Model overview
The code is at SilentCipher Repository Code for SilentCipher, a method of embedding watermarking in any audio with state-of-the-art robustness. Currently this repository supports audio at 16kHz and 44.1kHz. Checkout our paper for more details. We have posted some examples from existing watermarking algorithms and how they compare to our watermarking method at EXAMPLES In this paper, we address artefacts introduces by Deep learning-based watermarking methods and introduce a way to remove the need for perceptual losses which leads to stable training allowing us to achieve SOTA in terms of both perceptual quality and robustness against distortion. Unlike previous methods which work on 16kHz sampling rate, we also showcase our results on 44.1kHz sampling rates opening the path for practical applications.
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.
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.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
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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These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：来源未披露，须进一步核验。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。