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--- |
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language: |
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- cs |
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- en |
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tags: |
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- Czech |
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- English |
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- bilingual |
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- KKY |
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- FAV |
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license: cc-by-nc-sa-4.0 |
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--- |
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# wav2vec2-base-cs-en-100k |
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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). |
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It has been released along with a paper **A Comparative Analysis of Bilingual and Trilingual Wav2Vec Models for |
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Automatic Speech Recognition in Multilingual Oral History Archives** accepted to INTERSPEECH2024 conference. |
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## Paper |
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https://www.isca-archive.org/interspeech_2024/lehecka24_interspeech.pdf |
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Pre-print: http://arxiv.org/abs/2407.17160. |
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### All pre-trained models released along with the paper |
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- [fav-kky/wav2vec2-base-cs-50k](https://huggingface.co/fav-kky/wav2vec2-base-cs-50k) (monolingual Czech) |
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- [fav-kky/wav2vec2-base-de-50k](https://huggingface.co/fav-kky/wav2vec2-base-de-50k) (monolingual German) |
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- [fav-kky/wav2vec2-base-cs-en-100k](https://huggingface.co/fav-kky/wav2vec2-base-cs-en-100k) (bilingual Czech+English) |
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- [fav-kky/wav2vec2-base-cs-de-100k](https://huggingface.co/fav-kky/wav2vec2-base-cs-de-100k) (bilingual Czech+German) |
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- [fav-kky/wav2vec2-base-en-de-100k](https://huggingface.co/fav-kky/wav2vec2-base-en-de-100k) (bilingual English+German) |
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- [fav-kky/wav2vec2-base-cs-en-de-150k](https://huggingface.co/fav-kky/wav2vec2-base-cs-en-de-150k) (trilingual Czech+English+German) |
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## Citation |
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If you find this model useful, please cite our paper: |
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``` |
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@inproceedings{lehecka24_interspeech, |
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title = {A Comparative Analysis of Bilingual and Trilingual Wav2Vec Models for Automatic Speech Recognition in Multilingual Oral History Archives}, |
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author = {Jan Lehečka and Josef V. Psutka and Lubos Smidl and Pavel Ircing and Josef Psutka}, |
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year = {2024}, |
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booktitle = {Interspeech 2024}, |
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pages = {1285--1289}, |
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doi = {10.21437/Interspeech.2024-472}, |
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issn = {2958-1796}, |
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} |
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``` |
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## Usage |
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This model does not have a tokenizer as it was pretrained on audio alone. |
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In order to use this model for speech recognition, a tokenizer should be created |
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and the model should be [fine-tuned](https://huggingface.co/blog/fine-tune-wav2vec2-english) on labeled ASR data. |
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Inputs must be 16kHz mono audio files. |
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This model can be used e.g., to extract per-frame contextual embeddings from audio: |
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```python |
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from transformers import Wav2Vec2Model, Wav2Vec2FeatureExtractor |
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import torchaudio |
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("fav-kky/wav2vec2-base-cs-en-100k") |
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model = Wav2Vec2Model.from_pretrained("fav-kky/wav2vec2-base-cs-en-100k") |
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speech_array, sampling_rate = torchaudio.load("/path/to/audio/file.wav") |
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inputs = feature_extractor( |
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speech_array, |
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sampling_rate=16_000, |
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return_tensors="pt" |
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)["input_values"][0] |
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output = model(inputs) |
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embeddings = output.last_hidden_state.detach().numpy()[0] |
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``` |
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## Related works |
