Update README.md

README.md

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     The End-to-end Speech Challenge (ESC) is a benchmark for assessing ASR systems on a collection of eight speech recognition datasets. ESC consists of:
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+	Official Checkpoints
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 		href="https://huggingface.co/spaces/esc-bench/ESC"
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+	ESC Leaderboard
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-        The ESC datasets are sourced from 11 different domains and cover a range of audio and text distributions (speaking styles, background noise, transcription requirements). There is no restriction on architecture or training data: any system capable of processing audio inputs and generating the corresponding transcriptions is eligible to participate. The only constraint is that systems must use the same training and evaluation algorithms across datasets and may not use any dataset-specific pre- or post-processing. The datasets in ESC are selected to favour systems that can generalise to different datasets and domains. Overall, the objective of ESC is to encourage the research of more multi-domain ASR systems.
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+        The ESC datasets are sourced from 11 different domains and cover a range of audio and text distributions (speaking styles, background noise, transcription requirements). There is no restriction on architecture or training data: any system capable of processing audio inputs and generating the corresponding transcriptions is eligible to participate. The only constraint is that systems must use the same training and evaluation algorithms across datasets and may not use any dataset-specific pre- or post-processing. The objective of ESC is to encourage the research of more generalisable, multi-domain ASR systems.
+        <br />
         ESC was proposed in ESC: A Benchmark For Multi-Domain End-to-End Speech Recognition by ... For more information, see the official submission on <a  href="https://openreview.net/forum?id=9OL2fIfDLK"  class="underline">OpenReview.net</a> or the blog post at <a href="https://openreview.net/forum?id=9OL2fIfDLK" class="underline">ESC Benchmark (TODO)</a>.
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