MODEL PROFILE

whistle

Cactus-Compute · whistle

A speech-to-text model for mobiles, wearables, robots, smart home, automotive and microcontrollers. The whole model is a single 16.9 MB file, and it runs on the same CPU engine as Needle, from the same container and the same quantisation, with no dependencies and no GPU. - **Transcription**: 16 kHz mono audio of any length, one pass up to 30 seconds and windowed beyond it, in English, German, French, Spanish, Italian, Dutch and Polish. The language is detected unless you name it, and silence and steady noise return an empty transcript rather than an invented sentence; other sounds, music especially, can still come out as text, so put a voice activity detector or a wake word in front of an always-on microphone. Live audio streams for as long as you like, committing the words two passes agree on. - **Word timestamps**: every word with its start, end and probability, aligned from the decoder's own attention, so an app can highlight, seek or cut on a word. - **Speech embedding**: the encoder output, one row per 80 ms frame, for matching and retrieval without decoding a transcript at all. Keyword biasing takes the names, places, product words and jargon your users actually say and favours them during the search, so the ones that matter survive.

Speech recognitionlicense:apache-2.0
Context windowNot disclosed
Input → OutputAudio → Text
Model weightsNot verified
Profile updated2026/10/30 06:41:25
OVERVIEW

Model overview

A speech-to-text model for mobiles, wearables, robots, smart home, automotive and microcontrollers. The whole model is a single 16.9 MB file, and it runs on the same CPU engine as Needle, from the same container and the same quantisation, with no dependencies and no GPU. - **Transcription**: 16 kHz mono audio of any length, one pass up to 30 seconds and windowed beyond it, in English, German, French, Spanish, Italian, Dutch and Polish. The language is detected unless you name it, and silence and steady noise return an empty transcript rather than an invented sentence; other sounds, music especially, can still come out as text, so put a voice activity detector or a wake word in front of an always-on microphone. Live audio streams for as long as you like, committing the words two passes agree on. - **Word timestamps**: every word with its start, end and probability, aligned from the decoder's own attention, so an app can highlight, seek or cut on a word. - **Speech embedding**: the encoder output, one row per 80 ms frame, for matching and retrieval without decoding a transcript at all. Keyword biasing takes the names, places, product words and jargon your users actually say and favours them during the search, so the ones that matter survive.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。