Update README.md

README.md

@@ -4,7 +4,7 @@ language:
 thumbnail: null
 tags:
 - automatic-speech-recognition
-- transducer
+- transformer
 - Attention
 - pytorch
 - speechbrain
@@ -14,7 +14,7 @@ datasets:
 metrics:
 - name: Test WER
   type: wer
-  value: ' 17.58'
+  value: ' 16.00'
 ---
 
 <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>
@@ -29,7 +29,7 @@ The performance of the model is the following:
 
 | Release | Test CER | Test WER | GPUs |
 |:-------------:|:--------------:|:--------------:| :--------:|
-| 15.08.23 | 7.61 | 17.58 | 1xV100 32GB |
+| 15.08.23 | 4.20 | 16.00 | 1xV100 32GB |
 
 ## Credits
 The model is provided by [vitas.ai](https://www.vitas.ai/).
@@ -39,7 +39,7 @@ 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).
-- Transducers augment CTC by adding an autoregressive predictor and a join network.
+- transformer augment CTC by adding an autoregressive predictor and a join network.
 
 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.
@@ -92,7 +92,7 @@ pip install -e .
 3. Run Training:
 
 ```
-cd recipes/CommonVoice/ASR/transducer
+cd recipes/CommonVoice/ASR/transformer
 python train.py hparams/train_fr.yaml --data_folder=your_data_folder
 ```