Update README.md

README.md

@@ -28,7 +28,7 @@ The performance of the model is the following:
 
 | Release | Test WER | GPUs |
 |:-------------:|:--------------:| :--------:|
-| 20-05-22 | 3.08 | 1xV100 32GB |
+| 20-05-22 | 3.09 | 1xV100 32GB |
 
 ## Pipeline description
 
@@ -65,6 +65,28 @@ asr_model.transcribe_file('speechbrain/asr-crdnn-rnnlm-librispeech/example.wav')
 ### Inference on GPU
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
 
+### Training
+The model was trained with SpeechBrain (Commit hash: '2abd9f01').
+To train it from scratch follow these steps:
+1. Clone SpeechBrain:
+```bash
+git clone https://github.com/speechbrain/speechbrain/
+```
+2. Install it:
+```
+cd speechbrain
+pip install -r requirements.txt
+pip install -e .
+```
+
+3. Run Training:
+```
+cd recipes/LibriSpeech/ASR/seq2seq/
+python train.py hparams/train_BPE_1000.yaml --data_folder=your_data_folder
+```
+
+You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1SAndjcThdkO-YQF8kvwPOXlQ6LMT71vt?usp=sharing).
+
 ### Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
 
@@ -77,7 +99,7 @@ The SpeechBrain team does not provide any warranty on the performance achieved b
     year = {2021},
     publisher = {GitHub},
     journal = {GitHub repository},
-    howpublished = {\url{https://github.com/speechbrain/speechbrain}},
+    howpublished = {\\url{https://github.com/speechbrain/speechbrain}},
   }
 ```