Custom OpenAI-Compatible Endpoints

Add any OpenAI-compatible server — Ollama, LM Studio, OpenRouter, or your own — as a post-processing provider in HyperWhisper.

HyperWhisper can send your transcripts to any server that speaks the OpenAI chat-completions API for AI post-processing. This means you can use a local model running on your own machine, a self-hosted server, or a hosted gateway like OpenRouter — no changes to your Modes needed once the endpoint is configured.

What custom endpoints do

Custom endpoints are an alternative post-processing provider. When a Mode has AI cleanup enabled and its provider is set to a custom endpoint, HyperWhisper POSTs the raw transcript to your server using the standard OpenAI chat-completions format and uses the reply as the cleaned-up output.

They are not used for transcription itself — only for the AI cleanup/formatting pass that runs after speech recognition.

Adding an endpoint

macOS

1. Open the Model Library

   Open HyperWhisper and navigate to Model Library in the sidebar.

2. Find the OpenAI-compatible endpoints card

   Scroll to the OpenAI-compatible endpoints card near the bottom of the page. Click Add endpoint.

3. Choose a provider tab

   The sheet has three tabs:

   • LMStudio — pre-fills the base URL http://localhost:1234/v1 and name “LMStudio”; fetches your running models automatically.
   • Ollama — pre-fills http://localhost:11434 and name “Ollama”; fetches your running models automatically.
   • Custom — for OpenRouter, other hosted APIs, or any other OpenAI-compatible server; you enter the base URL and model name manually.
4. Fill in the fields

   Field	What to enter
   Name	A label you recognize, e.g. “My Ollama Server” or “OpenRouter GPT-4o”
   Base URL	The base URL of your server (see URL format)
   Model	The model identifier your server expects, e.g. llama3.2 or mistral-7b-instruct
   API Key	Optional. Leave blank for local servers that don’t require authentication.

   For LMStudio and Ollama tabs, HyperWhisper queries the server’s models list automatically — pick from the dropdown if models load, or type a model name manually as a fallback.

5. Test the connection (recommended)

   Click Test connection before saving. HyperWhisper sends a small request to your server and shows the response. A green indicator means the endpoint is reachable and responding correctly.

6. Save

   Click Add Endpoint (or Save Changes when editing). The endpoint appears in the card list.

Windows

1. Open the Model Library

   Open HyperWhisper and navigate to Model Library in the sidebar.

2. Add a custom endpoint

   Click Add endpoint in the Custom Endpoints section.

3. Choose a provider tab

   The window has three tabs:

   • LMStudio — pre-fills the base URL http://localhost:1234/v1 and name “LMStudio”; fetches your running models automatically.
   • Ollama — pre-fills http://localhost:11434 and name “Ollama”; fetches your running models automatically.
   • Custom — for OpenRouter, other hosted APIs, or any other OpenAI-compatible server; you enter the base URL and model name manually.
4. Fill in the fields

   Field	What to enter
   Name	A label you recognize, e.g. “My Ollama Server” or “OpenRouter GPT-4o”
   Base URL	The base URL of your server (see URL format)
   Model	The model identifier your server expects, e.g. llama3.2 or mistral-7b-instruct
   API Key	Optional. Leave blank for local servers that don’t require authentication.

   For LMStudio and Ollama tabs, HyperWhisper queries the server’s models list automatically — pick from the dropdown if models load, or type a model name manually as a fallback.

5. Test the connection (recommended)

   Click Test connection before saving. HyperWhisper sends a small request to your server and shows the response. A green indicator means the endpoint is reachable and responding correctly.

6. Save

   Click Add Endpoint (or Save Changes when editing). The endpoint appears in the model list.

URL format

Enter the base URL of your server. For Ollama and LMStudio tabs, HyperWhisper appends the correct path automatically. For the Custom tab, see the platform notes below:

Provider tab	Path appended	Example full URL stored
Ollama	/v1/chat/completions	http://localhost:11434/v1/chat/completions
LMStudio	/chat/completions	http://localhost:1234/v1/chat/completions
Custom (Windows)	/chat/completions (if not already present)	https://openrouter.ai/api/v1/chat/completions
Custom (macOS)	(none — URL used verbatim)	https://openrouter.ai/api/v1/chat/completions

On Windows, the Custom tab appends /chat/completions if your URL does not already end with it. On macOS, the URL you enter is stored exactly as-is, so include the full path if your server requires it.

API key storage

API keys are stored securely and never in plain text:

• macOS — keys are stored in the system Keychain.
• Windows — keys are stored in Windows Credential Manager.

To update or remove a key, edit the endpoint and change the API Key field. Leaving the field blank when editing removes the stored key.

Testing an endpoint

The test button is available both when adding a new endpoint and when editing an existing one. It sends a minimal request to your server:

POST <your endpoint URL>
{ "model": "<your model>", "messages": [{"role": "user", "content": "Say hello in one word."}], "max_tokens": 10, "temperature": 0.0 }

HyperWhisper expects a standard OpenAI-compatible response — a JSON object with a choices[0].message.content field. The last test result (pass or fail) is saved and shown next to the endpoint name so you can see at a glance whether it was working the last time you checked.

The test result is cleared automatically if you change the endpoint URL, since the prior result no longer applies to the new address.

Using a custom endpoint in a Mode

Once an endpoint is saved, it appears as a provider option when you edit a Mode’s post-processing settings. Select it the same way you would select any built-in AI provider.

See Transcription Modes for how to configure post-processing on a Mode.

Managing endpoints

Each endpoint in the list has three actions:

Action	What it does
Edit (pencil)	Change the name, URL, model, or API key
Duplicate	Creates a copy with a new name — useful for trying different models on the same server without re-entering the URL
Delete	Permanently removes the endpoint and its stored API key

Duplicating an endpoint copies the test status along with the settings, since the URL and model are the same. The API key is also copied securely to the new entry. If the key copy fails for any reason, the test status is cleared so you know to verify the duplicate before relying on it.

Deleting an endpoint removes it from the list and erases the stored API key. Any Modes that were using that endpoint will need to be updated to a different post-processing provider.

Common providers

Here are base URLs for popular OpenAI-compatible services. Use the Custom tab for all of these:

Provider	Base URL
OpenRouter	https://openrouter.ai/api/v1
Ollama (local)	http://localhost:11434
LM Studio (local)	http://localhost:1234/v1
Together AI	https://api.together.xyz/v1
Groq (OpenAI-compatible endpoint)	https://api.groq.com/openai/v1

For local servers (Ollama, LM Studio), use the Ollama or LMStudio tabs instead of Custom so that model auto-detection works.

Endpoint format

Custom endpoints must implement the OpenAI chat completions API shape:

POST <your-url>
Authorization: Bearer <api-key>   (omitted if no key)
Content-Type: application/json

{
  "model": "<your-model>",
  "messages": [{ "role": "user", "content": "..." }],
  ...
}

The response must match the standard OpenAI format:

{
  "choices": [
    {
      "message": {
        "content": "..."
      }
    }
  ]
}

Any server compatible with this format will work — Ollama, LM Studio, vLLM, llama.cpp server, OpenRouter, and similar.