Model overview
This is the model card for the Moonshine Streaming automatic speech recognition (ASR) models trained and released by Useful Sensors. Moonshine Streaming pairs a lightweight 50~Hz audio frontend with a sliding-window Transformer encoder to deliver low-latency streaming ASR on edge-class hardware. The encoder uses bounded local attention and no positional embeddings (an "ergodic" encoder), while an adapter injects positional information before a standard autoregressive decoder. This model card follows the recommendations from Model Cards for Model Reporting (Mitchell et al.). See the paper draft in this repository for full details. Moonshine Streaming is supported in Hugging Face Transformers. The following example matches the standard seq2seq ASR API and uses the streaming model checkpoint:
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。
View full indexed rankings →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.
Are these real-time billing prices?
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：mit。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。