metadata
dataset_info:
features:
- name: speaker_id
dtype: int64
- name: sentence
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: label
dtype: string
- name: gender
dtype: string
- name: tts
dtype: string
splits:
- name: train
num_bytes: 5004898198.44
num_examples: 11130
- name: validation
num_bytes: 1402111399.43
num_examples: 3181
- name: test
num_bytes: 695119245.014
num_examples: 1591
- name: fishaudio
num_bytes: 360425141
num_examples: 1000
- name: xtts
num_bytes: 217734249
num_examples: 1000
- name: mms
num_bytes: 300196557
num_examples: 1000
- name: speecht5
num_bytes: 184191717
num_examples: 1000
download_size: 7132788149
dataset_size: 8164676506.884
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: fishaudio
path: data/fishaudio-*
- split: xtts
path: data/xtts-*
- split: mms
path: data/mms-*
- split: speecht5
path: data/speecht5-*
from datasets import load_dataset
dataset = load_dataset("elsayedissa/AFAD-MSA")
DatasetDict({
train: Dataset({
features: ['speaker_id', 'sentence', 'audio', 'label', 'gender', 'tts'],
num_rows: 11130
})
validation: Dataset({
features: ['speaker_id', 'sentence', 'audio', 'label', 'gender', 'tts'],
num_rows: 3181
})
test: Dataset({
features: ['speaker_id', 'sentence', 'audio', 'label', 'gender', 'tts'],
num_rows: 1591
})
fishaudio: Dataset({
features: ['speaker_id', 'sentence', 'audio', 'label', 'gender', 'tts'],
num_rows: 1000
})
xtts: Dataset({
features: ['speaker_id', 'sentence', 'audio', 'label', 'gender', 'tts'],
num_rows: 1000
})
mms: Dataset({
features: ['speaker_id', 'sentence', 'audio', 'label', 'gender', 'tts'],
num_rows: 1000
})
speecht5: Dataset({
features: ['speaker_id', 'sentence', 'audio', 'label', 'gender', 'tts'],
num_rows: 1000
})
})