[KongchangAI]
ModelWhisper Base

Whisper Base

The base version of the Whisper series of speech recognition models introduced by OpenAI, with approximately 74M parameters and a size of 142 MB. It supports multiple languages and is widely used for speech transcription on edge devices.

Timeline (last 90 days)

Oct 22

MarkTechPost published a complete coding guide that systematically compared Cactus Whistle and Whisper Base across six dimensions: word error rate, CPU inference speed, streaming latency, timestamps, keyword recognition, and speech-to-tool-calling.

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Oct 22

For scenarios such as smart homes, in-car voice, and wearable devices, a 16.9 MB model can run directly on-device, eliminating network round-trips, which is a deployment form Whisper Base struggles to provide.

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Oct 22

Whisper Base remains a reliable baseline for general transcription, especially in multilingual, complex acoustic environments; Cactus Whistle, meanwhile, pushes the threshold of speech recognition down to a new order of magnitude in size.

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Oct 22

Whisper Base has about 74M parameters and a size of about 142 MB.

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Oct 22

On CPU, Whistle can achieve near-real-time transcription on an ordinary laptop or even embedded devices, whereas Whisper Base is more prone to backlog without a GPU.

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Oct 21

Whistle's performance is in the same league as Whisper Base and Moonshine rather than surpassing them, and its core advantage lies in extreme portability.

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Oct 21

Whistle's transcription quality is close to Whisper Base and Moonshine Tiny V2.

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