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---
language:
- cs
- en
tags:
- Czech
- English
- bilingual
- KKY
- FAV
license: cc-by-nc-sa-4.0
---
# wav2vec2-base-cs-en-100k
This is a bilingual Wav2Vec 2.0 base model pre-trained from 100 thousand hours of speech (50 thousand hours of Czech and 50 thousand hours of English).
It has been released along with a paper **A Comparative Analysis of Bilingual and Trilingual Wav2Vec Models for
Automatic Speech Recognition in Multilingual Oral History Archives** accepted to INTERSPEECH2024 conference.
## Paper
The pre-print of our paper is available at http://arxiv.org/abs/2407.17160.
### All pre-trained models released along with the paper
- [fav-kky/wav2vec2-base-cs-50k](https://huggingface.co/fav-kky/wav2vec2-base-cs-50k) (monolingual Czech)
- [fav-kky/wav2vec2-base-de-50k](https://huggingface.co/fav-kky/wav2vec2-base-de-50k) (monolingual German)
- [fav-kky/wav2vec2-base-cs-en-100k](https://huggingface.co/fav-kky/wav2vec2-base-cs-en-100k) (bilingual Czech+English)
- [fav-kky/wav2vec2-base-cs-de-100k](https://huggingface.co/fav-kky/wav2vec2-base-cs-de-100k) (bilingual Czech+German)
- [fav-kky/wav2vec2-base-en-de-100k](https://huggingface.co/fav-kky/wav2vec2-base-en-de-100k) (bilingual English+German)
- [fav-kky/wav2vec2-base-cs-en-de-150k](https://huggingface.co/fav-kky/wav2vec2-base-cs-en-de-150k) (trilingual Czech+English+German)
## Citation
If you find this model useful, please cite our paper:
```
@inproceedings{lehecka2024bitrilingual,
title = {{A Comparative Analysis of Bilingual and Trilingual Wav2Vec Models for Automatic Speech Recognition in Multilingual Oral History Archives}},
author = {
Jan Lehe\v{c}ka and
Josef V. Psutka and
Lubo\v{s} \v{S}m\'{i}dl and
Pavel Ircing and
Josef Psutka
},
booktitle={Proc. Interspeech 2024},
note={In Press},
year={2024},
url={https://arxiv.org/abs/2407.17160},
}
```
## Usage
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](https://huggingface.co/blog/fine-tune-wav2vec2-english) on labeled ASR data.
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-en-100k")
model = Wav2Vec2Model.from_pretrained("fav-kky/wav2vec2-base-cs-en-100k")
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]
```
## Related works |