Compare Swahili Speech Recognition

In this bulk run example, we compare several different Gooey.AI speech recognition + translations workflows (https://gooey.ai/speech), each of which uses a different AI speech model:

  1. Google Chirp
  2. Azure
  3. Whisper v3
  4. Seamless M4T

We then evaluate them at https://gooey.ai/eval/ (where you can see what works best....)


Gooey Workflows

Provide one or more Gooey.AI workflow runs.
You can add multiple runs from the same recipe (e.g. two versions of your copilot) and we'll run the inputs over both of them.

Speech Recognition and Translation

Whisper v3 (with english translation)

Speech Recognition and Translation

Swahili Speech Recognition & Translation via Google Chirp & Translate

Speech Recognition and Translation

Azure ASR (Swahili)


Input Data Spreadsheet

Upload or link to a CSV or google sheet that contains your sample input data.
For example, for Copilot, this would sample questions or for Art QR Code, would would be pairs of image descriptions and URLs.
Remember to includes header names in your CSV too.

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Preview: Here's what you uploaded

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Columns

Please select which CSV column corresponds to your workflow's input fields.
For the outputs, select the fields that should be included in the output CSV.
To understand what each field represents, check out our API docs.

Inputs

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Outputs

Evaluation Workflows

(optional) Add one or more Gooey.AI Evaluator Workflows to evaluate the results of your runs.

Speech Recognition Model Evaluator


Run cost = 1 credits

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