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README.md

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+# HuBERT fine-tuned on DUSHA dataset for speech emotion recognition in russian language
+
+The pre-trained model is this one - [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft)
+
+The DUSHA dataset used can be found [here](https://github.com/salute-developers/golos/tree/master/dusha#dataset-structure)
+
+# Fine-tuning
+
+Fine-tuned in Google Colab using Pro account with A100 GPU
+
+Freezed all layers exept projector, classifier and all 24 HubertEncoderLayerStableLayerNorm layers
+
+Used half of the train dataset
+
+# Training parameters
+
+2 epochs \
+train batch size = 8 \
+eval batch size = 8 \
+gradient accumulation steps = 4 \
+learning rate = 5e-5 without warm up and decay
+
+# Metrics
+
+Achieved \
+accuracy = 0.86 \
+balanced = 0.76 \
+macro f1 score = 0.81 \
+on test set, improving accucary and f1 score compared to dataset baseline
+
+# Usage
+
+```python
+from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor
+import torchaudio
+import torch
+
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-large-ls960-ft")
+model = HubertForSequenceClassification.from_pretrained("xbgoose/hubert-dusha-finetuned")
+num2emotion = {0: 'neutral', 1: 'angry', 2: 'positive', 3: 'sad', 4: 'other'}
+
+filepath = "path/to/audio.wav"
+
+waveform, sample_rate = torchaudio.load(filepath, normalize=True)
+transform = torchaudio.transforms.Resample(sample_rate, 16000)
+waveform = transform(waveform)
+
+inputs = feature_extractor(
+        waveform, 
+        sampling_rate=feature_extractor.sampling_rate, 
+        return_tensors="pt",
+        padding=True,
+        max_length=16000 * 10,
+        truncation=True
+    )
+
+logits = model(inputs['input_values'][0]).logits
+predictions = torch.argmax(logits, dim=-1)
+predicted_emotion = num2emotion[predictions.numpy()[0]]
+print(predicted_emotion)
+```