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--- |
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tags: |
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- espnet |
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- audio |
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- automatic-speech-recognition |
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language: |
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- et |
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license: apache-2.0 |
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metrics: |
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- wer |
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model-index: |
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- name: e-branchformer et |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: ERR2020 |
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type: audio |
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metrics: |
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- name: Wer |
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type: wer |
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value: 9.9 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# e-branchformer et |
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Espnet2 e-branchformer based recipe (https://github.com/espnet/espnet/tree/master/egs2/librispeech_100/asr1) trained Estonian ASR model using ERR2020 dataset |
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- WER on ERR2020: 9.9 |
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- WER on mozilla commonvoice_11: 20.8 |
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For usage: |
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- clone this repo (`git clone https://huggingface.co/rristo/espnet_ebranchformer_et`) |
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- go to repo (`cd espnet_ebranchformer_et`) |
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- build docker image for needed libraries (`build.sh` or `build.bat`) |
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- run docker container (`run.sh` or `run.sh`). This mounts current directory |
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- run notebook `example_usage.ipynb` for example usage |
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- currently expects audio to be in .wav format |
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## Model description |
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ASR model for Estonian, uses Estonian Public Broadcasting data ERR2020 data (around 340 hours of audio) |
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## Intended uses & limitations |
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Pretty much a toy model, trained on limited amount of data. Might not work well on data out of domain |
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(especially spontaneous/noisy data). |
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## Training and evaluation data |
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Trained on ERR2020 data, evaluated on ERR2020 and mozilla commonvoice test data. |
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## Training procedure |
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Used espnet e-branchformer based recipe (https://github.com/espnet/espnet/tree/master/egs2/librispeech_100/asr1) |
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### Training results |
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Look into folder exp/images. |
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Validation results are in exp/RESULTS.md |
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### Framework versions |
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- espnet2 |
