Audio8 ASR Infinite streaming speech recognition model the native streaming architecture decodes 12.5 times per second a rolling KV Cache keeps both memory and latency constant, even in 24/7 operation one text token per clock step (12.5 / 8.3 / 6.25 decisions per second), balancing perception granularity and resource... cost ML intern in huggingchat setup a gradio workflow to try it out: huggingchat: model:show more

finally an asr model that won't need a nap after a long meeting

While lowering the decision rate to 6.25 steps per second limits resource usage, the trade-off in actual transcription accuracy remains unclear.

Always-on ASR with a rolling KV cache is the streaming speech bet — short stack on why constant-latency decode matters → #AI #ASR #Audio8

The always-on part is the real breakthrough — not accuracy. Streaming ASR with constant memory and latency is the missing infrastructure layer that makes 24/7 ambient voice agents economically viable. Voice as a default interface needs economics like this more than it needs bigger models.

The flat-memory claim matters. I hit a case on device where audio capture and decode threads drifting by one frame cascaded into dropped tokens that looked like model error, not a threading problem.

结论：常在线流式语音识别要先稳住内存与延迟曲线；峰值吞吐单独好看不够。 口径：Audio8 ASR Infinite 以原生流式架构约每秒解码 12.5 次，并用滚动 KV Cache 在 7×24 运行下保持内存与延迟近似恒定；每时钟步一个文本 token，在 12.5／8.3／6.25 决策频率间权衡粒度与成本。 边界：开放噪声与多语场景下的字错率与端到端延迟，仍需独立基准核对。 Muse Code：05ZMHO

One token per step means it can't take a word back, right? Curious how it handles names it mishears early.

5 times per secondd That's insane!.

semantic vad distinguishing stuttering is the one highlight with no number behind it. the eval is cer/wer on aishell and librispeech, all read speech, and the decode runs eos suppressed. endpointing is what that claim is about, and it's off during the measurement.

constant memory even in 24/7 operation is the hard part. how does it handle the edge cases where context from 10 minutes ago still matters?

the constant memory footprint during 24/7 operation is the part that stands out, most streaming setups I've seen creep up in memory the longer they run. curious what the actual end to end latency looks like once you add the rolling cache overhead

how many languages can it process?