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---
language:
- ru
tags:
- SER
- speech
- audio
- russian
license: apache-2.0
pipeline_tag: audio-classification
base_model: facebook/hubert-large-ls960-ft
datasets:
- xbgoose/dusha
---
# 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-speech-emotion-recognition-russian-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)
``` |