datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
emova-alignment-7m
EMOVA-Alignment-7M
🤗 EMOVA-Models | 🤗 EMOVA-Datasets | 🤗 EMOVA-Demo
📄 Paper | 🌐 Project-Page | 💻 Github | 💻 EMOVA-Speech-Tokenizer-Github
Overview
EMOVA-Alignment-7M is a comprehensive dataset curated for omni-modal pre-training, including vision-language and speech-language alignment.
This dataset is created using open-sourced image-text pre-training datasets, OCR datasets, and 2,000 hours of ASR and TTS data.
This dataset is part of the EMOVA-Datasets… See the full description on the dataset page: https://huggingface.co/datasets/Emova-ollm/emova-alignment-7m.librispeech-alignments
Dataset Card for Librispeech Alignments
Librispeech with alignments generated by the Montreal Forced Aligner. The original alignments in TextGrid format can be found here
Dataset Details
Dataset Description
Librispeech is a corpus of read English speech, designed for training and evaluating automatic speech recognition (ASR) systems. The dataset contains 1000 hours of 16kHz read English speech derived from audiobooks.
The Montreal Forced Aligner (MFA) was used… See the full description on the dataset page: https://huggingface.co/datasets/gilkeyio/librispeech-alignments.multilingual_audio_alignments
Multilingual MFA-Aligned Speech Dataset (UNDER DEVELOPMENT)
A large-scale multilingual speech dataset with word-level and phoneme-level alignments produced using the Montreal Forced Aligner (MFA).
Dataset Description
This dataset consolidates multiple speech corpora across various languages, all processed through MFA to provide precise phoneme and word alignments. Each sample includes the original audio, transcript, and detailed timing information for both words and… See the full description on the dataset page: https://huggingface.co/datasets/takuM23/multilingual_audio_alignments.multilingual_audio_alignments
Multilingual MFA-Aligned Speech Dataset
A large-scale multilingual speech dataset with word-level and phoneme-level alignments produced using the Montreal Forced Aligner (MFA).
Dataset Description
This dataset consolidates multiple speech corpora across various languages, all processed through MFA to provide precise phoneme and word alignments. Each sample includes the original audio, transcript, and detailed timing information for both words and phonemes.… See the full description on the dataset page: https://huggingface.co/datasets/AAdonis/multilingual_audio_alignments.quran-alignment-benchmark
Quran Recitation Alignment Benchmark
Audio recordings of Quran recitation with a reviewed word-level ground truth: every recited word, in the order it was recited, with its start and end time, plus the reviewed segmentation and non-Quran regions. This is the corpus behind the Quran Recitation Alignment Benchmark; the task, scoring rules, leaderboard and submission format are documented there, not here.
16 recordings · 357 minutes · 18,421 recited words · Hafs ʿan ʿĀṣim ·… See the full description on the dataset page: https://huggingface.co/datasets/QUD-Technologies/quran-alignment-benchmark.librispeech-alignments_clean100
librispeech-alignments_clean100
This is a subset of librispeech-alignments (https://huggingface.co/datasets/gilkeyio/librispeech-alignments) which only includes train_clean_100 and test_clean splits for small experiments and tutorials.
Cite:
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={ICASSP},
year={2015}… See the full description on the dataset page: https://huggingface.co/datasets/ErfanAShams/librispeech-alignments_clean100.eval-forced-alignment
Hebrew Forced Alignment Evaluation Dataset
Human-verified, word-level time-aligned Hebrew speech clips.
To create this dataset, a dedicated labeling system (similar to Praat, but web-based) was
built. The system lets labelers fix the transcript and align each spoken word to the audio,
down to 1ms precision (though annotators typically work at ~10ms granularity).
The audio samples were gathered by randomly sampling from several of ivrit-ai's larger,
published open datasets. The… See the full description on the dataset page: https://huggingface.co/datasets/ivrit-ai/eval-forced-alignment.