What is the current behavior?

PrerecordedResponse.from_json(result) (link to code) is very slow, especially for larger inputs. This is due to the Dataclasses JSON library, where they are already aware of that performance issue but haven’t addressed it since 2020. In addition to .from_json(), the .to_dict() operation is also very slow, which one would use if they want to parse the output from the Deepgram SDK into their own Pydantic model.

In our case, for recordings lasting around 1 hour:

source = {"url": signed_url}
options = rerecordedOptions(
        model="nova-2-general",
        diarize=True,
        utterances=True,
        paragraphs=True)
deepgram.listen.rest.v("1").transcribe_url(source, options=options)

The .from_json() takes over 10 seconds. Pydantic parsing takes ~30ms.
For a 7-minute recording, the .from_json() operation took ~1.7 seconds, while Pydantic parsing took ~5ms.

This issue also affects the asynchronous version, where the problem is even more significant as it blocks the event loop for a long time.

Expected behavior

JSON serialization and deserialization shouldn't take that long, and CPU-heavy operations should definitely not block the event loop. Please consider using Pydantic or raw dataclasses.