Most voice datasets are built from isolated recordings. Hive Calls captures something different: real conversations between real people. 📞
Download the app and start talking:
play.google.com/store/apps/d…
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No fixed scripts or predefined topics.
Talk about your day, work, travel, hobbies, plans, or anything else that comes naturally. Real calls capture pauses, reactions, interruptions, laughter, and changes in tone.
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With everyone’s consent, qualifying calls may be recorded and used to build datasets for better voice AI.
The goal is simple: help AI learn not just what people say, but how real conversations actually happen.
Join Hive 🐝
play.google.com/store/apps/d…
Hive Calls is live! 📞🐝
play.google.com/store/apps/d…
Our new mobile app lets you call other DataHive users, have real conversations, and earn rewards for qualifying calls.
Hive Calls is live! 📞🐝
play.google.com/store/apps/d…
Our new mobile app lets you call other DataHive users, have real conversations, and earn rewards for qualifying calls.
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You can currently earn rewards for up to 40 qualifying minutes per day.
Calls must be real two-way conversations. Silence, prerecorded audio, scripts, or AI voices don’t count.
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With everyone’s consent, qualifying calls may be recorded and used to create datasets for training, testing, and improving voice AI systems.
Real conversations help capture how people actually speak.
Join Hive Calls - play.google.com/store/apps/d…
iOS App coming soon 🚀
Hive Calls is coming soon. 📞🐝
A new way to connect, talk, and contribute through real conversations is almost here.
Stay tuned. More details are coming shortly.
We’re building a feature that will change how you chat with each other and how you interact with the platform. New ways to engage, new ways to earn. The reveal is getting closer.
A WAV file and an MP3 can sound almost identical to us.
But for an AI model, they are not always the same.
Compression removes parts of the original signal, and those changes can affect what a model learns from the audio.
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There is another risk: if most recordings come from the same technical pipeline, the model may learn patterns created by the codec or device, not just human speech.
So audio diversity is also about formats, devices and processing conditions.
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Compression itself is not always bad.
The bigger issue is mismatch between training data and the audio a model sees in production.
The way audio is recorded, compressed and processed becomes part of the dataset itself.
Text models break sentences into tokens before processing them.
Speech AI is starting to work in a similar way.
Neural audio codecs turn continuous sound into smaller audio tokens that models can process and generate more efficiently.
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This also changes how we should think about training data.
If a codec sees mostly clean, scripted speech, it may be worse at representing laughter, hesitation, emotion, overlapping voices or noisy real-world conversations.
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Audio codecs are becoming much more than compression tools.
They increasingly decide which parts of human speech an AI model can actually understand and reproduce.
What the codec loses, the model may never get a chance to learn.