Update README.md

README.md

@@ -19,11 +19,11 @@ metrics:
 - Accuracy
 widget:
 - example_title: Australian English
-  src: australia_1.wav
+  src: data/australia_1.wav
 - example_title: African English
-  src: african_1.wav
+  src: data/african_1.wav
 - example_title: Canadian English
-  src: canada_1.wav
+  src: data/canada_1.wav
 ---
 
 
@@ -32,22 +32,29 @@ widget:
 
 # Accent Identification from Speech Recordings with ECAPA embeddings on CommonAccent
 
-This repository provides all the necessary tools to perform accent identification from speech recordings with SpeechBrain.
-The system uses a model pretrained on the CommonAccent dataset in English (16 accents).
+This repository provides all the necessary tools to perform accent identification from speech recordings with [SpeechBrain](https://github.com/speechbrain/speechbrain).
+The system uses a model pretrained on the CommonAccent dataset in English (16 accents). This system is based on the CommonLanguage Recipe located here: https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage
+
+
 The provided system can recognize the following 16 languages from short speech recordings:
 
 ```
 african australia bermuda canada england hongkong indian ireland malaysia newzealand philippines scotland singapore southatlandtic us wales
 ```
 
+<a href="https://github.com/JuanPZuluaga/accent-recog-slt2022"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green"> </a> Github repository link: https://github.com/JuanPZuluaga/accent-recog-slt2022
+
+
 ### To UPDATE ALL BELOW
 
 For a better experience, we encourage you to learn more about
 [SpeechBrain](https://speechbrain.github.io). The given model performance on the test set is:
 
-| Release | Accuracy (%)
+| Release (dd/mm/yyyy) | Accuracy (%)
 |:-------------:|:--------------:|
-| 30-06-21 | 85.0 | 
+| 01-08-2023 (this model) | 87 | 
+| 01-08-2023 (this model trained without data augmentation) | 85 | 
+| 01-08-2023 (this model trained from scratch, no paremeter transfer) | 82 | 
 
 
 ## Pipeline description
@@ -72,13 +79,13 @@ Please notice that we encourage you to read our tutorials and learn more about
 ```python
 import torchaudio
 from speechbrain.pretrained import EncoderClassifier
-classifier = EncoderClassifier.from_hparams(source="speechbrain/lang-id-commonlanguage_ecapa", savedir="pretrained_models/lang-id-commonlanguage_ecapa")
-# Italian Example
-out_prob, score, index, text_lab = classifier.classify_file('speechbrain/lang-id-commonlanguage_ecapa/example-it.wav')
+classifier = EncoderClassifier.from_hparams(source="Jzuluaga/accent-id-commonaccent_ecapa", savedir="pretrained_models/accent-id-commonaccent_ecapa")
+# Irish Example
+out_prob, score, index, text_lab = classifier.classify_file('Jzuluaga/accent-id-commonaccent_ecapa/data/ireland_1.wav')
 print(text_lab)
 
-# French Example
-out_prob, score, index, text_lab = classifier.classify_file('speechbrain/lang-id-commonlanguage_ecapa/example-fr.wav')
+# Malaysia Example
+out_prob, score, index, text_lab = classifier.classify_file('Jzuluaga/accent-id-commonaccent_ecapa/data/malaysia_1.wav')
 print(text_lab)
 ```
 
@@ -86,31 +93,38 @@ print(text_lab)
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
 
 ### Training
-The model was trained with SpeechBrain (a02f860e).
+
+The model was trained with SpeechBrain.
+
 To train it from scratch follow these steps:
+
 1. Clone SpeechBrain:
 ```bash
 git clone https://github.com/speechbrain/speechbrain/
 ```
+
 2. Install it:
-```
+```bash
 cd speechbrain
 pip install -r requirements.txt
 pip install -e .
 ```
 
-3. Run Training:
-```
-cd recipes/CommonLanguage/lang_id
-python train.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder
+3. Clone our repository in https://github.com/JuanPZuluaga/accent-recog-slt2022:
+
+```bash
+git clone https://github.com/JuanPZuluaga/accent-recog-slt2022
+cd CommonAccent/accent_id
+python train.py hparams/train_ecapa_tdnn.yaml
 ```
 
-You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1sD2u0MhSmJlx_3RRgwsYzevX81RM8-WE?usp=sharing).
+You can find our training results (models, logs, etc) in this repository's `Files and versions` page.
 
 ### Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
 
 #### Referencing ECAPA
+
 ```@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
   author    = {Brecht Desplanques and
                Jenthe Thienpondt and