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@@ -25,7 +25,7 @@ recognition from an end-to-end system pretrained on CommonVoice (French Language
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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).
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- The performance of the model is the following.
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  | Release | Test CER | Test WER | GPUs |
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  |:-------------:|:--------------:|:--------------:| :--------:|
@@ -34,9 +34,9 @@ The performance of the model is the following.
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  ## Pipeline description
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  This ASR system is composed of 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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- 2. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
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  N blocks of convolutional neural networks with normalization 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.
@@ -78,7 +78,7 @@ The SpeechBrain team does not provide any warranty on the performance achieved b
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  year = {2021},
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  publisher = {GitHub},
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  journal = {GitHub repository},
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- howpublished = {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\url{https://github.com/speechbrain/speechbrain}},
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  }
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  ```
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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).
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+ The performance of the model is the following:
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  | Release | Test CER | Test WER | GPUs |
31
  |:-------------:|:--------------:|:--------------:| :--------:|
 
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  ## Pipeline description
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  This ASR system is composed of 2 different but linked blocks:
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+ 1-Tokenizer (unigram) that transforms words into subword units and trained with
38
  the train transcriptions (train.tsv) of CommonVoice (FR).
39
+ 2- Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
40
  N blocks of convolutional neural networks with normalization and pooling on the
41
  frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain
42
  the final acoustic representation that is given to the CTC and attention decoders.
 
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  year = {2021},
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  publisher = {GitHub},
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  journal = {GitHub repository},
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+ howpublished = {\url{https://github.com/speechbrain/speechbrain}},
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  }
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  ```
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