---
language: "cs"
tags:
- Czech
- KKY
- FAV
license: "cc-by-nc-sa-4.0"
---

# wav2vec2-base-cs-80k-ClTRUS
**C**zech **l**anguage **TR**ransformer from **U**nlabeled **S**peech (ClTRUS) is a monolingual Czech Wav2Vec 2.0 base model pre-trained from 80 thousand hours of Czech speech.

This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. 

**Note:** This is a checkpoint of the model after 4 epochs over the whole dataset. If you want some earlier or later checkpoints, please feel free to contact the author (jlehecka(at)kky.zcu.cz).

## Pretraining data 
More than 80 thousand hours of unlabeled Czech speech:
- recordings from radio (22k hours),
- unlabeled data from VoxPopuli dataset (18.7k hours),
- TV shows (15k hours), 
- shadow speakers (12k hours), 
- sports (5k hours), 
- telephone data (2k hours),
- and a smaller amount of data from several other domains including the public CommonVoice dataset.

## Usage
Inputs must be 16kHz mono audio files.

This model can be used e.g. to extract per-frame contextual embeddings from audio:
```python
from transformers import Wav2Vec2Model, Wav2Vec2FeatureExtractor
import torchaudio

feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("fav-kky/wav2vec2-base-cs-80k-ClTRUS")
model = Wav2Vec2Model.from_pretrained("fav-kky/wav2vec2-base-cs-80k-ClTRUS")

speech_array, sampling_rate = torchaudio.load("/path/to/audio/file.wav")
inputs = feature_extractor(
    speech_array, 
    sampling_rate=16_000, 
    return_tensors="pt"
)["input_values"][0]

output = model(inputs)
embeddings = output.last_hidden_state.detach().numpy()[0]
```

## Speech recognition results
After fine-tuning, the model scored the following results on public datasets:
- Czech portion of CommonVoice v7.0: **WER = 3.8%**
- Czech portion of VoxPopuli: **WER = 8.8%**

See our paper for details.

## Paper 
The preprint of our paper (accepted to INTERSPEECH 2022) is available at http://arxiv.org/abs/2206.07627

## Citation
If you find this model useful, please cite our paper:
```
@inproceedings{wav2vec2-base-cs-80k-ClTRUS,
  title = {{Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech}},
  author = {
    Jan Lehe\v{c}ka and 
    Jan \v{S}vec and 
    Ale\v{s} Pra\v{z}\'ak and 
    Josef V. Psutka
  },
  booktitle={Proc. Interspeech 2022},
  pages={1831--1835},
  year = {2022},
  doi={10.21437/Interspeech.2022-10439}
}
```

## Related works
- [Transformer-based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project](https://arxiv.org/abs/2206.07666)
- [Yehor/wav2vec2-xls-r-base-uk-with-small-lm](https://huggingface.co/Yehor/wav2vec2-xls-r-base-uk-with-small-lm)