(Updated Jan 2025)
This page shows a test of many English speech‑to‑text systems.

We use the same English audio for every system. Then we compare each system’s text to a reference answer and give it a score between 0 and 1.
A higher score means the system is closer to the reference text and usually more accurate.

Ranking

With this benchmark, you can:

• See which system gets the best score
• Compare different models and pipelines side by side
• Choose the best system for your app, research, or product
• Download all results for deeper analysis

(Updated July 8, 2026)
This page presents a comparative demonstration of multiple LLM and speech-to-text systems combinations in Hausa → English translation pipelines, updated with the latest available Gooey model endpoints for Gemini 3.5 Flash and GPT-Realtime 2.1.

(Updated July 8, 2026)
This page presents a comparative demonstration of multiple LLM and speech-to-text systems combinations in Hausa → English translation pipelines, updated with the latest available Gooey model endpoints for Gemini 3.5 Flash and GPT-Realtime 2.1.

Hausa audio-to-English agent using Gemini 3.5 Flash (gemini_3_5_flash).

Hausa audio-to-English agent using Intron ASR plus the newer GPT-Realtime model endpoint (gpt_realtime_2).

Hausa audio-to-English agent using Omnilingual ASR plus the newer GPT-Realtime model endpoint (gpt_realtime_2).

Hausa audio-to-English agent using the newer GPT-Realtime model endpoint (gpt_realtime_2).

A bulk evaluator workflow that compares AI-generated answers (copilot responses) to a set of golden reference answers. Requires input data columns: "input_prompt" (the question/task) and "reference_answer" (the ideal response). The workflow uses custom evaluation prompts to compare outputs, scoring them for accuracy and penalizing hallucinations. Aggregates results to provide an overall performance metric for your AI answers.