--- license: apache-2.0 base_model: openai/whisper-base pipeline_tag: automatic-speech-recognition library_name: onnx tags: - dakera - ai-agent-memory - agent-memory - long-term-memory - rag - self-hosted - mcp - onnx - mirror ---

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whisper-base

Speech-to-text for Dakera ยท a verified mirror by Dakera, the self-hosted memory engine for AI agents

Dakera: dakera.ai Docs: dakera.ai/docs GitHub: Dakera-AI License: Apache-2.0 Original: openai/whisper-base

> An unmodified, checksum-verified copy of the files [Dakera](https://dakera.ai) uses from [openai/whisper-base](https://huggingface.co/openai/whisper-base). The model is by **OpenAI**; all credit goes to its authors. ## In Dakera Transcribes audio attachments into searchable memories (`DAKERA_ATTACHMENTS=true`, `DAKERA_WHISPER_MODEL=whisper-base`). The default speech model. Dakera fetches this speech recognition from this repository first and from the original repositories if it is unreachable, so installs never depend on a single source. Both are checked against the same SHA-256, and the [Dakera Docker image](https://github.com/Dakera-AI/dakera-deploy) ships the default models ready to run, with no download at startup. ## Files Copied byte for byte from: | File | Source | Size | |---|---|---| | `onnx/decoder_model.onnx` | [onnx-community/whisper-base](https://huggingface.co/onnx-community/whisper-base) | 208.3 MB | | `onnx/encoder_model.onnx` | [onnx-community/whisper-base](https://huggingface.co/onnx-community/whisper-base) | 82.5 MB | | `tokenizer.json` | [onnx-community/whisper-base](https://huggingface.co/onnx-community/whisper-base) | 2.5 MB |
SHA-256 checksums ```text 70d26763610c0d6bb407373b7f30d415252ee470e62a0f816c8a46b2caca7326 onnx/decoder_model.onnx a9f3b752833b49e880dec91ee5b6d936112be7c3ea07c221024ba493439f46fe onnx/encoder_model.onnx 27fc476bfe7f17299480be2273fc0608e4d5a99aba2ab5dec5374b4482d1a566 tokenizer.json ```
## Licence and credit Apache-2.0, the same as the original model (see [`LICENSE`](https://huggingface.co/DakeraAI/whisper-base/blob/main/LICENSE)). For what the model does, how it was trained and how to cite it, see the [original model card](https://huggingface.co/openai/whisper-base). ## About Dakera **[Dakera](https://dakera.ai) is a self-hosted memory engine for AI agents.** It gives agents long-term memory that survives sessions: hybrid vector and full-text retrieval, reranking, entity extraction and importance decay, in a single Rust binary that runs on your own servers, with an MCP server for Claude, Cursor and other agents. ```bash docker run -p 3000:3000 -e DAKERA_ROOT_API_KEY=dk-mykey ghcr.io/dakera-ai/dakera:latest ``` | | | |---|---| | ๐ŸŒ Website | [dakera.ai](https://dakera.ai) | | ๐Ÿ“š Documentation | [dakera.ai/docs](https://dakera.ai/docs) | | ๐Ÿ’ป GitHub | [github.com/Dakera-AI](https://github.com/Dakera-AI) | | ๐Ÿš€ Self-hosting (Docker, Kubernetes, Helm) | [dakera-deploy](https://github.com/Dakera-AI/dakera-deploy) | | ๐Ÿ”Œ MCP server | [dakera-mcp](https://github.com/Dakera-AI/dakera-mcp) | | ๐Ÿงฐ SDKs | [Python](https://github.com/Dakera-AI/dakera-py) ยท [TypeScript](https://github.com/Dakera-AI/dakera-js) ยท [Go](https://github.com/Dakera-AI/dakera-go) ยท [Rust](https://github.com/Dakera-AI/dakera-rs) | | ๐Ÿ’ผ LinkedIn | [Dakera AI](https://www.linkedin.com/company/dakera-ai) | Questions or feedback: [amine@dakera.ai](mailto:amine@dakera.ai).