Unverified50% confidenceFactExact time
By compressing the context from 128K to 32K, the developer brought total VRAM usage (including Whisper) below 12GB, but in the first version's experience it takes about 10 to 11 seconds from when the user finishes speaking to when the AI starts speaking.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/29/2026
First Seen
Valid until: 1/27/2027
Sources
Related Entities
Related Claims
UnverifiedThe scale of 31M parameters and 64 MB means the vocoder and embedding tables have been significantly compressed, possibly using parameter quantization (such as int8), knowledge distillation or a lightweight vocoder approach.71% similarUnverifiedWhisper原始FP32版本的Tiny模型约150MB，经INT8量化后可压缩至约40MB，在树莓派4B上可实现实时转写70% similarUnverifiedOpenAI's Whisper large-v3 model takes approximately 10–15 minutes to transcribe speech from a 1-hour video on an Apple M2 chip, compared to approximately 2 minutes on a cloud-based A100 GPU.69% similarUnverifiedHardware encoders (NVIDIA NVENC, Intel Quick Sync, Apple VideoToolbox) can compress encoding latency to 2–5 milliseconds, while pure software encoding (x264/x265) latency is typically 10–30 milliseconds.69% similar
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