AhanaVoice uses cross-modal neural compression to jointly model audio, video, and text — exploiting the deep statistical correlation between what you hear, see, and read at the same moment.
Classical audio codecs see only the waveform. AhanaVoice sees the video frame at the same timestamp, the subtitle at the same moment, and the audio sample — all at once.
A silence in the audio track correlates with a static video frame. AhanaVoice's ModalTransformer learns these relationships — using video context to sharpen audio predictions and vice versa.
Temporal-RoPE positional encoding keys on absolute millisecond timestamps. A video token at t=150ms and an audio token at t=150ms receive the same positional encoding — the model knows they're simultaneous.
Audio is converted to 80-dimensional log-mel spectrograms, then vector-quantized to a learned codebook. This produces 50 tokens per second — 160× fewer than raw PCM — without perceptual loss.
Speech audio and its transcript are 98%+ correlated. AhanaVoice compresses them jointly — the text prediction sharpens the audio model, and vice versa. Classical codecs discard this entirely.
A ModalTransformer variant tuned specifically for voice characteristics — prosody, phoneme transitions, speaker identity — achieves tighter compression on speech than a general audio model.
Audio, video, and text tracks are interleaved in a single .aarm container with SYNC tokens marking modal boundaries. One file, one format, all modalities.
AhanaVoice is coming in 2026. Join early access for beta access and preview pricing.
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