{
  "id": 235816,
  "title": "5 seconds full preprocessed dataset in .npy",
  "url": "/competitions/birdclef-2021/discussion/235816",
  "author_name": "",
  "post_date": "2021-05-01T11:14:56.427262800Z",
  "votes": 5,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Hi everybody,</p>\n<p>I cut every audio from train_short_audio to 5 sec piecies and preprocced them to (128,281) shape<br>\n<a href=\"https://www.kaggle.com/mishanyap/bird-clef-dataset-full\" target=\"_blank\">https://www.kaggle.com/mishanyap/bird-clef-dataset-full</a></p>\n<p>as well I transformed train_meta_data.csv to such format (simular to test dataset format). so it's more convinient to make generators from the data<br>\n<a href=\"https://www.kaggle.com/mishanyap/bird-clef-train-metadata\" target=\"_blank\">https://www.kaggle.com/mishanyap/bird-clef-train-metadata</a></p>\n<p>hope it could be useful for somebody</p>",
  "messages": [
    {
      "id": "1289790",
      "postDate": "05/01/2021 11:14:56",
      "content": "<p>Hi everybody,</p>\n<p>I cut every audio from train_short_audio to 5 sec piecies and preprocced them to (128,281) shape<br>\n<a href=\"https://www.kaggle.com/mishanyap/bird-clef-dataset-full\" target=\"_blank\">https://www.kaggle.com/mishanyap/bird-clef-dataset-full</a></p>\n<p>as well I transformed train_meta_data.csv to such format (simular to test dataset format). so it's more convinient to make generators from the data<br>\n<a href=\"https://www.kaggle.com/mishanyap/bird-clef-train-metadata\" target=\"_blank\">https://www.kaggle.com/mishanyap/bird-clef-train-metadata</a></p>\n<p>hope it could be useful for somebody</p>",
      "rawMarkdown": "Hi everybody,\n\nI cut every audio from train_short_audio to 5 sec piecies and preprocced them to (128,281) shape\nhttps://www.kaggle.com/mishanyap/bird-clef-dataset-full\n\nas well I transformed train_meta_data.csv to such format (simular to test dataset format). so it's more convinient to make generators from the data\nhttps://www.kaggle.com/mishanyap/bird-clef-train-metadata\n\nhope it could be useful for somebody",
      "votes": null
    },
    {
      "id": "1291682",
      "postDate": "05/03/2021 08:37:17",
      "content": "<p>Thanks a lot for sharing this <a href=\"https://www.kaggle.com/mishanyap\" target=\"_blank\">@mishanyap</a>… <br>\nYou have saved a lot of my time =)</p>",
      "rawMarkdown": "Thanks a lot for sharing this @mishanyap... \nYou have saved a lot of my time =)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1291682,
      "author_name": "nitindatta",
      "author_url": "",
      "post_date": "05/03/2021 08:37:17",
      "content": "<p>Thanks a lot for sharing this <a href=\"https://www.kaggle.com/mishanyap\" target=\"_blank\">@mishanyap</a>… <br>\nYou have saved a lot of my time =)</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1289790": "Hi everybody,\n\nI cut every audio from train_short_audio to 5 sec piecies and preprocced them to (128,281) shape\nhttps://www.kaggle.com/mishanyap/bird-clef-dataset-full\n\nas well I transformed train_meta_data.csv to such format (simular to test dataset format). so it's more convinient to make generators from the data\nhttps://www.kaggle.com/mishanyap/bird-clef-train-metadata\n\nhope it could be useful for somebody",
    "1291682": "Thanks a lot for sharing this @mishanyap... \nYou have saved a lot of my time =)"
  },
  "source": "meta"
}