whisper-tiny.en

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

An unmodified, checksum-verified copy of the files Dakera uses from openai/whisper-tiny.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-tiny.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 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-tiny.en	118.4 MB
onnx/encoder_model.onnx	onnx-community/whisper-tiny.en	32.9 MB
tokenizer.json	onnx-community/whisper-tiny.en	2.4 MB

SHA-256 checksums

14f1d425a4821feeba77cf93eeeaf812ca816f2e3fec382b4f0fa93d29de710e  onnx/decoder_model.onnx
8c361b9430a5ef6619ee64b7fe06c725df19f36d508cc8b847064b34a888a3fe  onnx/encoder_model.onnx
5eb60cec1e77aeeb6869a2bb5a8e01a84c3fe5d072d75369343021fe6f5310d0  tokenizer.json

Licence and credit

Apache-2.0, the same as the original model (see LICENSE). For what the model does, how it was trained and how to cite it, see the original model card.

About Dakera

Dakera 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.

docker run -p 3000:3000 -e DAKERA_ROOT_API_KEY=dk-mykey ghcr.io/dakera-ai/dakera:latest

🌐 Website	dakera.ai
📚 Documentation	dakera.ai/docs
💻 GitHub	github.com/Dakera-AI
🚀 Self-hosting (Docker, Kubernetes, Helm)	dakera-deploy
🔌 MCP server	dakera-mcp
🧰 SDKs	Python · TypeScript · Go · Rust
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Questions or feedback: amine@dakera.ai.