Update README.md

README.md

@@ -9,7 +9,7 @@ tags:
 - Transformer
 license: "apache-2.0"
 datasets:
-- commonvoice
+- Dvoice
 metrics:
 - wer
 - cer
@@ -30,15 +30,14 @@ SpeechBrain. For a better experience, we encourage you to learn more about
 
 # 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.
+- Tokenizer (unigram) that transforms words into subword units and is trained with the train transcriptions.
 - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset.
 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
-First of all, please install tranformers and SpeechBrain with the following command:
+First of all, please install transformers and SpeechBrain with the following command:
 ```
 pip install speechbrain transformers
 ```
@@ -48,15 +47,31 @@ Please notice that we encourage you to read the SpeechBrain tutorials and learn
 # Transcribing your own audio files (in Wolof)
 ```python
 from speechbrain.pretrained import EncoderASR
-asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-wolof", savedir="pretrained_models/asr-wav2vec2-dvoice-wol")
-asr_model.transcribe_file('./the_path_to_your_audio_file')
+asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-wolof", savedir="pretrained_models/asr-wav2vec2-dvoice-wolof")
+asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-wolof/example_wolof.wav')
 ```
 
 # Inference on GPU
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
 
 # Training
-To train the model from scratch, please see our GitHub tutorial [here](https://github.com/AIOXLABS/DVoice).
+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:
+```bash
+cd recipes/DVoice/ASR/CTC
+python train_with_wav2vec2.py hparams/train_wol_with_wav2vec.yaml --data_folder=/localscratch/ALFFA_PUBLIC/ASR/WOLOF/data/
+```
 
 # Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
@@ -74,22 +89,22 @@ The SpeechBrain team does not provide any warranty on the performance achieved b
 ```
 
 # About 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 medias. 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.
+DVoice is a community initiative that aims to provide African 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 retrieved from social media. 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.
+For this project, AIOX Labs and the SI2M Laboratory are joining forces to build the future of technologies together.
 
 # About AIOX Labs
-Based in Rabat, London and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies.
+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.
+- He is at the service of the growth of groups, the optimization of processes, or the improvement of the customer experience.
 - AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods.
-- Business ready data products with a solid algorithmic base and adaptability for the specific needs of each client.
+- Business-ready data products with a solid algorithmic base and adaptability for the specific needs of each client.
 - A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications.
 
 Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/)
 
 # 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.
+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, and 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)