Update README.md

README.md

@@ -16,21 +16,55 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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-# model
+## Model
 
-This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Timit dataset.
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Timit dataset. Check [this notebook](https://www.kaggle.com/code/vitouphy/phoneme-recognition-with-wav2vec2) for training detail.
 
-## Model description
+## Usage 
 
-More information needed
+**Approach 1:** Using HuggingFace's pipeline, this will cover everything end-to-end from raw audio input to text output.
 
-## Intended uses & limitations
+```python
+from transformers import pipeline
 
-More information needed
+# Load the model
+pipe = pipeline(model="vitouphy/wav2vec2-xls-r-300m-phoneme")
+# Process raw audio
+output = pipe("audio_file.wav", chunk_length_s=10, stride_length_s=(4, 2))
+```
+
+**Approach 2:** More custom way to predict phonemes.
+```python
+
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC 
+from datasets import load_dataset
+import torch
+import soundfile as sf
+
+# load model and processor
+processor = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-phoneme")
+model = Wav2Vec2ForCTC.from_pretrained("vitouphy/wav2vec2-xls-r-300m-phoneme")
+
+# Read and process the input
+audio_input, sample_rate = sf.read("audio_file.wav")
+inputs = processor(audio_input, sampling_rate=16_000, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+# Decode id into string
+predicted_ids = torch.argmax(logits, axis=-1)      
+predicted_sentences = processor.batch_decode(predicted_ids)
+print(predicted_sentences)
+
+```
 
 ## Training and evaluation data
+We use [DARPA TIMIT dataset](https://www.kaggle.com/datasets/mfekadu/darpa-timit-acousticphonetic-continuous-speech) for this model.
+- We split into **80/10/10** for training, validation, and testing respectively. 
+- That roughly corresponds to about **137/17/17** minutes. 
+- The model obtained **7.996%** on this test set.
 
-More information needed
 
 ## Training procedure