Added example

README.md

@@ -51,25 +51,50 @@ Further details are available in the corresponding [**paper**](https://huggingfa
 ### Usage
 
 ```python 
-import torch
-import torch.nn as nn
-from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
+    import torch
+    import torch.nn as nn
+    from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
+
+
+    # CONFIG and MODEL SETUP
+    model_name = 'amiriparian/ExHuBERT'
+    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-base-ls960")
+    model = AutoModelForAudioClassification.from_pretrained(model_name, trust_remote_code=True,
+                                                            revision="b158d45ed8578432468f3ab8d46cbe5974380812")
+
+    # Freezing half of the encoder for further transfer learning
+    model.freeze_og_encoder()
+
+    sampling_rate = 16000
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+
+
+
+    # Example application from a local audiofile
+    import numpy as np
+    import librosa
+    import torch.nn.functional as F
+    # Sample taken from the Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487
+    waveform, sr_wav = librosa.load("YAF_date_angry.wav")
+    # Max Padding to 3 Seconds at 16k sampling rate for the best results
+    waveform = feature_extractor(waveform, sampling_rate=sampling_rate,padding = 'max_length',max_length = 48000)
+    waveform = waveform['input_values'][0]
+    waveform = waveform.reshape(1, -1)
+    waveform = torch.from_numpy(waveform).to(device)
+    with torch.no_grad():
+        output = model(waveform)
+        output = F.softmax(output.logits, dim = 1)
+        output = output.detach().cpu().numpy().round(2)
+        print(output)
+
+        # [[0.      0.      0.      1.      0.      0.]]
+        #          Low          |          High                 Arousal
+        # Neg.     Neut.   Pos. |  Neg.    Neut.   Pos          Valence
+        # Disgust, Neutral, Kind| Anger, Surprise, Joy          Example emotions
 
 
 
-# CONFIG and MODEL SETUP
-model_name = 'amiriparian/ExHuBERT'
-feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-base-ls960")
-model = AutoModelForAudioClassification.from_pretrained(model_name, trust_remote_code=True,revision="b158d45ed8578432468f3ab8d46cbe5974380812")
-
-# Freezing half of the encoder for further transfer learning
-model.freeze_og_encoder()
-
-sampling_rate=16000 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = model.to(device)
-
-
 ```
 
 ### Citation Info