Update README.md

README.md

@@ -15,17 +15,15 @@ metrics:
 - cer
 ---
 
-
 <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>
 <br/><br/>
 
-# About
-### DVoice
+# DVoice
 DVoice is a community initiative that aims to provide Africa low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrived from social modias. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke.
 
 For this project, AIOX Labs the SI2M Laboratory are joining forces to build the future of technologies together.
 
-### AIOX Labs
+# AIOX Labs
 Based in Rabat, London and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies.
 
 - He is at the service of the growth of groups, the optimization of processes or the improvement of the customer experience.
@@ -35,12 +33,12 @@ Based in Rabat, London and Paris, AIOX-Labs mobilizes artificial intelligence te
 
 Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/)
 
-### SI2M Laboratory
+# SI2M Laboratory
 The Information Systems, Intelligent Systems and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network and System Security, Mathematical Modelling.
 
 Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique)
 
-### SpeechBrain
+# SpeechBrain
 SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
 Website: https://speechbrain.github.io/
 GitHub: https://github.com/speechbrain/speechbrain
@@ -55,7 +53,7 @@ SpeechBrain. For a better experience, we encourage you to learn more about
 |:-------------:|:---------------------------:| -----:| -----:| -----:|
 | v2.0 | 5.51 | 18.46 | 5.85 | 18.28 |
 
-## Pipeline description
+# Pipeline description
 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.
@@ -64,7 +62,7 @@ The obtained final acoustic representation is given to the CTC greedy decoder.
 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.
 
-## Install SpeechBrain
+# Install SpeechBrain
 First of all, please install tranformers and SpeechBrain with the following command:
 ```
 pip install speechbrain transformers
@@ -72,23 +70,23 @@ pip install speechbrain transformers
 Please notice that we encourage you to read the SpeechBrain tutorials and learn more about
 [SpeechBrain](https://speechbrain.github.io).
 
-### Transcribing your own audio files (in Darija)
+# Transcribing your own audio files (in Darija)
 ```python
 from speechbrain.pretrained import EncoderASR
 asr_model = EncoderASR.from_hparams(source="nairaxo/dvoice-darija", savedir="pretrained_models/asr-wav2vec2-dvoice-dar")
 asr_model.transcribe_file('./the_path_to_your_audio_file')
 ```
 
-### Inference on GPU
+# Inference on GPU
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
 
-### Training
+# Training
 To train the model from scratch, please see our GitHub tutorial [here](https://github.com/AIOXLABS/DVoice).
 
-### Limitations
+# Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
 
-#### Referencing SpeechBrain
+# Referencing SpeechBrain
 ```
 @misc{SB2021,
     author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
@@ -98,9 +96,4 @@ The SpeechBrain team does not provide any warranty on the performance achieved b
     journal = {GitHub repository},
     howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
   }
-```
-
-#### About SpeechBrain
-SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
-Website: https://speechbrain.github.io/
-GitHub: https://github.com/speechbrain/speechbrain
+```