update readme

README.md

@@ -24,7 +24,7 @@ SpeechBrain. For a better experience we encourage you to learn more about
 
 | Release | Test clean WER | Test other WER | GPUs |
 |:-------------:|:--------------:|:--------------:|:--------:|
-| 05-03-21 | 2.90 | 8.51 | 1xV100 16GB |
+| 05-03-21 | 2.55 | 5.99 | 2xV100 32GB |
 
 ## Pipeline description
 
@@ -32,11 +32,8 @@ This ASR system is composed with 3 different but linked blocks:
 1. Tokenizer (unigram) that transforms words into subword units and trained with
 the train transcriptions of LibriSpeech.
 2. Neural language model (Transformer LM) trained on the full 10M words dataset.
-3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
-N blocks of convolutional neural networks with normalisation and pooling on the
-frequency domain. Then, a bidirectional LSTM with projection layers is connected
-to a final DNN to obtain the final acoustic representation that is given to
-the CTC and attention decoders.
+3. Acoustic model made of a transformer encoder and a joint decoder with CTC +
+transformer. Hence, the decoding also incorporate the CTC probabilities.
 
 ## Intended uses & limitations
 
@@ -61,7 +58,7 @@ Please notice that we encourage you to read our tutorials and learn more about
 ```python
 from speechbrain.pretrained import EncoderDecoderASR
 
-asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-transformerlm-librispeech")
+asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-transformer-transformerlm-librispeech")
 asr_model.transcribe_file("path_to_your_file.wav")
 
 ```