In this bulk run example, we compare Gooey.AI speech recognition + translations workflows (https://gooey.ai/speech), each of which uses a different AI speech model:
We then evaluate them at https://gooey.ai/eval/ (where you can see what works best....)
Run
Examples
API
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
Chichewa ASR via MMS-Large + Google Translate
Seamless M4T v1 - Chichewa Speech Reco
Google USM - Chichewa ASR
➕ Add a Workflow
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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Submit Links in Bulk
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.
Documents
Audio
Raw Output Text
Run URL
🤲 Show All Columns
Selected Model
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Language
Translation Model
Source Translation Language
Target Translation Language
Google Translate Target
Glossary Document
Output Format
Price
Run Time
Error Msg
Output Text
(optional) Add one or more Gooey.AI Evaluator Workflows to evaluate the results of your runs.
Speech Recognition Model Evaluator
➕ Add an Eval
⚙️ Settings
Run cost = 1 credits
🏃 Submit
By submitting, you agree to Gooey.AI's terms & privacy policy.
https://storage.googleapis.com/dara-c1b52.appspot.com/daras_ai/media/9f21278c-00d1-11ef-a60d-02420a000174/evaluator-20.csv
https://gooey.ai/eval/?run_id=ypwnjkbhna6n&uid=kKZgp2h1H2YxZYxZ2DbiRfUfeDM2
Generated in 230.6s on
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ℹ️ Details
Building complex AI workflows like copilot) and then evaluating each iteration is complex.Workflows are affected by the particular LLM used (GPT4 vs PalM2), their vector DB knowledge sets (e.g. your google docs), how synthetic data creation happened (e.g. how you transformed your video transcript or PDF into structured data), which translation or speech engine you used and your LLM prompts. Every change can affect the quality of your outputs.
To get started:
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