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--- |
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license: cc-by-sa-4.0 |
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language: "de" |
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thumbnail: |
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tags: |
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- automatic-speech-recognition |
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- CTC |
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- Attention |
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- pytorch |
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- speechbrain |
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metrics: |
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- wer |
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--- |
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# German ASR |
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This model is trained on the Mozilla Common Voice 8.0, the Spoken Wikipedia Corpus and the m-ailabs corpus. |
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In contrast to our first model this one can transcribe German Umlauts. Additionally it was trained on a newer Version of the Mozilla Common Voice. |
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- https://nats.gitlab.io/swc/ |
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- https://commonvoice.mozilla.org/de/datasets |
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- https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset/ |
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We do not provide a language model. |
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# Performance |
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This model has a WER of 7.09% and is slightly better then the model from our paper: |
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https://huggingface.co/jfreiwa/asr-crdnn-german |
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# Model application |
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## Install SpeechBrain |
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First of all, please install SpeechBrain with the following command: |
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``` |
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pip install speechbrain |
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``` |
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Please notice that we encourage you to read the tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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## Using the model |
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``` |
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from speechbrain.pretrained import EncoderDecoderASR |
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asr_model = EncoderDecoderASR.from_hparams(source="jfreiwa/asr-crdnn-german-umlaute", savedir="pretrained_models/asr-crdnn-german-umlaute") |
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asr_model.transcribe_file("jfreiwa/asr-crdnn-german/example-de.wav") |
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``` |
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## Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
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# Limitations |
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We do not provide any warranty on the performance achieved by this model when used on other datasets. |
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# **About SpeechBrain** |
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- Website: https://speechbrain.github.io/ |
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- Code: https://github.com/speechbrain/speechbrain/ |
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- HuggingFace: https://huggingface.co/speechbrain/ |
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# **Citing SpeechBrain** |
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Please, cite SpeechBrain if you use it for your research or business. |
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```bibtex |
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@misc{speechbrain, |
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title={{SpeechBrain}: A General-Purpose Speech Toolkit}, |
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author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, |
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year={2021}, |
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eprint={2106.04624}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.AS}, |
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note={arXiv:2106.04624} |
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} |
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``` |
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# **Citing our paper** |
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Please, cite our original paper, when you use our models in your research. |
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```bibtex |
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@inproceedings{freiwald2022, |
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author={J. Freiwald and P. Pracht and S. Gergen and D. Kolossa}, |
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title={Open-Source End-To-End Learning for Privacy-Preserving German {ASR}}, |
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year=2022, |
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booktitle={DAGA 2022} |
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} |
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``` |
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# Acknowledgements |
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This work was funded by the German Federal Ministry of Education and Research (BMBF) |
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within the “Innovations for Tomorrow’s Production, Services, and |
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Work” Program (02L19C200), a project that is implemented by |
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the Project Management Agency Karlsruhe (PTKA). The authors |
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are responsible for the content of this publication. |
