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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

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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....

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....

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....