Update README.md

README.md

@@ -4,8 +4,42 @@ language:
 - en
 pipeline_tag: audio-classification
 ---
-The model was developed for the Odyssey 2024 Emotion Recognition competition trained on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html).
-
+The model was developed for the Odyssey 2024 Emotion Recognition competition trained on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html).<br>
 This particular model is the multi-attributed based model which predict arousal, dominance and valence in a range of approximately 0...1. 
 
-For more details:  [paper/soon]() and [tutorial](https://github.com/MSP-UTD/MSP-Podcast_Challenge/tree/main).
+For more details:  [paper/soon]() and [GitHub](https://github.com/MSP-UTD/MSP-Podcast_Challenge/tree/main).
+
+
+# Usage
+```python
+from transformers import AutoModelForAudioClassification
+import librosa, torch
+
+#load model
+model = AutoModelForAudioClassification.from_pretrained("3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes", trust_remote_code=True)
+
+#get mean/std
+mean = model.config.mean
+std = model.config.std
+
+
+#load an audio file
+audio_path = "/path/to/audio.wav"
+raw_wav, _ = librosa.load(audio_path, sr=16000)
+
+#normalize the audio by mean/std
+norm_wav = (raw_wav - mean) / (std+0.000001)
+
+#generate the mask
+mask = torch.ones(1, len(norm_wav))
+wavs = torch.tensor(norm_wav).unsqueeze(0)
+
+
+#predict
+with torch.no_grad():
+    pred = model(wavs, mask)
+
+print(model.config.id2label) #arousal, dominance, valence
+print(pred)
+
+```