datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
fleurs
FLEURS
Fleurs is the speech version of the FLoRes machine translation benchmark.
We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages.
Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is
used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven… See the full description on the dataset page: https://huggingface.co/datasets/google/fleurs.svq
Simple Voice Questions
Simple Voice Questions (SVQ) is a set of short audio questions recorded in 26 locales across 17 languages under multiple audio conditions. It serves as a core evaluation componenet for Massive Sound Embedding Benchmark (MSEB).
Technical Specifications
Feature
Details
Locales
26
Languages
17
Total Speakers
~700 (Capped at 250 recordings per speaker)
Audio Conditions
Clean, Background Speech, Media, Traffic Noise
Gender… See the full description on the dataset page: https://huggingface.co/datasets/google/svq.WaxalNLP
Waxal Datasets
The WAXAL dataset is a large-scale multilingual speech corpus for African languages, introduced in the paper WAXAL: A Large-Scale Multilingual African Language Speech Corpus.
Dataset Description
The Waxal project provides datasets for both Automated Speech Recognition (ASR)
and Text-to-Speech (TTS) for African languages. The goal of this dataset's
creation and release is to facilitate research that improves the accuracy and
fluency of speech and… See the full description on the dataset page: https://huggingface.co/datasets/google/WaxalNLP.red_ace_asr_error_detection_and_correction
RED-ACE
Dataset Summary
This dataset can be used to train and evaluate ASR Error Detection or Correction models. It was introduced in the RED-ACE paper (Gekhman et al, 2022).
The dataset contains ASR outputs on the LibriSpeech corpus (Panayotov et al., 2015) with annotated transcription errors.
Dataset Details
The LibriSpeech corpus was decoded using Google Cloud Speech-to-Text API, with the default and video models.
The word-level confidence was enabled… See the full description on the dataset page: https://huggingface.co/datasets/google/red_ace_asr_error_detection_and_correction.khmer-speech-large-english-google-translations
Dataset Card for khmer-speech-large-english-google-translation
Audio recordings of khmer speech with varying speakers and background noises.
English transcriptions were transcribed from the Khmer labels using Google Translate.
Based off of seanghay/khmer-speech-large.
Dataset Details
Dataset Sources
Huggingface: seanghay/khmer-speech-large
Usage
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/djsamseng/khmer-speech-large-english-google-translations.myanmar-speech-dataset-google-fleursPlease visit to the GitHub repository for other Myanmar Langauge datasets.
Myanmar Speech Dataset (Google Fleurs)
This dataset consists exclusively of Myanmar speech recordings, extracted from the larger multilingual Google Fleurs dataset.
For the complete multilingual dataset and additional information, please visit the original dataset repository
of Google Fleurs HuggingFace page.
Original Source
Fleurs is the speech version of the FLoRes machine translation benchmark.… See the full description on the dataset page: https://huggingface.co/datasets/chuuhtetnaing/myanmar-speech-dataset-google-fleurs.google_myanmar_asr_voices
Google Myanmar ASR Dataset (WebDataset Version)
This repository provides a clean, user-friendly, and robust version of the Google Myanmar ASR Dataset, which is derived from the OpenSLR-80 Burmese Speech Corpus.
This version has been carefully re-processed into the WebDataset format. Each sample consists of a .wav audio file and a clean .json metadata file, packaged into sharded .tar archives. This format is highly efficient for large-scale training of ASR models.… See the full description on the dataset page: https://huggingface.co/datasets/freococo/google_myanmar_asr_voices.