Update README.md

README.md

@@ -1,3 +1,57 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- am
+- sw
+- wo
+tags:
+- speech pretraining
+- african
+- fongbe
+- swahili
+- wolof
+- wav2vec2
+- ahmaric
+---
+
+# Wav2vec2-large
+
+## Model description
+This model is a pre-trained instance of the Wav2vec 2.0 architecture, specifically focused on processing and understanding four major African languages: Fongbe, Swahili,
+Amharic, and Wolof. The model leverages unlabelled audio data in these languages to learn rich, language-specific representations before any fine-tuning on downstream tasks.
+
+## Training data
+
+The model was pre-trained using a diverse set of audio recordings from [ALFFA](https://github.com/getalp/ALFFA_PUBLIC) dataset.
+
+* Fongbe: A Gbe language, primarily spoken in Benin and parts of Nigeria and Togo.
+* Swahili: A Bantu language, widely spoken across East Africa including Tanzania, Kenya, Uganda, Rwanda, and Burundi.
+* Amharic: The official language of Ethiopia, belonging to the Semitic branch of the Afroasiatic language family.
+* Wolof: Predominantly spoken in Senegal, The Gambia, and Mauritania.
+
+## Model Architecture
+This model uses the large version of wav2vec 2.0 architecture developed by Facebook AI, which includes a multi-layer convolutional neural network that processes 
+raw audio signals to produce contextual representations. These representations are then used to predict the original audio input before any labels are provided,
+following a self-supervised training methodology.
+
+
+## Usage
+This model is intended for use in Automatic Speech Recognition (ASR), audio classification, and other audio-related tasks in Fongbe, Swahili, Amharic, and Wolof. 
+To use this model for fine-tuning on a specific task, you can load it via the Hugging Face Transformers library:
+
+```
+from transformers import Wav2Vec2Processor, Wav2Vec2Model
+
+processor = Wav2Vec2Processor.from_pretrained("your-username/wav2vec2-african-languages")
+model = Wav2Vec2Model.from_pretrained("your-username/wav2vec2-african-languages")
+
+```
+
+## Performance
+The model's performance was evaluated using a held-out validation set of audio recordings. 
+The effectiveness of the pre-trained representations was measured in terms of their ability to be fine-tuned to specific tasks such as ASR. 
+Note that detailed performance metrics will depend on the specifics of the fine-tuning process and the quality of the labeled data used.
+
+## Limitations
+The model might exhibit variability in performance across different languages due to varying amounts of training data available for each language.
+Performance may degrade with audio inputs that significantly differ from the types of recordings seen during training (e.g., telephone quality audio, noisy environments).