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thiagobarbosa
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whisper-base-google-fleurs-pt-br

Automatic Speech Recognition
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whisper
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  1. README.md +13 -12
  2. pytorch_model.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 [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4732
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- - Wer: 18.2079
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2.5e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - lr_scheduler_warmup_steps: 100
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- - training_steps: 4000
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|
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- | 0.382 | 0.57 | 400 | 0.4085 | 18.3348 |
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- | 0.1182 | 1.15 | 800 | 0.4220 | 17.7613 |
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- | 0.0991 | 1.72 | 1200 | 0.4479 | 19.6230 |
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- | 0.0424 | 2.3 | 1600 | 0.4551 | 18.2173 |
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- | 0.0458 | 2.87 | 2000 | 0.4732 | 18.2079 |
 
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  ### Framework versions
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  - Transformers 4.36.2
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- - Pytorch 2.1.2+cu121
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  - Datasets 2.16.1
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  - Tokenizers 0.15.0
 
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  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6283
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+ - Wer: 25.9071
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2.5e-05
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+ - train_batch_size: 12
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+ - eval_batch_size: 12
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 120
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+ - training_steps: 2400
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|
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+ | 0.0871 | 2.72 | 400 | 0.4838 | 24.4078 |
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+ | 0.0066 | 5.44 | 800 | 0.5647 | 25.5452 |
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+ | 0.0013 | 8.16 | 1200 | 0.5981 | 25.6110 |
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+ | 0.0008 | 10.88 | 1600 | 0.6143 | 25.6533 |
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+ | 0.0006 | 13.61 | 2000 | 0.6245 | 25.7661 |
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+ | 0.0006 | 16.33 | 2400 | 0.6283 | 25.9071 |
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  ### Framework versions
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  - Transformers 4.36.2
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+ - Pytorch 2.1.1
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  - Datasets 2.16.1
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  - Tokenizers 0.15.0
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