Aisha |

100% sure of this because i am a contributor and i got $$$ in my balance. You can also be a contributor you are not late

@Abba_kakaa soon.......😊

DX to USDC will be on-chain soon.

@_dialectra This is still early. Which usually means more opportunities for early users Follow so you don’t miss it 👀 @_dialectra

@_dialectra Why this is interesting Most platforms: → You create content for free Dialectra: → You get rewarded for contributing And the best part? you don’t need skills. If you can speak your dialect, you’re qualified.

Everyone is talking about Dialectra… But most people still don’t understand what it actually is. Here’s a simple breakdown 🧵👇

@_dialectra Exactly. Data is the foundation without it, AI is just guesswork. Building this kind of infrastructure for African languages is good

You can’t build powerful AI without quality data. Especially for African languages. Dialectra is solving this by: 🔵 Collecting native speech 🔵 Verifying dialect accuracy 🔵 Structuring datasets for AI training We’re not building apps. We’re building infrastructure.

@_dialectra Exactly. Data is the foundation without it, AI is just guesswork.

You can’t build powerful AI without quality data. Especially for African languages. Dialectra is solving this by: 🔵 Collecting native speech 🔵 Verifying dialect accuracy 🔵 Structuring datasets for AI training We’re not building apps. We’re building infrastructure.

@Salimboyafrica @_dialectra This is the kind of AI Africa needs,Local voices real impact.

Africa has thousands of languages and dialects yet most modern AI systems barely understand them. This is where Dialectra.io comes in @_dialectra is building the voice infrastructure for African languages collecting, validating, and training AI models using real voices from African communities. Through its testnet, people can participate by contributing their daily speech, cultural voice tones, and native dialects to help train AI systems that truly understand how Africans speak. Why does this matter? Take Hausa for example. Hausa ranks around 19th among the most spoken languages in the world, with millions of speakers. Most Hausa speakers live in Nigeria (55–67 million) and Niger (14–22 million), with strong communities across Ghana, Cameroon, Benin, Chad, and other West African countries. Yet despite its massive global presence, Hausa like many African languages is still underrepresented in modern AI systems Dialectra is changing that By building datasets, validating native voices and training dialect-aware AI models, Dialectra aims to make African languages visible, usable and powerful in the digital future This is more than technology. It is about preserving culture, empowering communities and bringing billions of African voices into the AI era. Join the movement. Let’s bring billions of voices together.

@_dialectra The future of all west african AI models voice

The future of AI speaks our languages. We are proud to unveil Dialectra, the definitive data layer for African voice. Our platform empowers all developers to build, benchmark, and scale dialect-aware AI models across West Africa. Watch this demo to see the future of voice!

@Abba_kakaa Take a look i am sure you are going to like it

There’s a growing conversation around speech data who needs it, why it matters, and what it actually takes to build it right. At Dialectra, we’re focused on one thing: building high-quality, dialect-aware speech data infrastructure for African languages. Here’s what that really means in practice: What we’re building, we collect and structure speech data across dialects using native speakers, capturing not just audio, but rich metadata like region, speaker profile, and speaking conditions. Every sample goes through validation, annotation, and benchmarking to ensure it’s usable for real-world AI systems. We’ve already built this foundation with 90,000+ voice datasets and 1,000+ contributors in under 30 days making it faster and more reliable for teams to build on top. We’re currently in beta and working towards full rollout in next few months, expanding datasets, improving benchmarking, and supporting more languages. At the end of the day, this isn’t just about data it’s about building the infrastructure layer for voice AI in Africa. Learn more Dialectra.io

@_dialectra @Msageer_ welcome to the team

Please join us in welcoming our Social Media Manager, @Msageer_ As we continue building Dialectra from scaling our dialect-aware datasets to advancing benchmarking and AI systems strong communication and community engagement are more important than ever.. Welcome to the team.

@_dialectra welcome sir

Let's welcome Dr Umar (PhD, FRSA) in Forensic linguist ( specialising in forensic speech science and LADO. Consultant at JP French International, advancing reliable speech data for linguistic evidence in UK.. Welcome to the team..

Are you a Hausa speaker across West and Central Africa? If you’re in Niger, Ghana, Cameroon, Chad, Benin, Togo, Burkina Faso, Côte d’Ivoire, or anywhere else Hausa is spoken this is for you. Dialectra is building high-quality, dialect-rich voice datasets for African languages, and we’re inviting native Hausa speakers to be part of it. You can: • contribute your voice • help review and validate recordings • support accurate dialect representation • and earn while doing it Your voice helps improve speech recognition, voice assistants, and digital services for millions of people who speak Hausa every day. This isn’t just data collection it’s building the future of African language AI. Join us and contribute today dialectra.io

@Abba_kakaa Seeing this play out while contributing. Strong spoken performance, but transcription standardization is the real bottleneck. Solid direction from Dialectra on fixing this.

Recent insights from our Dialectra benchmarking pipeline are revealing an important pattern in Hausa speech data performance. Across multiple evaluation cycles, we’re observing clear variation in transcription performance by dialect. General Hausa (Kano and widely standardized variants) continues to achieve strong WER/DRR scores and high consistency in transcription tasks. Sakkwatanci, Zamfaranci, and Katsinanci dialects show lower transcription accuracy and participation efficiency, particularly in text-based annotation workflows. Interestingly, these same dialect groups perform significantly better in corpus script recordings, indicating that the challenge is not speech production but transcription alignment and standardization. This suggests a few key dynamics: ^ stronger familiarity with spoken forms than standardized orthography ^ higher variation in lexical and phonetic representation ^potential gaps in annotation guidelines or reviewer calibration for these dialects On the other hand, Fulfulde datasets are showing relatively stable performance, with fewer discrepancies between recording and transcription quality a useful baseline for comparison. What We’re Doing Next Our current focus is on closing the dialect performance gap within Hausa before expanding further. We are actively working on: * refining dialect-specific transcription guidelines * improving validator calibration and review workflows * introducing dialect-aware annotation support * analyzing error patterns to reduce WER variance across dialect groups The goal is simple; Consistent quality across all dialects not just the standardized ones. Once we achieve alignment within Hausa, the next phase will extend these systems to Swahili and other African languages. More updates soon as we continue to build and benchmark at scale at @_dialectra

@Abba_kakaa Thank you boss,

Thank you for still staying with us.. Let me see what have you got 👀 Check yours Dialectra.io

@Abba_kakaa Yes make some time to take a look am sure you will love it

Hi, my name is Bakaka and I’m the founder of Dialectra, a platform building structured dialect intelligence infrastructure for African languages.. Have a take a look at what we are building here dialectra.io