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jrs-a
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wav2vec2_batangueno

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Community
jrs-a commited on
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End of training

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  1. README.md +13 -13
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -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-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5121
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- - Wer: 0.2514
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  ## Model description
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@@ -50,17 +50,17 @@ 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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- | 3.8266 | 2.67 | 500 | 2.8513 | 1.0 |
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- | 1.587 | 5.35 | 1000 | 0.8559 | 0.5943 |
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- | 0.5172 | 8.02 | 1500 | 0.5303 | 0.3903 |
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- | 0.2865 | 10.7 | 2000 | 0.5031 | 0.3716 |
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- | 0.2002 | 13.37 | 2500 | 0.4735 | 0.2800 |
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- | 0.1566 | 16.04 | 3000 | 0.4878 | 0.2955 |
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- | 0.1163 | 18.72 | 3500 | 0.5329 | 0.2889 |
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- | 0.0948 | 21.39 | 4000 | 0.4727 | 0.2679 |
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- | 0.0797 | 24.06 | 4500 | 0.4704 | 0.2679 |
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- | 0.0669 | 26.74 | 5000 | 0.5199 | 0.2580 |
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- | 0.0579 | 29.41 | 5500 | 0.5121 | 0.2514 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5694
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+ - Wer: 0.2635
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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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+ | 3.8312 | 2.66 | 500 | 2.7217 | 1.0 |
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+ | 1.5348 | 5.32 | 1000 | 0.7723 | 0.5788 |
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+ | 0.5104 | 7.98 | 1500 | 0.5589 | 0.4308 |
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+ | 0.2845 | 10.64 | 2000 | 0.5485 | 0.3803 |
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+ | 0.1995 | 13.3 | 2500 | 0.5043 | 0.3273 |
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+ | 0.1493 | 15.96 | 3000 | 0.5247 | 0.3045 |
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+ | 0.1195 | 18.62 | 3500 | 0.5410 | 0.2984 |
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+ | 0.0974 | 21.28 | 4000 | 0.5905 | 0.2804 |
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+ | 0.0784 | 23.94 | 4500 | 0.5702 | 0.2780 |
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+ | 0.0601 | 26.6 | 5000 | 0.5678 | 0.2659 |
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+ | 0.0578 | 29.26 | 5500 | 0.5694 | 0.2635 |
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  ### Framework versions
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