
# wav2vec2-base-sk-17k
This is a monolingual Slovak Wav2Vec 2.0 base model pre-trained from 17 thousand of hours of Slovak 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 data. 

The model was initialized from Czech pre-trained model [fav-kky/wav2vec2-base-cs-80k-ClTRUS](https://huggingface.co/fav-kky/wav2vec2-base-cs-80k-ClTRUS). We found this cross-language transfer learning approach better than pre-training from scratch. See our paper for details.

## Pretraining data 
Almost 18 thousand hours of unlabeled Slovak speech:
- unlabeled data from VoxPopuli dataset (12.2k hours),
- recordings from TV shows (4.5k hours),
- oral history archives (800 hours),
- CommonVoice 13.0 (24 hours)

## 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-sk-17k")
model = Wav2Vec2Model.from_pretrained("fav-kky/wav2vec2-base-sk-17k")

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:
- Slovak portion of CommonVoice v13.0: **WER = 8.82%**
- Slovak portion of VoxPopuli: **WER = 8.88%**

See our paper for details.

## Paper 
The preprint of our paper (accepted to TSD 2023) is available at https://arxiv.org/abs/2306.04399.

## Citation
If you find this model useful, please cite our paper:
```
@inproceedings{wav2vec2-base-sk-17k,
  title = {{Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak}},
  author = {
    Jan Lehe\v{c}ka and 
    Josef V. Psutka and 
    Josef Psutka
  },
  booktitle = {{Text, Speech, and Dialogue}},
  publisher = {{Springer International Publishing}},
  year = {2023},
  note = {(in press)},
  url = {https://arxiv.org/abs/2306.04399},
}
```

## Related papers
- [INTERSPEECH 2022 - Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech](https://www.isca-speech.org/archive/pdfs/interspeech_2022/lehecka22_interspeech.pdf)
- INTERSPEECH 2023 - Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech

## Related models
- [fav-kky/wav2vec2-base-cs-80k-ClTRUS](https://huggingface.co/fav-kky/wav2vec2-base-cs-80k-ClTRUS)

{"language": "sk", "license": "cc-by-nc-sa-4.0", "tags": ["Slovak", "KKY", "FAV"]}
transformers pytorch wav2vec2 pretraining Slovak KKY FAV sk arxiv:2306.04399 license:cc-by-nc-sa-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #pretraining #Slovak #KKY #FAV #sk #arxiv-2306.04399 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us 


# wav2vec2-base-sk-17k
This is a monolingual Slovak Wav2Vec 2.0 base model pre-trained from 17 thousand of hours of Slovak 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 data. 

The model was initialized from Czech pre-trained model fav-kky/wav2vec2-base-cs-80k-ClTRUS. We found this cross-language transfer learning approach better than pre-training from scratch. See our paper for details.

## Pretraining data 
Almost 18 thousand hours of unlabeled Slovak speech:
- unlabeled data from VoxPopuli dataset (12.2k hours),
- recordings from TV shows (4.5k hours),
- oral history archives (800 hours),
- CommonVoice 13.0 (24 hours)

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

This model can be used e.g. to extract per-frame contextual embeddings from audio:


## Speech recognition results
After fine-tuning, the model scored the following results on public datasets:
- Slovak portion of CommonVoice v13.0: WER = 8.82%
- Slovak portion of VoxPopuli: WER = 8.88%

See our paper for details.

## Paper 
The preprint of our paper (accepted to TSD 2023) is available at URL

If you find this model useful, please cite our paper:


## Related papers
- INTERSPEECH 2022 - Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech
- INTERSPEECH 2023 - Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech

## Related models
- fav-kky/wav2vec2-base-cs-80k-ClTRUS

# wav2vec2-base-sk-17k
This is a monolingual Slovak Wav2Vec 2.0 base model pre-trained from 17 thousand of hours of Slovak 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 data. 

The model was initialized from Czech pre-trained model fav-kky/wav2vec2-base-cs-80k-ClTRUS. We found this cross-language transfer learning approach better than pre-training from scratch. See our paper for details. ## Pretraining data 
Almost 18 thousand hours of unlabeled Slovak speech:
- unlabeled data from VoxPopuli dataset (12.2k hours),
- recordings from TV shows (4.5k hours),
- oral history archives (800 hours),
- CommonVoice 13.0 (24 hours) ## Usage
Inputs must be 16kHz mono audio files.

This model can be used e.g. to extract per-frame contextual embeddings from audio: ## Speech recognition results
After fine-tuning, the model scored the following results on public datasets:
- Slovak portion of CommonVoice v13.0: WER = 8.82%
- Slovak portion of VoxPopuli: WER = 8.88%

See our paper for details. ## Paper 
The preprint of our paper (accepted to TSD 2023) is available at URL

If you find this model useful, please cite our paper: ## Related papers
- INTERSPEECH 2022 - Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech
- INTERSPEECH 2023 - Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech ## Related models
- fav-kky/wav2vec2-base-cs-80k-ClTRUS
TAGS
#transformers #pytorch #wav2vec2 #pretraining #Slovak #KKY #FAV #sk #arxiv-2306.04399 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us 
 # wav2vec2-base-sk-17k
This is a monolingual Slovak Wav2Vec 2.0 base model pre-trained from 17 thousand of hours of Slovak 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 data. 

The model was initialized from Czech pre-trained model fav-kky/wav2vec2-base-cs-80k-ClTRUS. We found this cross-language transfer learning approach better than pre-training from scratch. See our paper for details. ## Pretraining data 
Almost 18 thousand hours of unlabeled Slovak speech:
- unlabeled data from VoxPopuli dataset (12.2k hours),
- recordings from TV shows (4.5k hours),
- oral history archives (800 hours),
- CommonVoice 13.0 (24 hours) ## Usage
Inputs must be 16kHz mono audio files.

This model can be used e.g. to extract per-frame contextual embeddings from audio: ## Speech recognition results
After fine-tuning, the model scored the following results on public datasets:
- Slovak portion of CommonVoice v13.0: WER = 8.82%
- Slovak portion of VoxPopuli: WER = 8.88%

See our paper for details. ## Paper 
The preprint of our paper (accepted to TSD 2023) is available at URL

If you find this model useful, please cite our paper: ## Related papers
- INTERSPEECH 2022 - Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech
- INTERSPEECH 2023 - Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech ## Related models
- fav-kky/wav2vec2-base-cs-80k-ClTRUS