whisper-tiny

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. 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).

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	118.4 MB
onnx/encoder_model.onnx	onnx-community/whisper-tiny	32.9 MB
tokenizer.json	onnx-community/whisper-tiny	2.5 MB

SHA-256 checksums

ab79e3f2a9a3d98f159f853a3172120a38af7eb5f7863d706aa7d39c228f009e  onnx/decoder_model.onnx
6642befb640f950d4a8cbbd17834d59e7e75f575b81ccf213e06b050623ab1dd  onnx/encoder_model.onnx
27fc476bfe7f17299480be2273fc0608e4d5a99aba2ab5dec5374b4482d1a566  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

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