hertz-dev

An open-source full-duplex audio generation foundational model.

CommonProductProgrammingAudio ProcessingSpeech Recognition

Hertz-dev is a full-duplex, audio-only transformer foundational model open-sourced by Standard Intelligence, featuring 8.5 billion parameters. This model represents scalable cross-modal learning technology capable of converting mono 16kHz speech into an 8Hz latent representation at a bitrate of 1kbps, outperforming other audio encoders. Key advantages of hertz-dev include low latency, high efficiency, and accessibility for researchers to fine-tune and build upon. Contextual information indicates that Standard Intelligence is committed to developing general intelligence that benefits humanity, with hertz-dev being a substantial step in that direction.

hertz-codec: A convolutional audio autoencoder that converts mono 16kHz speech into an 8Hz latent representation with a bitrate of approximately 1kbps.
hertz-vae: An 1.8 billion parameter transformer decoder featuring context with 8192 sampled latent representations that predicts the next encoded audio frame.
hertz-dev: A 6.6 billion parameter transformer stack
primarily initialized with weights from a pre-trained language model and trained over 20 million hours of audio for one epoch.
Theoretical latency of 65ms
with an average actual latency of 120ms
which is lower than any public model
making it suitable for real-time interaction.
An open-source model that easily allows researchers to fine-tune and build
representing the future of real-time voice interaction.

The target audience includes researchers
developers
and businesses interested in audio processing
speech recognition
and generation. Hertz-dev’s open-source nature

Researchers use hertz-dev to fine-tune audio models for specific speech recognition tasks.
Developers leverage hertz-dev to create real-time voice interaction applications
such as smart assistants or virtual customer service agents.
Businesses utilize hertz-dev for audio data compression and transmission to enhance communication efficiency.

1. Visit the GitHub page of hertz-dev and clone or download the code.
2. Install the necessary dependencies and environment as specified in the documentation.
3. Run the hertz-dev model to perform encoding and decoding tests on audio data.
4. Fine-tune the model as needed to suit specific application scenarios.
5. Evaluate the performance using audio samples generated by hertz-dev.

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