Update README.md

README.md

@@ -45,10 +45,10 @@ More information needed
 
 ## Training and evaluation data
 
-The model was trained on version 7 of the Luganda dataset of Mozilla common voices dataset. We used the train and validation dataset for training and the test dataset for validation.
+The model was trained on version 7 of the Luganda dataset of Mozilla common voices dataset. We used the train and validation dataset for training and the test dataset for validation. 
 
 ## Training procedure
-The model was finetuned for 10 epochs using a learning rate of 5e-05. We used the AdamW optimizer.
+We trained the model on a 32 GB V100 GPU for 10 epochs using a learning rate of 5e-05. We used the AdamW optimizer. 
 
 ### Training hyperparameters
 
@@ -82,3 +82,36 @@ The following hyperparameters were used during training:
 - Pytorch 2.2.1+cu121
 - Datasets 2.17.0
 - Tokenizers 0.15.2
+
+### Usage
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+test_dataset = load_dataset("common_voice", "lg", split="test[:10]") 
+
+processor = Wav2Vec2Processor.from_pretrained("dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0") 
+model = Wav2Vec2ForCTC.from_pretrained("dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the audio files as arrays
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset["sentence"][:2])
+```