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Custom Models Pro

Pro subscribers can import their own speech-to-text model instead of using one of the models in Echophrase’s built-in registry. This is for advanced users who want a specific model - a fine-tune, a niche-language model, or something not yet in our registry.
This is a Pro feature. See the beta page for a tier comparison, or the Model Selection page for the bundled models available on every tier.

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What’s an ONNX model?

ONNX (Open Neural Network Exchange) is an open, portable file format for machine learning models. It lets a model trained in one framework run in a different runtime - in this case, ONNX Runtime, which Echophrase already uses for its built-in models. A speech-to-text model in ONNX form usually looks like:
  • model.onnx - the model itself (required)
  • model.onnx.data - an external weights file for larger models (optional, only present if the model was exported that way)
  • tokenizer.json and/or config.json - files describing how to turn the model’s raw output into text (optional, but recommended - without them the app falls back to best-effort decoding)
You don’t need to understand the format to use it - Echophrase reads whatever files you point it at and tells you if something’s missing or broken.
Custom models in Echophrase v1 must be ONNX. Other formats such as safetensors or GGUF are not supported for custom models.

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Where to find models

  • Hugging Face model hub
    • filtered to automatic speech recognition models published in ONNX format. This is the easiest place to browse.
  • ONNX Model Zoo - a curated collection of pre-trained ONNX models maintained by the ONNX community.
Look for a repo or download that includes a .onnx file (plus, ideally, a tokenizer.json) built for automatic speech recognition.

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How to import a model

Open Settings > Custom Model and choose one of two ways to bring in a model:

Local file

Browse to a .onnx file on disk, or drag and drop it into the settings window. If the model has a companion .onnx.data weights file, a tokenizer.json, or a config.json, add those too (or drop the whole folder).

Hugging Face repo

Paste a repo URL or org/repo name, such as openai/whisper-tiny.en. Echophrase downloads the repo’s files directly. The repo must contain exactly one .onnx file - if it has more than one, download the one you want manually and import it as a local file instead.
Once you import a model, Echophrase actually loads it in ONNX Runtime to confirm it’s a valid, loadable ONNX model, then stores a copy in the app’s local cache. This validation step catches corrupted downloads and files that aren’t ONNX models at all - it does not evaluate transcription quality. A successfully imported model becomes your active model immediately - no extra selection step. You can import as many models as you like and switch between them: every imported model is listed with its own Use / Remove in the import window, in Settings > Custom Models, and via the custom row in the AI Models picker.
On WSL, Windows Explorer cannot drag files into WSL windows, so the import window shows a Browse button instead of a drop zone.
Custom models have an 8 GB size limit.

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The compatibility contract

Echophrase runs custom models through a single generic inference path, since it can’t know in advance how every possible model is built. For a model to produce usable transcriptions, it must:
  • Accept a raw 16 kHz mono waveform as input
  • Expose that input under the tensor name audio/input/mel
  • Produce text or token output that Echophrase can decode into words
If a model doesn’t follow this contract, Echophrase will still load and run it - it just won’t crash - but the output may be garbled, empty, or nonsensical. That’s a sign the model is incompatible with Echophrase’s inference path, not a bug in the app. If you hit this, double-check that the model was actually exported for raw-waveform ASR input and not, say, mel-spectrogram or log-mel features computed outside the model.

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Coming soon: Transform models

A second custom model slot - for Transform models, which post-process transcribed text (grammar correction, custom rewriting rules, and similar) - is planned for a future release. Custom Transform models are not yet supported; see Transform Model for the built-in version.

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Security notes

Custom models run entirely on your device through ONNX Runtime, the same engine that powers Echophrase’s built-in models - nothing about a custom model is uploaded or sent to Echophrase’s servers. That said, please keep a few things in mind:
  • Only import models you trust. A .onnx file is a computation graph, and a malicious or broken one could still misbehave at runtime even though Echophrase validates that it loads successfully in ONNX Runtime.
  • Validation confirms format, not quality. Echophrase checks that a file is a real, loadable ONNX model - it can’t vet what the model was trained on, how accurate it is, or whether its output can be trusted.
  • Prefer known sources. Hugging Face repos with a listed author, description, and download count are generally a safer bet than an unfamiliar file from an untrusted link.