datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
thai-aligner-bench
Thai Aligner Bench
🚧 Development in progress.
How accurately can a forced aligner place Thai token and word boundaries in
speech? This is a self-contained benchmark: one Python file
(aligner_bench.py) plus 1,572 clips of Thai speech with frame-exact timing
ground truth. No Thai NLP stack or other code is needed — just
numpy soundfile torch torchaudio transformers.
The ground truth is what makes the dataset useful: the audio was rendered by a
TTS model whose duration predictor… See the full description on the dataset page: https://huggingface.co/datasets/wayu-ai/thai-aligner-bench.voice-code-bench
VoiceCodeBench
VoiceCodeBench is a test-only benchmark for evaluating whether automatic
speech recognition (ASR) systems preserve exact structured values in English
workplace speech.
Paper: VoiceCodeBench: Evaluating Exact Structured-Token Recovery in Automatic Speech Recognition
The benchmark targets cases where a transcript is software input: callback
numbers, email addresses, command-line flags, file paths, URLs, account
identifiers, dates, measurements, and similar values… See the full description on the dataset page: https://huggingface.co/datasets/besimple-ai/voice-code-bench.enhanced-audiosnippets-long-2-8M
Enhanced Audiosnippets Long 2.8M
Enhanced version of mitermix/audiosnippets_long_2_8M with speech enhancement, emotion annotations, speaker embeddings, and comprehensive metadata analysis.
Dataset Summary
Metric
Value
Total samples
2,633,037
Total audio hours
4,932 h
Duration range
3.0s - 1124.3s
Mean duration
6.7s
Audio format
WAV, 48kHz mono
Tar files
1,410
Processing Pipeline
Each audio sample was processed through:
Speech… See the full description on the dataset page: https://huggingface.co/datasets/ai-music4you3/enhanced-audiosnippets-long-2-8M.bangla-10k
Bangla-10K: A Challenging, Metadata-Rich Corpus of Read and Conversational Bengali Speech from India and Bangladesh
Bangla-10K is a 10,816-hour Bengali speech corpus with
624,951 recordings from India and Bangladesh: a 10,070.8-hour core corpus
(567,323 recordings) and a separately collected 745.1-hour evaluation set
(57,628 recordings). It combines scripted single-speaker read speech with
natural multi-speaker conversations for Bengali automatic speech recognition
(ASR).
The… See the full description on the dataset page: https://huggingface.co/datasets/psdn-ai/bangla-10k.maleo-short-1.5H
Dataset Card for Maleo Short 1.5H
Dataset Description
Dataset Summary
Maleo Short 1.5H is a manually curated, rigorously annotated speaker diarization dataset designed to benchmark State-of-the-Art (SOTA) models against complex, "in-the-wild" media domains. While modern diarization pipelines excel in controlled acoustic environments (like telephony or reading corpora), they heavily struggle with the overlapping speech, sound effects, and rapid speaker shifts… See the full description on the dataset page: https://huggingface.co/datasets/maleo-ai/maleo-short-1.5H.fleurs-r-neucodec-all-languages
FLEURS-R NeuCodec All Languages
FLEURS-R metadata, source audio and precomputed NeuCodec speech tokens for 102
locales, plus a speaker label FLEURS itself does not ship.
Layout
data/{locale}-{split}.parquet — metadata, one row per utterance (this is what the
viewer shows).
audio/{locale}-{split}.zip — source FLEURS-R audio, 24kHz mono PCM16 WAV, members
named audio/{locale}/{split}/{id}.wav (the path column).
neucodec/{locale}-{split}-rank{N}.zip — NeuCodec… See the full description on the dataset page: https://huggingface.co/datasets/malaysia-ai/fleurs-r-neucodec-all-languages.knesset-plenums
About
This dataset is derived from raw a/v recordings and human-generated protocols of the Knesset (the Israeli house of representatives) plenums as part of the ivrit.ai project.
Consider visiting the preview space for this dataset here
Method
Data dumps from the Knesset contain A/V recordings, alongside proprietary protocols with timestamps.
We extract the audio stream, and clean up timestamp mistakes (such as backward jumps, or out-of-order timestamp artifacts).
The… See the full description on the dataset page: https://huggingface.co/datasets/ivrit-ai/knesset-plenums.crowd-recital-yi
About
This dataset was created by crowd-sourced recording sessions in Yiddish as part of the ivrit.ai Crowd Recital project.
Volunteers read on normal desktop or mobile setting Wikipedia articles while time-stamping every sentence read.
Later this data is normalized by aligning the gathered captions with the audio using Stable Whisper (See Below).
The recording project is an ongoing effort and new data will be appended to this dataset periodically as it is being generated.… See the full description on the dataset page: https://huggingface.co/datasets/ivrit-ai/crowd-recital-yi.UniDic-tdmelodic
tdmelodic Pre-computed Accents Dataset
This repository provides pre-computed, inference-ready CSV files generated by tdmelodic (Tokyo Dialect MELOdic accent DICtionary generator) mapping over the NEologd vocabulary.
Generating these files locally requires running neural network inference (tdmelodic-convert), which typically takes several hours to complete depending on the hardware. We have pre-generated these dictionary files and made them available here to eliminate the setup… See the full description on the dataset page: https://huggingface.co/datasets/aipracticecafe/UniDic-tdmelodic.crowd-whatsapp-yi
About
This dataset was created by crowd-sourced Whatsapp voice recordings in Yiddish as part of the ivrit.ai project.
Volunteers read a message sent to them from a predefined set of messages, recording themselves using Whasapp voice message sent to the collecting bot.
Later this data is normalized by aligning the captions with the audio using Stable Whisper (See Below).
The recording project is an ongoing effort and new data will be appended to this dataset periodically as it is… See the full description on the dataset page: https://huggingface.co/datasets/ivrit-ai/crowd-whatsapp-yi.yogera_runyankore_ailab_4_0_1luel-multilingual-tts-samples
Multilingual TTS Samples (Luel)
License: All Rights Reserved. Proprietary. Access only for authorized parties; no redistribution or use without permission. See LICENSE.
A multilingual text-to-speech / read-speech dataset of short scripted utterances across 7 languages. Each sample is a single-speaker recording of a written prompt, paired with rich speaker and recording metadata. Useful for TTS training and evaluation, ASR adaptation, dialect/accent studies, and read-speech… See the full description on the dataset page: https://huggingface.co/datasets/Luel-ai/luel-multilingual-tts-samples.yogera_runyankore_ailabzwesui-grupa-5-it-ai
Wykorzystanie ASR do transkrypcji polskich nagrań o tematyce AI
Korpus do ewaluacji systemów ASR języka polskiego stworzony w ramach warsztatów
Ewaluacja Systemów Rozpoznawania Mowy (UAM WMI, edycja 2026, zespół 5).
Zbiór powstał jako część kursu - publikujemy go publicznie, żeby inni badacze
polskiego ASR mogli z niego korzystać i porównywać wyniki na wspólnym benchmarku.
Cel i pytania badawcze
Cel główny:
Porównanie jakości 3 systemów ASR dla spontanicznej… See the full description on the dataset page: https://huggingface.co/datasets/slapekm/zwesui-grupa-5-it-ai.