Update README.md

README.md

@@ -4,4 +4,61 @@ datasets:
 metrics:
 - wer
 pipeline_tag: automatic-speech-recognition
----
+---
+
+# Wav2vec2-Bert-Fongbe
+
+This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://arxiv.org/abs/2108.06209). This has a WER of 24.20 on [Aloresb dataset](https://huggingface.co/datasets/alaleye/aloresb), fongbe language.
+
+## Model description
+This model is a fine-tuned version of the wav2vec2-BERT architecture on the AlorésB dataset for the Fongbe language. Fongbe, a Gbe language, is predominantly spoken in the southern region of Benin. The model has been fine-tuned specifically for Automatic Speech Recognition (ASR) tasks in this language.
+It can be useful for transcription services, research, and linguistic studies involving Fongbe.
+
+### Details
+
+* Model Name: wav2vec2-bert-fongbe
+* Base Model: facebook/w2v-bert-2.0
+* Fine-tuned on: Aloresb dataset
+* Languages: Fongbe
+* Architecture: Wav2vec2 + BERT
+* Fine-tuning Dataset: Aloresb (Fongbe)
+
+### How to use
+
+```
+import torch
+import soundfile as sf
+from transformers import AutoModelForCTC, Wav2Vec2BertProcessor
+
+model_name = "OctaSpace/wav2vec2-bert-fongbe"
+
+asr_model = AutoModelForCTC.from_pretrained(model_name).to(device)
+processor = Wav2Vec2BertProcessor.from_pretrained(model_name)
+
+audio_input, _ = sf.read(file)
+
+inputs = processor([audio_input], sampling_rate=16_000).input_features
+features = torch.tensor(inputs)
+
+with torch.no_grad():
+  logits = asr_model(features).logits
+
+predicted_ids = torch.argmax(logits, dim=-1)
+predictions = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+
+```
+
+### Training Procedure
+
+The model was fine-tuned on the Aloresb dataset, which contains audio recordings and transcriptions in Fongbe.
+
+### Training Parameters:
+
+Optimizer: AdamW
+Learning Rate: 3e-5
+Batch Size: 3
+Epochs: 3
+Evaluation Results
+The model was evaluated using the Word Error Rate (WER) metric on a test set. Here are the results:
+
+WER: 24.20%