---
license: apache-2.0
base_model: openai/whisper-base.en
pipeline_tag: automatic-speech-recognition
library_name: onnx
tags:
- dakera
- ai-agent-memory
- agent-memory
- long-term-memory
- rag
- self-hosted
- mcp
- onnx
- mirror
---
whisper-base.en
Speech-to-text for Dakera ยท a verified mirror by Dakera, the self-hosted memory engine for AI agents
> An unmodified, checksum-verified copy of the files [Dakera](https://dakera.ai) uses from [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en). 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.en`).
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.en](https://huggingface.co/onnx-community/whisper-base.en) | 208.3 MB |
| `onnx/encoder_model.onnx` | [onnx-community/whisper-base.en](https://huggingface.co/onnx-community/whisper-base.en) | 82.5 MB |
| `tokenizer.json` | [onnx-community/whisper-base.en](https://huggingface.co/onnx-community/whisper-base.en) | 2.4 MB |
SHA-256 checksums
```text
381195952316e01d2cd5f3e51611e56959e660909a1f636de151af13166d23cc onnx/decoder_model.onnx
1cc86302d480b061452d348638064383ab41b6f3333ddd0e423532d14edaf535 onnx/encoder_model.onnx
5eb60cec1e77aeeb6869a2bb5a8e01a84c3fe5d072d75369343021fe6f5310d0 tokenizer.json
```
## Licence and credit
Apache-2.0, the same as the original model (see [`LICENSE`](https://huggingface.co/DakeraAI/whisper-base.en/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.en).
## 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
```
| | |
|---|---|
| ๐ Website | [dakera.ai](https://dakera.ai) |
| ๐ Documentation | [dakera.ai/docs](https://dakera.ai/docs) |
| ๐ป GitHub | [github.com/Dakera-AI](https://github.com/Dakera-AI) |
| ๐ Self-hosting (Docker, Kubernetes, Helm) | [dakera-deploy](https://github.com/Dakera-AI/dakera-deploy) |
| ๐ MCP server | [dakera-mcp](https://github.com/Dakera-AI/dakera-mcp) |
| ๐งฐ 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) |
| ๐ผ LinkedIn | [Dakera AI](https://www.linkedin.com/company/dakera-ai) |
Questions or feedback: [amine@dakera.ai](mailto:amine@dakera.ai).