A Coding Guide to Cactus Whistle: Does a 16.9 MB Speech Model Hold Up Against Whisper Base? Word Error Rate, Speed, Streaming, Timestamps, Keywords, and Speech-to-Tool Calls on a CPU
In this tutorial, we use Whistle, Cactus Compute’s speech-to-text model, a 16.9 MB file that runs on a CPU, transcribes seven languages, and returns word timestamps and probabilities.
Proof1 independent outlet
Key points
- The model card claims it beats Whisper base on LibriSpeech; we test it on seeded test-clean and test-other samples, comparing Whistle with Whisper tiny and base on identical clips, judged by paired speaker-cluster intervals with Holm correction.
- We find a lead over greedy Whisper base on test-other, no significant gap against beam search, and a speed advantage that narrows as clips grow.
- Beyond the headline, we probe a 30-second window where the pinned engine silently drops the middle of longer audio, plus streaming latency, timestamps, keyword biasing, other languages, noise, embeddings, and speech-to-tool calls.
- Our figures are one-run samples from an Apple M4, and published full-split numbers are quoted, not measured.
Sources (1)
- [1]A Coding Guide to Cactus Whistle: Does a 16.9 MB Speech Model Hold Up Against Whisper Base? Word Error Rate, Speed, Streaming, Timestamps, Keywords, and Speech-to-Tool Calls on a CPUMarkTechPost · Oct 22, 04:17 AM
In this tutorial, we use Whistle, Cactus Compute’s speech-to-text model, a 16.9 MB file that runs on a CPU, transcribes seven languages, and returns word timestamps and probabilities.
The model card claims it beats Whisper base on LibriSpeech; we test it on seeded test-clean and test-other samples, comparing Whistle with Whisper tiny and base on identical clips, judged by paired speaker-cluster intervals with Holm correction.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 22, 2026NVIDIA/TensorRT-LLM v1.4.0rc1
- Oct 20, 2026Gradium Open-Sources Phonon, a 31M-Parameter On-Device TTS Model Under Apache 2.0
- Oct 20, 2026[AINews] DeepSeek DeepInfer: Open Source SOTA Inference running 155T toks/day/cluster making $22m/day at ~80% margin for a 81 day payback
- Oct 20, 2026Do Diffusion LLMs Scale? Testing Mercury 3 + Decide
- Oct 20, 2026AnyLanguageModel 1.0: One Year of Local Models on Apple Platforms
- Oct 20, 2026MPS in PyTorch 2.15
