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  "fetched_at": "2026-10-19T06:29:03Z",
  "source_url": "https://www.kaggle.com/competitions/automatic-speech-recognition-s24/data",
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    {
      "name": "data-description",
      "content": "The dataset is given in a similar format to previous homework in this course.\n\n## Subdirectories\n\n*   **train-clean-50** - the training set\n*   **train-clean-100** - the training set\n*   **dev-clean** - the training set\n*   **test-clean** - the training set\n\n## Files\n\n*   `encoder_weights.pth` - reference CNN-LSTM encoder weights",
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      "name": "Evaluation",
      "content": "You will be evaluated based on the Levenshtein distance between your output sequences and the ground truth sequences for the test dataset of LibriSpeech.\n\n---\n\n## Submission File\nFor each ID in the test set, you must predict a character sequence.\n\nThe file should contain a header and have the following format:\n\n    index,label\n    0,string1\n    1,string2\n    2,string3\n    etc.",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "abstract",
      "content": "**Attention-based Speech Recognition**\n\nIn this competition you will work on a sequence-to-sequence conversion problem, in which you must\ntrain attention-based (transformer) models to transduce speech recordings into word sequences, spelled out alphabetically. This competition is a part of Carnegie Mellon's Introduction to Deep Learning (11-785) course.",
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