---
dataset_info:
  features:
  - name: input_features
    sequence:
      sequence: float32
  - name: labels
    sequence: int64
  splits:
  - name: test
    num_bytes: 10227942104
    num_examples: 6656
  download_size: 2056491222
  dataset_size: 10227942104
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---
The input_features are nothing but the values generated after passing the dataset's audio array through a whisper processor's feature extraction and the field 'labels' consists of the tokenized(using whisper tokenizer) ground truths.
The following is the link for what I did with the sarvah dataset and how I trained it on whisper-large-v3-turbo.
The training steps for whisper-large-v3 are same.
https://colab.research.google.com/drive/1oD0v7MWZ9WJqk7tZYThwgTUM85PTEhMN?usp=sharing