# Model Card for Respeecher/ukrainian-data2vec

This model can be used as Feature Extractor model for Ukrainian language audio data

It can also be used as Backbone for downstream tasks, like ASR, Audio Classification, etc.

### How to Get Started with the Model

```python
from transformers import AutoProcessor, Data2VecAudioModel
import torch
from datasets import load_dataset, Audio

dataset = load_dataset("mozilla-foundation/common_voice_11_0", "uk", split="validation")
# Resample
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))

processor = AutoProcessor.from_pretrained("Respeecher/ukrainian-data2vec")
model = Data2VecAudioModel.from_pretrained("Respeecher/ukrainian-data2vec")

# audio file is decoded on the fly
inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)
```