TLDR: Appen Inc. and DataoceanAI have provided high-quality English ASR datasets covering scripted and conversational speech over multiple accents. To prevent potential risks of benchmaxxing or test-set contamination, we will keep these datasets private for a high-quality measure of performance on multiple tasks.

We’re not updating the average WER at this time: by default, the leaderboard’s Average WER remains computed on public datasets only. You can optionally include the private datasets using the toggle to see their impact 👀

Since its launch in September 2023, the Open ASR Leaderboard has been visited over 710K times. We’re blown away by the community’s interest and motivation to keep pushing speech recognition 🗣️

Two words sum up the objectives (but also challenges) in maintaining a benchmark like the Open ASR Leaderboard:

Standardization: models can have different conventions for their usage and outputs, e.g. with/without punctuation and casing. Datasets have the same challenges and can be structured differently. To this end, all test sets have been gathered into a single dataset on the Hub for easy access and previewing. Moreover, to standardize model outputs and dataset transcripts, we use a normalizer that (among other things) removes punctuation and casing, and maps to American spelling. It is based on the normalizer of Whisper.

Openness: the UI code and evaluation scripts are open-sourced. This has helped not only to incorporate new models, but also to improve the quality of the evaluation procedure through community feedback and contributions.

Standardization and openness are essential for meaningful benchmarking, but they also make benchmarks more susceptible to benchmark-specific optimization ("benchmaxxing"), where models improve leaderboard performance without corresponding gains in real-world robustness.

Source link