datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
open-asr-leaderboard-multilingual-datasets
ASR Leaderboard Datasets
This repository contains test splits from multiple speech corpora, including FLEURS, Common Voice (MCV), and Multilingual LibriSpeech (MLS).
How to Load
To load a specific subset, use load_dataset with the corresponding config_name in the format <set>_<lang>.
from datasets import load_dataset
# Load the FLEURS dataset for Bulgarian
fleurs_bg = load_dataset("nithinraok/asr-leaderboard-datasets", "fleurs_bg")
print(fleurs_bg)
# Load the… See the full description on the dataset page: https://huggingface.co/datasets/hf-audio/open-asr-leaderboard-multilingual-datasets.Zambia-MultiLingual-ASR-Dataset
🇿🇲 Zambia Multilingual ASR Dataset
A continuously growing and curated multilingual speech corpus for Zambian languages, designed to advance Automatic Speech Recognition (ASR) research through community-driven data collection and real-world evaluation.
Overview
The Zambia Multilingual ASR Dataset is an open, continuously evolving speech corpus developed as part of the ZamVoice project.
The dataset supports research and development of Automatic Speech… See the full description on the dataset page: https://huggingface.co/datasets/buumba641/Zambia-MultiLingual-ASR-Dataset.open-asr-leaderboard-multilingual-datasets
Open ASR Leaderboard Armenian Test Datasets
This private repository holds leaderboard-compatible Armenian test
configurations while their integration is being validated.
Configurations
fleurs_hy
Source: google/fleurs,
configuration hy_am, test split
Reviewed reference changes:
Metric-AI/fleurs-corrections,
test split
932 recordings; all 314 reviewed corrections were matched to the original
source transcript and applied
mcv_hy… See the full description on the dataset page: https://huggingface.co/datasets/Metric-AI/open-asr-leaderboard-multilingual-datasets.multilingual-asr
multilingual-asr
CoVoST 2 and Common Voice 17.0 Swahili and Hausa, re-packaged under one feature schema so the
configs can be concatenated into a single multi-task training mix. Four configs, ~206 hours of
distinct audio, 8.44 GB of Parquet. No Bambara.
Load
from datasets import load_dataset
asr = load_dataset("djelia/multilingual-asr", "covost2-transcription", split="train")
sw_test = load_dataset("djelia/multilingual-asr", "swahili", split="test")… See the full description on the dataset page: https://huggingface.co/datasets/djelia/multilingual-asr.