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 The End-to-end Speech Challenge (ESC) is a benchmark for assessing a single ASR system on a collection of eight different speech recognition datasets. The ESC datasets are sourced from different domains and cover a range of audio and text distributions (speaking styles, background noise, transcription requirements). ESC consists of:
 
-- A Hugging Face dataset to easily download and use pre-prepared audio-text data
+- A [Hugging Face dataset](https://huggingface.co/datasets/esc-bench/esc-datasets) to download and use pre-prepared ESC audio-text data
 
-- Checkpoints and scripts to reproduce runs for the five official baseline systems
+- [Checkpoints and scripts](https://huggingface.co/models?other=esc) to reproduce runs for the five official baseline systems
 
-- A leaderboard for ranking systems according to overall performance on the benchmark
+- An [ESC leaderboard](https://huggingface.co/spaces/esc-bench/ESC) for ranking systems according to overall performance on the benchmark
 
 ESC was proposed in _ESC: A Benchmark For Multi-Domain End-to-End Speech Recognition_ by ... For more information, see the official submission on [OpenReview.net](https://openreview.net/forum?id=9OL2fIfDLK).