wav2vec2-base-cs-50k

This is a monolingual Czech Wav2Vec 2.0 base model pre-trained from 50 thousand hours of Czech speech. 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 (monolingual Czech)
• fav-kky/wav2vec2-base-de-50k (monolingual German)
• fav-kky/wav2vec2-base-cs-en-100k (bilingual Czech+English)
• fav-kky/wav2vec2-base-cs-de-100k (bilingual Czech+German)
• fav-kky/wav2vec2-base-en-de-100k (bilingual English+German)
• 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 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:

from transformers import Wav2Vec2Model, Wav2Vec2FeatureExtractor
import torchaudio

feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("fav-kky/wav2vec2-base-cs-50k")
model = Wav2Vec2Model.from_pretrained("fav-kky/wav2vec2-base-cs-50k")

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