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Cactus Compute's Whistle speech-to-text model outperforms Whisper on CPU

By PulseAugur Editorial · [1 sources] ·

Cactus Compute has developed Whistle, a compact 16.9 MB speech-to-text model designed to run efficiently on a CPU. Benchmarks on the LibriSpeech test-other dataset show Whistle achieving a 10.47% word error rate, outperforming the base Whisper model by 2.19 points when using greedy decoding. Additionally, Whistle demonstrated a 5.7x speed advantage over Whisper on ten-second audio clips. AI

IMPACT Offers a more efficient, CPU-bound alternative for speech-to-text tasks, potentially lowering inference costs.

RANK_REASON The item describes a new model release and its benchmark performance against an existing model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Cactus Compute's Whistle speech-to-text model outperforms Whisper on CPU

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The item describes a new model release and its benchmark performance against an existing model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    Cactus Compute's Whistle, a 16.9 MB speech-to-text model that runs on a CPU, was benchmarked against Whisper base. On LibriSpeech test-other it scored a 10.47%

    Cactus Compute's Whistle, a 16.9 MB speech-to-text model that runs on a CPU, was benchmarked against Whisper base. On LibriSpeech test-other it scored a 10.47% word error rate, 2.19 points better than greedy Whisper base, while beam search showed no gap. It ran 5.7 times faster o…

    LINKS marktechpost.com/…/cactus-whistle-vs-whis…

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