The world's largest open-source, multi-language voice dataset for democratizing AI speech recognition.
Mozilla Common Voice is a cornerstone of the 2026 decentralized AI ecosystem, serving as a massive, multi-language corpus of transcribed speech. Built on a technical architecture of crowdsourced contribution and peer-to-peer validation, the platform addresses the 'data poverty' that often hampers smaller organizations and researchers in the Speech-to-Text (STT) and Automatic Speech Recognition (ASR) sectors. Unlike proprietary silos held by Big Tech, Common Voice releases its data under a CC-0 (Public Domain) license, allowing for unrestricted commercial and academic use. By 2026, the project has expanded significantly into spontaneous speech collection and multi-dialectal metadata tagging, enabling the development of more nuanced and inclusive Large Language Models (LLMs) and Small Language Models (SLMs). The technical workflow involves rigorous sentence collection, voice recording via web/mobile interfaces, and a three-stage validation pipeline to ensure high-fidelity signal-to-noise ratios. Its market position is critical for fine-tuning models like OpenAI's Whisper or Meta's MMS, specifically for under-represented languages where commercial datasets are non-existent.
Each voice sample is optionally tagged with age, gender, and accent data using a standardized schema.
The industry-standard forensic benchmark for evaluating temporal and spatial synthetic media artifacts.
The industry-standard high-fidelity benchmark for training next-generation synthetic media detection models.
The industry-standard benchmark for training and validating state-of-the-art deepfake detection models.
A massive-scale open-source corpus for multilingual Automatic Speech Recognition (ASR) research.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Every clip requires at least two independent positive votes from other users to be moved into the 'Validated' bucket.
Users can download only the new data added since the last release version rather than the entire multi-terabyte corpus.
A 2025-2026 expansion allowing users to submit unscripted audio with post-hoc transcription.
Analytical tools provided to measure the coverage of phonemes and rare words within a specific language set.
A collaborative platform for sourcing public domain text to ensure recordings do not violate copyright.
Modular architecture allowing community leaders to launch localized versions for low-resource languages.
General-purpose models often fail with thick regional accents (e.g., Scottish, deep Southern US).
Registry Updated:2/7/2026
Validate against the Common Voice test split.
IoT devices often support only major languages, excluding millions of potential users.
Existing STT models often show higher error rates for female voices or specific age groups.