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StofEzz
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mascir_fr_wav2vec_version1000

Automatic Speech Recognition
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PyTorch
TensorBoard
wav2vec2
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update model card README.md

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@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4441
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- - Wer: 0.3622
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  ## Model description
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@@ -50,36 +50,23 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | No log | 2.0 | 250 | 4.6558 | 1.0 |
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- | 5.4653 | 4.0 | 500 | 3.1189 | 1.0 |
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- | 5.4653 | 6.0 | 750 | 1.3807 | 0.9344 |
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- | 1.6415 | 8.0 | 1000 | 0.6832 | 0.5689 |
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- | 1.6415 | 10.0 | 1250 | 0.4986 | 0.48 |
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- | 0.3065 | 12.0 | 1500 | 0.4968 | 0.4711 |
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- | 0.3065 | 14.0 | 1750 | 0.4470 | 0.4533 |
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- | 0.1441 | 16.0 | 2000 | 0.4832 | 0.4433 |
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- | 0.1441 | 18.0 | 2250 | 0.5433 | 0.45 |
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- | 0.0938 | 20.0 | 2500 | 0.4734 | 0.4344 |
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- | 0.0938 | 22.0 | 2750 | 0.4745 | 0.4111 |
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- | 0.0727 | 24.0 | 3000 | 0.4236 | 0.4044 |
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- | 0.0727 | 26.0 | 3250 | 0.4692 | 0.4133 |
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- | 0.0556 | 28.0 | 3500 | 0.4411 | 0.3967 |
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- | 0.0556 | 30.0 | 3750 | 0.4722 | 0.3822 |
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- | 0.0422 | 32.0 | 4000 | 0.4845 | 0.3978 |
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- | 0.0422 | 34.0 | 4250 | 0.4818 | 0.4 |
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- | 0.0325 | 36.0 | 4500 | 0.4638 | 0.3944 |
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- | 0.0325 | 38.0 | 4750 | 0.4737 | 0.38 |
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- | 0.0284 | 40.0 | 5000 | 0.4615 | 0.3822 |
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- | 0.0284 | 42.0 | 5250 | 0.4491 | 0.3722 |
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- | 0.0235 | 44.0 | 5500 | 0.4480 | 0.3744 |
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- | 0.0235 | 46.0 | 5750 | 0.4630 | 0.3711 |
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- | 0.0172 | 48.0 | 6000 | 0.4421 | 0.3644 |
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- | 0.0172 | 50.0 | 6250 | 0.4441 | 0.3622 |
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  ### Framework versions
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  - Transformers 4.31.0
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  - Pytorch 2.0.1+cu118
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- - Datasets 2.14.2
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  - Tokenizers 0.13.3
 
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  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4677
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+ - Wer: 0.37
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 5.3896 | 4.0 | 500 | 3.0842 | 1.0 |
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+ | 1.6969 | 8.0 | 1000 | 0.6327 | 0.5956 |
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+ | 0.3056 | 12.0 | 1500 | 0.5024 | 0.49 |
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+ | 0.1441 | 16.0 | 2000 | 0.5241 | 0.45 |
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+ | 0.091 | 20.0 | 2500 | 0.4997 | 0.44 |
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+ | 0.0676 | 24.0 | 3000 | 0.5173 | 0.4456 |
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+ | 0.0603 | 28.0 | 3500 | 0.4487 | 0.4122 |
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+ | 0.0378 | 32.0 | 4000 | 0.4554 | 0.3933 |
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+ | 0.0328 | 36.0 | 4500 | 0.4395 | 0.3822 |
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+ | 0.0275 | 40.0 | 5000 | 0.4910 | 0.3889 |
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+ | 0.0198 | 44.0 | 5500 | 0.4861 | 0.3722 |
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+ | 0.019 | 48.0 | 6000 | 0.4677 | 0.37 |
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.31.0
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  - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.3
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  - Tokenizers 0.13.3