Model overview
Hubert(Hidden-Unit BERT)는 Facebook에서 제안한 Speech Representation Learning 모델입니다. Hubert는 기존의 음성 인식 모델과 달리, 음성 신호를 raw waveform에서 바로 학습하는 self-supervised learning 방식을 사용합니다. 해당 모델은 과학기술정보통신부의 재원으로 한국지능정보사회진흥원의 지원을 받아 구축된 자유대화 음성(일반남여), 다화자 음성합성 데이터, 방송 콘텐츠 대화체 음성인식 데이터 에서 약 4,000시간을 추출해 학습되었습니다.
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：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。