|
|
--- |
|
|
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 |
