Update README.md

README.md

@@ -7,45 +7,32 @@ sdk: static
 pinned: false
 ---
 
-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). The only constraint on systems is that they must use the same training and evaluation algorithms across datasets and may not use any dataset-specific pre- or post-processing, assuming the data distributions are a-priori unknown. The goal of ESC is to promote generalisable ASR systems that can be applied in a multi-domain setting.
-
-ESC consists of:
-
-- 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](https://huggingface.co/models?other=esc) to reproduce runs for the five official baseline systems
-
-- 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).
-
 <div class="grid lg:grid-cols-3 gap-x-4 gap-y-7">
 	<p class="lg:col-span-3">
-    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.<br />
-    The ESC datasets are sourced from different domains and cover a range of audio and text distributions (speaking styles, background noise, transcription requirements). The only constraint on systems is that they must use the same training and evaluation algorithms across datasets and may not use any dataset-specific pre- or post-processing, assuming the data distributions are a-priori unknown. The goal of ESC is to promote generalisable ASR systems that can be applied in a multi-domain setting.
+    The End-to-end Speech Challenge (ESC) is a benchmark for assessing ASR systems on a collection of eight different speech recognition datasets. 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. 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 promote generalisable ASR systems that can be applied in a multi-domain setting.
 	</p>
-	<a href="https://spacy.io/" class="block overflow-hidden group">
+	<a href="https://huggingface.co/datasets/esc-bench/esc-datasets" class="block overflow-hidden group">
 		<div
 			class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center bg-[#ECFAFF]"
 		>
 			<img alt="" src="/front/assets/promo/spacy_logo.png" class="w-40" />
 		</div>
-		<div class="underline">spaCy website</div>
+		<div class="underline">ESC Datasets</div>
 	</a>
 	<a
-		href="https://github.com/explosion/spacy-huggingface-hub"
+		href="https://huggingface.co/models?other=esc"
 		class="block overflow-hidden"
 	>
 		<div class="flex items-center h-40 bg-[#ECFAFF] rounded-lg px-4 mb-2">
 			<pre
 				class="break-words leading-1 whitespace-pre-line text-xs text-gray-800">
-	python -m spacy huggingface-hub push [whl_path] [--org] [--msg] [--local-repo] [--verbose]
+	some stuff
 		</pre>
 		</div>
-		<div class="underline">Upload packages on the hub</div>
+		<div class="underline">Official Checkpoints</div>
 	</a>
 	<a
-		href="https://huggingface.co/models?filter=spacy"
+		href="https://huggingface.co/spaces/esc-bench/ESC"
 		class="block overflow-hidden group"
 	>
 		<div
@@ -57,33 +44,9 @@ ESC was proposed in _ESC: A Benchmark For Multi-Domain End-to-End Speech Recogni
 				class="w-full h-40 object-cover overflow-hidden"
 			/>
 		</div>
-		<div class="underline">Find all spaCy models in the Hub</div>
+		<div class="underline">ESC Leaderboard</div>
 	</a>
-	<div class="lg:col-span-3">
-		<p class="mb-4">
-			To upload your spaCy pipelines to the Hub, you can use the <a
-				href="https://github.com/explosion/spacy-huggingface-hub"
-				>spacy-huggingface-hub</a
-			>
-			library.
-		</p>
-		<div
-			class="p-4 bg-gradient-to-b from-gray-50-to-white border border-gray-100 rounded-lg relative mb-4"
-		>
-			<pre
-				class="break-words leading-1 whitespace-pre-line text-xs md:text-sm text-gray-800">
-	pip install spacy-huggingface-hub
-	huggingface-cli login
-	python -m spacy package ./en_ner_fashion ./output --build wheel
-	cd ./output/en_ner_fashion-0.0.0/dist
-	python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl
-		</pre>
-		</div>
-		<p>
-			More information: <a
-				href="https://spacy.io/usage/v3-1#huggingface-hub"
-				class="underline">spaCy 3.1 release post</a
-			>
-		</p>
-	</div>
+    <p class="lg:col-span-3">
+    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>.
+	</p>
 </div>