adding the link to a pretrained model for testing

Webpage/02-pytorch-asr.md

@@ -396,7 +396,51 @@ To test of an audio clip, you can record yourself as a mp3 and call main_ctc :
 mymachine:~:mylogin$ python3 main_ctc.py test --modelpath a/path/to/a/best_model.pt --audiofile myclip.mp3
 ```
 
-If you used non default values for the other arguments (the ones for the model), you must provide them again. We need them in the test function for recreating the exact same model than the one used during training.
+If you used non default values for the other arguments (the ones for the model), you must provide them again. We need them in the test function for recreating the exact same model than the one used during training. Audio clips are provided in the test fold. You can check the list of the test audio clips with their transcripts in the `/mounts/Datasets4/CommonVoice/v15.0/fr/test.tsv` file.
+
+If you do not have yet a trained model, you can download [the best_ctc_42077.pt](https://centralesupelec-my.sharepoint.com/:u:/g/personal/jeremy_fix_centralesupelec_fr/EScVCV_zdYZDpWXBlynPmMYBjCbXVDc0SkTuS6JRn43CSg?e=u7eUWF) checkpoint. This model can be tested with the command line below :
+
+```console
+mymachine:~:mylogin$ python main_ctc.py test --modelpath best_ctc_42077.pt --audiofile /mounts/Datasets4/CommonVoice/v15.0/fr/clips/common_voice_fr_37118026.mp3
+Log prob    Sequence
+7.82      ¶une contrefa❌on fut publiee en belgique.¦
+```
+
+The summary file of this run is given below. This model has been trained with SpecAugment, Dropout(0.5), L2 regularization (0.0001), gradient norm clipping with all the available audio clips. The training was performed for almost 4 days.
+
+	## Summary of the model architecture
+	=================================================================
+	Layer (type:depth-idx)                   Param #
+	=================================================================
+	CTCModel                                 --
+	├─Sequential: 1-1                        --
+	│    └─Conv2d: 2-1                       14,464
+	│    └─BatchNorm2d: 2-2                  64
+	│    └─Hardtanh: 2-3                     --
+	│    └─Dropout2d: 2-4                    --
+	│    └─Conv2d: 2-5                       236,576
+	│    └─BatchNorm2d: 2-6                  64
+	│    └─Hardtanh: 2-7                     --
+	│    └─Dropout2d: 2-8                    --
+	├─GRU: 1-2                               64,929,792
+	├─Sequential: 1-3                        --
+	│    └─Linear: 2-9                       90,156
+	=================================================================
+	Total params: 65,271,116
+	Trainable params: 65,271,116
+	Non-trainable params: 0
+	=================================================================
+	 Wandb run name : amber-galaxy-55
+
+
+
+	## Executed command :
+	main_ctc.py --logname ctc_42077_0 --batch_size 128 --num_epochs 100 --base_lr 0.0001 --min_duration 0.0 --max_duration 60.0 --nlayers_rnn 4 --nhidden_rnn 1024 --weight_decay 0.0001 --dropout 0.5 --grad_clip 20 --datasetversion v15.0 --seed 12345 --train_augment --scheduler train
+
+	## Args : 
+	 Namespace(audiofile=None, base_lr=0.0001, batch_size=128, beamsearch=False, beamwidth=10, cell_type='GRU', command='train', datasetroot='/mounts/Datasets4/CommonVoice/', datasetversion='v15.0', debug=False, dropout=0.5, grad_clip=20.0
+	, logname='ctc_42077_0', max_duration=60.0, min_duration=0.0, modelpath=None, nhidden_rnn=1024, nlayers_rnn=4, nmels=20, nthreads=6, num_epochs=100, scheduler=True, seed=12345, train_augment=True, weight_decay=0.0001)
+
 
 ## Extras