Create README.md

README.md

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+This model is fine tuned on the IEMOCAP_speaker_indpt_Ses05F_Ses05M.pickle dataset, which use Ses05F as validation speaker and Ses05M as test speaker. So it is a speaker independent model.
+
+The initial pre-trained model is facebook/wav2vec2-base. The fine tune dataset only contains 4 common emotions of IEMOCAP (happy, angry, sad, neutral), **without frustration**. No audio augmentation is applied. Fine-tune dataset audios are also not padded or trimed to fixed length. The length setting is done when fine tuning the transoformer using max_length = 8 sec in the feature extractor. 
+
+After **10** epoches of training, the validation accuracy is around **67%**. 
+
+In order to impliment this model: run the following code in a python script:
+
+```
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+import librosa
+import torch
+
+target_sampling_rate = 16000
+model_name = 'canlinzhang/Sorenson_fine_tune_wav2vec2-on_IEMOCAP_no_aug_no_fru_2'
+my_token = my_token
+audio_path = your_audio_path
+
+#build id and label dicts    
+id2label = {0:'neu', 1:'ang', 2:'sad', 3:'hap'}
+label2id = {'neu':0, 'ang':1, 'sad':2, 'hap':3}
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+
+model = AutoModelForAudioClassification.from_pretrained(model_name, use_auth_token = my_token)
+
+y_ini, sr_ini = librosa.load(audio_path, sr=target_sampling_rate)
+
+inputs = feature_extractor(y_ini, sampling_rate=target_sampling_rate, return_tensors="pt")
+
+logits = model(**inputs).logits
+
+predicted_class_ids = torch.argmax(logits).item()
+
+pred_class = id2label[predicted_class_ids]
+
+print(pred_class)
+```