Create README.md

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: audio-classification
+tags:
+- wavlm
+- msp-podcast
+- emotion-recognition
+- audio
+- speech
+- valence
+- lucas
+- speech-emotion-recognition
+---
+The model was trained on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) for the Odyssey 2024 Emotion Recognition competition baseline<br>
+This particular model is the single-task specialized valence model, which predict valence in a range of approximately 0...1. 
+
+
+
+# Benchmarks
+CCC based on Test3 and Development sets of the Odyssey Competition
+<table style="width:500px">
+  <tr><th colspan=6 align="center" >Sinle-Task Setup</th></tr>
+  <tr><th colspan=3 align="center">Test 3</th><th colspan=3 align="center">Development</th></tr>
+  <tr>   <td>Val</td> <<td>Val</td>  </tr>
+  <tr>  <td> 0.607</td>  <td>0.709</td>  </tr>
+</table>
+ 
+
+
+For more details:  [demo](https://huggingface.co/spaces/3loi/WavLM-SER-Multi-Baseline-Odyssey2024), [paper/soon]() and [GitHub](https://github.com/MSP-UTD/MSP-Podcast_Challenge/tree/main).
+
+
+```
+@InProceedings{Goncalves_2024,
+            author={L. Goncalves and A. N. Salman and A. {Reddy Naini} and L. Moro-Velazquez and T. Thebaud and L. {Paola Garcia} and N. Dehak and B. Sisman and C. Busso},
+            title={Odyssey2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results},
+            booktitle={Odyssey 2024: The Speaker and Language Recognition Workshop)},
+            volume={To appear},
+            year={2024},
+            month={June},
+            address =  {Quebec, Canada},
+}
+```
+
+
+# Usage
+```python
+from transformers import AutoModelForAudioClassification
+import librosa, torch
+
+#load model
+model = AutoModelForAudioClassification.from_pretrained("3loi/SER-Odyssey-Baseline-WavLM-Valence", 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=model.config.sampling_rate)
+
+#normalize the audio by mean/std
+norm_wav = (raw_wav - mean) / (std+0.000001)
+
+#generate the mask
+mask = torch.ones(1, len(norm_wav))
+
+#batch it (add dim)
+wavs = torch.tensor(norm_wav).unsqueeze(0)
+
+
+#predict
+with torch.no_grad():
+    pred = model(wavs, mask)
+
+print(model.config.id2label) 
+print(pred)
+#{0: 'valence'}
+#tensor([[0.3670]])
+```