jonatasgrosman
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update evaluation
Browse files- README.md +10 -5
- log_mozilla-foundation_common_voice_8_0_it_test_predictions.txt +0 -0
- log_mozilla-foundation_common_voice_8_0_it_test_predictions_greedy.txt +0 -0
- mozilla-foundation_common_voice_8_0_it_test_eval_results.txt +2 -2
- mozilla-foundation_common_voice_8_0_it_test_eval_results_greedy.txt +2 -2
README.md
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metrics:
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- name: Test WER
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type: wer
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value:
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- name: Test CER
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type: cer
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value: 2.
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- name: Test WER (+LM)
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type: wer
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value:
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- name: Test CER (+LM)
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type: cer
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value:
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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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# XLS-R-1B-ITALIAN
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## Evaluation Commands
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metrics:
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- name: Test WER
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type: wer
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value: 9.04
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- name: Test CER
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type: cer
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value: 2.20
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- name: Test WER (+LM)
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type: wer
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value: 6.75
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- name: Test CER (+LM)
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type: cer
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value: 1.76
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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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# XLS-R-1B-ITALIAN
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Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Italian using the [Common Voice 8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
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When using this model, make sure that your speech input is sampled at 16kHz.
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This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
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The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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## Evaluation Commands
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log_mozilla-foundation_common_voice_8_0_it_test_predictions.txt
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log_mozilla-foundation_common_voice_8_0_it_test_predictions_greedy.txt
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mozilla-foundation_common_voice_8_0_it_test_eval_results.txt
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WER: 0.
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CER: 0.
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WER: 0.0675129084844723
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CER: 0.01761353561922042
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mozilla-foundation_common_voice_8_0_it_test_eval_results_greedy.txt
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WER: 0.
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CER: 0.
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WER: 0.09043783123819138
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CER: 0.022088713826821264
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