metadata
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: finetune
data_files:
- split: train
path: finetune/train-*
- split: validation
path: finetune/validation-*
- split: test
path: finetune/test-*
dataset_info:
- config_name: default
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: duration
dtype: float32
- name: student_id
dtype: string
- name: date
dtype: string
- name: time
dtype: string
- name: module
dtype: string
- name: investigation
dtype: string
- name: part
dtype: string
splits:
- name: train
num_bytes: 43732745924.625
num_examples: 181323
- name: validation
num_bytes: 5529107838.5
num_examples: 23652
- name: test
num_bytes: 5385316354
num_examples: 22592
download_size: 50216196525
dataset_size: 54647170117.125
- config_name: finetune
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: duration
dtype: float32
splits:
- name: train
num_bytes: 12603945060.375
num_examples: 51029
- name: validation
num_bytes: 2082115799.625
num_examples: 8459
- name: test
num_bytes: 2224927204
num_examples: 9200
download_size: 16055315812
dataset_size: 16910988064
NOTE: This is not a public dataset, I couldn't share access.
There are two config for this dataset:
By default, is loading the raw dataset with basically everything of the dataset
There is another configuration is for finetune use, which is a preprocessed myst subset with
- cleaned transcription (in the format w2v2 output)
- duration, only 2~20 sec audio entries will be seen in this subset
- *local file name: only for reference use locally.
from datasets import load_dataset
myst = load_dataset("MagicLuke/MyST", split="test")
myst = load_dataset("MagicLuke/MyST", "finetune", split="test")