Mozilla Common Voice
Common Voice is Mozilla's effort to build an open, public-domain speech corpus that anyone can use to train voice technology. Speakers record themselves reading short sentences, and a dual-review process (two independent approvals) validates each clip before it enters a release. Because everything is released under CC0, you can use the audio and transcripts for any purpose, including commercial products, with no attribution required.
Each release is versioned and covers a growing set of languages, with hours per language ranging from a handful to thousands. The recordings are paired with transcripts, so the data feeds directly into automatic speech recognition (ASR, turning spoken audio into text) and text-to-speech training pipelines.
Note that from October 2025, Common Voice datasets are only available through the Mozilla Data Collective, not HuggingFace. If you previously pulled releases from HuggingFace, you will need to switch to the Data Collective to get the latest data.
Related sources
LibriSpeech
A collection of 1,000 hours of read English speech, released under CC BY 4.0 and stored using the open-source FLAC audio encoder. Labels are aligned at the sentence level. Derived from LibriVox public domain audiobooks, it is the standard benchmark for English automatic speech recognition.
LJ Speech
An open dataset of 13,100 short audio clips of a single speaker reading passages from seven non-fiction books. Every clip is transcribed and clips run from 1 to 10 seconds. Released into the public domain, it is the standard single-speaker text-to-speech benchmark.
OpenSLR
Open Speech and Language Resources, a hosting site for speech and language datasets, software, and models. It is the home of LibriSpeech and dozens of other language-specific speech corpora, making it a central catalogue for finding openly available voice data.
The People's Speech
A large, freely licensed English speech recognition dataset from MLCommons, assembled from openly licensed sources and totalling more than 30,000 hours. Built as a permissively licensed alternative for training commercial automatic speech recognition systems.