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  This repository provides all the necessary tools to perform automatic speech
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  recognition from an end-to-end system pretrained on CommonVoice (FR) within
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- SpeechBrain. For a better experience we encourage you to learn more about
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  [SpeechBrain](https://speechbrain.github.io). The given ASR model performance are:
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  | Release | Test CER | Test WER | GPUs |
@@ -27,19 +27,19 @@ SpeechBrain. For a better experience we encourage you to learn more about
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  ## Pipeline description
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- This ASR system is composed with 2 different but linked blocks:
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  1. Tokenizer (unigram) that transforms words into subword units and trained with
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  the train transcriptions (train.tsv) of CommonVoice (FR).
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  3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
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- N blocks of convolutional neural networks with normalisation and pooling on the
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  frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain
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  the final acoustic representation that is given to the CTC and attention decoders.
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  ## Intended uses & limitations
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- This model has been primilarly developed to be run within SpeechBrain as a pretrained ASR model
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  for the French language. Thanks to the flexibility of SpeechBrain, any of the 2 blocks
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- detailed above can be extracted and connected to you custom pipeline as long as SpeechBrain is
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  installed.
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  ## Install SpeechBrain
 
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  This repository provides all the necessary tools to perform automatic speech
20
  recognition from an end-to-end system pretrained on CommonVoice (FR) within
21
+ SpeechBrain. For a better experience, we encourage you to learn more about
22
  [SpeechBrain](https://speechbrain.github.io). The given ASR model performance are:
23
 
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  | Release | Test CER | Test WER | GPUs |
 
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  ## Pipeline description
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+ This ASR system is composed of 2 different but linked blocks:
31
  1. Tokenizer (unigram) that transforms words into subword units and trained with
32
  the train transcriptions (train.tsv) of CommonVoice (FR).
33
  3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
34
+ N blocks of convolutional neural networks with normalization and pooling on the
35
  frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain
36
  the final acoustic representation that is given to the CTC and attention decoders.
37
 
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  ## Intended uses & limitations
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+ This model has been primarily developed to be run within SpeechBrain as a pretrained ASR model
41
  for the French language. Thanks to the flexibility of SpeechBrain, any of the 2 blocks
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+ detailed above can be extracted and connected to your custom pipeline as long as SpeechBrain is
43
  installed.
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  ## Install SpeechBrain