Datasets:
File size: 1,625 Bytes
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---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: cleaned_text
dtype: string
- name: speaker_age
dtype: string
- name: speaker_gender
dtype: string
- name: speaker_dialect
dtype: string
- name: input_features
sequence:
sequence: float32
- name: input_length
dtype: float64
- name: labels
sequence: int64
- name: cleaned_labels
sequence: int64
splits:
- name: validation
num_bytes: 5862458096.364273
num_examples: 5024
download_size: 2002683497
dataset_size: 5862458096.364273
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
license: apache-2.0
task_categories:
- automatic-speech-recognition
language:
- ar
tags:
- WhisperTiny
- WhisperSmall
- WhisperBase
- WhisperMedium
- OpenAI
- ASR
- Arabic
- Preprocessed
size_categories:
- 1K<n<10K
---
# Details
This is the SADA 2022 dataset with the input_features whish are log mels and the cleaned_labels which is the tokenized version of the cleaned_text. You can directly use this as the validation dataset when training Whisper Tiny, Small, Base & Medium models, as they all use the same tokenizer. Please double check this as well from the original model repo.
In addtition, the following filters were applied to this data:
- All audios are less than 30 seconds and greater than 0 seconds.
- All cleaned_text have token lengths less than 448 and greater than 0.
- All rows with 'nan' in cleaned_text or cleaned_text only having whitespace or being empty were dropped. |