Update README.md

README.md

@@ -4,7 +4,6 @@ thumbnail:
 pipeline_tag: automatic-speech-recognition
 tags:
 - CTC
-- Attention
 - pytorch
 - speechbrain
 - Transformer
@@ -19,7 +18,7 @@ metrics:
 <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
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-# wav2vec 2.0 with CTC/Attention trained on CommonVoice English (No LM)
+# wav2vec 2.0 with CTC trained on CommonVoice English (No LM)
 
 This repository provides all the necessary tools to perform automatic speech
 recognition from an end-to-end system pretrained on CommonVoice (English Language) within
@@ -37,8 +36,8 @@ The performance of the model is the following:
 This ASR system is composed of 2 different but linked blocks:
 - Tokenizer (unigram) that transforms words into subword units and trained with
 the train transcriptions (train.tsv) of CommonVoice (EN).
-- Acoustic model (wav2vec2.0 + CTC/Attention). A pretrained wav2vec 2.0 model ([wav2vec2-lv60-large](https://huggingface.co/facebook/wav2vec2-large-lv60)) is combined with two DNN layers and finetuned on CommonVoice En. 
-The obtained final acoustic representation is given to the CTC and attention decoders.
+- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-lv60-large](https://huggingface.co/facebook/wav2vec2-large-lv60)) is combined with two DNN layers and finetuned on CommonVoice En. 
+The obtained final acoustic representation is given to the CTC decoder.
 
 The system is trained with recordings sampled at 16kHz (single channel).
 The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.