179/475 [00:11<00:05, 54.42it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed weird-clap.wav in category clap\n","Processed electro-cricket-clap2.wav in category clap\n","Processed lo_filt_clap.wav in category clap\n","Processed gated-clap.wav in category clap\n","Processed biggest-clap.wav in category clap\n","Processed qy20clap.wav in category clap\n","Processed quiet-pop.wav in category clap\n","Processed earsplitting-schlusselcrush.wav in category clap\n","Processed gotsa-big-clap.wav in category clap\n","Processed electro-cricket-clap1.wav in category clap\n","Processed brightclap2.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  39%|███▉      | 185/475 [00:11<00:05, 53.19it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed electro-cricket-clap3.wav in category clap\n","Processed ruddy-handclap.wav in category clap\n","Processed clap909.wav in category clap\n","Processed queer-clap.wav in category clap\n","Processed static_clap.wav in category clap\n","Processed Clap 12.wav in category clap\n","Processed Clap 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clap\n","Processed Clap 3.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  43%|████▎     | 206/475 [00:11<00:07, 37.24it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Clap 8.wav in category clap\n","Processed Clap 16.wav in category clap\n","Processed Clap 6.wav in category clap\n","Processed ZDOT - CLAP (WATER).wav in category clap\n","Processed ZDOT - CLAP (SAND).wav in category clap\n","Processed ZDOT - CLAP (SCOPE).wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  44%|████▍     | 210/475 [00:12<00:08, 31.94it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed ZDOT - CLAP (BASE).wav in category clap\n","Processed ZDOT - CLAP (MEDIC).wav in category clap\n","Processed ZDOT - CLAP (POINTS).wav in category clap\n","Processed ZDOT - CLAP (EAGLE).wav in category clap\n","Processed FLAVAD_clap_warehouse.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  46%|████▌     | 218/475 [00:12<00:08, 30.39it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed FLAVAD_clap_perky.wav in category clap\n","Processed FLAVAD_clap_sharp.wav in category clap\n","Processed FLAVAD_clap_air.wav in category clap\n","Processed FLAVAD_clap_kwok.wav in category clap\n","Processed FLAVAD_clap_glass.wav in category clap\n","Processed Cymatics - San Holo Free Clap 1.wav in category clap\n","Processed Cymatics - San Holo Free Clap 4.wav in category clap\n","Processed Cymatics - San Holo Free Clap 5.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  48%|████▊     | 227/475 [00:12<00:07, 34.73it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymatics - San Holo Free Clap 3.wav in category clap\n","Processed Cymatics - San Holo Free Clap 6.wav in category clap\n","Processed Cymatics - San Holo Free Clap 2.wav in category clap\n","Processed Cymatics - Odyssey Tight Clap 4.wav in category clap\n","Processed Cymatics - Odyssey Flam Clap 2.wav in category clap\n","Processed Cymatics - Odyssey Big Clap 3.wav in category clap\n","Processed Cymatics - Odyssey Big Clap 5.wav in category clap\n","Processed Cymatics - Odyssey Big Clap 6.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  49%|████▉     | 235/475 [00:12<00:07, 32.01it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymatics - Odyssey Tight Clap 3.wav in category clap\n","Processed Cymatics - Odyssey Big Clap 1.wav in category clap\n","Processed Cymatics - Odyssey Tight Clap 1.wav in category clap\n","Processed Cymatics - Odyssey Big Clap 4.wav in category clap\n","Processed Cymatics - Odyssey Flam Clap 3.wav in category clap\n","Processed Cymatics - Odyssey Big Clap 2.wav in category clap\n","Processed Cymatics - Odyssey Tight Clap 2.wav in category clap\n","Processed Cymatics - Odyssey Flam Clap 1.wav in category clap\n","Processed Cymatics - Odyssey Tight Clap 5.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  51%|█████     | 242/475 [00:13<00:10, 21.52it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Hihat Clap House 120 bpm.wav in category clap\n","Processed Hihat and Clap 80 bpm.wav in category clap\n","Processed odh_clap_bonus.wav in category clap\n","Processed odh_clap_pleasure.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  52%|█████▏    | 249/475 [00:13<00:09, 24.95it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed odh_clap_tribeall.wav in category clap\n","Processed odh_clap_meta.wav in category clap\n","Processed odh_clap_ahara.wav in category clap\n","Processed odh_clap_cagefree.wav in category clap\n","Processed odh_clap_cagefree2.wav in category clap\n","Processed odh_clap_ranger.wav in category clap\n","Processed odh_clap_lies.wav in category clap\n","Processed odh_clap_chances.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  54%|█████▎    | 255/475 [00:13<00:08, 26.27it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed odh_clap_degree.wav in category clap\n","Processed odh_clap_lies2.wav in category clap\n","Processed PML_Organica_2_Clap_003.wav in category clap\n","Processed PML_Organica_2_Clap_004.wav in category clap\n","Processed PML_Organica_2_Clap_002.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  55%|█████▌    | 263/475 [00:13<00:06, 30.35it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed PML_Organica_2_Clap_001.wav in category clap\n","Processed PML_Organica_2_Clap_007.wav in category clap\n","Processed PML_Organica_2_Clap_008.wav in category clap\n","Processed PML_Organica_2_Clap_009.wav in category clap\n","Processed PML_Organica_2_Clap_006.wav in category clap\n","Processed PML_Organica_2_Clap_005.wav in category clap\n","Processed PML_Organica_2_Clap_010.wav in category clap\n","Processed High 808 Clap.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  56%|█████▌    | 267/475 [00:14<00:06, 30.34it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed High 909 Clap.wav in category clap\n","Processed Haunted Clap.wav in category clap\n","Processed Cymatics - Drill Clap (Tin).wav in category clap\n","Processed Cymatics - Drill Clap (Burst).wav in category clap\n","Processed Cymatics - Drill Clap (Full).wav in category clap\n","Processed Cymatics - Drill Clap (Sheen).wav in category clap\n","Processed Cymatics - Drill Clap (Horror).wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  58%|█████▊    | 275/475 [00:14<00:06, 30.87it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Cymatics - Drill Clap (Paper).wav in category clap\n","Processed Cymatics - Drill Clap (Ashes).wav in category clap\n","Processed Cymatics - Drill Clap (Legend).wav in category clap\n","Processed Cymatics - Drill Clap (King).wav in category clap\n","Processed Cymatics - Drill Clap (Teensie).wav in category clap\n","Processed Cymatics - Drill Clap (Tears).wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  59%|█████▊    | 279/475 [00:14<00:07, 27.27it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed AM_PT_Clap_10.wav in category clap\n","Processed AM_PT_Clap_1.wav in category clap\n","Processed AM_PT_Clap_12.wav in category clap\n","Processed AM_PT_Clap_11.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  59%|█████▉    | 282/475 [00:14<00:09, 20.99it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed AM_PT_Clap_13.wav in category clap\n","Processed AM_PT_Clap_14.wav in category clap\n","Processed AM_PT_Clap_5.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  60%|██████    | 285/475 [00:15<00:12, 15.09it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed AM_PT_Clap_7.wav in category clap\n","Processed AM_PT_Clap_3.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  60%|██████    | 287/475 [00:15<00:13, 14.17it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed AM_PT_Clap_6.wav in category clap\n","Processed AM_PT_Clap_4.wav in category clap\n","Processed AM_PT_Clap_2.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  61%|██████▏   | 291/475 [00:15<00:14, 12.58it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed AM_PT_Clap_8.wav in category clap\n","Processed AM_PT_Clap_9.wav in category clap\n","Processed Mecha3 Clap 23.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  62%|██████▏   | 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in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  64%|██████▍   | 305/475 [00:16<00:13, 12.32it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Mecha3 Clap 6.wav in category clap\n","Processed Mecha3 Clap 10.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  65%|██████▍   | 307/475 [00:16<00:16, 10.43it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Mecha3 Clap 5.wav in category clap\n","Processed Mecha3 Clap 9.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  65%|██████▌   | 309/475 [00:17<00:18,  9.13it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Mecha3 Clap 8.wav in category clap\n","Processed Mecha3 Clap 11.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  65%|██████▌   | 311/475 [00:17<00:19,  8.43it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Mecha3 Clap 12.wav in category clap\n","Processed Mecha3 Clap 20.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  66%|██████▌   | 313/475 [00:17<00:19,  8.16it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Mecha3 Clap 13.wav in category clap\n","Processed Mecha3 Clap 4.wav in category clap\n","Processed Mecha3 Clap 14.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  67%|██████▋   | 319/475 [00:18<00:09, 16.00it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed Mecha3 Clap 1.wav in category clap\n","Processed VWB1 Claps 009.wav in category clap\n","Processed VWB1 Claps 010.wav in category clap\n","Processed VWB1 Claps 020.wav in category clap\n","Processed VWB1 Claps 012.wav in category clap\n","Processed VWB1 Claps 063.wav in category clap\n","Processed VWB1 Claps 007.wav in category clap\n","Processed VWB1 Claps 015.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  69%|██████▉   | 327/475 [00:18<00:06, 22.91it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 104 -16ms.wav in category clap\n","Processed VWB1 Claps 021.wav in category clap\n","Processed VWB1 Claps 016.wav in category clap\n","Processed VWB1 Claps 062.wav in category clap\n","Processed VWB1 Claps 051.wav in category clap\n","Processed VWB1 Claps 070.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  71%|███████   | 335/475 [00:18<00:05, 27.33it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 024.wav in category clap\n","Processed VWB1 Claps 011.wav in category clap\n","Processed VWB1 Claps 022.wav in category clap\n","Processed VWB1 Claps 101 -9ms.wav in category clap\n","Processed VWB1 Claps 017.wav in category clap\n","Processed VWB1 Claps 064.wav in category clap\n","Processed VWB1 Claps 080 -10ms.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  72%|███████▏  | 341/475 [00:18<00:04, 27.23it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 135.wav in category clap\n","Processed VWB1 Claps 082 -15ms.wav in category clap\n","Processed VWB1 Claps 013.wav in category clap\n","Processed VWB1 Claps 006.wav in category clap\n","Processed VWB1 Claps 008.wav in category clap\n","Processed VWB1 Claps 014.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  73%|███████▎  | 349/475 [00:18<00:04, 31.41it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 018.wav in category clap\n","Processed VWB1 Claps 025.wav in category clap\n","Processed VWB1 Claps 027.wav in category clap\n","Processed VWB1 Claps 109 -12ms.wav in category clap\n","Processed VWB1 Claps 031.wav in category clap\n","Processed VWB1 Claps 083.wav in category clap\n","Processed VWB1 Claps 019.wav in category clap\n","Processed VWB1 Claps 030.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  75%|███████▌  | 357/475 [00:19<00:03, 35.07it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 052 -17ms.wav in category clap\n","Processed VWB1 Claps 001.wav in category clap\n","Processed VWB1 Claps 023.wav in category clap\n","Processed VWB1 Claps 076.wav in category clap\n","Processed VWB1 Claps 059 -42ms.wav in category clap\n","Processed VWB1 Claps 093 -19ms.wav in category clap\n","Processed VWB1 Claps 118.wav in category clap\n","Processed VWB1 Claps 094 -15ms.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  77%|███████▋  | 365/475 [00:19<00:03, 36.38it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 071.wav in category clap\n","Processed VWB1 Claps 081.wav in category clap\n","Processed VWB1 Claps 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clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  83%|████████▎ | 393/475 [00:20<00:02, 34.33it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 085 -20ms.wav in category clap\n","Processed VWB1 Claps 045 -23ms.wav in category clap\n","Processed VWB1 Claps 113.wav in category clap\n","Processed VWB1 Claps 123 -45ms.wav in category clap\n","Processed VWB1 Claps 079 -18ms.wav in category clap\n","Processed VWB1 Claps 086 -15ms.wav in category clap\n","Processed VWB1 Claps 060 -27ms.wav in category clap\n","Processed VWB1 Claps 105 -10ms.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap:  84%|████████▍ | 401/475 [00:20<00:02, 34.29it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 068 -43ms.wav in category clap\n","Processed VWB1 Claps 034.wav in category clap\n","Processed VWB1 Claps 005.wav in category clap\n","Processed VWB1 Claps 129.wav in category 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27.01it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 150.wav in category clap\n","Processed VWB1 Claps 026.wav in category clap\n","Processed VWB1 Pre Rattle Claps 06.wav in category clap\n","Processed VWB1 Claps 119 -19ms.wav in category clap\n","Processed VWB1 Pre Rattle Claps 01.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\rProcessing clap:  99%|█████████▉| 472/475 [00:22<00:00, 28.42it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 124 -27ms.wav in category clap\n","Processed VWB1 Pre Rattle Claps 09.wav in category clap\n","Processed VWB1 Claps 137 -47ms.wav in category clap\n","Processed VWB1 Claps 092.wav in category clap\n","Processed VWB1 Claps 032 -244ms.wav in category clap\n","Processed VWB1 Claps 033 -334ms.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["Processing clap: 100%|██████████| 475/475 [00:22<00:00, 20.86it/s]"]},{"output_type":"stream","name":"stdout","text":["Processed VWB1 Claps 028.wav in category clap\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]},{"cell_type":"markdown","source":["# Drum Dataset Class"],"metadata":{"id":"YvS753rrz-70"}},{"cell_type":"code","source":["class DrumDataset(Dataset):\n","    def __init__(self, audio_df, transform=None):\n","        self.audio_df = audio_df\n","        self.transform = transform\n","\n","    def __len__(self):\n","        return len(self.audio_df)\n","\n","    def __getitem__(self, idx):\n","        mel_spectrogram = self.audio_df.iloc[idx]['mel_spectrogram']\n","        label = self.audio_df.iloc[idx]['label']\n","\n","        if self.transform:\n","            mel_spectrogram = self.transform(mel_spectrogram)\n","\n","        # Convert label to a numerical value\n","        label_idx = categories.index(label)\n","\n","        return mel_spectrogram, label_idx\n","\n","def to_tensor(spectrogram):\n","    spectrogram = spectrogram.astype(np.float32)\n","    return torch.tensor(spectrogram).unsqueeze(0)  # Add channel dimension\n"],"metadata":{"id":"U8wt32enLxQ4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Split Data and Create DataLoader"],"metadata":{"id":"pdjx9JQTzvWj"}},{"cell_type":"code","source":["# Split data\n","train_df, test_df = train_test_split(audio_df, test_size=0.2, stratify=audio_df['label'], random_state=42)\n","train_df, val_df = train_test_split(train_df, test_size=0.2, stratify=train_df['label'], random_state=42)\n","\n","# Create datasets and dataloaders\n","train_dataset = DrumDataset(train_df, transform=to_tensor)\n","val_dataset = DrumDataset(val_df, transform=to_tensor)\n","test_dataset = DrumDataset(test_df, transform=to_tensor)\n","\n","train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n","val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n","test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n"],"metadata":{"id":"OSM7mYThvMLK"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# CNN Model"],"metadata":{"id":"6D4qDYV1JfmH"}},{"cell_type":"code","source":["# Compute model paramters\n","def compute_model_params(model):\n","  params = 0\n","  for p in model.parameters():\n","    params+= p.numel()\n","  return params"],"metadata":{"id":"4gSXp0M67yRs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class DrumCNN(nn.Module):\n","    def __init__(self):\n","        super(DrumCNN, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)\n","        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n","        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)\n","        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n","        self.fc1 = nn.Linear(128 * 16 * 16, 512)\n","        self.fc2 = nn.Linear(512, len(categories))  # Number of classes\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))\n","        x = self.pool(F.relu(self.conv2(x)))\n","        x = self.pool(F.relu(self.conv3(x)))\n","        x = x.view(-1, 128 * 16 * 16)\n","        x = F.relu(self.fc1(x))\n","        x = self.fc2(x)\n","        return x"],"metadata":{"id":"7DDiEgixL1fQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(f'Using device: {device}')\n","\n","model_basic = DrumCNN().to(device)\n","\n","n_params = compute_model_params(model_basic)\n","print(\"DrumCNN Parameters: \" + str(n_params))\n","\n","# Print the model summary\n","summary(model_basic, input_size=(1, 128, 128))"],"metadata":{"id":"OFrTZtN690Gf","executionInfo":{"status":"ok","timestamp":1717068283855,"user_tz":-120,"elapsed":308,"user":{"displayName":"Alex Montoya","userId":"06075551048510865080"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"56af0cd6-7a31-4856-d040-57b3f39d15fc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Using device: cpu\n","DrumCNN Parameters: 16873478\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1         [-1, 32, 128, 128]             320\n","         MaxPool2d-2           [-1, 32, 64, 64]               0\n","            Conv2d-3           [-1, 64, 64, 64]          18,496\n","         MaxPool2d-4           [-1, 64, 32, 32]               0\n","            Conv2d-5          [-1, 128, 32, 32]          73,856\n","         MaxPool2d-6          [-1, 128, 16, 16]               0\n","            Linear-7                  [-1, 512]      16,777,728\n","            Linear-8                    [-1, 6]           3,078\n","================================================================\n","Total params: 16,873,478\n","Trainable params: 16,873,478\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.06\n","Forward/backward pass size (MB): 8.75\n","Params size (MB): 64.37\n","Estimated Total Size (MB): 73.18\n","----------------------------------------------------------------\n"]}]},{"cell_type":"code","source":["## Adding Batch Norm and Dropout\n","\n","class DrumCNN_BN_Dropout(nn.Module):\n","    def __init__(self):\n","        super(DrumCNN_BN_Dropout, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)\n","        self.bn1 = nn.BatchNorm2d(32)\n","        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n","        self.bn2 = nn.BatchNorm2d(64)\n","        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)\n","        self.bn3 = nn.BatchNorm2d(128)\n","        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n","        self.fc1 = nn.Linear(128 * 16 * 16, 512)\n","        self.dropout = nn.Dropout(0.5)\n","        self.fc2 = nn.Linear(512, len(categories))\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.bn1(self.conv1(x))))\n","        x = self.pool(F.relu(self.bn2(self.conv2(x))))\n","        x = self.pool(F.relu(self.bn3(self.conv3(x))))\n","        x = x.view(-1, 128 * 16 * 16)\n","        x = self.dropout(F.relu(self.fc1(x)))\n","        x = self.fc2(x)\n","        return x\n"],"metadata":{"id":"NFGe0LbbA5fE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(f'Using device: {device}')\n","\n","model_bn_dropout = DrumCNN_BN_Dropout().to(device)\n","\n","n_params = compute_model_params(model_bn_dropout)\n","print(\"DrumCNN_BN_Dropout Parameters: \" + str(n_params))\n","\n","# Print the model summary\n","summary(model_bn_dropout, input_size=(1, 128, 128))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"goUWDLNlBy_O","executionInfo":{"status":"ok","timestamp":1717073702151,"user_tz":-120,"elapsed":360,"user":{"displayName":"ÀLEX MONTOYA PÉREZ","userId":"15889589265134071867"}},"outputId":"99f2537f-e3dc-4d42-fbc1-41764d5faf1a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Using device: cpu\n","DrumCNN_BN_Dropout Parameters: 16873926\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1         [-1, 32, 128, 128]             320\n","       BatchNorm2d-2         [-1, 32, 128, 128]              64\n","         MaxPool2d-3           [-1, 32, 64, 64]               0\n","            Conv2d-4           [-1, 64, 64, 64]          18,496\n","       BatchNorm2d-5           [-1, 64, 64, 64]             128\n","         MaxPool2d-6           [-1, 64, 32, 32]               0\n","            Conv2d-7          [-1, 128, 32, 32]          73,856\n","       BatchNorm2d-8          [-1, 128, 32, 32]             256\n","         MaxPool2d-9          [-1, 128, 16, 16]               0\n","           Linear-10                  [-1, 512]      16,777,728\n","          Dropout-11                  [-1, 512]               0\n","           Linear-12                    [-1, 6]           3,078\n","================================================================\n","Total params: 16,873,926\n","Trainable params: 16,873,926\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.06\n","Forward/backward pass size (MB): 15.76\n","Params size (MB): 64.37\n","Estimated Total Size (MB): 80.19\n","----------------------------------------------------------------\n"]}]},{"cell_type":"code","source":["class SEBlock(nn.Module):\n","    def __init__(self, in_channels, reduction=16):\n","        super(SEBlock, self).__init__()\n","        self.fc1 = nn.Conv2d(in_channels, in_channels // reduction, kernel_size=1)\n","        self.fc2 = nn.Conv2d(in_channels // reduction, in_channels, kernel_size=1)\n","\n","    def forward(self, x):\n","        out = F.adaptive_avg_pool2d(x, 1)\n","        out = F.relu(self.fc1(out))\n","        out = torch.sigmoid(self.fc2(out))\n","        return x * out\n","\n","class DrumCNN_DWSE(nn.Module):\n","    def __init__(self):\n","        super(DrumCNN_DWSE, self).__init__()\n","\n","        # Initial convolution block\n","        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, stride=1, padding=2)\n","        self.bn1 = nn.BatchNorm2d(32)\n","        self.se1 = SEBlock(32)\n","\n","        # Depthwise Separable Convolution Blocks\n","        self.dwconv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1, groups=16) # Depthwise convolution\n","        self.pwconv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=1, stride=1) # Pointwise convolution\n","        self.bn2 = nn.BatchNorm2d(64)\n","        self.se2 = SEBlock(64)\n","\n","        self.dwconv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, groups=64) # Depthwise convolution\n","        self.pwconv4 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=1, stride=1) # Pointwise convolution\n","        self.bn4 = nn.BatchNorm2d(128)\n","        self.se4 = SEBlock(128)\n","\n","        self.dwconv5 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1, groups=128) # Depthwise convolution\n","        self.pwconv5 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=1, stride=1) # Pointwise convolution\n","        self.bn5 = nn.BatchNorm2d(256)\n","        self.se5 = SEBlock(256)\n","\n","        self.dwconv6 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, groups=256) # Depthwise convolution\n","        self.pwconv6 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1, stride=1) # Pointwise convolution\n","        self.bn6 = nn.BatchNorm2d(512)\n","        self.se6 = SEBlock(512)\n","\n","        # Max Pooling layers\n","        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # Output: 16x16x32\n","        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # Output: 8x8x128\n","        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # Output: 4x4x256\n","\n","        # Global Average Pooling\n","        self.global_pool = nn.AdaptiveAvgPool2d(1) # Global Average Pooling\n","\n","        # Fully Connected Layer\n","        self.fc = nn.Linear(in_features=512, out_features=128) # 256 to 10 (output classes)\n","        self.fc2 = nn.Linear(in_features=128, out_features=len(categories)) # 256 to 10 (output classes)\n","\n","\n","        self.dropout = nn.Dropout(0.3)\n","\n","    def forward(self, x):\n","        # Initial conv block\n","        x = F.relu(self.bn1(self.conv1(x)))\n","        x = self.se1(x)\n","\n","        # Depthwise Separable Convolution blocks\n","        x = F.relu(self.bn2(self.pwconv2(self.dwconv2(x))))\n","        x = self.pool1(x)\n","        x = self.se2(x)\n","\n","        x = F.relu(self.bn4(self.pwconv4(self.dwconv4(x))))\n","        x = self.pool2(x)\n","        x = self.se4(x)\n","\n","        x = F.relu(self.bn5(self.pwconv5(self.dwconv5(x))))\n","        x = self.se5(x)\n","\n","        x = F.relu(self.bn6(self.pwconv6(self.dwconv6(x))))\n","        x = self.pool3(x)\n","        x = self.se6(x)\n","\n","        # Global Average Pooling\n","        x = self.global_pool(x)\n","        x = torch.flatten(x, 1) # Flatten the tensor\n","\n","        # Fully Connected Layer\n","        x = self.dropout(x)\n","        x = self.fc(x)  # Primera capa completamente conectada\n","        x = F.relu(x)  # Función de activación ReLU\n","        x = self.fc2(x)  # Segunda capa completamente conectada\n","\n","        return x\n"],"metadata":{"id":"Uw6YObV7ZeRj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(f'Using device: {device}')\n","\n","#model_bn_dropout = DrumCNN_BN_Dropout().to(device)\n","model_DWSE = DrumCNN_DWSE().to(device)\n","\n","n_params = compute_model_params(model_DWSE)\n","print(\"DrumCNN_DWSE Parameters: \" + str(n_params))\n","\n","# Print the model summary\n","summary(model_DWSE, input_size=(1, 128, 128))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717594192557,"user_tz":-120,"elapsed":778,"user":{"displayName":"Alex Montoya","userId":"06075551048510865080"}},"outputId":"add5d46f-33ac-47d1-e473-6d2a5526fc31","id":"tz90q2MvZ2RT"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Using device: cuda\n","DrumCNN_DWSE Parameters: 294084\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1         [-1, 32, 128, 128]             832\n","       BatchNorm2d-2         [-1, 32, 128, 128]              64\n","            Conv2d-3              [-1, 2, 1, 1]              66\n","            Conv2d-4             [-1, 32, 1, 1]              96\n","           SEBlock-5         [-1, 32, 128, 128]               0\n","            Conv2d-6         [-1, 32, 128, 128]             608\n","            Conv2d-7         [-1, 64, 128, 128]           2,112\n","       BatchNorm2d-8         [-1, 64, 128, 128]             128\n","         MaxPool2d-9           [-1, 64, 64, 64]               0\n","           Conv2d-10              [-1, 4, 1, 1]             260\n","           Conv2d-11             [-1, 64, 1, 1]             320\n","          SEBlock-12           [-1, 64, 64, 64]               0\n","           Conv2d-13           [-1, 64, 64, 64]             640\n","           Conv2d-14          [-1, 128, 64, 64]           8,320\n","      BatchNorm2d-15          [-1, 128, 64, 64]             256\n","        MaxPool2d-16          [-1, 128, 32, 32]               0\n","           Conv2d-17              [-1, 8, 1, 1]           1,032\n","           Conv2d-18            [-1, 128, 1, 1]           1,152\n","          SEBlock-19          [-1, 128, 32, 32]               0\n","           Conv2d-20          [-1, 128, 32, 32]           1,280\n","           Conv2d-21          [-1, 256, 32, 32]          33,024\n","      BatchNorm2d-22          [-1, 256, 32, 32]             512\n","           Conv2d-23             [-1, 16, 1, 1]           4,112\n","           Conv2d-24            [-1, 256, 1, 1]           4,352\n","          SEBlock-25          [-1, 256, 32, 32]               0\n","           Conv2d-26          [-1, 256, 32, 32]           2,560\n","           Conv2d-27          [-1, 512, 32, 32]         131,584\n","      BatchNorm2d-28          [-1, 512, 32, 32]           1,024\n","        MaxPool2d-29          [-1, 512, 16, 16]               0\n","           Conv2d-30             [-1, 32, 1, 1]          16,416\n","           Conv2d-31            [-1, 512, 1, 1]          16,896\n","          SEBlock-32          [-1, 512, 16, 16]               0\n","AdaptiveAvgPool2d-33            [-1, 512, 1, 1]               0\n","          Dropout-34                  [-1, 512]               0\n","           Linear-35                  [-1, 128]          65,664\n","           Linear-36                    [-1, 6]             774\n","================================================================\n","Total params: 294,084\n","Trainable params: 294,084\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.06\n","Forward/backward pass size (MB): 67.02\n","Params size (MB): 1.12\n","Estimated Total Size (MB): 68.20\n","----------------------------------------------------------------\n"]}]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"Sf1Rd6rJJkrt"}},{"cell_type":"code","source":["# model = model_basic\n","model = model_bn_dropout\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","num_epochs = 20\n","train_losses = []\n","val_losses = []\n","val_accuracies = []\n","\n","\n","for epoch in range(num_epochs):\n","    model.train()\n","    running_loss = 0.0\n","    for inputs, labels in train_loader:\n","        optimizer.zero_grad()\n","        outputs = model(inputs)\n","        loss = criterion(outputs, labels)\n","        loss.backward()\n","        optimizer.step()\n","        running_loss += loss.item()\n","    train_losses.append(running_loss / len(train_loader))\n","\n","    model.eval()\n","    val_loss = 0.0\n","    correct = 0\n","    total = 0\n","    with torch.no_grad():\n","        for inputs, labels in val_loader:\n","            outputs = model(inputs)\n","            loss = criterion(outputs, labels)\n","            val_loss += loss.item()\n","            _, predicted = torch.max(outputs.data, 1)\n","            total += labels.size(0)\n","            correct += (predicted == labels).sum().item()\n","    val_losses.append(val_loss / len(val_loader))\n","    val_accuracies.append(100 * correct / total)\n","\n","    print(f\"Epoch {epoch+1}, Loss: {train_losses[-1]}, Validation Loss: {val_losses[-1]}, Validation Accuracy: {val_accuracies[-1]}%\")\n"],"metadata":{"id":"zub9kUzUL4oP","executionInfo":{"status":"ok","timestamp":1717077971291,"user_tz":-120,"elapsed":713791,"user":{"displayName":"ÀLEX MONTOYA PÉREZ","userId":"15889589265134071867"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"fb455345-ea7b-4fff-a5c4-aac8e676809d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1, Loss: 2.326423357141779, Validation Loss: 0.9010178794463476, Validation Accuracy: 67.28971962616822%\n","Epoch 2, Loss: 0.8992700082190493, Validation Loss: 0.5413315544525782, Validation Accuracy: 81.70894526034714%\n","Epoch 3, Loss: 0.665297897889259, Validation Loss: 0.45627417353292304, Validation Accuracy: 85.98130841121495%\n","Epoch 4, Loss: 0.6147311713467253, Validation Loss: 0.4080389514565468, Validation Accuracy: 86.78237650200268%\n","Epoch 5, Loss: 0.5540592633980386, Validation Loss: 0.36685584609707195, Validation Accuracy: 87.04939919893191%\n","Epoch 6, Loss: 0.5060647104648833, Validation Loss: 0.35471261696269113, Validation Accuracy: 89.31909212283044%\n","Epoch 7, Loss: 0.48329837604406034, Validation Loss: 0.3489046562463045, Validation Accuracy: 89.18558077436582%\n","Epoch 8, Loss: 0.4828413723314062, Validation Loss: 0.3742162895699342, Validation Accuracy: 87.58344459279039%\n","Epoch 9, Loss: 0.4380337585635642, Validation Loss: 0.41184119259317714, Validation Accuracy: 88.38451268357811%\n","Epoch 10, Loss: 0.4298778296785152, Validation Loss: 0.31618624242643517, Validation Accuracy: 90.52069425901202%\n","Epoch 11, Loss: 0.39878031437067274, Validation Loss: 0.33014582718412083, Validation Accuracy: 89.0520694259012%\n","Epoch 12, Loss: 0.4423628329755144, Validation Loss: 0.2996182693168521, Validation Accuracy: 90.52069425901202%\n","Epoch 13, Loss: 0.4019593854850911, Validation Loss: 0.32553637865930796, Validation Accuracy: 89.98664886515354%\n","Epoch 14, Loss: 0.3764504942488163, Validation Loss: 0.3088632359479864, Validation Accuracy: 91.32176234979973%\n","Epoch 15, Loss: 0.3509686475897089, Validation Loss: 0.32114196351418894, Validation Accuracy: 90.25367156208277%\n","Epoch 16, Loss: 0.34691659749505366, Validation Loss: 0.3392845469837387, Validation Accuracy: 91.0547396528705%\n","Epoch 17, Loss: 0.3377900019763632, Validation Loss: 0.33208386537929374, Validation Accuracy: 90.78771695594125%\n","Epoch 18, Loss: 0.3256867321485535, Validation Loss: 0.3496430739760399, Validation Accuracy: 90.92122830440587%\n","Epoch 19, Loss: 0.3196094644830582, Validation Loss: 0.32219651124129695, Validation Accuracy: 90.78771695594125%\n","Epoch 20, Loss: 0.2728192121028266, Validation Loss: 0.3771088433762391, Validation Accuracy: 91.8558077436582%\n"]}]},{"cell_type":"code","source":["# model = model_basic\n","model = model_DWSE\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.Adam(model.parameters(), lr=0.005)\n","\n","# Define el scheduler que ajustará la tasa de aprendizaje\n","scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)\n","\n","\n","num_epochs = 30\n","train_losses = []\n","val_losses = []\n","val_accuracies = []\n","\n","\n","\n","for epoch in range(num_epochs):\n","    model.train()\n","    running_loss = 0.0\n","    for inputs, labels in train_loader:\n","        inputs, labels = inputs.to(device), labels.to(device)\n","        optimizer.zero_grad()\n","\n","        outputs = model(inputs)\n","        loss = criterion(outputs, labels)\n","        loss.backward()\n","        optimizer.step()\n","        running_loss += loss.item()\n","    train_losses.append(running_loss / len(train_loader))\n","\n","    model.eval()\n","    val_loss = 0.0\n","    correct = 0\n","    total = 0\n","    with torch.no_grad():\n","        for inputs, labels in val_loader:\n","            inputs, labels = inputs.to(device), labels.to(device)\n","            outputs = model(inputs)\n","            loss = criterion(outputs, labels)\n","            val_loss += loss.item()\n","            _, predicted = torch.max(outputs.data, 1)\n","            total += labels.size(0)\n","            correct += (predicted == labels).sum().item()\n","    val_losses.append(val_loss / len(val_loader))\n","    val_accuracies.append(100 * correct / total)\n","\n","    scheduler.step()\n","\n","\n","    print(f\"Epoch {epoch+1}, Loss: {train_losses[-1]}, Validation Loss: {val_losses[-1]}, Validation Accuracy: {val_accuracies[-1]}%\")\n","    scheduler.step()\n","\n","\n","print(f\"Tasa de aprendizaje actual: {scheduler.get_last_lr()[0]}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717594105895,"user_tz":-120,"elapsed":230878,"user":{"displayName":"Alex Montoya","userId":"06075551048510865080"}},"outputId":"7a587b67-d603-420d-8999-5442a82d5c9a","id":"EIe7YnkAaCba"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1, Loss: 0.38645808684064986, Validation Loss: 0.33055884142716724, Validation Accuracy: 88.25100133511349%\n","Epoch 2, Loss: 0.28796222695010776, Validation Loss: 0.28830887128909427, Validation Accuracy: 89.18558077436582%\n","Epoch 3, Loss: 0.23659013195874842, Validation Loss: 0.3701799015204112, Validation Accuracy: 89.18558077436582%\n","Epoch 4, Loss: 0.2388187867212803, Validation Loss: 0.37189923226833344, Validation Accuracy: 87.85046728971963%\n","Epoch 5, Loss: 0.25386593142088426, Validation Loss: 0.5028845071792603, Validation Accuracy: 79.57276368491321%\n","Epoch 6, Loss: 0.1850534144868242, Validation Loss: 0.28015393142898876, Validation Accuracy: 91.58878504672897%\n","Epoch 7, Loss: 0.1452752352553479, Validation Loss: 0.25366295749942463, Validation Accuracy: 91.58878504672897%\n","Epoch 8, Loss: 0.138559208310982, Validation Loss: 0.3425916023552418, Validation Accuracy: 89.31909212283044%\n","Epoch 9, Loss: 0.1287229512520927, Validation Loss: 0.2812004629522562, Validation Accuracy: 92.5233644859813%\n","Epoch 10, Loss: 0.13247049679147435, Validation Loss: 0.2898390634606282, Validation Accuracy: 90.78771695594125%\n","Epoch 11, Loss: 0.09003935841486808, Validation Loss: 0.31310378946363926, Validation Accuracy: 92.3898531375167%\n","Epoch 12, Loss: 0.06389873015119675, Validation Loss: 0.28897089169671136, Validation Accuracy: 92.5233644859813%\n","Epoch 13, Loss: 0.05591225301093878, Validation Loss: 0.33023604440192383, Validation Accuracy: 91.7222963951936%\n","Epoch 14, Loss: 0.07473769237069373, Validation Loss: 0.331536619613568, Validation Accuracy: 91.7222963951936%\n","Epoch 15, Loss: 0.05053452184384174, Validation Loss: 0.3128675079594056, Validation Accuracy: 91.0547396528705%\n","Epoch 16, Loss: 0.03811839171388048, Validation Loss: 0.3374231780568759, Validation Accuracy: 91.32176234979973%\n","Epoch 17, Loss: 0.03371921949524512, Validation Loss: 0.300807467661798, Validation Accuracy: 92.12283044058745%\n","Epoch 18, Loss: 0.02247378913229926, Validation Loss: 0.3258930929005146, Validation Accuracy: 92.3898531375167%\n","Epoch 19, Loss: 0.022351324652381082, Validation Loss: 0.33416840930779773, Validation Accuracy: 91.7222963951936%\n","Epoch 20, Loss: 0.02830510397263347, Validation Loss: 0.356808348869284, Validation Accuracy: 92.12283044058745%\n","Epoch 21, Loss: 0.016389218084712293, Validation Loss: 0.35444430261850357, Validation Accuracy: 92.5233644859813%\n","Epoch 22, Loss: 0.014952746665778947, Validation Loss: 0.35728116892278194, Validation Accuracy: 92.65687583444593%\n","Epoch 23, Loss: 0.017490718721788614, Validation Loss: 0.35581941219667595, Validation Accuracy: 92.25634178905207%\n","Epoch 24, Loss: 0.017306954698993803, Validation Loss: 0.36080458760261536, Validation Accuracy: 92.5233644859813%\n","Epoch 25, Loss: 0.01428693755371298, Validation Loss: 0.37080863180259865, Validation Accuracy: 91.8558077436582%\n","Epoch 26, Loss: 0.012433478908434987, Validation Loss: 0.373090739051501, Validation Accuracy: 92.65687583444593%\n","Epoch 27, Loss: 0.011826695548618173, Validation Loss: 0.37283603226145107, Validation Accuracy: 92.25634178905207%\n","Epoch 28, Loss: 0.009907485631126117, Validation Loss: 0.3721771811445554, Validation Accuracy: 92.5233644859813%\n","Epoch 29, Loss: 0.014266064499524679, Validation Loss: 0.3811162579804659, Validation Accuracy: 91.98931909212283%\n","Epoch 30, Loss: 0.009252612119877433, Validation Loss: 0.3784377556294203, Validation Accuracy: 92.25634178905207%\n","Tasa de aprendizaje actual: 7.8125e-05\n"]}]},{"cell_type":"markdown","source":["# Results Visualization"],"metadata":{"id":"Jla947rH7dN-"}},{"cell_type":"markdown","source":["## Plot Training and Validation Loss"],"metadata":{"id":"zIHBiWQy9BLU"}},{"cell_type":"code","source":["plt.figure(figsize=(10, 5))\n","plt.plot(train_losses, label='Training Loss')\n","plt.plot(val_losses, label='Validation Loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.title('Training and Validation Loss over Epochs')\n","plt.legend()\n","plt.show()"],"metadata":{"id":"fnJ-oADz7caD","executionInfo":{"status":"ok","timestamp":1717077972598,"user_tz":-120,"elapsed":1307,"user":{"displayName":"ÀLEX MONTOYA PÉREZ","userId":"15889589265134071867"}},"colab":{"base_uri":"https://localhost:8080/","height":487},"outputId":"6a1d4cff-f1c0-4dc7-f4da-563cc207bc27"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Plot Validation Accuracy"],"metadata":{"id":"hb83CN9V9G9l"}},{"cell_type":"code","source":["plt.figure(figsize=(10, 5))\n","plt.plot(val_accuracies, label='Validation Accuracy')\n","plt.plot(val_accuracies, label='Validation Accuracy')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy (%)')\n","plt.title('Validation Accuracy over Epochs')\n","plt.legend()\n","plt.show()"],"metadata":{"id":"dXrZCkIw7gjk","executionInfo":{"status":"ok","timestamp":1717077972601,"user_tz":-120,"elapsed":4,"user":{"displayName":"ÀLEX MONTOYA PÉREZ","userId":"15889589265134071867"}},"colab":{"base_uri":"https://localhost:8080/","height":487},"outputId":"4cbb0153-1233-48a8-b001-cbe98bc91031"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Evaluation"],"metadata":{"id":"4KNDFp2eMAEs"}},{"cell_type":"code","source":["model.eval()\n","all_preds = []\n","all_labels = []\n","\n","with torch.no_grad():\n","    for inputs, labels in test_loader:\n","        outputs = model(inputs)\n","        _, predicted = torch.max(outputs.data, 1)\n","        all_preds.extend(predicted.cpu().numpy())\n","        all_labels.extend(labels.cpu().numpy())\n","\n","# Calculate test accuracy\n","test_accuracy = accuracy_score(all_labels, all_preds)\n","print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n","\n","# Generate confusion matrix\n","conf_matrix = confusion_matrix(all_labels, all_preds, labels=[categories.index(cat) for cat in categories])\n","\n","# Plot confusion matrix\n","plt.figure(figsize=(10, 8))\n","sns.heatmap(conf_matrix, annot=True, fmt='d', xticklabels=categories, yticklabels=categories)\n","plt.xlabel('Predicted')\n","plt.ylabel('Actual')\n","plt.title('Confusion Matrix')\n","plt.show()\n"],"metadata":{"id":"8adJvBmZL_ON","executionInfo":{"status":"ok","timestamp":1717077988619,"user_tz":-120,"elapsed":16022,"user":{"displayName":"ÀLEX MONTOYA PÉREZ","userId":"15889589265134071867"}},"colab":{"base_uri":"https://localhost:8080/","height":735},"outputId":"ae5e7f51-b2cb-43a3-d5ac-fff37a53bc5c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Test Accuracy: 88.37%\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Save and Load the Model"],"metadata":{"id":"bXWJoFy70QAi"}},{"cell_type":"code","source":["# Save the trained model\n","torch.save(model.state_dict(), 'model_bn_dropout.pth')\n","\n","# To load the model\n","model.load_state_dict(torch.load('model_bn_dropout.pth'))\n","model.eval()\n"],"metadata":{"id":"Ck5YF17gvIg2","executionInfo":{"status":"ok","timestamp":1717077994741,"user_tz":-120,"elapsed":6123,"user":{"displayName":"ÀLEX MONTOYA PÉREZ","userId":"15889589265134071867"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"72b86a8c-c87f-403d-9535-efdac8b0d119"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DrumCNN_BN_Dropout(\n","  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (bn3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (fc1): Linear(in_features=32768, out_features=512, bias=True)\n","  (dropout): Dropout(p=0.5, inplace=False)\n","  (fc2): Linear(in_features=512, out_features=6, bias=True)\n",")"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","source":["# Grid Search"],"metadata":{"id":"PAxVZuS9Jn_H"}},{"cell_type":"markdown","source":["# Sound Sorting"],"metadata":{"id":"K5eGiBQDJe28"}},{"cell_type":"code","source":["#put your imput sounds into the input dir\n","#then folders will automatically be created and your sounds will be moved to output_soudns in the predicted catagories\n","\n","input_dir = '/content/drive/Shareddrives/DeepLearning/FinalProject/Data/input_sounds'\n","output_dir = '/content/drive/Shareddrives/DeepLearning/FinalProject/Data/output_sounds'\n","categories = ['kick', 'hat', 'snare', 'cymbal', 'tom', 'clap']\n","fixed_width = 128  # Fixed width for the mel spectrograms\n","\n","def generate_mel_spectrogram(y, sr, n_mels=128):\n","    S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels)\n","    S_DB = librosa.power_to_db(S, ref=np.max)\n","    return S_DB\n","\n","def pad_spectrogram(spectrogram, max_len=fixed_width):\n","    if spectrogram.shape[1] < max_len:\n","        pad_width = max_len - spectrogram.shape[1]\n","        padded_spectrogram = np.pad(spectrogram, ((0, 0), (0, pad_width)), mode='constant')\n","    else:\n","        padded_spectrogram = spectrogram[:, :max_len]\n","    return padded_spectrogram\n","\n","class DrumCNN_BN_Dropout(nn.Module):\n","    def __init__(self):\n","        super(DrumCNN_BN_Dropout, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)\n","        self.bn1 = nn.BatchNorm2d(32)\n","        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n","        self.bn2 = nn.BatchNorm2d(64)\n","        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)\n","        self.bn3 = nn.BatchNorm2d(128)\n","        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n","        self.fc1 = nn.Linear(128 * 16 * 16, 512)\n","        self.dropout = nn.Dropout(0.5)\n","        self.fc2 = nn.Linear(512, len(categories))\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.bn1(self.conv1(x))))\n","        x = self.pool(F.relu(self.bn2(self.conv2(x))))\n","        x = self.pool(F.relu(self.bn3(self.conv3(x))))\n","        x = x.view(-1, 128 * 16 * 16)\n","        x = self.dropout(F.relu(self.fc1(x)))\n","        x = self.fc2(x)\n","        return x\n","\n","# Load the model\n","model = DrumCNN_BN_Dropout()\n","model.load_state_dict(torch.load(base_dir+'/drum_cnn.pth'))\n","model.eval()\n","\n","# Function to transform mel spectrogram to tensor\n","def to_tensor(spectrogram):\n","    spectrogram = spectrogram.astype(np.float32)\n","    return torch.tensor(spectrogram).unsqueeze(0).unsqueeze(0)  # Add channel and batch dimensions\n","\n","# Process each audio file in input_sounds directory\n","for file_name in tqdm(os.listdir(input_dir), desc=\"Classifying audio files\"):\n","    file_path = os.path.join(input_dir, file_name)\n","    if os.path.isfile(file_path):\n","        try:\n","            # Load and preprocess the audio file\n","            y, sr = librosa.load(file_path, sr=None)\n","            mel_spectrogram = generate_mel_spectrogram(y, sr)\n","            mel_spectrogram = pad_spectrogram(mel_spectrogram, max_len=fixed_width)\n","            mel_spectrogram_tensor = to_tensor(mel_spectrogram)\n","\n","            # Make prediction\n","            with torch.no_grad():\n","                outputs = model(mel_spectrogram_tensor)\n","                _, predicted = torch.max(outputs.data, 1)\n","\n","            # Convert prediction to label\n","            predicted_label = categories[predicted.item()]\n","\n","            # Create the target directory if it doesn't exist\n","            target_dir = os.path.join(output_dir, predicted_label)\n","            os.makedirs(target_dir, exist_ok=True)\n","\n","            # Move the file to the target directory\n","            shutil.move(file_path, os.path.join(target_dir, file_name))\n","\n","            print(f\"Classified {file_name} as {predicted_label}\")\n","        except Exception as e:\n","            print(f\"Could not process {file_name}: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TSpUhE6XJjbY","executionInfo":{"status":"ok","timestamp":1717150916208,"user_tz":-120,"elapsed":2311,"user":{"displayName":"MILES CHURCHLAND","userId":"06343733599797419764"}},"outputId":"d1bdd454-7736-4203-87d1-818db458f799"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Classifying audio files:  33%|███▎      | 2/6 [00:00<00:00,  7.88it/s]"]},{"output_type":"stream","name":"stdout","text":["Classified HiTom 707.wav as tom\n","Classified HiTom 707X.wav as tom\n"]},{"output_type":"stream","name":"stderr","text":["Classifying audio files:  67%|██████▋   | 4/6 [00:00<00:00,  6.90it/s]"]},{"output_type":"stream","name":"stdout","text":["Classified LowTom 707.wav as tom\n","Classified MidTom 707X.wav as tom\n"]},{"output_type":"stream","name":"stderr","text":["Classifying audio files: 100%|██████████| 6/6 [00:00<00:00,  6.95it/s]"]},{"output_type":"stream","name":"stdout","text":["Classified MidTom 707.wav as tom\n","Classified LowTom 707X.wav as tom\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]}]}